The dataset contains hourly river water level data for 3 monitoring sites; Mukurumudzi River (3KD06 Shimba Hills & KISCOL Weir) Ramisi River (Eshu Bridge) in Kwale County, Kenya

The dataset contains climate data (Humidity, Rainfall, Rainfall Rate, Dewpoint, Atmospheric Pressure, Temperature, Wind Direction, Wind Gust, Wind Chill, Solar Radiation, Windspeed, Heat Index, UV & UVI) at daily temporal resolution from Maplin Professional Solar Powered Wi-Fi Weather Stations installed at Munje and Galu within the study area.

The file consists of data sets from Kwale County, Kenya that describe biophysical characteristics of the catchment overlaid as layers. These include Basin, Sub-basins extent, Soil, DEM, Landuse, Slope, Rivers, Outlets and Monitoring Points. The data are in raster, shapefile, polygon, polyline and point format.

The data sets contain the rating curves and equations, approved stage (H) discharge (Q) data for the purposed of building river rating information for three hydrometric stations: 3KD06 and KISCOL weir on Mukurumudzi River and Eshu Bridge on Ramisi River, in Kwale. See also Wara et al. 2019: https://doi.org/10.4236/jwarp.2019.114028

The dataset consists of daily rainfall data for 23 manual rain gauge stations installed by Gro for GooD project within and about the study area. The installed stations covering four river catchments name Ramisi River, Mukurumudzi River, Mtawa River and Mwachema River in Kwale County. The dataset period is from January 2016 to November 2018. Gro for GooD: Groundwater Risk Management for Growth and Development

This dataset contains digital terrain data that describes the topography of the area under study at 90m resolution based on SRTM 90m Digital Elevation Data from the CGIAR-CSI (Consultative Group on International Agricultural Research - Consortium for Spatial Information). Gro for GooD: Groundwater Risk Management for Growth and Development, https://upgro.org/consortium/gro-for-good/

Results of Electrical Resistivity Tomography (ERT) conducted in Kwale County, Kenya December 2015 and June 2016 by University of Nairobi and Water Resources Management Authority as part of the Gro for GooD project (https://upgro.org/consortium/gro-for-good/) to characterize the aquifers in the study area. There were eight transects of length 1.2 to 6km, running W-E and NNE-SSW parallel to coastline. ERT data was analysed using RES2D inversion software. Gro for GooD - Groundwater Risk Management for Growth and Development

Results of Vertical Electrical Soundings (VES) study conducted in Kwale County, Kenya in July and August 2017 by University of Nairobi and Water Resources Management Authority as part of the Gro for GooD project (https://upgro.org/consortium/gro-for-good/) to determine the existence of deeper aquifers.

The data sets contain the daily record of meters of groundwater columns for 7 Heron logger transducers installed in different boreholes and wells in the study area. Missing data denoted -9999. The Barlog data for atmospheric pressure (Atmospheric Pressure data measured by Heron Barologger for the period of April 2014 to November 2018 at Munje Jabalini.) is also included. "Uncomp.HT.WTR. Above Transducer" corresponds to the actual pressure the dipperLog is measuring. "Barologger Data" corresponds to the Barlog data for atmospheric pressure at Munje Jabalini "Comp.Depth.WTR Below the Datum" is the "Depth below datum" entered in the logger setup less "Comp.HT.WTR. Above Transducer". The data was collected by Albert Folch and Nuria Ferrer (UPC), Mike Lane and Calvince Wara (Rural Focus Ltd). The PI on the Gro for GooD project was Prof. Rob Hope, University of Oxford.

The data set contains Soil Data used in the Gro for GooD Project in Kwale, Kenya based on KENSOTER database and soil survey in study area. The KENSOTER dataset, specific for Kenya, was compiled by the Kenya Soil Survey (KSS) and ISRIC and released in 2006 where ISRIC plays a lead role in methodology development and programme implementation (http://www.isric.org/projects/soil-and-terrain-soter-database-programme). The dataset includes over 600 soil components, including synthetic profiles, which have been derived from soil survey reports and expert knowledge. The second version of the dataset which has been made available includes additional soil profile database and is also used for the assessment of soil carbon stocks. The 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, aluminium 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. The data have recently been used for the Green Water Credit (GWC) programme in the Upper Tana River Valley. This dataset was prepared for the Gro for GooD project by Mike Thomas, Rural Focus Ltd., Kenya; John Gathenya, JKUAT, Kenya. Gro for GooD: Groundwater Risk Management for Growth and Development