the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An in-situ daily dataset for benchmarking temporal variability of groundwater recharge
Abstract. Accurate estimate of groundwater recharge is crucial for prediction of groundwater table dynamics and dependent eco-hydrological processes. Despite its importance, benchmark data for groundwater recharge at fine (~ daily) temporal resolution is lacking. We present a first-of-a-kind daily groundwater recharge per unit specific yield (RpSy) data over periods of 2–38 years at 485 groundwater monitoring wells in the US. The RpSy data for these locations are calculated from the daily groundwater table time series using the water table fluctuations (WTF) method. Although direct validation of the data is not possible, since it is the first of its kind, we compare the RpSy data with the monthly USGS product to identify similarities and differences. The RpSy dataset may serve as a benchmark for validating the temporal consistency of recharge products and daily simulation results from land surface and integrated hydrologic models.
- Preprint
(1651 KB) - Metadata XML
-
Supplement
(1025 KB) - BibTeX
- EndNote
Status: open (until 13 Oct 2024)
Data sets
An in-situ daily dataset for benchmarking temporal variability of groundwater recharge Pragnaditya Malakar, Aatish Anshuman, Mukesh Kumar, Georgios Boumis, T. Prabhakar Clement, Arik Tashi, Hitesh Thakur, Nagaraj Bhat, and Lokendra Rathore https://doi.org/10.5281/zenodo.13323242
Model code and software
An in-situ daily dataset for benchmarking temporal variability of groundwater recharge Pragnaditya Malakar, Aatish Anshuman, Mukesh Kumar, Georgios Boumis, T. Prabhakar Clement, Arik Tashi, Hitesh Thakur, Nagaraj Bhat, and Lokendra Rathore https://doi.org/10.5281/zenodo.13323242
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
184 | 51 | 8 | 243 | 15 | 4 | 5 |
- HTML: 184
- PDF: 51
- XML: 8
- Total: 243
- Supplement: 15
- BibTeX: 4
- EndNote: 5
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1