2018
DOI: 10.5194/hess-22-2689-2018
|View full text |Cite
|
Sign up to set email alerts
|

Predicting groundwater recharge for varying land cover and climate conditions – a global meta-study

Abstract: Abstract. Groundwater recharge is one of the important factors determining the groundwater development potential of an area. Even though recharge plays a key role in controlling groundwater system dynamics, much uncertainty remains regarding the relationships between groundwater recharge and its governing factors at a large scale. Therefore, this study aims to identify the most influential factors of groundwater recharge, and to develop an empirical model to estimate diffuse rainfall recharge at a global scale… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

12
89
2

Year Published

2018
2018
2023
2023

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 105 publications
(103 citation statements)
references
References 51 publications
12
89
2
Order By: Relevance
“…The collected data were manually examined to ensure that the coverage of cloud and snow was lower than 10%. Land cover classification map was obtained from 500-m MODIS land cover data (MCD12Q1) [37], which has been used in many studies to stratify vegetation types [38,39]. The terrain information was represented by the 30-m Shuttle Radar Topography Mission (SRTM) digital terrain model (DTM) data collected from the United States Geological Survey.…”
Section: Ancillary Datasetsmentioning
confidence: 99%
“…The collected data were manually examined to ensure that the coverage of cloud and snow was lower than 10%. Land cover classification map was obtained from 500-m MODIS land cover data (MCD12Q1) [37], which has been used in many studies to stratify vegetation types [38,39]. The terrain information was represented by the 30-m Shuttle Radar Topography Mission (SRTM) digital terrain model (DTM) data collected from the United States Geological Survey.…”
Section: Ancillary Datasetsmentioning
confidence: 99%
“…The runoff fraction is 0.42, which is at the lower end compared to other models (Haddeland et al, 2011), but can be explained because CWatM takes into account evaporation from lakes and rivers. Groundwater recharge amounts to 19,000 km 3 /yr, which is higher than some of the GHMs 575 (Mohan et al, 2018), such as WaterGAP or FAO statistics, but lower than PCR-GLOBWB2 (Sutanudjaja et al, 2018) or MATSIRO . Figure 2 shows the spatial distribution of discharge and groundwater recharge which is similar to the distributions shown in Koirala et al (2012) and Mohan et al (2018).…”
Section: Global Water Balancementioning
confidence: 79%
“…Groundwater recharge amounts to 19,000 km 3 /yr, which is higher than some of the GHMs 575 (Mohan et al, 2018), such as WaterGAP or FAO statistics, but lower than PCR-GLOBWB2 (Sutanudjaja et al, 2018) or MATSIRO . Figure 2 shows the spatial distribution of discharge and groundwater recharge which is similar to the distributions shown in Koirala et al (2012) and Mohan et al (2018). It is important to note that water withdrawals from the agricultural sector (irrigation and livestock), industry, and domestic sector (households) has been increasing over the years.…”
Section: Global Water Balancementioning
confidence: 79%
“…Clean water resources are becoming scarcer due to the increasing demand for its use and to environmental degradation [1]. It has been estimated that 1/3 of all countries will have to adapt these studies have used numerical groundwater models or dynamically linked them to hydrological models to estimate recharge variations under different climate and land cover conditions [32][33][34][35][36]. For example, Döll (2008) modeled global groundwater recharge using the WaterGAP Global Hydrological Model (WGHM), which has failed to reliably estimate recharge in semi-arid regions [37].…”
Section: Introductionmentioning
confidence: 99%
“…For example, Döll (2008) modeled global groundwater recharge using the WaterGAP Global Hydrological Model (WGHM), which has failed to reliably estimate recharge in semi-arid regions [37]. In that study, the influence of vegetation was not taken into account, even though many studies have showed the importance of this variable for estimating the groundwater recharge [32,[38][39][40][41][42]. Moreover, Chowdhury et al (2010) delineated groundwater recharge zones in West Medinipur district, India, using a GIS approach mixed with remote sensing and multi-criteria decision making techniques [22].…”
Section: Introductionmentioning
confidence: 99%