The dataset provides raster gridded estimates of open water and inundated vegetation for the Barotseland Region in Western Zambia. There are a total of 55 images covering the period 2016-2019 at a spatial resolution of 10m. The images were generated using an automatic classification routine applied to Sentinel-1 radar imagery, with classification refinements made using ancillary datasets such as the Global Urban Footprint, and the Height Above Nearest Drainage terrain derivative generated using SRTM digital elevation data. These data are valuable for a range of applications including public health and water resources. Full details about this dataset can be found at https://doi.org/10.5285/4ef558d2-05d4-4ae2-988e-a5c2450b95dd

The data resource contains daily time-series of simulated streamflow, ground water levels and estimated demands, from humans, livestock and irrigation across the Narmada Basin, India. The data were generated using the Global Water Availability Assessment (GWAVA) Model 5. For the Upper Narmada, a baseline of 1970-2013 is presented along with a future time slice of 2028- 2060. For the whole Narmada, a baseline of 1981-2013 and future period of 2021-2099 is included. The data were produced to help predict how climate and land use change in the region would impact on future water security. The research was funded by NERC research grant NE/R000131/1 Full details about this dataset can be found at https://doi.org/10.5285/9fc7ab01-c622-46f1-a904-0bcd54073da3

This dataset contains information about the luminescence signals measured from the Lake Suigetsu sediment cores across four time periods: the last 500 years (537 to -47 cal BP), the Laschamp geomagnetic excursion (44,828 to 35,550 cal BP), the limit of varves (73,130 to 69,413 yr BP) and glacial termination I (139,499 to 118,001 yr BP). Sampling intervals varied between time periods (see supporting documentation for more information). The luminescence signals were quantified using Portable Optically Stimulated Luminescence (POSL) analysis of bulk sediment using blue light and infrared exposures, and laboratory profiling analysis of prepared quartz fine fractions (using blue light exposures) and polymineral fine fractions (using blue light and infrared exposures). This data was collected to determine if these methods could be used to detect past catchment environmental change. Alongside this dataset, we estimated dose rate at six points across the four time periods studied using elemental concentrations. This data was collected to see if the luminescence signals measured from the Lake Suigetsu cores could be used to determine burial age. The work was supported by the NERC IAPETUS2 Doctoral Training Partnership. Full details about this dataset can be found at https://doi.org/10.5285/73ee6512-a7c6-464c-bae0-38862ab8b87a

This data set contains satellite-derived information on geomorphic river mobility for ten catchments in the Philippines. We applied the locational probability approach to map the proportion of time that a river channel occupies a particular location. We quantified satellite-derived locational probabilities for 600 km2 of riverbed. The information is useful for predicting and developing resilience to river-related hazards in dynamic landscapes. We provide example Google Earth Engine (GEE) and MATLAB codes to replicate satellite-derived locational probability analyses, and provide outputs for each catchment. Data sets include: (1) example GEE codes to run satellite imagery analyses; (2) example MATLAB codes and data to generate locational probabilities; (3) example MATLAB codes and data to produce longitudinal analyses; and, (4) processed locational probability outputs for the ten catchments. The work was supported by the Natural Environment Research Council (NERC) and Department of Science and Technology - Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD) – Newton Fund grant NE/S003312. Full details about this dataset can be found at https://doi.org/10.5285/a2bcc66e-4dcc-4ed1-897d-cdf36dde246d

This is a set of six ASCII grids describing the peak flood event for six return periods (2-100 years) at each point of the river network in the state of Kerala, India. Estimates were derived in a similar way to the Flood Estimation Handbook* approach. The data is measured in cubic metres per second, and is given on an unprojected resolution of 15 arc-seconds per grid cell. This work was supported by the Natural Environment Research Council as part of the LAWIS programme delivering National Capability. * (Flood Estimation Handbook. Centre for Ecology & Hydrology, 1999, ISBN: 9781906698003) Full details about this dataset can be found at https://doi.org/10.5285/cba9f9db-4706-4f1e-aaf4-fd7769e00db0

This dataset contains fluvial flood maps of the present day 1 in 20 year return period, and corresponding flood extents for 3 SSP (Shared Socioeconomic Pathway)/RCP(Representative Concentration Pathway) scenarios for the future (2070-2100). Change in flood return periods are estimated using CMIP6 projections and subsequently used to extract flood maps from a global flood model. Full details about this dataset can be found at https://doi.org/10.5285/0d5d69ae-7f50-40ee-a0c9-2522de138f27

Data on peat depth from >250 locations in the Pastaza-Marañón Basin, Amazonian Peru. The data were collected during a series of field campaigns in 2019 and 2020. These data, along with similar data collected under other projects, were used to train a predictive model of peat distribution. Locations of a small number of other sites are given without peat depth measurements (i.e. with NA in the column Peat_depth_cm); these sites relate to data reported elsewhere in the ‘Carbon Storage in Amazonian Peatlands’ data collection. Full details about this dataset can be found at https://doi.org/10.5285/ab13a06f-392f-4bc6-b1bf-06dd8b020307

The shapefiles contain the classification and locations of each river style determined by the authors. The data were used to characterise the river styles in Bislak River, Philippines. Shapefiles were clipped to the catchment boundary from different national government agencies to produce different thematic maps. Catchment properties such as land use (from the National Mapping and Resource Information Authority (NAMRIA)), geology (from the Mines and Geosciences Bureau), fault (from Philippine Institute of Volcanology and Seismology, rainfall isohyets, slope map, and the digital elevation model (also from NAMRIA) were used for regional and catchment analysis. The data only covers the whole Bislak catchment, Philippines. The CSV contains data used for the stream power analysis where stream power is a factor of slope and discharge. Full details about this dataset can be found at https://doi.org/10.5285/31ae71aa-74a9-466b-9a3a-25d2b1a9406e

This dataset comprises multiple baseline and future ensembles of hydrological model estimates of monthly mean and annual maximum river flows (m3s-1) on a 0. 0.008333° × 0. 0.008333° grid (approximate grid of 1 km × 1 km) across Peninsular Malaysia. Specifically, these are provided for historical (1971 to 2005) and projected future (2006 to 2099) periods, for 3 Representative Concentration Pathways (RCPs). This dataset is the output from the Hydrological Modelling Framework for Malaysia, or “HMF-Malaysia” model. The projected future hydrology simulations are provided for CORDEX-SEA (Coordinated Regional Downscaling Experiment – South East Asia) three RCPs (RCP2.6, RCP4.5 and RCP8.5) assuming (i) current artificial influences (CAI) such as water transfers and diversions and (ii) planned future artificial influences (FAI). This dataset is an output from the hydrological modelling study from the Malaysia - Flood Impacts Across Scales (FIAS) project. Full details about this dataset can be found at https://doi.org/10.5285/9b70bebe-189c-4ae8-9aee-1bb1db7b1ad5

This dataset contains river (fluvial) and surface water (pluvial) flooding maps for the central highlands of Vietnam and surrounding provinces. Flood depth is estimated at 30m horizontal grid spacing for 10 return periods, ranging from the 1 in 5 year to the 1 in 1000 year return period flood. These maps are of relevance to planners and policy makers to estimate which areas of most at risk of flooding and can contribute towards policy such as the sustainable development goals. Full details about this dataset can be found at https://doi.org/10.5285/74e4e6ec-a119-4dc7-8ada-9513252b1b60