Kenya environment data

**ADAM ID: FL-20231121-KEN-03** Flood covering 543793.0 sq m on Nov 21 2023 in Kenya. It impacted 242812 people.

This product has been developed using the (Revised Universal Soil Loss Equation) RUSLE. Among the key parameters used include: soil erosivity, slope, rainfall intensity, population and land cover. Online distribution information:File Data Format: Zipped GeoTIFFOnline location: Click to Download

This product has been developed using the (Revised Universal Soil Loss Equation) RUSLE. Among the key parameters used include: soil erosivity, slope, rainfall intensity, population and land cover. Online distribution information:File Data Format: Zipped GeoTIFF Online location: Click to Download

This product has been developed using the (Revised Universal Soil Loss Equation) RUSLE. Among the key parameters used include: soil erosivity, slope, rainfall intensity, population and land cover. Online distribution information:File Data Format: Zipped GeoTIFFOnline location: Click to Download

This product has been developed using the (Revised Universal Soil Loss Equation) RUSLE. Among the key parameters used include: soil erosivity, slope, rainfall intensity, population and land cover. Online distribution information:File Data Format: Zipped GeoTIFFOnline location: Click to Download

NASA's nighttime lights imagery has a wide range of uses, including tracking urban growth, assessing electrification, monitoring disasters, and studying the biological impacts of light pollution.

This dataset is the output from the Global Flood Model intercomparison project undertaken on behalf of the Global Flood Partnership. The methodology is described in Trigg et al., 2016, "The credibility challenge for global fluvial flood risk analysis," published in the Environmental Research Letters Journal. Each file is a geospatial, WGS84, 1/1200 decimal degrees resolution (~90m) GeoTIFF raster with a classified integer value representing how many out of the 6 models agree that a cell is wet (note 0 - dry/no data)

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

This product has been developed using the (Revised Universal Soil Loss Equation) RUSLE. Among the key parameters used include: soil erosivity, slope, rainfall intensity, population and land cover. Online distribution information:File Data Format: Zipped GeoTIFF Online location: Click to Download

Polygon boundaries of gazetted and community forests in Kenya. Each polygon represents a named forest with its gazetted status and area. Published by Orion Terrametrics.

This Maps Viewer shows all the Agro-Ecological Zones in Kenya dataset

Datasets and map of dam break near solai in Kenya on May 9th 2018.

This file contains flood hazard extent for Garissa, Kilifi and Tana River counties for the period 2018, 2019 and 2023. This data was obtained by merging flood extent from different sources among them UNOSAT. This data is in vector format.

**UNOSAT code: FL20231124KEN** This application provides geospatial information about the ongoing floods in Kenya. Important note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations

Disaster loss and damage dataset for Kenya

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This application provides geospatial information on the ongoing floods in Kenya. Important note: The boundaries and names shown, and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The United Nations Satellite Centre. UNOSAT is not responsible for the misuse or misrepresentation of the map.

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi and Kiambu County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 17 km² of land appears to be flooded. UNITAR-UNOSAT identified 3,663 affected structures, about 44 km of affected roads, and 7 affected bridges. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 2 km² of land appears to be flooded. UNITAR-UNOSAT identified 3,387 affected structures, about 7 km of affected roads, and 9 affected bridges. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi and Machakos County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 22 km² of land appears to be flooded. UNITAR-UNOSAT identified 4,246 affected structures, about 94 km of affected roads. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi and Kiambu County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 3 km² of cropland appears to be inundated This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Kiambu County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 2 km² of cropland appears to be inundated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Kiambu County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 13 km² of land appears to be flooded. UNITAR-UNOSAT identified 1,815 affected structures, about 24 km of affected roads, and 6 affected bridges. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi and Machakos County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 4 km² of cropland appears to be inundated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Over the past three decades, large areas of gazetted forest reserves have been deforested and converted to other uses while unprotected indigenous forests are continuously degraded by illegal and legal of logging of valuable timber trees resulting in reduced carbon stocks and degraded biodiversity values. Forest cover loss in Kenya can be largely attributed to agricultural expansion which is motivated by the subsistence or the market economy. The best example of agricultural expansion fuelled by subsistence and commercial farming interests is in the Mau Conservancy. Unprecedented levels of officially sanctioned excisions of gazetted forests by 2001 was motivated by interests in commercial tea and wheat cultivation, in addition to small scale farming for subsistence and surplus production for the market. Landuse Landcover maps are useful in monitoring and tracking changes within forest ecosystems.

Over the past three decades, large areas of gazetted forest reserves have been deforested and converted to other uses while unprotected indigenous forests are continuously degraded by illegal and legal of logging of valuable timber trees resulting in reduced carbon stocks and degraded biodiversity values. Forest cover loss in Kenya can be largely attributed to agricultural expansion which is motivated by the subsistence or the market economy. The best example of agricultural expansion fuelled by subsistence and commercial farming interests is in the Mau Conservancy. Unprecedented levels of officially sanctioned excisions of gazetted forests by 2001 was motivated by interests in commercial tea and wheat cultivation, in addition to small scale farming for subsistence and surplus production for the market. Landuse Landcover maps are useful in monitoring and tracking changes within forest ecosystems.

Over the past three decades, large areas of gazetted forest reserves have been deforested and converted to other uses while unprotected indigenous forests are continuously degraded by illegal and legal of logging of valuable timber trees resulting in reduced carbon stocks and degraded biodiversity values. Forest cover loss in Kenya can be largely attributed to agricultural expansion which is motivated by the subsistence or the market economy. The best example of agricultural expansion fuelled by subsistence and commercial farming interests is in the Mau Conservancy. Unprecedented levels of officially sanctioned excisions of gazetted forests by 2001 was motivated by interests in commercial tea and wheat cultivation, in addition to small scale farming for subsistence and surplus production for the market. Landuse Landcover maps are useful in monitoring and tracking changes within forest ecosystems.

The Soil and Terrain database for Kenya (KENSOTER), version 2.0, at scale 1:1 million, replaces version 1.0 . The update include changes in the GIS file and in the attribute database. The topographic base of KENSOTER was adapted to a version congruent to the Digital Chart of the World. The KENSOTER attribute database has changed with respect to the number of pedons stored and pedon attributes. The KENSOTER version 2.0 database contains a number of measured soil moisture contents at various tensions.

This harmonized set of soil parameter estimates for the Upper Tana river catchment, Kenya. The data set was derived from the 1:250 000 scale Soil and Terrain Database for the Upper Tana (SOTER_UT, ver. 1.1; Dijkshoorn et al. 2011) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of the Upper Tana, Kenya, covering some 18,900 km2, has been mapped in SOTER using 191 unique SOTER units. Each map unit may comprise of up to three different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts. Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SOTER_UT have been characterized using 146 profiles consisting of 109 real and 37 so-called synthetic profiles. The latter were used to represent some 18% per cent of the study area. Comprehensive sets of measured attribute data are seldom available for most profiles (109) collated in SOTER_UT, as these were not considered in the source materials. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil property estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database. The current taxotransfer procedure only considers profiles in WISE that: (a) have FAO soil unit names (43) identical to those mapped for the Upper Tana in SOTER, and (b) originate from regions having similar Köppen climate zones (n= 5745). Property estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and volumetric water content (-33 kPa to -1.5 MPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and studies of global environmental change. The soil property estimates can be linked to the spatial data (map), using GIS, through the unique SOTER-unit code; database applications should consider the full map unit composition and depth range.

This harmonized set of soil parameter estimates for Kenya (KENSOTER), at scale 1:1M, compiled by the Kenya Soil Survey. The land surface of the Republic of Kenya - excluding lakes and towns - has been characterized using 397 unique SOTER units corresponding with 623 soil components. The major soils have been described using 495 profiles, which include 178 synthetic profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports and expert knowledge. Gaps in the measured soil profile data have been filled using a step-wise procedure which includes three main stages: (1) collate additional measured soil analytical data where available; (2) fill gaps using expert knowledge and common sense; (3) fill the remaining gaps using a scheme of taxotransfer rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminum saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments, and available water capacity. These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure. Taxotransfer rules have been flagged to provide an indication of the possible confidence in the derived data. Results are presented as summary files and can be linked to the 1:1M scale SOTER map for Kenya in a GIS, through the unique SOTER-unit code. The secondary data are considered appropriate for studies at the national scale (1:1M). Correlation of soil analytical data, however, should be done more rigorously when more detailed scientific work is considered.