WMS This is a description of your Web Map Server. WFS WMS GEOSERVER

NONE NONE text/xml image/png application/atom+xml application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png image/vnd.jpeg-png8 text/html; subtype=openlayers text/html; subtype=openlayers2 text/html; subtype=openlayers3 text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html application/json XML INIMAGE BLANK JSON This is a description of your Web Map Server. EPSG:4326 EPSG:3785 EPSG:3857 EPSG:900913 EPSG:32647 EPSG:32736 CRS:84 -180.0 4009360.09736286 -1930110.57247653 90.0000000000001 geonode:Africa_Cities The layer of all cities in Africa from the Department of Survey 2019 Africa_Cities features EPSG:4267 CRS:84 -17.450577138548546 49.384525686215916 -33.97566215784602 37.29712778052752 other other other other text/xml other other geonode:Chitipa_forests No abstract providedMinistry of Forest is running a project on forest regeneration in all the districts of Malawi. this data was digitized by the department of surveys with the help of the forest department at both National and District levels. the layer depicts all the the sites for forest regeneration in Chitipa Districts features Chitipa_forests EPSG:20936 CRS:84 33.00047412127426 33.701349916301666 -10.420546547091718 -9.36724833387906 other other other other other text/xml other geonode:Malawi_Sentinel2_LULC20160 No abstract provided GeoTIFF WCS Malawi_Sentinel2_LULC2016 EPSG:4326 CRS:84 32.671499962487 35.915019651266995 -17.127509988741 -9.363618298641 other other other other other text/xml other geonode:Open_Defication This layer shows percentage use of different types of toilet facilities in Malawi. Data from 2018 Malawi Population and Housing Census Open_Defication features EPSG:20936 CRS:84 32.66592705701498 35.95084470064585 -17.141409224326523 -9.35577793939739 other other other other text/xml other other geonode:Phalombe_Forest Ministry of Forest is running a project on forest regeneration in all the districts of Malawi. this data was digitized by the department of surveys with the help of the forest department at both National and District levels. the layer depicts all the the sites for forest regeneration in Phalombe Districts. Phalombe_Forest features EPSG:139337 CRS:84 35.50361790899661 35.83748991893261 -15.88576283524053 -15.534556614654708 other other other other text/xml other other geonode:Thyolo_Forest Ministry of Forest is running a project on forest regeneration in all the districts of Malawi. this data was digitized by the department of surveys with the help of the forest department at both National and District levels. the layer depicts all the the sites for forest regeneration in Thyolo Districts Thyolo_Forest features EPSG:139337 CRS:84 34.906873689699395 35.325435518969506 -16.413059547664176 -15.923578269595996 other other other other text/xml other other geonode:a__1973_landcovergeo This shapefile shows National 1972/1973 MSS land cover. _1973_landcovergeo features EPSG:4326 CRS:84 32.6739452477594 35.9171402019396 -17.1235018417457 -9.36669169924584 other other other other text/xml other other geonode:agri_markets No abstract provided agri_markets features EPSG:32736 CRS:84 32.88716568188796 35.74528425056311 -16.915441282658897 -9.671458574205111 other other other other text/xml other other geonode:agricultural_schemes No abstract provided agricultural_schemes features EPSG:32736 CRS:84 33.15686102250473 35.5980376292297 -16.551719608642454 -9.75246598471735 other other other text/xml other other other geonode:agro_ecological_zones No abstract provided agro_ecological_zones features EPSG:4326 CRS:84 32.6537569772456 35.784636768315 -17.1155162411061 -9.36729596843301 other other other other other text/xml other geonode:agro_opp_shp This dataset represents the opportunity area for implementing the agricultural technologies restoration intervention as modeled for the National Forest Landscape Restoration Assessment. Agricultural technologies refer to any type of intercropping of trees with crops and include conservation agriculture (CA), farmer-managed natural regeneration (FMNR), and agroforestry (AF). agro_opp_shp features EPSG:32736 CRS:84 32.67983321584302 35.91605508903754 -17.13646730474364 -9.367740911470582 other other other other text/xml other other geonode:agroclimatic_zonesgeo This shapefile shows the Agroclimatic zones of Malawi which were edited and attributed digital version agroclimatic_zonesgeo features EPSG:4326 CRS:84 32.6739452477594 35.9171402019396 -17.1235018417457 -9.36669169924584 other other other other text/xml other other geonode:basemap_1 No abstract provided EPSG:32736 CRS:84 34.748014095520666 35.00235909228521 -15.000782888728695 -14.746674163895694 other other other other other text/xml other geonode:basemap_10 No abstract provided EPSG:32736 CRS:84 34.24848253255459 34.50191076771584 -16.000216329081507 -15.747043279377268 other other other other other text/xml other geonode:basemap_11 No abstract provided EPSG:32736 CRS:84 34.248503584675056 34.501878509804534 -15.75022408369369 -15.497067449697061 other other other other other text/xml other geonode:basemap_12 No abstract provided EPSG:32736 CRS:84 34.498193971123214 34.75220626394944 -15.750532303689868 -15.496810656621536 other other other other other other other text/xml other geonode:basemap_13 No abstract provided EPSG:32736 CRS:84 34.74789499817612 35.00249808969544 -15.750794205522242 -15.496504057975574 other other other text/xml other other other other other geonode:basemap_14 No abstract provided EPSG:32736 CRS:84 34.49818242797835 34.75222396021801 -16.000507655255966 -15.746755975941982 other other other other other text/xml other other text/xml other geonode:basemap_15 No abstract provided EPSG:32736 CRS:84 34.74785931753861 35.00254486642503 -16.000798486767305 -15.74647913966254 other other other other other other text/xml other geonode:basemap_16 No abstract provided EPSG:32736 CRS:84 34.99768549034 35.25273164376584 -15.251053067639235 -14.996346433941689 other other other other other text/xml other geonode:basemap_17 No abstract provided EPSG:32736 CRS:84 35.247411463132195 35.50300236328176 -15.25132500486473 -14.996052465125635 other other other other other text/xml other geonode:basemap_18 No abstract provided EPSG:32736 CRS:84 34.9976552747565 35.252763186634404 -15.501087372600418 -15.246276419128069 other other other other other text/xml other geonode:basemap_19 No abstract provided EPSG:32736 CRS:84 35.24735110891238 35.503069600207354 -15.501362735085761 -15.246004979474288 other other other other other text/xml other geonode:basemap_20 No abstract provided EPSG:32736 CRS:84 35.49706872214335 35.75336928484327 -15.501627934615389 -15.245743953757358 other other other other other text/xml other geonode:basemap_21 No abstract provided EPSG:32736 CRS:84 34.99755742153828 35.25286558103263 -16.001110090886097 -15.746190499435484 other other other other other text/xml other geonode:basemap_22 No abstract provided EPSG:32736 CRS:84 35.24726037429293 35.50315998309098 -16.001388934241085 -15.745881940105985 other other other other other text/xml other geonode:basemap_23 No abstract provided EPSG:32736 CRS:84 35.4969487735602 35.75348986128944 -16.00167203559047 -15.74558993058295 other other other other other text/xml other geonode:basemap_24 No abstract provided EPSG:32736 CRS:84 35.74664321719183 36.003805246141674 -16.00198568123999 -15.745322114430532 other other other other other text/xml other geonode:basemap_25 No abstract provided EPSG:32736 CRS:84 34.248436555543606 34.501929147914794 -16.250232766296932 -15.997009209119762 other other other other other text/xml other geonode:basemap_26 No abstract provided EPSG:32736 CRS:84 34.414894729811266 34.75229112422137 -16.501105322685653 -16.24606915824142 other other other other other text/xml other geonode:basemap_27 No abstract provided EPSG:32736 CRS:84 34.498129329750334 34.75224461101072 -16.250544537723005 -15.996708218714753 other other other other other text/xml other geonode:basemap_28 No abstract provided EPSG:32736 CRS:84 34.747852079181676 35.00259507936307 -16.25081686960537 -15.996410419293856 other other other other other text/xml other geonode:basemap_29 No abstract provided EPSG:32736 CRS:84 34.74778804652904 35.00260414983857 -16.500818134986385 -16.246359761413192 other other other other other text/xml other geonode:basemap_3 No abstract provided EPSG:32736 CRS:84 35.24754396619941 35.502865224853984 -14.501289418492073 -14.246244492532215 other other other other other text/xml other geonode:basemap_30 No abstract provided EPSG:32736 CRS:84 34.964101305179945 35.25303967856291 -17.001479771042153 -16.745650752346716 other other other other other text/xml other geonode:basemap_31 No abstract provided EPSG:32736 CRS:84 34.997525010748184 35.2528790822498 -16.25110223900417 -15.99611591290124 other other other other other text/xml other geonode:basemap_32 No abstract provided EPSG:32736 CRS:84 35.24722333659381 35.5032289059743 -16.2514173793463 -15.995822323141423 other other other other other text/xml other geonode:basemap_33 No abstract provided EPSG:32736 CRS:84 34.997478565065606 35.31967697049496 -16.50187309770455 -16.245363931913076 other other other other other text/xml other geonode:basemap_34 No abstract provided EPSG:32736 CRS:84 35.49689538173582 35.83697423699474 -16.252823846795643 -15.994460134713462 other other other other other text/xml other geonode:basemap_35 No abstract provided EPSG:32736 CRS:84 34.997435475114514 35.33644011874852 -16.752089816479383 -16.49508476506025 other other other other other text/xml other geonode:basemap_36 No abstract provided EPSG:32736 CRS:84 35.247058817152634 35.36984813113716 -16.99986133783035 -16.747209763113958 other other other other other text/xml other geonode:basemap_37 No abstract provided EPSG:32736 CRS:84 34.997349333963555 35.25306720439355 -17.2511846552445 -16.995892713506326 other other other other other text/xml other geonode:basemap_38 No abstract provided EPSG:32736 CRS:84 35.24703267626487 35.3699231717066 -17.24986035322509 -16.99719903761361 other other other other other text/xml other geonode:basemap_39 No abstract provided EPSG:4326 CRS:84 34.748051260232536 35.00233818896727 -14.750743154608282 -14.496723537120456 other other other other other text/xml other geonode:basemap_4 No abstract provided EPSG:32736 CRS:84 34.99777915285753 35.252629296149024 -14.75102623440494 -14.496432934601906 other other other other other text/xml other geonode:basemap_5 No abstract provided EPSG:32736 CRS:84 34.99773672340468 35.25264959747542 -15.001059814917589 -14.746407017831665 other other other other other text/xml other geonode:basemap_6 No abstract provided EPSG:32736 CRS:84 35.247429082398504 35.50295115501281 -15.001312218079565 -14.746111780989462 other other other other other text/xml other geonode:basemap_7 No abstract provided EPSG:32736 CRS:84 34.74798517022292 35.002430875717224 -15.250773328368023 -14.996599199689678 other other other other other text/xml other geonode:basemap_8 No abstract provided EPSG:32736 CRS:84 30.5356726 38.323107400000005 -15.73734355 -9.896767449999999 other other other other other text/xml other geonode:basemap_9 No abstract provided EPSG:32736 CRS:84 34.7479441122105 35.00246435132478 -15.500772251205477 -15.24654617787647 other other other other other text/xml other geonode:biodiversitymca_nflra_2017 Data represent a multi-criteria analysis of Biodiversity opportunity areas as described on pp40 and pp122 (Annex 18) of Malawi's National Forest Landscape Restoration Assessment (2017). https://portals.iucn.org/library/node/46837 Call number: IUCN-2017-029ed Landuse Biodiversity agriculture Forest restoration EPSG:32736 CRS:84 32.66340596368842 36.008984646144256 -17.144876157039747 -9.355793811063013 other other other other other text/xml other geonode:bird_locations SIBNHES- Locations Surveyed for Birds Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2) features bird_locations EPSG:32736 CRS:84 34.59474208876152 35.510552690039766 -15.407249321785327 -14.423439459283431 other other other other text/xml other other geonode:blantyre No abstract provided blantyre features EPSG:32736 CRS:84 34.71648442834991 35.144800128393825 -16.01714442465166 -15.345342855853472 other other other other other text/xml other geonode:blantyre_boundary No abstract provided blantyre_boundary features EPSG:32736 CRS:84 34.71648442834991 35.144800128393825 -16.01714442465166 -15.345342855853472 other other other other text/xml other other geonode:blantyresoils No abstract provided blantyresoils features EPSG:32736 CRS:84 34.350599922023775 35.86436189007983 -16.414507070508023 -15.189826638526116 other other other other other text/xml other geonode:bpc2_1 No abstract provided bpc2_1 features EPSG:32736 CRS:84 35.12034838474528 35.410778240324845 -15.132822480609544 -15.019417048751247 other other other other other text/xml other geonode:bpc2_4 No abstract provided features bpc2_4 EPSG:32736 CRS:84 35.12034838474528 35.410778240324845 -15.132822480609544 -15.019417048751247 other other other other other text/xml other geonode:btcity_dem1 DEM for Blantyre City. Elevation EPSG:4326 CRS:84 34.96238177386204 35.12851173724834 -15.893486120982711 -15.696728845910673 other other other other other text/xml other geonode:btyint_1 No abstract provided btyint_1 features EPSG:32736 CRS:84 34.735871790069204 34.886196987725 -15.796949641680365 -15.58514560548459 other other other other other text/xml other geonode:building_poly_pt_as_points Data generate from OSM using the HOT export (http://export.hotosm.org/en/jobs/8398). Buildings as point and polygons were combined to a single point layer using QGIS2. building_poly_pt_as_points features EPSG:4326 CRS:84 32.629485 4009360.09736286 -1930110.57247653 -9.2756863 other other other other text/xml other other geonode:butterfly_locations SIBNHES- Locations Surveyed for Butterflies Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2) butterfly_locations features EPSG:4326 CRS:84 35.16602 35.30754 -15.21204 -14.25332 other other other other text/xml other other geonode:canal_buffer30m water canal to be constructed in chikwawa district under shire valley project canal_buffer30m features EPSG:20936 CRS:84 34.67797160934372 34.7882934191479 -16.23223434832762 -15.894233519526606 other other other other other text/xml other geonode:catchmentintervention Catchment level intervention in four priority catchments of SRBMP catchmentintervention features EPSG:32736 CRS:84 34.5668974518803 35.29047098807096 -15.770285128689533 -15.02907282819622 other other other other other text/xml other geonode:census Subset of District Population for Lower SHire Districts based http://www.masdap.mw/maps/218 population EPSG:4326 CRS:84 34.24847872402849 35.36897872402849 -17.12734564270053 -14.73694564270053 other other other other other text/xml other geonode:census_dist No abstract provided census_dist features EPSG:4326 CRS:84 32.6716182282675 35.9184171567682 -17.1262788148958 -9.36366167264395 other other other other other text/xml other geonode:census_ta This data shows TA boundaries for Malawi census_ta features EPSG:4326 CRS:84 32.6716182278052 35.9184171567682 -17.1262788148958 -9.36366167264395 other other other other text/xml other other geonode:chiradzulu Creative features chiradzulu EPSG:32736 CRS:84 35.09619755016322 35.330934348066435 -16.003594741812357 -15.520025639905464 other other other other text/xml other other geonode:chiradzulu_1 Chiradzulu District is in Southern part of Malawi and it borders with Zomba, Blantyre,Thyolo and Mulanje. chiradzulu_1 features EPSG:32736 CRS:84 35.09619755016322 35.330934348066435 -16.003594741812357 -15.520025639905464 other other other other other text/xml other geonode:chiradzulu_district Its a district found in the Southern part of Malawi, it shares boundary with Thyolo, Zomba and Blantyre. The languages used there are Lomwe and Yao chiradzulu_district features EPSG:32736 CRS:84 35.09619755016322 35.330934348066435 -16.003594741812357 -15.520025639905464 other other other other text/xml other other geonode:chiradzuru Chiradzulu District is the original home of Malawi's hero John Chilembwe. Its one of the Districts as Malawian history concern chiradzuru features EPSG:32736 CRS:84 35.09619755016322 35.330934348066435 -16.003594741812357 -15.520025639905464 other other other other text/xml other other geonode:chitipa chitipa district boundary, extracted 17 in salima chitipa features EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other text/xml other other geonode:chitipa_1 No abstract provided features chitipa_1 EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other text/xml other other geonode:chitipa_10 No abstract provided chitipa_10 features EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other other text/xml other geonode:chitipa_11 No abstract provided chitipa_11 features EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other other text/xml other geonode:chitipa_12 No abstract provided chitipa_12 features EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other other text/xml other geonode:chitipa_2 No abstract provided chitipa_2 features EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other other text/xml other geonode:chitipa_3 No abstract provided chitipa_3 features EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other other text/xml other geonode:chitipa_8 No abstract provided features chitipa_8 EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other text/xml other other geonode:chitipa_9 No abstract provided features chitipa_9 EPSG:32736 CRS:84 32.94343479951775 33.962350776348444 -10.57450986520869 -9.363016526483234 other other other other text/xml other other geonode:chngint_1 No abstract provided chngint_1 features EPSG:32736 CRS:84 35.08430865150213 35.288956333337545 -15.37331371159595 -15.166409564819993 other other other other text/xml other other geonode:commforest_opp_shp This dataset represents the opportunity area for implementing community forest and woolot interventions as modeled for the National Forest Landscape Restoration Assessment. Community forests (such as graveyards and village forest areas (VFAs)) and woodlots are areas of customary or private land set aside and managed for wood and range of provisioning, regulating and non-wood-cultural services including, non-timber products, medicinal plants and burial. They may be managed by a Traditional Authority, a community, a family or an individual. Community non-cultural forests and woodlots, if planned and managed properly, can provide a regular supply of products (e.g., poles, timber, fuel wood, fruit, etc.) for household consumption and/or for sale. Both through provision of wood products and income, community forests can reduce pressure on forest reserves and other protected areas. Credits: Ministry of Natural Resources, Energy and Mining - Malawi, International Union for Conservation of Nature (IUCN) and World Resources Institute (WRI) in support of the Forest Landscape Restoration Opportunities Assessment for Malawi, 2017. commforest_opp_shp features EPSG:32736 CRS:84 32.67870365523484 35.86452493236301 -17.137290976031974 -9.370032959833324 other other other other text/xml other other geonode:degradation_biodiversity Forest landscape restoration opportunity for biodiversity, this analysis multiplies the magnitude of coincident biodiversity criteria in the Biodiversity Opportunity Areas map (as defined in the National Forest Landscape Restoration Assessment, 2017) with the magnitude of coincident criteria in the Functional Degradation map. The result demonstrates a level of priority for FLR intervention activities that would conceivably support both biodiversity priorities and the restoration of degraded land. Landuse Biodiversity Forest restoration degradation EPSG:32736 CRS:84 32.668445924953325 35.948170038402374 -17.137421605389022 -9.358751169863769 other other other other text/xml other other geonode:degradation_foodsecurity Forest landscape restoration opportunity for food security, this analysis multiplies the magnitude of coincident food security criteria in the Food Security Opportunity Areas map with the magnitude of coincident criteria in the Functional Degradation map. The result demonstrates a level of priority for FLR intervention activities that would conceivably support both food security and the restoration of degraded land. degradation_foodsecurity WCS GeoTIFF EPSG:32736 CRS:84 32.67585951868813 35.94346620770085 -17.137433623926448 -9.360092020974411 other other other other text/xml other other geonode:degradation_fs_res_bio_add Data shows the sum of the three priority landscape restoration scenarios that formed the objectives of FLR in Malawi. These data demonstrate priority areas for addressing all of the FLR themes in Malawi, as described in the National Assessment and are underpinned by an assessment of functional degradation . These data indicate the highest priority areas based on an additive treatment of the product of three multi-criteria analyses (food security*degradation + resilience*degradation + and biodiversity*degradation). Based on the input data, restoration in the highest priority areas would most benefit all of the input criteria, including drivers of degradation. food security Biodiversity Forest restoration resilience EPSG:32736 CRS:84 32.67585951868813 35.942927079221775 -17.137433623926448 -9.361975194893155 other other other other text/xml other other geonode:degradation_resilience Forest landscape restoration opportunity for resilience, this analysis multiplies the magnitude of coincident resilience criteria in the Resilience Opportunity Areas map (as defined in the National Forest Landscape Restoration Assessment, 2017) with the magnitude of coincident criteria in the Functional Degradation map. The result demonstrates a level of priority for FLR intervention activities that would conceivably support both resilience and the restoration of degraded land. Climate Change Landuse restoration degradation EPSG:32736 CRS:84 32.668445924953325 35.94588522086028 -17.137421605389022 -9.361952122978229 other other other other text/xml other other geonode:dr_events_unepgrid_mw This dataset includes an estimate of global drought annual repartition based on Standardized Precipitation Index. It is based on two sources: 1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia). 2) A GIS modeling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology. This product was designed by UNEP/GRID-Europe for the Project of Risk Evaluation, Vulnerability, Information & Early Warning (PreView). It was modeled using global data. Credit: UNEP/GRID-Europe. drought features dr_events_unepgrid_mw EPSG:4326 CRS:84 27.5 39.8256900000015 -21.0 -4.0 other other other other text/xml other other geonode:eas_bnd This layer shows the distribution of the population over the country of Malawi on the basis of enumeration area boundary. Data producer is the National Statistical Office. features eas_bnd EPSG:4326 CRS:84 32.6716182278052 35.918417156765 -17.1262788148958 -9.36366167264395 other other other other text/xml other other geonode:em_1 No abstract provided features em_1 EPSG:21036 CRS:84 34.794896953288585 35.17016628431262 -16.624851447412947 -15.992128807449067 other other other other other text/xml other geonode:emsr116_01blantyre_01del_00ovr_monit01_flooded_areas Heavy rains over the last few weeks have led to severe flooding across Malawi. To date, an estimated 173.700 people have been displaced. The floods have also caused extensive damage to crops, livestock and infrastructure. The southern districts of Nsanje, Chikwawa, Phalombe and Zomba are the most affected. Some areas are inaccessible, impeding the conduction of assessments [http://emergency.copernicus.eu/mapping/list-of-components/EMSR116]. emsr116_01blantyre_01del_00ovr_monit01_flooded_areas features EPSG:32736 CRS:84 34.998755697804384 35.31683101427756 -17.130658405364805 -16.239920599410446 other other other text/xml other other other geonode:europe_national_provinces_capitals No abstract provided europe_national_provinces_capitals features EPSG:4326 CRS:84 -25.692211151123 56.0960006713867 35.3380012512207 70.0820007324219 other other other other other text/xml other geonode:existing_hydropower_stations No abstract provided existing_hydropower_stations features EPSG:4326 CRS:84 34.1194144697459 34.8311712572415 -15.9713713099806 -10.3910003857966 other other other other other text/xml other geonode:existing_hydropower_stations_1 No abstract provided features existing_hydropower_stations_1 EPSG:4326 CRS:84 34.1194144697459 34.8311712572415 -15.9713713099806 -10.3910003857966 other other other other text/xml other other geonode:fl1010irmt_clipmalawi This dataset includes an estimate of the global risk induced by flood hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe. flood climate risk EPSG:4326 CRS:84 31.416666666665833 37.08333333333246 -17.583333333333613 -8.916666666666984 other other other other other text/xml other geonode:fl_frequency_clipmalawi This dataset includes an estimate of flood frequency. It is based on three sources: 1) A GIS modeling using a statistical estimation of peak-flow magnitude and a hydrological model using HydroSHEDS dataset and the Manning equation to estimate river stage for the calculated discharge value. 2) Observed flood from 1999 to 2007, obtained from the Dartmouth Flood Observatory (DFO). 3) The frequency was set using the frequency from UNEP/GRID-Europe PREVIEW flood dataset. In area where no information was available, it was set to 50 years returning period. Unit is expected average number of event per 100 years. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing UNEP/GRID-Europe, with key support from USGS EROS Data Center, Dartmouth Flood Observatory 2008. flood climate frequency EPSG:4326 CRS:84 31.49166666666582 37.00833333333247 -17.508333333333624 -8.991666666666973 other other other other other text/xml other geonode:flood_radarsat_worldbank GLIDE#: FL-2015-000006-MWI. DLR derived flood extent using RADARSAT data for Malawi floods Jan 2015. Source: RADARSAT-2 / TerraSAR-X Acquired: RADARSAT-2: 13/01/2015 TerraSAR-X: 10/01/2015 Copyright: RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2015) - All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency. TerraSAR-X © German Aerospace Center (DLR), 2015 Airbus Defence and Space / Infoterra GmbH flood_radarsat_worldbank features EPSG:4326 CRS:84 34.609657225 35.5894699012086 -16.7782090912426 -16.0185259 other other other other text/xml other other geonode:flood_terrasarx_worldbank GLIDE#: FL-2015-000006-MWI. DLR derived flood extent using TerraSARX data for Malawi floods Jan 2015. Source: TerraSAR-X Acquired: TerraSAR-X: 10/01/2015 Copyright: TerraSAR-X © German Aerospace Center (DLR), 2015 Airbus Defence and Space / Infoterra GmbH features flood_terrasarx_worldbank EPSG:32736 CRS:84 34.565321132121824 35.343200602990606 -16.832461435373542 -16.041031207428855 other other other other other text/xml other geonode:flooded_areas_zomba_by_copernicus_20150118_1 Heavy rains during the month of January 2015 have led to severe flooding across Malawi. The floods have also caused extensive damage to crops, livestock and infrastructure. The southern districts of Nsanje, Chikwawa, Phalombe and Zomba are the most affected. Some areas are inaccessible, impeding the conduction of assessments [http://emergency.copernicus.eu/mapping/ems-product-component/EMSR116_02ZOMBA_DELINEATION_OVERVIEW-MONIT01/2]. flooded_areas_zomba_by_copernicus_20150118_1 features EPSG:32736 CRS:84 35.5362543191888 35.836894666996095 -15.675778950572239 -15.353616139914601 other other other other other text/xml other geonode:focal_group_discussions SIBNHES- Large Mammal Focus Group Discussions Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2) focal_group_discussions features EPSG:4326 CRS:84 35.2365547135523 35.5866771915193 -15.1942969145092 -14.4091759685678 other other other other other text/xml other geonode:foodsecuritymca Data represent a multi-criteria analysis of food security as described on pp36 and pp122 (Annex 18), Table A11 of Malawi's National Forest Landscape Restoration Assessment (2017). https://portals.iucn.org/library/node/46837 Call number: IUCN-2017-029ed Landuse food security Agriculture restoration EPSG:32736 CRS:84 32.675858734567676 35.95377080601839 -17.137885868879376 -9.36046356667382 other other other other text/xml other other geonode:forestmgmt_opp_shp This dataset represents the opportunity area for implementing the forest management restoration intervention as modeled for the National Forest Landscape Restoration Assessment. Forest management interventions include three types: 1) Regeneration of recently degraded or deforested areas through managed natural regeneration and plantings; 2) Improved management of existing plantations; and 3) Protection of existing natural forests inside and outside forest reserves and other protected areas. forestmgmt_opp_shp features EPSG:32736 CRS:84 32.66713994692649 35.947611821498135 -17.137286540971044 -9.36966385045657 other text/xml other other other other other geonode:gauging_stations_status Gauge stations status in Malawi. Data under Shire River Basin Management Programme features gauging_stations_status EPSG:4326 CRS:84 34.1634 35.6735 14.4792 16.7335 other other other other text/xml other other geonode:gaul_malawi_poly The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects. The GAUL compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments) and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth and lowers levels. The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System and e) distribute data to the users (see TechnicalArticleG2014.pdf). Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see TechnicalArticleG2014.pdf and G2014_DisputedAreas.dbf). GAUL is released once a year and the target beneficiary of GAUL data is the UN community and other authorized international and national partners. Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data. features gaul_malawi_poly EPSG:4326 CRS:84 32.673950195 35.916820526 -17.1249999999999 -9.36754131299995 other other other other text/xml other other geonode:gdp_grid_flt_clipmalawi An Estimate of Gross Domestic Product (GDP) Derived From Satellite Data gross product domestic economy EPSG:4326 CRS:84 31.495832487350015 37.00416579864998 -17.50416629665 -8.995832997350007 other other other other other text/xml other geonode:geonames No abstract provided features geonames EPSG:4326 CRS:84 32.71059 36.11667 -17.15 -9.41667 other other other other text/xml other other geonode:geonames_a The layer is a GeoNames extraction of the feature class (fclass='A') representing administrative areas names. geonames_a features EPSG:4326 CRS:84 33.07998 35.66667 -16.75 -9.92726 other other other other other text/xml other geonode:geonames_h The layer is a GeoNames extraction of the feature class (fclass='H') representing streams, lakes, bays... geonames_h features EPSG:4326 CRS:84 32.79246 36.11667 -17.13333 -9.47152 other other other other other text/xml other geonode:geonames_l No abstract provided features geonames_l EPSG:4326 CRS:84 32.85732 35.7119 -16.82318 -10.05178 other other other other text/xml other other geonode:geonames_p The layer is a GeoNames extraction of the feature class (fclass='P') representing population centres such as cities, villages, settlements. geonames_p features EPSG:4326 CRS:84 32.71059 35.8709 -17.13333 -9.50844 other other other other text/xml other other geonode:geonames_s The layer is a GeoNames extraction of the feature class (fclass='S') representing spots, buildings, farms geonames_s features EPSG:4326 CRS:84 32.84368 35.85 -17.15 -9.47155 other other other other other text/xml other geonode:geonames_t The layer is a GeoNames extraction of the feature class (fclass='T') representing elevation points such as mountains, hills, rocks. features geonames_t EPSG:4326 CRS:84 32.8 35.86667 -17.11667 -9.41667 other other other other text/xml other other geonode:groundwater_monitoring_wells_Coordinates Ground water monitoring well sites from Department of water 2015 features groundwater_monitoring_wells_Coordinates EPSG:20936 CRS:84 32.89842357666478 35.71742966450818 -16.934225341687952 -9.683290124957574 other other other other text/xml other other geonode:healthfac This dataset shows the location, name and type of the health facilities in the country of Malawi. healthfac features EPSG:32736 CRS:84 32.88569106462749 35.89811041165719 -17.123582475335652 -9.483258942419347 other other other other text/xml other other geonode:hydrogeology No abstract provided hydrogeology features EPSG:22236 CRS:84 32.66686491490766 35.95035453116584 -17.140365895536334 -9.3554921607352 other other other other other text/xml other geonode:hydropowerschemesapprox No abstract provided hydropowerschemesapprox features EPSG:22236 CRS:84 33.628368887906525 35.30444608510442 -16.23792443090868 -9.589337597522915 other other other other other text/xml other geonode:inrbrl_1 REQUIRED: A brief narrative summary of the data set. inrbrl_1 features EPSG:21036 CRS:84 34.733156855447824 35.31490969977126 -17.13155609488341 -16.009640649014614 other other other other text/xml other other geonode:irrigation_scheme1 This data set was produced from Digital Aerial Photography using AutCard by SEPRET Consultant hired by Department of Water. Department of Surveys verified it and add the attribute table. irrigation_scheme1 features EPSG:32736 CRS:84 34.77054333856432 35.0838413466516 -16.567427795448058 -16.048234057692703 other other other other text/xml other other geonode:landcover This Land cover layer provides details of the various types of land cover in Malawi. landcover features EPSG:32736 CRS:84 32.66841053439094 35.949631154376625 -17.135667010244898 -9.355176053324312 other other other other text/xml other other geonode:landuse No abstract provided landuse features EPSG:4326 CRS:84 32.61137 35.76494 -16.47592 -8.518 other other other other text/xml other other geonode:landuse_00 No abstract provided landuse_00 features EPSG:32736 CRS:84 34.251373275926404 35.317920649954424 -17.134218359365907 -15.732392288317689 other other other other text/xml other other geonode:licenseexport No abstract provided features licenseexport EPSG:20936 CRS:84 32.84920625118352 35.93525683273848 -17.136327783947372 -9.354198035612942 other other other other other text/xml other geonode:likoma No abstract provided likoma features EPSG:32736 CRS:84 34.60597379036725 34.76045877756435 -12.097962395204052 -12.003963140813454 other other other other other text/xml other geonode:likoma_1 This is Likoma Island, the smallest District in Malawi likoma_1 features EPSG:32736 CRS:84 34.60597379036725 34.76045877756435 -12.097962395204052 -12.003963140813454 other other other other other text/xml other geonode:lilongwe This is a boundary of Lilongwe rural whose attribute table contain population densities between 2008 and 2035. features lilongwe EPSG:32736 CRS:84 33.24573797761886 34.284265065955786 -14.600182376435116 -13.481554143916354 other other other other other text/xml other geonode:lilongwe_4 No abstract provided lilongwe_4 features EPSG:32736 CRS:84 33.2446972031354 34.285026537140524 -14.597786537692464 -13.48036854323461 other other other other text/xml other other geonode:lilongwe_district This is the district of Lilongwe features lilongwe_district EPSG:32736 CRS:84 33.244722220146606 34.28506486912705 -14.600501684903966 -13.483082981248394 other other other other other text/xml other geonode:lilongwesoils This dataset shows the soil types present in the area around Lilongwe. lilongwesoils features EPSG:32736 CRS:84 33.24664591034851 34.9329374180671 -15.334544806464665 -13.477147638199641 other other other other other text/xml other geonode:livelihoodzones_bnd This layer contains information about boundaries of livelihood zones livelihoodzones_bnd features EPSG:4326 CRS:84 32.6716182208022 35.9179798737588 -17.126537270693 -9.36431291203553 other other other other text/xml other other geonode:lswint1 No abstract provided lswint1 features EPSG:32736 CRS:84 34.58886908832063 34.83612653415753 -15.202607999999831 -15.077316691073886 other other other other other text/xml other geonode:lunyangwa_dam This is Lunyangwa water reservoir in Mzuzu City. It is one of the Biggest Dams in Malawi. It supplies water to the entire Mzuzu city and the surrounding areas. It is nicknamed as Galiva Dam because it was constructed by a company called Guliver. lunyangwa_dam features EPSG:4209 CRS:84 34.05126045302568 34.06741826867696 -11.445340988372863 -11.429851571891971 other other other other text/xml other other geonode:m_land No abstract provided m_land features EPSG:4326 CRS:84 -179.999989387104 180.0 -89.9999325500059 83.6341006528229 other other other other other text/xml other geonode:m_ocean No abstract provided features m_ocean EPSG:4326 CRS:84 -179.999989387104 180.0 -85.2219803557921 90.0000000000001 other other other other other text/xml other geonode:machinga_district This is Machinga District. features machinga_district EPSG:32736 CRS:84 35.15475134363132 35.92265217318074 -15.25275779102475 -14.552749400697438 other other other other text/xml other other geonode:machingasoils This dataset shows the soil types that are present in the area of Machinga. features machingasoils EPSG:32736 CRS:84 34.79700853305835 35.92854805935974 -15.68811783030318 -13.469758189354375 other other other other text/xml other other geonode:main_cities_point This layer contains the main cities in the country of Malawi main_cities_point features EPSG:4326 CRS:84 33.2761 35.310131 -15.80837 -9.699278 other other other other text/xml other other geonode:major_lakes Vector data extracted from OpenStreetMap using Overpass Turbo API (http://overpass-turbo.eu) and filtered by the following water = lake A lake: a body of relatively still fresh or salt water of considerable size, localized in a basin that is surrounded by land. This value should be considered default for natural=water, when no water or other descriptive tags are specified. features major_lakes EPSG:4326 CRS:84 33.8885 35.858422 -15.5248484 -9.4931623 other other other other text/xml other other geonode:majorcatchmentareas This dataset shows the main catchment areas in the whole country. majorcatchmentareas features EPSG:22236 CRS:84 32.66686467869863 35.95035662353519 -17.14049869925956 -9.355492160970076 other other other other other text/xml other geonode:malawi_add_l Agriculture Development Divisions provided by the Surveys Department malawi_add_l features EPSG:4326 CRS:84 32.6725716613619 35.9179792712298 -17.1265401073974 -9.36431291204032 other other other other text/xml other other geonode:malawi_cities This point layer represents Malawi Cities: Lilongwe, Blantyre and Mzuzu. features malawi_cities EPSG:32736 CRS:84 33.77257174968748 35.01722503712999 -15.803039586003248 -11.44675036409559 other other other other text/xml other other geonode:malawi_disticts_boundary This polygon layer represents the District Boundary for Malawi. features malawi_disticts_boundary EPSG:32736 CRS:84 32.67083367452945 35.95029099894159 -17.145034036703997 -9.35371764545262 other other other other text/xml other other geonode:malawi_district_boundaries District Boundary data provided by Department of Surveys, Lilongwe features malawi_district_boundaries EPSG:32736 CRS:84 32.66704210373993 35.95053239575951 -17.140506089342356 -9.355500494198179 other other other other text/xml other other geonode:malawi_district_bundaries No abstract provided features malawi_district_bundaries EPSG:32736 CRS:84 32.66704210373993 35.95053239575951 -17.140506089342356 -9.355500494198179 other other other other other text/xml other geonode:malawi_exposure This layer contains the results of an analysis performed by RMSI on the exposure of the country of Malawi towards floods. You can download the entire study report from here: http://23.22.63.123/documents/3 features malawi_exposure EPSG:4326 CRS:84 32.6725716613619 35.9179792712298 -17.1265401073974 -9.36431291204143 other other other other text/xml other other geonode:malawi_globcover_l4_2009 GlobCover is an ESA initiative which began in 2005 in partnership with JRC, EEA, FAO, UNEP, GOFC-GOLD and IGBP. The GlobCover project has developed a service capable of delivering global composites and land cover maps using as input observations from the 300m MERIS sensor on board the ENVISAT satellite mission. landuse land cover EPSG:4326 CRS:84 31.998611111111103 35.9986111111111 -17.998611111111117 -7.998611111111117 other other other other text/xml other other geonode:malawi_government_buildings This is the data collected during the E Government project. where the department of surveys with the help of NSO and The Department of Land Resource collected all government offices. The data will be used to calculate distance for the connectivity of Internet. features malawi_government_buildings EPSG:20936 CRS:84 32.713984580904885 35.90403141911515 -17.139632520424662 -9.437870540756077 other other other text/xml other other other geonode:malawi_hillshade_4326 The JRC (Joint Research Center), together with the International Center for Tropical Agriculture, Colombia, provides processed SRTM 90m Digital Elevation Data for the entire world. The SRTM digital elevation data, originally produced by NASA, has been processed to fill data voids, and to facilitate it's ease of use by a wide group of potential users. Areas with regions of no data in the original SRTM data have been filled using high resolution auxiliary data for US, Canada, Mexico, Europe, Australia, New Zealand and GTOPO30 using interpolation methods described by Reuter et al. (2007). The last version of the database (v4) is available at 90 m or 250 m resolution; previous versions are distributed at 500 m or 1 km. elevation EPSG:4326 CRS:84 32.0 36.0 -18.0 -8.0 other other other other text/xml other other geonode:malawi_karonga_flo_wd_fluvial_12m_t0005 Karonga fluvial flood risk 5 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 5 years; otherwise expressed as its probability of occurring being 1/5, or 20% in any one year. This means that, in any given year, there is a 1% chance that it will happen, regardless of when the last similar event was. flood EPSG:4326 CRS:84 33.88583333333332 33.95061111111096 -9.962611111110977 -9.903944444444441 other other other other text/xml other other geonode:malawi_karonga_flo_wd_fluvial_12m_t0020 Karonga District fluvial flood risk 20 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of 20 years; otherwise expressed as its probability of occurring being 1/20, or 5% in any one year. This means that, in any given year, there is a 5% chance that it will happen, regardless of when the last similar event was. flood Malawi Karonga Flood EPSG:4326 CRS:84 33.88583333333332 33.95061111111096 -9.962611111110977 -9.903944444444441 other other other other text/xml other other geonode:malawi_karonga_flo_wd_fluvial_12m_t0100 Karonga fluvial flood risk 100 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 100 years; otherwise expressed as its probability of occurring being 1/100, or 1% in any one year. This means that, in any given year, there is a 1% chance that it will happen, regardless of when the last similar event was. Or, put differently, it is 10 times less likely to occur than a flood with a return period of 10 years (or a probability of 10%) flood EPSG:4326 CRS:84 33.88583333333332 33.95061111111049 -9.962611111111492 -9.903944444444441 other other other other text/xml other other geonode:malawi_karonga_flo_wd_fluvial_12m_t0500 Karonga fluvial flood risk 500 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 500 years; otherwise expressed as its probability of occurring being 1/500, or 0.2% in any one year. This means that, in any given year, there is a 0.2% chance that it will happen, regardless of when the last similar event was. emsr116_01blantyre_01del_00ovr_monit01_flooded_areas EPSG:4326 CRS:84 33.88583333333332 33.95061111111049 -9.962611111111492 -9.903944444444441 other other other other text/xml other other geonode:malawi_lake This layer contains the shape of the Lake Malawi malawi_lake features EPSG:4326 CRS:84 33.8923719086551 35.345359958884 -15.0189924579988 -9.49192953075453 other other other other text/xml other other geonode:malawi_landcover_1990_schema_1 Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Schema I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3 pixel by 3 pixel filter to eliminate salt and pepper effect and remove isolated pixels. landcover landuse gaul_malawi_poly EPSG:32736 CRS:84 32.57638207701636 36.04581015261043 -17.23512169124135 -9.26239896101552 other other other other other text/xml other geonode:malawi_landcover_1990_schema_2 Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Schema I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3 pixel filter to eliminate salt and pepper effect and remove isolated pixels. landcover landuse gaul_malawi_poly EPSG:32736 CRS:84 32.57638207701636 36.04581015261043 -17.23512169124135 -9.26239896101552 other other other other other text/xml other geonode:malawi_landcover_2000_schema_1 Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Schema I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3pixel filter to eliminate salt and pepper effect and remove isolated pixels. landcover landuse gaul_malawi_poly EPSG:32736 CRS:84 30.24514678242187 38.3770458745377 -17.314846786003397 -9.182947693887565 other other other other other text/xml other geonode:malawi_landcover_2000_schema_2 Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Schema I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3pixel filter to eliminate salt and pepper effect and remove isolated pixels. landcover landuse gaul_malawi_poly EPSG:32736 CRS:84 32.57666426386541 36.04581015261043 -17.2351222851003 -9.262669948336866 other other other other other text/xml other geonode:malawi_landcover_2010_schema_1 Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Schema I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3pixel filter to eliminate salt and pepper effect and remove isolated pixels. landcover landuse gaul_malawi_poly EPSG:32736 CRS:84 32.57666426386541 36.04581015261043 -17.2351222851003 -9.262669948336866 other other other other other text/xml other geonode:malawi_landcover_2010_schema_2 Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF)sector. The coverage for the Land Cover maps is six Eastern and Southern Africa (ESA) countries: Malawi, Rwanda, Zambia, Namibia, Botswana and Tanzania. The Land Cover maps have been developed from Landsat Imagery (30m by 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Land Cover maps are developed for two epochs: 2010 and 2000. For each year flexibility in image selection is allowed from previous and next year in each epoch. An epoch for 1990 is available in some of the project countries: Malawi and Rwanda. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC) 6 land over categories for Scheme I: Forestland, Grassland, Wetland, Cropland, Settlement and Other land. Classification Scheme II is informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines. Scheme II is such that it meets the country specific mapping standards and can be rolled back to the IPCC categories. Final map is taken through a 3pixel by 3pixel filter to eliminate salt and pepper effect and remove isolated pixels. landcover landuse gaul_malawi_poly EPSG:32736 CRS:84 32.57666426386541 36.04581015261043 -17.2351222851003 -9.262669948336866 other other other other text/xml other other geonode:malawi_major_roads This poly line layer represents Malawi Major roads. features malawi_major_roads EPSG:32736 CRS:84 32.79135710673193 35.86828197707541 -17.137509210748583 -9.377430547639085 other other other other other other text/xml geonode:malawi_mangochi_flo_wd_pluvial_12m_t0002 Mangochi District pluvial flood risk 2 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of 2 years; otherwise expressed as its probability of occurring being 1/2, or 50% in any one year. This means that, in any given year, there is a 50% chance that it will happen, regardless of when the last similar event was Mangochi emsr116_01blantyre_01del_00ovr_monit01_flooded_areas Malawi EPSG:4326 CRS:84 35.204275573444434 35.3006088104466 -14.524159220000463 -14.452159291999997 other other other other text/xml other other geonode:malawi_mangochi_flo_wd_pluvial_12m_t0010 Mangochi District pluvial flood risk 10 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 10 years; otherwise expressed as its probability of occurring being 1/10, or 10% in any one year. This means that, in any given year, there is a 10% chance that it will happen, regardless of when the last similar event was flood EPSG:4326 CRS:84 35.204275573444434 35.3006088104466 -14.524159220000463 -14.452159291999997 other other other other text/xml other other geonode:malawi_mangochi_flo_wd_pluvial_12m_t0020 Mangochi District pluvial flood risk 20 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of 20 years; otherwise expressed as its probability of occurring being 1/20, or 5% in any one year. This means that, in any given year, there is a 5% chance that it will happen, regardless of when the last similar event was emsr116_01blantyre_01del_00ovr_monit01_flooded_areas EPSG:4326 CRS:84 35.204275573444434 35.3006088104466 -14.524159220000463 -14.452159291999997 other other other other text/xml other other geonode:malawi_mangochi_flo_wd_pluvial_12m_t0050 Mangochi pluvial flood risk 50 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 50 years; otherwise expressed as its probability of occurring being 1/50, or 2% in any one year. This means that, in any given year, there is a 2% chance that it will happen, regardless of when the last similar event was. flood EPSG:4326 CRS:84 35.204275573444434 35.3006088104466 -14.524159220000463 -14.452159291999997 other other other other text/xml other other geonode:malawi_national_building_material Poorly constructed dwellings/houses are more sensitive to climate hazards. Houses whose walls are made of mud or unburnt bricks have a higher probability of damage incase of hazards like floods than for instance those made of burnt bricks, and concrete. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). SERVIR is a joint USAID-NASA project. For more information on SERVIR GeoTIFF WCS malawi_national_building_material EPSG:4326 CRS:84 32.67668701171875 35.92668701171875 -17.124999999999996 -9.364999999999997 other other other other text/xml other other geonode:malawi_national_malaria_susceptibility Higher poverty levels are likely to be associated with higher sensitivity. Estimated levels of Plasmodium falciparum malaria endemicity within the limits of stable transmission. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). SERVIR is a joint USAID-NASA project. For more information on SERVIR, visit http://www.servirglobal.netNo abstract provided geossNoMonetaryCharge geossDataCore fire geo-disasters servircat vulnerability rcmrd-servir emsr116_01blantyre_01del_00ovr_monit01_flooded_areas geossAttribution temperature atlas drought precipitation gaul_malawi_poly exposure EPSG:4326 CRS:84 32.67668701171875 35.92668701171875 -17.124999999999996 -9.364999999999997 text/xml other other other other other other geonode:malawi_national_poverty_levels No abstract provided GeoTIFF malawi_national_poverty_levels WCS EPSG:4326 CRS:84 32.67668701171875 35.92668701171875 -17.124999999999996 -9.364999999999997 other other other other text/xml other other geonode:malawi_national_poverty_levels0 High poverty levels are likely to be associated with high sensitivity to climate hazards. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). For references, use: Regional Center for Mapping of Resources for Development (RCMRD), Malawi Department of Disaster Management GeoTIFF malawi_national_poverty_levels WCS EPSG:4326 CRS:84 32.67668701171875 35.92668701171875 -17.124999999999996 -9.364999999999997 other other other other text/xml other other geonode:malawi_national_soil_organic_carbon High Soil organic matter is generally associated with higher crop yields and greater soil moisture retention. In Malawi, areas with high productivity are more sensitive to disruptions as a result of climate change and climate variability. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). o abstract provided GeoTIFF WCS malawi_national_soil_organic_carbon EPSG:4326 CRS:84 32.67668701171875 35.92668701171875 -17.124999999999996 -9.364999999999997 other other other other text/xml other other geonode:malawi_national_soil_organic_carbon0 High Soil organic matter is generally associated with higher crop yields and greater soil moisture retention. In Malawi, areas with high productivity are more sensitive to disruptions as a result of climate change and climate variability. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). SERVIR is a joint USAID-NASA project. For more information on SERVIR, visithttp://www.servirglobal.net/ GeoTIFF WCS malawi_national_soil_organic_carbon EPSG:4326 CRS:84 32.67668701171875 35.92668701171875 -17.124999999999996 -9.364999999999997 other other other other text/xml other other geonode:malawi_natural No abstract provided malawi_natural features EPSG:4326 CRS:84 32.6831752 35.8995658 -16.7350433 -9.3679649 other other other other other text/xml other geonode:malawi_osm_points_1950_utm OSM building points of Malawi generated from OSM buildings in malawi features EPSG:20936 CRS:84 32.81609716099739 35.920848943292725 -17.150237214545953 -9.47526537832943 other other other other text/xml other other geonode:malawi_rdp This file represents the rural development program areas, defined by the Ministry of Agriculture of Malawi. features malawi_rdp EPSG:4326 CRS:84 32.67395 35.916821 -17.125 -9.367541 other other other other text/xml other other geonode:malawi_resiliencemca Data represent a multi-criteria analysis of resilience as described on pp38 and pp122 (Annex 18), Table A12 of Malawi's National Forest Landscape Restoration Assessment (2017). https://portals.iucn.org/library/node/46837 Call number: IUCN-2017-029ed Climate Change Landuse disaster restoration resilience EPSG:32736 CRS:84 32.66702402654607 35.954834537965596 -17.137878704358027 -9.362341381723676 other other other other text/xml other other geonode:malawi_salima_flo_wd_fluvial_12m_t0005 Salima fluvial flood risk 5 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, flood return period of 5 years; otherwise expressed as its probability of occurring being 1/5, or 20% in any one year. This means that, in any given year, there is a 20% chance that it will happen, regardless of when the last similar event was. flood EPSG:4326 CRS:84 34.40949859044443 34.47205408344528 -13.819604369000945 -13.749826660999997 other other other other text/xml other other geonode:malawi_salima_flo_wd_fluvial_12m_t0010 Salima District fluvial flood risk 10 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 10 years; otherwise expressed as its probability of occurring being 1/10, or 10% in any one year. This means that, in any given year, there is a 10% chance that it will happen, regardless of when the last similar event was. flood EPSG:4326 CRS:84 34.40949859044443 34.47205408344528 -13.819604369000945 -13.749826660999997 other other other other text/xml other other geonode:malawi_salima_flo_wd_fluvial_12m_t0050 Salima fluvial flood risk 50 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 50 years; otherwise expressed as its probability of occurring being 1/50, or 2% in any one year. This means that, in any given year, there is a 2% chance that it will happen, regardless of when the last similar event was. flood EPSG:4326 CRS:84 34.40949859044443 34.47205408344528 -13.819604369000945 -13.749826660999997 other other other other text/xml other other geonode:malawi_salima_flo_wd_fluvial_12m_t0100 Salima fluvial flood risk 100 years return period probability generated as part of Northern region and Lake Malawi Flood risk management project by RASOR (rasor-project.eu) for Global Facility for Disaster Risk reduction and the government of Malawi between June 2015- June 2016. RASOR has developed the flood risk model by adapting a newly developed 12m resolution TanDEM-X Digital Elevation Model (DEM) to flood risk management applications and using it as a base layer to interrogate data sets and develop specific flood disaster scenarios. RASOR overlays archived and near-real time very-high resolution optical and radar satellite data, combined with in-situ data for Malawi local applications. The probability of a flood event occurring is expressed as a return period. A flood return is the inverse of probability (generally expressed in %), it gives the estimated time interval between events of a similar size or intensity. For example, the return period of a flood might be 100 years; otherwise expressed as its probability of occurring being 1/100, or 1% in any one year. This means that, in any given year, there is a 1% chance that it will happen, regardless of when the last similar event was. Or, put differently, it is 10 times less likely to occur than a flood with a return period of 10 years (or a probability of 10%) flood EPSG:4326 CRS:84 34.40949859044443 34.47205408344528 -13.819604369000945 -13.749826660999997 other other other other text/xml other other geonode:malawi_srtm30meters This data represents the 30 meters Digital Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM). This data-set was derived through mosaicking of individual SRTM tiles for a particular country and clipping the mosaicked tiles using the country boundary extent.No abstract provided geossNoMonetaryCharge geo-agriculture geo-disasters servircat geo-water geo-ecosystems rcmrd-servir EPSG:4326 CRS:84 32.67625 35.91847222222222 -17.132916666666667 -9.365138888888906 other other other other text/xml other other geonode:malawi_srtm_4236 The JRC (Joint Research Center), together with the International Center for Tropical Agriculture, Colombia, provides processed SRTM 90m Digital Elevation Data for the entire world. The SRTM digital elevation data, originally produced by NASA, has been processed to fill data voids, and to facilitate it's ease of use by a wide group of potential users. Areas with regions of no data in the original SRTM data have been filled using high resolution auxiliary data for US, Canada, Mexico, Europe, Australia, New Zealand and GTOPO30 using interpolation methods described by Reuter et al. (2007). The last version of the database (v4) is available at 90 m or 250 m resolution; previous versions are distributed at 500 m or 1 km. Elevation EPSG:4326 CRS:84 32.0 36.0 -18.0 -8.0 other other other other text/xml other other geonode:malawiaverageyearlytemperature_cut2_prj This layer shows the average yearly temperature for the country of Malawi Climate Temperature EPSG:4326 CRS:84 32.60000000000001 35.99000000000001 -17.200000000000003 -9.304 other other other other text/xml other other geonode:malawidegradation_mca Data represent a multi-criteria analysis of functional landscape degradation as described on pp33 and pp122 (Annex 18), Table A10 of Malawi's National Forest Landscape Restoration Assessment (2017). https://portals.iucn.org/library/node/46837 Call number: IUCN-2017-029ed development Landuse agriculture restoration degradation EPSG:32736 CRS:84 32.668445924953325 35.948170038402374 -17.137421605389022 -9.358751169863769 other other other other text/xml other other geonode:malawimaizeexposure Malawi Maize Exposure malawimaizeexposure features EPSG:4326 CRS:84 32.6725716609534 35.9179792709266 -17.1265401077398 -9.36431291191138 other other other other text/xml other other geonode:mammal_transects SIBNHES- Large Mammal Transects Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2) mammal_transects features EPSG:4326 CRS:84 35.2803661489006 35.4876924847317 -15.2094985399759 -14.4283439853666 other other other other text/xml other other geonode:mchinji_district This file is a representation of Mchinji district boundary. Attributes attached to it are population densities, area and perimeter. mchinji_district features EPSG:32736 CRS:84 33.24573797761886 34.284265065955786 -14.600182376435116 -13.481554143916354 other other other other other text/xml other geonode:mlw_airports No abstract provided features mlw_airports EPSG:4326 CRS:84 33.468834 35.389946 -15.680083 -9.954028 other other other other text/xml other other geonode:mlwplaces_point This layer provides location of major trading centers and places in Malawi. It also includes location of cities and district centers also popularly known as BOMAs. Part of this data was collected using Handheld GPS units which have a positional accuracy of around 15 metres while some was digitised from hardcopy maps. mlwplaces_point features EPSG:4326 CRS:84 32.816667 35.868408 -17.083981 -9.466667 other other other other text/xml other other geonode:mulanje_boundary No abstract provided mulanje_boundary features EPSG:32736 CRS:84 35.27171621478225 35.822281078105995 -16.214748559323105 -15.590693567787222 other other other other text/xml other other geonode:mw_contours No abstract provided mw_contours features EPSG:4326 CRS:84 32.6741577230339 35.8430604692544 -17.1346480166301 -9.38026909346553 other other other other other text/xml other 150000.0 geonode:mw_district Malawi Second and Third Level Administrative Boundaries with City Regions features mw_district EPSG:4326 CRS:84 32.6739499991893 35.9168210008107 -17.1250000019394 -9.36754129806063 other other other other other text/xml other geonode:mw_health_wgs84 This dataset shows the location, address and contacts of the health facilities in Malawi. features mw_health_wgs84 EPSG:4326 CRS:84 32.8874999981601 35.8674999991746 -17.0988891936947 -9.49444416995358 other text/xml other other other other other geonode:mw_railways_50kv1 No abstract provided features mw_railways_50kv1 EPSG:20936 CRS:84 32.82267218628355 35.92256416909071 -17.139744934343053 -13.699369202078598 other other other other text/xml other other geonode:mw_soils This layer contain a description of the soil types in the country of Malawi mw_soils features EPSG:4326 CRS:84 32.6739701223612 35.9182495369888 -17.1328246865103 -9.36523548021429 other other other other other text/xml other geonode:mw_urban_city_towns Cities and towns boundaries to help delineate urban rural areas mw_urban_city_towns features EPSG:4326 CRS:84 32.8650588989258 35.6657867431641 -16.9457130432129 -9.68781566619873 other other other other text/xml other other geonode:mw_villagespoints This layer contains the location and information about villages in the country of Malawi mw_villagespoints features EPSG:4326 CRS:84 32.7116104939231 35.8730151044098 -17.1227557159228 -9.38506641898287 other other other other text/xml other other 1865.0 2004976.0 geonode:mwi_adm0 This vector layer provides international boundary for Malawi. This data was produced by the Malawi Surveys department. mwi_adm0 features EPSG:4326 CRS:84 32.671524 35.915046 -17.127211 -9.36379599999987 other other other other text/xml other other geonode:mwi_phy_vegetationloss_geonode_20140612 This layer contains information about the land degradation phenomenon observed during the Integrated Context Analysis (ICA) run in Malawi in 2014. Data source: European Space Agency, 1990-2010. Local land degradation data were not available for the purposes of this analysis, therefore a deforestation analysis was performed using remotely sensed land cover data. The key indicators used for the analysis were the percentage and the overall surface of vegetation cover loss mwi_phy_vegetationloss_geonode_20140612 features EPSG:4326 CRS:84 32.6739501948456 35.9168205259465 -17.12499999985 -9.36754131258147 other other other other text/xml other other geonode:mwi_trs_roadaccessconstraints_wfp This dataset is based on an extraction of OpenStreetMap. In addition, it contains access constraints status in the column "status" and is edited regularly with information received by partners. features mwi_trs_roadaccessconstraints_wfp EPSG:4326 CRS:84 33.8419761657715 35.8577461242676 -17.1261348724365 -13.6587066650391 other other other other text/xml other other geonode:mwi_wfp_malnutrition_geonode_20140612 No abstract provided mwi_wfp_malnutrition_geonode_20140612 features EPSG:4326 CRS:84 32.6739501948456 35.9168205259465 -17.12499999985 -9.36754131258147 other other other other other text/xml other geonode:mwilv3 The Small Area Estimates of Poverty and Inequality dataset consists of spatially referenced consumption-based measures of poverty and inequality for subnational administrative areas of various countries throughout Africa, Asia, Europe, North America and South America. These measures are derived, on a country-level basis, from a combination of census and survey data using small area estimation techniques developed for poverty mapping. The collection of data sets have been compiled, integrated and standardized from the original data providers into a unified, spatially referenced and globally consistent dataset. The collection is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with a number of external data providers. mwilv3 features EPSG:4326 CRS:84 32.6739500000003 35.9168200000004 -17.125 -9.36753999999928 other other other other text/xml other other geonode:mzimba_district Mzimba District is located in the northern region of Malawi, Its the biggest district in this country Malawi, Ngoni and Tumbuka are the languages used in this district features mzimba_district EPSG:32736 CRS:84 33.2253833253479 34.106579943747896 -12.738658612806967 -10.958718002233862 other other other other text/xml other other geonode:mzimba_district_boundary Mzimba district boundary clipped from the Malawi district boundary features mzimba_district_boundary EPSG:20936 CRS:84 33.22526786905955 34.10647448143796 -12.741337610782692 -10.961393986355109 other other other text/xml other other other geonode:natural OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. The maps are created using data from portable GPS devices, aerial photography, other free sources or simply from local knowledge. Both rendered images and the vector graphics are available for download. natural features EPSG:4326 CRS:84 32.00541 35.972 -16.73504 -8.212 other other other other other text/xml other geonode:ndvicompositedryseason_90_00_16 The following raster represents the vegetation evolution in Malawi during the dry seasons of 1990, 2000 and 2016. GeoTIFF ndvicompositedryseason_90_00_16 WCS EPSG:4326 CRS:84 31.925945534550973 36.82616557789455 -17.518226018671612 -8.862868424651612 other other other other text/xml other other geonode:ne_10m_admin_0_countries No abstract provided ne_10m_admin_0_countries features EPSG:4326 CRS:84 -180.0 180.0 -90.0 83.6341006530001 other other other other text/xml other other geonode:nkhata_bay_district NkhataBay is a lakeshore district in the northern region of Malawi. It is bordered by Rumphi to the northt, Mzimba to the northeast and southeast and Nkhotakota the south. nkhata_bay_district features EPSG:32736 CRS:84 33.733104329522135 34.32938618303847 -12.235101587808352 -10.992394568506173 other other other other other text/xml other geonode:npfr1 No abstract provided npfr1 features EPSG:32736 CRS:84 34.35530494054919 35.61011194785471 -16.975107650998755 -14.360505060421744 other other other other other text/xml other geonode:nrifld_1 No abstract provided nrifld_1 emsr116_01blantyre_01del_00ovr_monit01_flooded_areas features EPSG:4326 CRS:84 34.86 35.27 -16.96 -16.2 other other other other text/xml other other geonode:nsange_3 No abstract provided features nsange_3 EPSG:20936 CRS:84 34.75136260553039 35.251336501520534 -16.934715110915782 -15.425531540411 other other other other other text/xml other geonode:nsanje No abstract provided features nsanje EPSG:32736 CRS:84 34.90200481884074 35.318254663380934 -17.12888447768347 -16.29568931572708 other other other other text/xml other other geonode:nsanje_1 No abstract provided nsanje_1 features EPSG:32736 CRS:84 34.90200481884074 35.318254663380934 -17.12888447768347 -16.29568931572708 other other other other text/xml other other geonode:nsanje_2 No abstract provided nsanje_2 features EPSG:20936 CRS:84 35.087737990115215 35.34644509266578 -15.383848283621122 -15.276230085071564 other other other other text/xml other other geonode:ntcheu Ntchisi is a district in the central region of Malawi. It is located in the northern part of Dowa, South east of Kasungu and south west of Nkhotakota. features ntcheu EPSG:32736 CRS:84 33.6992349894734 34.12885161956993 -13.535017113379869 -13.013137826973228 other other other other text/xml other other geonode:osm_poi No abstract provided features EPSG:4326 CRS:84 32.629485 35.6872482 -16.8376423 -9.25730610000002 other other other other text/xml other other geonode:osm_waterway_lines Data derived from OpenStreetMap (export.hotosm.org). Selected features were extracted using QGIS. License under ODbL V1. features osm_waterway_lines EPSG:4326 CRS:84 32.6073353 36.0345261 -17.2664608 -9.18864770000002 other other other other text/xml other other geonode:point_ft No abstract provided point_ft features EPSG:32736 CRS:84 35.07849410713278 35.18696145810661 -15.984732287508114 -15.752291864657066 other other other other other text/xml other geonode:pp_blk7 Flight plan for 1995 Aerial images Block 7 covering Mzimba, Nkhatabay and part of Dwangwa District pp_blk7 features EPSG:32736 CRS:84 33.23586382296759 34.75083740463994 -12.578675499575754 -10.997733621396158 other other other other text/xml other other geonode:pp_blk8 1995 Aerial Imagery Flight Plan features pp_blk8 EPSG:32736 CRS:84 33.27411196868341 34.24910253519961 -11.052397122510977 -10.24875575452456 other other other other text/xml other other geonode:prcatch_1 No abstract provided prcatch_1 features EPSG:32736 CRS:84 34.488020311491454 35.32239082785133 -15.825889059831697 -15.072620436449924 other other other other text/xml other other geonode:primary_schools_ll Location of Primary Schools (2013) primary_schools_ll features EPSG:4326 CRS:84 32.7194374815491 35.8659575904468 -17.1254487716706 -9.38189613176767 other other other other text/xml other other geonode:produce_markets SIBNHES- Location of Economic Valuation Surveys Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2) produce_markets features EPSG:32736 CRS:84 35.183097674926636 35.613766989087104 -15.41427754183575 -14.364147258079761 other other other other text/xml other other geonode:proposed_forest_reserves Malawi Proposed Forest Reserves proposed_forest_reserves features EPSG:32736 CRS:84 33.03286040028239 35.18078199488131 -15.756768012541729 -9.466908683144666 other other other other other text/xml other geonode:proposed_hydropower_station No abstract provided features proposed_hydropower_station EPSG:4326 CRS:84 33.5853382978529 35.0587965900739 -15.8595708886644 -9.60816206727466 other other other other text/xml other other geonode:protected_areas This layer shows protected areas in the country of Malawi protected_areas features EPSG:4326 CRS:84 32.6716182278052 35.7853830317719 -16.9653031588231 -9.90915004931652 other other other other other text/xml other geonode:q100_atkins_flood_hazard Flood hazard rating on the Shire River area, based on the application of a 2-dimensional model [http://www.atkinsglobal.com/environment]. The flood return period for this dataset is 100 years. The final rating is stored in the HAZARD2D field: values lower than 0.75 correspond to very low hazard - caution; values in the range 0.75 to 1.25 correspond to danger for some - includes children, the elderly and infirm; values in the range 1.25 to 2.0 correspond to danger for most - includes the general public; values higher than 2.0 correspond to danger for all - includes the emergency services. See document "Integrated Flood Risk Management Plan for the Shire Basin project - Interim Report" on the documents section of this platform. features q100_atkins_flood_hazard EPSG:32736 CRS:84 34.429553867944925 35.3638356797606 -17.162514534460247 -15.036130658262637 other other other text/xml other other other geonode:q100_atkins_flood_outline Flood envelope polygon showing the extent of flooding with respect to a 100 years return period. q100_atkins_flood_outline features EPSG:32736 CRS:84 34.42829247752465 35.36540506409638 -17.162160716384374 -15.035759575977535 other other other other text/xml other other geonode:q10_atkins_flood_hazard Flood hazard rating on the Shire River area, based on the application of a 2-dimensional model [http://www.atkinsglobal.com/environment]. The flood return period for this dataset is 10 years. The final rating is stored in the HAZARD2D field: values lower than 0.75 correspond to very low hazard - caution; values in the range 0.75 to 1.25 correspond to danger for some - includes children, the elderly and infirm; values in the range 1.25 to 2.0 correspond to danger for most - includes the general public; values higher than 2.0 correspond to danger for all - includes the emergency services. See document "Integrated Flood Risk Management Plan for the Shire Basin project - Interim Report" on the documents section of this platform. features q10_atkins_flood_hazard EPSG:32736 CRS:84 34.43146570006568 35.36019175864218 -17.03625392375424 -15.036151579496082 other other other other other text/xml other geonode:q10_atkins_flood_outline Flood envelope polygon showing the extent of flooding with respect to a 10 years return period. features q10_atkins_flood_outline EPSG:32736 CRS:84 34.430809095455395 35.36136208040306 -17.04258874292225 -15.036598465918122 other other other other other text/xml other geonode:q20_atkins_flood_outline Flood envelope polygon showing the extent of flooding with respect to a 100 years return period. features q20_atkins_flood_outline EPSG:32736 CRS:84 34.43080366863533 35.36248825008795 -17.132050914839784 -15.035785423872936 other other other other other text/xml other geonode:q500_atkins_flood_hazard Flood hazard rating on the Shire River area, based on the application of a 2-dimensional model [http://www.atkinsglobal.com/environment]. The flood return period for this dataset is 500 years. The final rating is stored in the HAZARD2D field: values lower than 0.75 correspond to very low hazard - caution; values in the range 0.75 to 1.25 correspond to danger for some - includes children, the elderly and infirm; values in the range 1.25 to 2.0 correspond to danger for most - includes the general public; values higher than 2.0 correspond to danger for all - includes the emergency services. See document "Integrated Flood Risk Management Plan for the Shire Basin project - Interim Report" on the documents section of this platform. q500_atkins_flood_hazard features EPSG:32736 CRS:84 34.42866701667744 35.363835679749165 -17.16252092610088 -15.03574662716621 other other other other text/xml other other geonode:q500_atkins_flood_outline Flood envelope polygon showing the extent of flooding with respect to a 500 years return period. q500_atkins_flood_outline features EPSG:32736 CRS:84 34.42829247752466 35.36540506409638 -17.162160716384374 -15.035759575977535 other other other other other text/xml other geonode:q50_atkins_flood_hazard Flood hazard rating on the Shire River area, based on the application of a 2-dimensional model [http://www.atkinsglobal.com/environment]. The flood return period for this dataset is 50 years. The final rating is stored in the HAZARD2D field: values lower than 0.75 correspond to very low hazard - caution; values in the range 0.75 to 1.25 correspond to danger for some - includes children, the elderly and infirm; values in the range 1.25 to 2.0 correspond to danger for most - includes the general public; values higher than 2.0 correspond to danger for all - includes the emergency services. See document "Integrated Flood Risk Management Plan for the Shire Basin project - Interim Report" on the documents section of this platform. q50_atkins_flood_hazard features EPSG:32736 CRS:84 34.43071460831789 35.362288853139965 -17.162506136563433 -15.036146414278337 other other other other text/xml other other geonode:q50_atkins_flood_outline Flood envelope polygon showing the extent of flooding with respect to a 50 years return period. q50_atkins_flood_outline features EPSG:32736 CRS:84 34.43231230829264 35.36286858050779 -17.16214861438417 -15.38294210201787 other other other other other text/xml other geonode:q5_atkins_flood_hazard Flood hazard rating on the Shire River area, based on the application of a 2-dimensional model [http://www.atkinsglobal.com/environment]. The flood return period for this dataset is 5 years. The final rating is stored in the HAZARD2D field: values lower than 0.75 correspond to very low hazard - caution; values in the range 0.75 to 1.25 correspond to danger for some - includes children, the elderly and infirm; values in the range 1.25 to 2.0 correspond to danger for most - includes the general public; values higher than 2.0 correspond to danger for all - includes the emergency services. See document "Integrated Flood Risk Management Plan for the Shire Basin project - Interim Report" on the documents section of this platform. features q5_atkins_flood_hazard EPSG:32736 CRS:84 34.431687316682854 35.35862056585812 -16.93712653777621 -15.043142779360283 other other other other text/xml other other geonode:q5_atkins_flood_outline Flood envelope polygon showing the extent of flooding with respect to a 5 years return period. q5_atkins_flood_outline features EPSG:32736 CRS:84 34.43084166265132 35.35921572916732 -16.93848637852435 -15.041485238325828 other other other other text/xml other other geonode:q75_atkins_flood_outline Flood envelope polygon showing the extent of flooding with respect to a 75 years return period. q75_atkins_flood_outline features EPSG:32736 CRS:84 34.429966579527566 35.365405064096386 -17.16214861438417 -15.035759575977535 other other other other other text/xml other geonode:railways This layer contains information about the railway network in the country of Malawi railways features EPSG:20936 CRS:84 32.82276355775996 35.92256416909071 -17.13974501444552 -13.708607683250372 other other other other other text/xml other geonode:rain2 precipitation_mw Climate Precipitation Rain EPSG:4326 CRS:84 32.6 35.92 -17.195533333333334 -9.305 other other other other text/xml other other geonode:raingauges This dataset shows the position and the number of rain gauges in the whole country. raingauges features EPSG:32736 CRS:84 32.813890845709736 35.774479247976004 -17.09396984888607 -9.62347798049785 other other other other text/xml other other geonode:regeneration_potential_survey_plots SIBNHES- Locations of Regeneration Potential Survey Plots Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project (Year 2) regeneration_potential_survey_plots features EPSG:4326 CRS:84 34.727867 35.505861 -15.347558 -14.423817 other other other other text/xml other other geonode:regions_bnd_project This layer contains the administrative boundaries for the region of the country of Malawi regions_bnd_project features EPSG:4326 CRS:84 32.6725716613619 35.9179792712298 -17.1265401073974 -9.36431291204143 other other other other text/xml other other geonode:river_stream_opp_shp This dataset represents the opportunity area for implementing river- and stream-bank interventions as modeled for the National Forest Landscape Restoration Assessment. River- and stream-bank restoration focuses on establishing buffers of trees along streams and rivers courses to stabilize the soil, either through active planting or natural regeneration. The benefits of these protective buffers include decreased erosion and sedimentation into waterways, which improves water quality and quantity. This practice is particularly important in watersheds with downstream hydropower and reservoir infrastructure, where sedimentation is a major impediment to their efficiency and sustainability. river_stream_opp_shp features EPSG:32736 CRS:84 32.724679378489945 35.925895725545196 -17.135336489292392 -9.35628203895326 other other other other other text/xml other geonode:roads OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. The maps are created using data from portable GPS devices, aerial photography, other free sources or simply from local knowledge. Both rendered images and the vector graphics are available for download. roads features EPSG:4326 CRS:84 32.0 35.99993 -17.9997 -8.00023 other other other other other text/xml other geonode:rs2_20150121_flood_unosat (Taken from http://goo.gl/ATRNMy) UNOSAT Product ID: 2152 - English Published: 21 Jan, 2015 GLIDE: FL20150112MOZ FL-2015-000006-MWI. FootPrint (Lat x Long, WSG84 Geographic, decimal degrees) TopLeft: -17.8783776 x 35.4900186 BottomRight: -16.9266849 x 34.8972707 This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and southern Malawi along the Chire River as detected by Radarsat-2 imagery acquired 21 January 2015. Between 11 December 2014 and 21 January 2015 flood waters affected roughly 55000 hectares of lands in the five listed districts. About 31 villages are located within the flooded zone and according to the World Population database around 55,000 people are located within these potentially affected areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT. Satellite Data (1): Radarsat-2 Imagery Dates: 21 January 2015 Resolution: 12.5 m Copyright: MacDonald, Dettwiler and Associates Ltd. Source: KSAT Analysis : UNITAR / UNOSAT Production: UNITAR / UNOSAT Analysis conducted with ArcGIS v10.2 features rs2_20150121_flood_unosat EPSG:404000 CRS:84 34.5475605490001 35.752308187 -17.8722248649999 -16.8233196989999 other other other other other text/xml other geonode:salima Map of Salima District where you find Livingstonia Beach Hotel, Mpatsa Lodge, Blue Waters Lodge salima features EPSG:32736 CRS:84 34.10882535592462 34.63427435588531 -14.12622832829367 -13.408448554433642 other other other other text/xml other other geonode:salimasoils This dataset describes the soil types in the area around Salima. salimasoils features EPSG:32736 CRS:84 33.75399462177122 35.084335665710334 -14.88988367060368 -12.144754079487 other other other other text/xml other other geonode:secondary_schools_ll Secondary Schools (2013) secondary_schools_ll features EPSG:4326 CRS:84 32.8261698977891 35.8028373946714 -17.1106339293673 -9.40501071868339 other other other other other text/xml other geonode:sed_export_malawi_aoi_lakes Utilising the InVEST sediment delivery ratio model, developed by The Natural Capital Project, these data demonstrate sediment export to stream networks in Malawi. Model documentation can be found here: http://data.naturalcapitalproject.org/nightly-build/invest-users-guide/html/sdr.html development Water ecosystem services Hydrology EPSG:32736 CRS:84 32.66702484484225 35.950108770298726 -17.13741927056647 -9.355244827779558 other other other other text/xml other other geonode:sed_retention_malawi_aoi_lakes Utilising the InVEST sediment delivery ratio model, developed by The Natural Capital Project, these data demonstrate sediment retention to stream networks in Malawi. Model documentation can be found here: http://data.naturalcapitalproject.org/nightly-build/invest-users-guide/html/sdr.html Development Water ecosystem services Hydrology EPSG:32736 CRS:84 32.66702484484225 35.950108770298726 -17.13741927056647 -9.355244827779558 other other other other text/xml other other geonode:shire_agric_markets_dd No abstract provided shire_agric_markets_dd features EPSG:404000 CRS:84 34.50967 35.73376 -16.903 -14.4809 other other other other text/xml other other geonode:shire_areas_highly_prone_to_floods_dd No abstract provided features shire_areas_highly_prone_to_floods_dd EPSG:404000 CRS:84 35.0269488089203 35.3140375851257 -17.1276736301744 -16.3342153875628 other other other other other text/xml other geonode:shire_gw_observation_wells_dd Ground water observation wells by SRBMP shire_gw_observation_wells_dd features EPSG:404000 CRS:84 34.5162810594292 35.6071829495333 -16.9220284754861 -14.3671225278708 other other other other text/xml other other geonode:shire_interventions Interventions currently under Shire River Basin Management Program. features shire_interventions EPSG:20936 CRS:84 34.5309006416127 35.293643696550795 -15.806913069693792 -15.030391689928285 other other other other other text/xml other geonode:shire_irrigation_schemes_mining_dd No abstract provided shire_irrigation_schemes_mining_dd features EPSG:4326 CRS:84 34.674552 35.729848 -16.547194 -14.906327 other text/xml other other other other other geonode:shire_streamflow_gauges_dd No abstract provided shire_streamflow_gauges_dd features EPSG:404000 CRS:84 33.533780850883 35.8 -16.9486111111111 -14.8097222222222 other other other other text/xml other other geonode:shire_water_resources_units_dd No abstract provided shire_water_resources_units_dd features EPSG:404000 CRS:84 34.2487325050001 35.8193335950001 -17.1292439119999 -14.350580119 other other other other other text/xml other geonode:shiregeology This map represents geology units in the Shire River Valley. shiregeology features EPSG:32736 CRS:84 34.2191123171771 35.95987836535799 -17.697498640477953 -14.596230111045433 other other other other text/xml other other geonode:shirevalleysoils This dataset describes soil units in the Shire River Valley shirevalleysoils features EPSG:32736 CRS:84 34.251375581364165 35.31795053886002 -17.14122218222686 -15.732388367913556 other other other other other text/xml other geonode:sibnhes_interpolated_ghi_values SIBNHES- Interpolated Genetic Heat Index Values Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project features sibnhes_interpolated_ghi_values EPSG:32736 CRS:84 34.238890683946096 35.84901307804887 -17.132134460315726 -13.475312645440662 other other other other other text/xml other geonode:sibnhes_project_sites SIBNHES Project Sites Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project sibnhes_project_sites features EPSG:4326 CRS:84 34.3707132658695 35.5783804425855 -16.9614966748052 -14.3804455454086 other other other other text/xml other other geonode:sibnhes_rapid_botanical_surveys SIBNHES- Rapid Botanical Surveys Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project sibnhes_rapid_botanical_surveys features EPSG:32736 CRS:84 34.41534743100616 35.530368472411354 -16.970603134494365 -14.42343945918309 other other other other other text/xml other geonode:sibnhes_shire_ecological_value SIBNHES- Shire Ecological Value Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project GeoTIFF WCS sibnhes_shire_ecological_value EPSG:32736 CRS:84 34.230361532608356 35.966110882722724 -17.143791560246402 -13.469705055404177 other other other other text/xml other other geonode:sibnhes_shire_forested_areas_2015 SIBNHES- Shire Forested Areas 2015 Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project features sibnhes_shire_forested_areas_2015 EPSG:32736 CRS:84 34.23885981249013 35.84903397357931 -17.13350781169304 -13.475104045376703 other other other other other text/xml other geonode:sibnhes_shire_lulc_2015 SIBNHES- Shire Land Use Land Cover 2015 Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project sibnhes_shire_lulc_2015 features EPSG:32736 CRS:84 34.2386965033583 35.94944813915794 -17.133978783251965 -13.47398381426662 other other other other text/xml other other geonode:sibnhes_year_1_natural_habitat_survey_plots SIBNHES- Year 1 Natural Habitat Survey Plots Strengthening the Information Base of Natural Habitats, Biodiversity and Environmental Services in the Shire Basin Project sibnhes_year_1_natural_habitat_survey_plots features EPSG:4326 CRS:84 34.4237833097577 35.5074659921229 -16.9638832937926 -14.4265259709209 other other other other text/xml other other geonode:soil_water_cons_opp_shp This dataset represents the opportunity area for implementing the soil and water conservation interventions as modeled for the National Forest Landscape Restoration Assessment. Soil and water conservation interventions involve establishing small-scale infrastructure such as check dams, terraces, infiltration trenches, and contour bunds along slopes and hillsides for the purposes of regulating water flow during heavy rains to prevent intense erosion and gully formation. These types of infrastructure are particularly important where croplands are located at the base of these hillsides and thus are more vulnerable to soil and nutrient loss and crop damage from heavy or rapid water flow. The check dams and terraces serve to reduce the force of water flow downslope while the infiltration ditches and contour bunds absorb and accumulate soil and water. Planting vetiver grass and other vegetation along the slopes also adds to the absorption and mitigation benefits. soil_water_cons_opp_shp features EPSG:32736 CRS:84 32.671091390961124 35.85661355473043 -17.13567169636864 -9.370092768597464 other other other other text/xml other other geonode:sports_fields Data downloaded from OSM, Features extracted in QGIS, Licence under ODbL version1 features sports_fields EPSG:4326 CRS:84 32.7655584 35.6468149 -16.1850401 -9.27039280000001 other other other other other other other text/xml other geonode:srtm_malawi No abstract provided features srtm_malawi EPSG:4326 CRS:84 32.0 35.997192383 -18.0 -8.0 other other other other text/xml other other geonode:thyolo Hub of Tea Fields features thyolo EPSG:32736 CRS:84 34.900347234562666 35.33512279185939 -16.412159379275376 -15.848094359746552 other other other other text/xml other other geonode:villagegrmc2 Villages selected for implementation of Grievance Redressal Mechanism around Kamuzu barrage under SRBMP features villagegrmc2 EPSG:32736 CRS:84 35.1444696601798 35.38622501896946 -15.083766236758098 -15.021334484569378 other other other other other text/xml other geonode:waterresourceareas This dataset describes the Water Resource Areas in Malawi. It was produced during the Water Resources Investment Strategy project [by Atkins 2012] features waterresourceareas EPSG:22236 CRS:84 32.666864678238376 35.95035662353149 -17.140498699024953 -9.3554921607352 other other other other other text/xml other geonode:waterresourceunits This dataset describes the Water Resource Units in Malawi. It was produced during the Water Resources Investment Strategy project [by Atkins 2012]. waterresourceunits features EPSG:22236 CRS:84 32.666864678238376 35.95035662353149 -17.140498699024953 -9.3554921607352 other other other other text/xml other other geonode:waterways No abstract provided waterways features EPSG:4326 CRS:84 32.00019 36.0026057202923 -17.99693 -8.00076 other other other other other text/xml other geonode:wetlands_100 Malawi wetlands features wetlands_100 EPSG:4326 CRS:84 32.7054856535275 35.9183696205969 -17.1723065456674 -9.47542016180002 other other other other text/xml other other geonode:wmkint_1 No abstract provided features wmkint_1 EPSG:32736 CRS:84 34.532512875749944 34.681898584322695 -15.485343904465541 -15.248303445547402 other other other other other text/xml other geonode:world No abstract provided flood_radarsat_worldbank features EPSG:4326 CRS:84 -31.2890300750732 30.1287117004395 27.6374969482422 69.060302734375 other other other other text/xml other other