Data and code repositories

We are delighted to share our data for all published (and frequently unpublished) work with anyone, and we do so via the research data repository (figshare) as well as email. We also make our code available to everyone on the Github platform.

The high-resolution (1km) groundwater storage and depletion maps across Irrigated Indus Basin (IIB) during 2002-2019

We provided high-resolution (1km) groundwater storage (GWS) and depletion (DEPgw) maps across Irrigated Indus Basin (IIB) for 2002-2019. We applied two independent methodologies (1) spatial downscaling for improving GRACE-based GWS data, and (2) SWAT (Soil Water Assessment Tool) and pixel-based water balance approach for DEPgw estimates. GWS were estimated from the GRACE data, then downscaled to 1 km × 1 km using data-driven spatial downscaling models. We combined the downscaled GRACE-based GWS estimates with results from a calibrated SWAT hydrological model to estimate the DEPgw. The resultant maps delineate the groundwater depletion hotspots of cropping systems in the 55 canal command areas of IIB and could contribute to sustainable water use and agricultural development in the region.

Data is publically available at https://doi.org/10.6084/m9.figshare.22301020.v4

Data format: .tif formal (WGS1984),

Coverage: 2002-2019 (Annual)

Citation:

Arfan, Arshad; Mirchi, Ali (2023): The high-resolution (1km) groundwater storage and depletion maps across Irrigated Indus Basin (IIB) during 2002-2019. figshare. Dataset. https://doi.org/10.6084/m9.figshare.22301020.v4

Readers can refer to the following publication for more details on the methods.

Arshad, A., Mirchi, A., Samimi, M. and Ahmad, B., 2022. Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the Irrigated Indus Basin (IIB). Science of the Total Environment, 838, p.156044.

FUNDING

National Science Foundation (NSF Award 2114701) of the United States

TRMM at 1km-Resolution: High-resolution precipitation data in a data-scarce Indus Basin reconstructed through data-driven spatial downscaling and remote sensing

We provided high-resolution (1km) gridded precipitation data across the entire Indus Basin for 2002-2019. We investigated the performance of a data-driven spatial downscaling procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geo-graphically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. The high-resolution gridded precipitation data generated by the proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin.

Data is publically available at https://doi.org/10.6084/m9.figshare.24570397.v3

Data format: .tif formal (WGS1984),

Coverage: 2002-2019 (Annual)

Citation:

Arfan Arshad; Zhang, Wanchang; Noor, Rabeea (2023). TRMM at 1km-Resolution: High-resolution precipitation data in a data-scarce Indus Basin reconstructed through data-driven spatial downscaling and remote sensing. figshare. Dataset. https://doi.org/10.6084/m9.figshare.24570397.v3

Readers can refer to the following publication for more details on the methods.

[1] Arshad, A., Zhang, W., Zhang, Z., Wang, S., Zhang, B., Cheema, M.J.M. and Shalamzari, M.J., 2021. Reconstructing high-resolution gridded precipitation data using an improved downscaling approach over the high altitude mountain regions of Upper Indus Basin (UIB). Science of The Total Environment, 784, p.147140.

[2] Noor R, Arshad A, Shafeeque M, Liu J, Baig A, Ali S, Maqsood A, Pham QB, Dilawar A, Khan SN, Anh DT. Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin. Remote Sensing. 2023 Jan 5;15(2):318.

FUNDING

This study was jointly financed by the National Key R & D Program of China [Grant No. 2016YFA0602302] and the Key R & D and Transformation Program of Qinghai Province [Grant No. 2020-SF-C37]. Acknowledgment: The authors are grateful to the Water and Power Development Authority (WAPDA), Pakistan Meteorological Department (PMD), and China Metrological Department (CMD) for providing observed hydro-metrological data in the upper Indus basin.

Contact: aarshad@okstate.edu; arfanarshad52@gmail.com

High-Resolution (1km) Data of Groundwater Level Changes from 2003-2020 for Indus Basin

his high-resolution groundwater data is a result of a comprehensive study aimed at addressing the challenges posed by the spatial and temporal limitations of groundwater monitoring. The data covers the Indus Basin from 2003 to 2020, providing biannual (July and Oct) estimates of groundwater level (GWL) changes. To overcome the data gaps, we employed a cutting-edge approach that combines machine learning, local covariates, and exiting piezometers GWL data. The geographically weighted random forest (RFgw) model, a hybrid machine learning model, was the primary tool used to generate high-resolution (1 km2) and temporally continuous GWL estimates. The accuracy and reliability of this data have been rigorously assessed. This dataset is not limited to monitored sites but extends to unmonitored locations, offering valuable insights into GWL changes in regions without in-situ measurements. The incorporation of high-resolution covariates into the RFgw model allowed for reliable estimates of GWL changes at unmonitored sites.

Data is publically available at figshare. https://doi.org/10.6084/m9.figshare.24224674.v2

Data format: .tif formal (WGS1984),

Coverage: 2003-2020 (Bi-Annual; July and October)

Citation:

Readers can refer to the following publication for more details on the methods.

[1] Arshad, A., Mirchi, A., Vilcaez, J., Akbar, M.U. and Madani, K., 2023. Reconstructing high-resolution groundwater level data using a hybrid random forest model to quantify distributed groundwater changes in the Indus Basin. Journal of Hydrology, p.130535.

FUNDING

This study was jointly financed by National Science Foundation (NSF Award 2114701) of the United States. Acknowledgment: The authors are grateful to the Punjab Irrigation Department-PAK and Indian Water Resource Information System (IWRIS) for providing observed groundwater level data in the Indus basin.