Data provided are monthly surface water layers extracted from Sentinel1A SAR data for 3 districts in India (Shivamogga, Sindhudurg, Wayanad) for the year 2017 and 2018. Surface water body layers were mapped using an average monthly threshold value extracted from the image backscatter histogram. The average threshold value excluded the monsoon months due to the difference in water and not water area. The threshold value was slightly lesser than the mean threshold value. The end product was validated using field data which resulted in user and producer accuracies. Monthly surface water body layers were not produced for a few months due to the non-availability of Sentinel 1 data. The work was supported by MRC, AHRC, BBSRC, ESRC and NERC [grant number MR/P024335/1] and NERC - SUNRISE project [grant number NE/R000131/1] Full details about this dataset can be found at https://doi.org/10.5285/3c23fea1-5b27-4b01-b9ef-fc13346cfedc

This dataset contains data on geomorphological characteristics and flow-related variables along the Beas River (Punjab, India) between Pong dam and Harike barrage in January 2020. The variables provided include cross-sectional area, water depth, river channel width, river flow velocity and dry-season discharge measured at ten reference sites with stable banks and straight, linear channels without islands or other mid-channel structures. Full details about this dataset can be found at https://doi.org/10.5285/f899fbc5-7034-45c0-a15c-9ee1d92a693f

Data are presented for daily rainfall, stream discharge and hydraulic conductivity of soils from catchments located in the Upper Nilgiris Reserve Forest in the state of Tamil Nadu. The catchments are dominated by four land cover types, shola, grassland, pine and wattle. The data were collected between May 2014 and December 2016. Tipping bucket wired rain gauges were used to measure rainfall. Stream discharge was measured from stilling wells and capacitance probe-based water level recorders. A mini-disk infiltrometer was used to measure the hydraulic conductivity of soils. Dry season data has not been included in this dataset as its focus is on extreme rain events. The data were collected as part of a series of eco-hydrology projects that explored the impact of land cover on rain-runoff response, carbon sequestration and nutrient and sediment discharge. The dataset presented here was collected by a team of three to five researchers and field assistants who were engaged in the installation of the data loggers and their regular operation and maintenance. Four research agencies have partnered across multiple projects to sustain the data collection efforts that started in June 2013 and continue (June 2020). These are the Foundation for Ecological Research, Advocacy and Learning - Pondicherry, the Ashoka Trust for Research in Ecology and the Environment - Bangalore, the Lancaster Environmental Centre, Lancaster University - UK, and the National Centre for Biological Sciences - Bangalore. Funding was provided by Ministry of Earth Sciences Government of India from the Changing Water Cycle programme (Grant Ref: MoES/NERC/16/02/10 PC-II) and the Hydrologic footprint of Invasive Alien Species project (MOES/PAMC/H&C/85/2016-PC-II). Additional funding was provided by UKRI Natural Environment Research Council grant NE/I022450/1 (Western Ghats-Capacity within the NERC Changing Water Cycle programme) and WWF-India as part of the Noyyal-Bhavani program.This research took place inside protected areas in the Nilgiri Division for which permissions and support were provided continually by the Tamil Nadu Forest Department, particularly the office of the District Forest Officer, Udhagamandalam. Full details about this dataset can be found at https://doi.org/10.5285/9257a999-2844-4be1-80d1-fd29e2ccf9ef

The dataset contains stable isotope data from surface and groundwater samples collected in the Gandak Basin, north India. The data was collected between March 2017 and February 2019. These measurements were taken to improve understanding of surface and subsurface water interconnections and movement through river and canal networks and underlying aquifers. The data were collected as part of the NERC sponsored project Coupled Human and Natural Systems Environment (CHANSE), grant number NE/N01670X/1 Full details about this dataset can be found at https://doi.org/10.5285/09ae86d6-896f-430f-aab4-c5b46c265213

This dataset contains Land Cover/Land Use (LCLU) maps for Sindhudurg, Shivamogga and Wayanad, India. LCLU products are state-of-the-art statically stable and area weighted accuracy assessed products. The LCLU product was generated for Kyasanur Forest Disease (KFD), a Zoonotic disease. KFD is an “ecotonal” disease. Diverse forest-plantation mosaics, zone moist evergreen forest and plantation, and low coverage of dry deciduous forest will cause higher risks for KFD. Our LCLU product aimed to separate diverse forest types and plantation and we achieved high accuracy (>90%). The study covers Sindhudurg, Shivamogga, and Wayanad Western Ghats district which belong to Indian state Maharashtra, Karnataka, and Kerala respectively. Full details about this dataset can be found at https://doi.org/10.5285/cacb66de-aea0-41d5-97b3-9eacd4683aaf