Assessing Groundwater Drought Hazard in the Case of Groundwater Storage Trends caused by Human Water Use as well as Climate Variability and Change - Data set ...
Over the last decades, increasing groundwater abstractions, and to a lesser extent climate variability and change, have led to groundwater depletion (GWD), especially in major irrigation areas. Such negative trends in groundwater storage (GWS) are problematic in the context of groundwater drought de...
| Main Authors: | , |
|---|---|
| Format: | Dataset |
| Language: | English |
| Published: |
Goethe-Universität Frankfurt
2024
|
| Subjects: | |
| Online Access: | https://dx.doi.org/10.25716/gude.1t1f-0e1t https://gude.uni-frankfurt.de/handle/gude/392 |
| Summary: | Over the last decades, increasing groundwater abstractions, and to a lesser extent climate variability and change, have led to groundwater depletion (GWD), especially in major irrigation areas. Such negative trends in groundwater storage (GWS) are problematic in the context of groundwater drought detection since they can superimpose climate-induced drought signals including climate-induced groundwater pumping. As this is currently not considered in large-scale drought early warning systems (LDEWSs), we used time series of monthly GWS from the global hydrological model WaterGAP 2.2e to investigate how groundwater drought can best be quantified in an LDEWS covering GWD regions. Groundwater drought hazard indicators (GDHIs) based on three variants of GWS were analyzed: (1) GWS as impacted by human water use (GWS_ant), (2) naturalized GWS assuming no human water use (GWS_nat), and (3) GWS_ltc, in which the linear trend of GWS_ant is removed. Here, the reader can download 1) monthly time series of GWS_nat during ... : General remarks: "-99" or "-999" in the data refer to NA (not applicable, not computable). 1) GWS_nat 1980-2019: Monthly time series of GWS_nat from a naturalized WG22e model run. 2) Groundwater drought hazard indicators (GDHIs) as computed by WaterGAP 2.2e (climate data GSWP3-W5E5) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1980-2009 and the evaluation period 2010-2019: - EP1_ant, EP1_nat, EP1_ltc, EP12_prec (unitless; values need to be multiplied by 100 to be transformed into percent) - D1_ant,D1_nat (in months) - RP1_ant, RP1_nat (in years) 3) Nine R scripts: - Eight out of the nine scripts are numbered and should be used subsequently as indicated. 4) GWS text files as input for R scripts to compute the EP1 variants: - GWS data (G_GROUND_WATER_STORAGE_mm_gswp3w5e5_[...].txt) compiled from WG22e output (ant and nat variants) as well as GWS_ltc computed based on GWS_ant for 1980-2009 and 1980-2019. 5) Other data: - ... |
|---|