Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-5107-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-5107-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The impact of future changes in climate variables and groundwater abstraction on basin-scale groundwater availability
Steven Reinaldo Rusli
CORRESPONDING AUTHOR
Hydrology and Environmental Hydraulics, Wageningen University, 6708 PB Wageningen, the Netherlands
Civil Engineering Department, Faculty of Engineering, Parahyangan Catholic University, Bandung 40141, Indonesia
Victor F. Bense
Hydrology and Environmental Hydraulics, Wageningen University, 6708 PB Wageningen, the Netherlands
Syed M. T. Mustafa
Hydrology and Environmental Hydraulics, Wageningen University, 6708 PB Wageningen, the Netherlands
Albrecht H. Weerts
Hydrology and Environmental Hydraulics, Wageningen University, 6708 PB Wageningen, the Netherlands
Department of Inland Water Systems, Operational Water Management, Deltares, Delft, the Netherlands
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Mostafa Gomaa Daoud, Fakhereh Alidoost, Yijian Zeng, Bart Schilperoort, Christiaan Van der Tol, Maciek W. Lubczynski, Mhd Suhyb Salama, Eric D. Morway, Christian D. Langevin, Prajwal Khanal, Zengjing Song, Lianyu Yu, Hong Zhao, Gualbert Oude Essink, Victor F. Bense, Michiel van der Molen, and Zhongbo Su
EGUsphere, https://doi.org/10.5194/egusphere-2025-4179, https://doi.org/10.5194/egusphere-2025-4179, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study investigates the groundwater role in soil-plant-atmosphere continuum. An integrated ecohydrological modelling approach was developed by coupling STEMMUS-SCOPE to MODFLOW 6 and applied at three sites over 8 years. The coupled model improved simulations of soil moisture and temperature, evapotranspiration, carbon fluxes and fluorescence. The findings highlight the groundwater critical role in ecosystem dynamics and its contribution to advancing water, energy and carbon cycle modelling.
Devi Purnamasari, Adriaan J. Teuling, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 29, 1483–1503, https://doi.org/10.5194/hess-29-1483-2025, https://doi.org/10.5194/hess-29-1483-2025, 2025
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This paper introduces a method to identify irrigated areas by combining hydrology models with satellite temperature data. Our method was tested in the Rhine basin and aligns well with official statistics. It performs best in regions with large farms and less well in areas with small farms. Observed differences to existing data are influenced by data resolution and methods.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
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Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Marjanne J. Zander, Pety J. Viguurs, Frederiek C. Sperna Weiland, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-274, https://doi.org/10.5194/hess-2023-274, 2023
Manuscript not accepted for further review
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Flash floods are damaging natural hazard which often occur in the European Alps. High resolution climate model output is combined with high resolution distributed hydrological models to model changes in flash flood frequency and intensity. Results show a similar flash flood frequency for autumn in the future, but a decrease in summer. However, the future discharge simulations indicate an increase in the flash flood severity in both summer and autumn leading to more severe flash flood impacts.
Bas J. M. Wullems, Claudia C. Brauer, Fedor Baart, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 27, 3823–3850, https://doi.org/10.5194/hess-27-3823-2023, https://doi.org/10.5194/hess-27-3823-2023, 2023
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In deltas, saltwater sometimes intrudes far inland and causes problems with freshwater availability. We created a model to forecast salt concentrations at a critical location in the Rhine–Meuse delta in the Netherlands. It requires a rather small number of data to make a prediction and runs fast. It predicts the occurrence of salt concentration peaks well but underestimates the highest peaks. Its speed gives water managers more time to reduce the problems caused by salt intrusion.
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, and Pieter Hazenberg
Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, https://doi.org/10.5194/hess-26-4407-2022, 2022
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In recent years gridded hydrological modelling moved into the realm of hyper-resolution modelling (<10 km). In this study, we investigate the effect of varying grid-cell sizes for the wflow_sbm hydrological model. We used a large sample of basins from the CAMELS data set to test the effect that varying grid-cell sizes has on the simulation of streamflow at the basin outlet. Results show that there is no single best grid-cell size for modelling streamflow throughout the domain.
Mar J. Zander, Pety J. Viguurs, Frederiek C. Sperna Weiland, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-207, https://doi.org/10.5194/hess-2022-207, 2022
Manuscript not accepted for further review
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We perform a modelling study to research potential future changes in flash flood occurrence in the European Alps. We use new high-resolution numerical climate simulations, which can simulate the type of local, intense rainstorms which trigger flash floods, combined with high-resolution hydrological modelling. We find that flash floods would become less frequent in summers in our future climate scenario, with little change in autumns. However, the maximal severity would increase in both seasons.
Alessandro Montemagno, Christophe Hissler, Victor Bense, Adriaan J. Teuling, Johanna Ziebel, and Laurent Pfister
Biogeosciences, 19, 3111–3129, https://doi.org/10.5194/bg-19-3111-2022, https://doi.org/10.5194/bg-19-3111-2022, 2022
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We investigated the biogeochemical processes that dominate the release and retention of elements (nutrients and potentially toxic elements) during litter degradation. Our results show that toxic elements are retained in the litter, while nutrients are released in solution during the first stages of degradation. This seems linked to the capability of trees to distribute the elements between degradation-resistant and non-degradation-resistant compounds of leaves according to their chemical nature.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
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Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Ruben Imhoff, Claudia Brauer, Klaas-Jan van Heeringen, Hidde Leijnse, Aart Overeem, Albrecht Weerts, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 25, 4061–4080, https://doi.org/10.5194/hess-25-4061-2021, https://doi.org/10.5194/hess-25-4061-2021, 2021
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Significant biases in real-time radar rainfall products limit the use for hydrometeorological forecasting. We introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors to correct radar rainfall products and to benchmark other correction algorithms. When tested for 12 Dutch basins, estimated rainfall and simulated discharges with CARROTS generally outperform those using the operational mean field bias adjustments.
Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, https://doi.org/10.5194/hess-25-1069-2021, 2021
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We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
Jeffrey M. McKenzie, Barret L. Kurylyk, Michelle A. Walvoord, Victor F. Bense, Daniel Fortier, Christopher Spence, and Christophe Grenier
The Cryosphere, 15, 479–484, https://doi.org/10.5194/tc-15-479-2021, https://doi.org/10.5194/tc-15-479-2021, 2021
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Groundwater is an underappreciated catalyst of environmental change in a warming Arctic. We provide evidence of how changing groundwater systems underpin surface changes in the north, and we argue for research and inclusion of cryohydrogeology, the study of groundwater in cold regions.
Mikkel Toft Hornum, Andrew Jonathan Hodson, Søren Jessen, Victor Bense, and Kim Senger
The Cryosphere, 14, 4627–4651, https://doi.org/10.5194/tc-14-4627-2020, https://doi.org/10.5194/tc-14-4627-2020, 2020
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In Arctic fjord valleys, considerable amounts of methane may be stored below the permafrost and escape directly to the atmosphere through springs. A new conceptual model of how such springs form and persist is presented and confirmed by numerical modelling experiments: in uplifted Arctic valleys, freezing pressure induced at the permafrost base can drive the flow of groundwater to the surface through vents in frozen ground. This deserves attention as an emission pathway for greenhouse gasses.
Cited articles
Abidin, H. Z., Gumilar, I., Andreas, H., Murdohardono, D., and Fukuda, Y.: On causes and impacts of land subsidence in Bandung Basin, Indonesia, Environ. Earth Sci., 68, 1545–1553, https://doi.org/10.1007/s12665-012-1848-z, 2013. a
Adachi, S. A., Nishizawa, S., Ando, K., Yamaura, T., Yoshida, R., Yashiro, H., Kajikawa, Y., and Tomita, H.: An evaluation method for uncertainties in regional climate projections, Atmos. Sci. Lett., 20, e877, https://doi.org/10.1002/asl.877, 2019. a
Ansari, A. H. M., Umar, R., us Saba, N., and Sarah, S.: Assessment of Current and Future Groundwater Stress through Varied Scenario Projections in Urban and Rural Environment in Parts of Meerut District, Uttar Pradesh in Ganges Sub-basin, J. Geol. Soc. India, 97, 927–934, https://doi.org/10.1007/s12594-021-1793-0, 2021. a
Anurag, H. and Ng, G.-H. C.: Assessing future climate change impacts on groundwater recharge in Minnesota, J. Hydrol., 612, 128112, https://doi.org/10.1016/j.jhydrol.2022.128112, 2022. a
Aslam, R. A., Shrestha, S., Usman, M. N., Khan, S. N., Ali, S., Sharif, M. S., Sarwar, M. W., Saddique, N., Sarwar, A., Ali, M. U., and Arshad, A.: Integrated SWAT-MODFLOW Modeling-Based Groundwater Adaptation Policy Guidelines for Lahore, Pakistan under Projected Climate Change, and Human Development Scenarios, Atmosphere, 13, 2001, https://doi.org/10.3390/atmos13122001, 2022. a, b
Baghel, T., Babel, M. S., Shrestha, S., Salin, K. R., Virdis, S. G., and Shinde, V. R.: A generalized methodology for ranking climate models based on climate indices for sector-specific studies: An application to the Mekong sub-basin, Sci. Total Environ., 829, 154551, https://doi.org/10.1016/j.scitotenv.2022.154551, 2022. a
Bakker, M., Post, V., Langevin, C. D., Hughes, J. D., White, J. T., Starn, J. J., and Fienen, M. N.: Scripting MODFLOW Model Development Using Python and FloPy, Groundwater, 54, 733–739, https://doi.org/10.1111/gwat.12413, 2016. a
Bhave, A. G., Mishra, A., and Groot, A.: Sub-basin scale characterization of climate change vulnerability, impacts and adaptation in an Indian River basin, Reg. Environ. Change, 13, 1087–1098, https://doi.org/10.1007/s10113-013-0416-8, 2013. a
Bierkens, M. F. P. and Wada, Y.: Non-renewable groundwater use and groundwater depletion: a review, Environ. Res. Lett., 14, 063002, https://doi.org/10.1088/1748-9326/ab1a5f, 2019. a
Brewington, L., Keener, V., and Mair, A.: Simulating Land Cover Change Impacts on Groundwater Recharge under Selected Climate Projections, Maui, Hawaii, Remote Sens., 11, 3048, https://doi.org/10.3390/rs11243048, 2019. a
Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E., Li, L., and Tarko, A.: Copernicus Global Land Service: Land Cover 100m: version 3 Globe 2015–2019: Product User Manual, https://doi.org/10.5281/zenodo.3938963, 2020. a
Buser, C. M., Künsch, H. R., and Weber, A.: Biases and Uncertainty in Climate Projections, Scand. J. Stat., 37, 179–199, https://doi.org/10.1111/j.1467-9469.2009.00686.x, 2010. a
Chang, S. W., Chung, I.-M., Kim, M.-G., and Yifru, B. A.: Vulnerability assessment considering impact of future groundwater exploitation on coastal groundwater resources in northeastern Jeju Island, South Korea, Environ. Earth Sci., 79, 498, https://doi.org/10.1007/s12665-020-09254-2, 2020. a
Chen, H., Xue, Y., and Qiu, D.: Numerical simulation of the land subsidence induced by groundwater mining, Cluster Comput., 26, 3647–3656, https://doi.org/10.1007/s10586-022-03771-4, 2022. a
Cook, P., Black, E., Verhoef, A., Macdonald, D., and Sorensen, J.: Projected increases in potential groundwater recharge and reduced evapotranspiration under future climate conditions in West Africa, J. Hydrol. Regional Studies, 41, 101076, https://doi.org/10.1016/j.ejrh.2022.101076, 2022. a
Copernicus Climate Change Service: ERA5-Land hourly data from 2001 to present, Copernicus [data set], https://doi.org/10.24381/CDS.E2161BAC, 2019. a
Copernicus Climate Change Service: CMIP6 climate projections, Copernicus [data set], https://doi.org/10.24381/CDS.C866074C, 2021. a, b, c
Custodio, E., del Carmen Cabrera, M., Poncela, R., Puga, L.-O., Skupien, E., and del Villar, A.: Groundwater intensive exploitation and mining in Gran Canaria and Tenerife, Canary Islands, Spain: Hydrogeological, environmental, economic and social aspects, Sci. Total Environ., 557–558, 425–437, https://doi.org/10.1016/j.scitotenv.2016.03.038, 2016. a
Davamani, V., John, J. E., Poornachandhra, C., Gopalakrishnan, B., Arulmani, S., Parameswari, E., Santhosh, A., Srinivasulu, A., Lal, A., and Naidu, R.: A Critical Review of Climate Change Impacts on Groundwater Resources: A Focus on the Current Status, Future Possibilities, and Role of Simulation Models, Atmosphere, 15, 122, https://doi.org/10.3390/atmos15010122, 2024. a
de Bruin, H. A. R., Trigo, I. F., Bosveld, F. C., and Meirink, J. F.: A Thermodynamically Based Model for Actual Evapotranspiration of an Extensive Grass Field Close to FAO Reference, Suitable for Remote Sensing Application, J. Hydrometeorol., 17, 1373–1382, https://doi.org/10.1175/jhm-d-15-0006.1, 2016. a, b, c
de Luna, R. M. R., Garnés, S. J. d. A., Cabral, J. J. d. S. P., and dos Santos, S. M.: Groundwater overexploitation and soil subsidence monitoring on Recife plain (Brazil), Nat. Hazards, 86, 1363–1376, https://doi.org/10.1007/s11069-017-2749-y, 2017. a
de Vries, W. T. and Schrey, M.: Geospatial Approaches to Model Renewable Energy Requirements of the New Capital City of Indonesia, Frontiers in Sustainable Cities, 4, https://doi.org/10.3389/frsc.2022.848309, 2022. a
Döll, P., Hoffmann-Dobrev, H., Portmann, F., Siebert, S., Eicker, A., Rodell, M., Strassberg, G., and Scanlon, B.: Impact of water withdrawals from groundwater and surface water on continental water storage variations, J. Geodyn., 59–60, 143–156, https://doi.org/10.1016/j.jog.2011.05.001, 2012. a
Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, 2021. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Scientific Data, 2, 1, https://doi.org/10.1038/sdata.2015.66, 2015. a, b, c
Gash, J. H. C.: An analytical model of rainfall interception by forests, Q. J. Roy. Meteor. Soc., 105, 43–55, https://doi.org/10.1002/qj.49710544304, 1979. a
Gaye, C. B. and Tindimugaya, C.: Review: Challenges and opportunities for sustainable groundwater management in Africa, Hydrogeol. J., 27, 1099–1110, https://doi.org/10.1007/s10040-018-1892-1, 2019. a
Gebremicael, T., Mohamed, Y., and der Zaag, P. V.: Attributing the hydrological impact of different land use types and their long-term dynamics through combining parsimonious hydrological modelling, alteration analysis and PLSR analysis, Sci. Total Environ., 660, 1155–1167, https://doi.org/10.1016/j.scitotenv.2019.01.085, 2019. a
Gleeson, T., Cuthbert, M., Ferguson, G., and Perrone, D.: Global Groundwater Sustainability, Resources, and Systems in the Anthropocene, Annu. Rev. Earth Pl. Sc., 48, 431–463, https://doi.org/10.1146/annurev-earth-071719-055251, 2020. a
Gumilar, I., Abidin, H. Z., Hutasoit, L. M., Hakim, D. M., Sidiq, T. P., and Andreas, H.: Land Subsidence in Bandung Basin and its Possible Caused Factors, Proced. Earth Plan. Sc., 12, 47–62, https://doi.org/10.1016/j.proeps.2015.03.026, 2015. a
Hackbarth, T. X. and de Vries, W. T.: An Evaluation of Massive Land Interventions for the Relocation of Capital Cities, Urban Science, 5, 25, https://doi.org/10.3390/urbansci5010025, 2021. a
Hashimoto, R., Kazama, S., Hashimoto, T., Oguma, K., and Takizawa, S.: Planning methods for conjunctive use of urban water resources based on quantitative water demand estimation models and groundwater regulation index in Yangon City, Myanmar, J. Clean. Prod., 367, 133123, https://doi.org/10.1016/j.jclepro.2022.133123, 2022. a
Hassaballah, K., Mohamed, Y., Uhlenbrook, S., and Biro, K.: Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia–Sudan), Hydrol. Earth Syst. Sci., 21, 5217–5242, https://doi.org/10.5194/hess-21-5217-2017, 2017. a
Hawkins, E., Smith, R. S., Gregory, J. M., and Stainforth, D. A.: Irreducible uncertainty in near-term climate projections, Clim. Dynam., 46, 3807–3819, https://doi.org/10.1007/s00382-015-2806-8, 2016. a
Healy, A., Upton, K., Capstick, S., Bristow, G., Tijani, M., MacDonald, A., Goni, I., Bukar, Y., Whitmarsh, L., Theis, S., Danert, K., and Allan, S.: Domestic groundwater abstraction in Lagos, Nigeria: a disjuncture in the science-policy-practice interface?, Environ. Res. Lett., 15, 045006, https://doi.org/10.1088/1748-9326/ab7463, 2020. a
Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017. a
Hua, L., Zhao, T., and Zhong, L.: Future changes in drought over Central Asia under CMIP6 forcing scenarios, J. Hydrol. Regional Studies, 43, 101191, https://doi.org/10.1016/j.ejrh.2022.101191, 2022. a
Hughes, A., Mansour, M., Ward, R., Kieboom, N., Allen, S., Seccombe, D., Charlton, M., and Prudhomme, C.: The impact of climate change on groundwater recharge: National-scale assessment for the British mainland, J. Hydrol., 598, 126336, https://doi.org/10.1016/j.jhydrol.2021.126336, 2021. a
Imhoff, R. O., van Verseveld, W. J., van Osnabrugge, B., and Weerts, A. H.: Scaling Point-Scale (Pedo)transfer Functions to Seamless Large-Domain Parameter Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River, Water Resour. Res., 56, 4, https://doi.org/10.1029/2019wr026807, 2020. a
Indonesia: Detail masterplan on Indonesia capital city, https://www.ikn.go.id/ (last access: 19 June 2024), 2022. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, in press, https://doi.org/10.1017/9781009157896, 2021. a, b
Iqbal, Z., Shahid, S., Ahmed, K., Ismail, T., Ziarh, G. F., Chung, E.-S., and Wang, X.: Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia, Atmos. Res., 254, 105525, https://doi.org/10.1016/j.atmosres.2021.105525, 2021. a
Jackson, C. R., Bloomfield, J. P., and Mackay, J. D.: Evidence for changes in historic and future groundwater levels in the UK, Prog. Phys. Geog., 39, 49–67, https://doi.org/10.1177/0309133314550668, 2015. a
Jyrkama, M. I. and Sykes, J. F.: The impact of climate change on spatially varying groundwater recharge in the grand river watershed (Ontario), J. Hydrol., 338, 237–250, https://doi.org/10.1016/j.jhydrol.2007.02.036, 2007. a
Kawai, H., Yukimoto, S., Koshiro, T., Oshima, N., Tanaka, T., Yoshimura, H., and Nagasawa, R.: Significant improvement of cloud representation in the global climate model MRI-ESM2, Geosci. Model Dev., 12, 2875–2897, https://doi.org/10.5194/gmd-12-2875-2019, 2019. a
Kodir, A., Hadi, N., Astina, I., Taryana, D., Ratnawati, N., and Idris: The dynamics of community response to the development of the New Capital (IKN) of Indonesia, in: Development, Social Change and Environmental Sustainability, Routledge, 57–61, https://doi.org/10.1201/9781003178163-13, 2021. a
Krasting, J. P., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Dunne, K. A., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Silvers, L., Wyman, B., Zeng, Y., Adcroft, A., Dunne, J. P., Dussin, R., Guo, H., He, J., Held, I. M., Horowitz, L. W., Lin, P., Milly, P., Shevliakova, E., Stock, C., Winton, M., Wittenberg, A. T., Xie, Y., and Zhao, M.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1407, 2018. a
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019. a, b, c
Lange, S.: ISIMIP3BASD, Zenodo [code], https://doi.org/10.5281/zenodo.4686991, 2021. a
Latif, M.: Uncertainty in climate change projections, J. Geochem. Explor., 110, 1–7, https://doi.org/10.1016/j.gexplo.2010.09.011, 2011. a
Lili, Y., Minhua, L., Fei, C., Yueyuan, D., and Cuimei, L.: Practices of groundwater over-exploitation control in Hebei Province, Water Policy, 22, 591–601, https://doi.org/10.2166/wp.2020.183, 2020. a
Liu, K., Zhang, J., and Wang, M.: Drivers of Groundwater Change in China and Future Projections, Remote Sens., 14, 4825, https://doi.org/10.3390/rs14194825, 2022. a
López López, P., Wanders, N., Schellekens, J., Renzullo, L. J., Sutanudjaja, E. H., and Bierkens, M. F. P.: Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations, Hydrol. Earth Syst. Sci., 20, 3059–3076, https://doi.org/10.5194/hess-20-3059-2016, 2016. a
Mancuso, M., Santucci, L., and Carol, E.: Effects of intensive aquifers exploitation on groundwater salinity in coastal wetlands, Hydrol. Process., 34, 2313–2323, https://doi.org/10.1002/hyp.13727, 2020. a
Marcos-Garcia, P., Pulido-Velazquez, M., Sanchis-Ibor, C., García-Mollá, M., Ortega-Reig, M., Garcia-Prats, A., and Girard, C.: From local knowledge to decision making in climate change adaptation at basin scale. Application to the Jucar River Basin, Spain, Climatic Change, 176, 38, https://doi.org/10.1007/s10584-023-03501-8, 2023. a
Martinez, R. and Masron, I. N.: Jakarta: A city of cities, Cities, 106, 102868, https://doi.org/10.1016/j.cities.2020.102868, 2020. a
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and Stoffelen, A.: Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target, Geophys. Res. Lett., 41, 6229–6236, https://doi.org/10.1002/2014gl061322, 2014. a
Meixner, T., Manning, A. H., Stonestrom, D. A., Allen, D. M., Ajami, H., Blasch, K. W., Brookfield, A. E., Castro, C. L., Clark, J. F., Gochis, D. J., Flint, A. L., Neff, K. L., Niraula, R., Rodell, M., Scanlon, B. R., Singha, K., and Walvoord, M. A.: Implications of projected climate change for groundwater recharge in the western United States, J. Hydrol., 534, 124–138, https://doi.org/10.1016/j.jhydrol.2015.12.027, 2016. a
Meng, L., Shi, J., Zhai, Y., Zuo, R., Wang, J., Guo, X., Teng, Y., Gao, J., Xu, L., and Guo, B.: Ammonium Reactive Migration Process and Functional Bacteria Response along Lateral Runoff Path under Groundwater Exploitation, Sustainability, 14, 8609, https://doi.org/10.3390/su14148609, 2022. a
Momejian, N., Abou Najm, M., Alameddine, I., and El-Fadel, M.: Groundwater Vulnerability Modeling to Assess Seawater Intrusion: a Methodological Comparison with Geospatial Interpolation, Water Resour. Manag., 33, 1039–1052, https://doi.org/10.1007/s11269-018-2165-4, 2019. a
Mustafa, S. M. T., Abdollahi, K., Verbeiren, B., and Huysmans, M.: Identification of the influencing factors on groundwater drought and depletion in north-western Bangladesh, Hydrogeol. J., 25, 1357–1375, https://doi.org/10.1007/s10040-017-1547-7, 2017. a
Mustafa, S. M. T., Hasan, M. M., Saha, A. K., Rannu, R. P., Van Uytven, E., Willems, P., and Huysmans, M.: Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios, Hydrol. Earth Syst. Sci., 23, 2279–2303, https://doi.org/10.5194/hess-23-2279-2019, 2019. a, b, c
Mutaqin, D. J., Muslim, M. B., and Rahayu, N. H.: Analisis Konsep Forest City dalam Rencana Pembangunan Ibu Kota Negara, Bappenas Working Papers, 4, 13–29, https://doi.org/10.47266/bwp.v4i1.87, 2021. a
Nugroho, H.: Pemindahan Ibu Kota Baru Negara Kesatuan Republik Indonesia ke Kalimantan Timur: Strategi Pemenuhan Kebutuhan dan Konsumsi Energi, Bappenas Working Papers, 3, 33–41, https://doi.org/10.47266/bwp.v3i1.53, 2020. a
Olarinoye, T., Foppen, J. W., Veerbeek, W., Morienyane, T., and Komakech, H.: Exploring the future impacts of urbanization and climate change on groundwater in Arusha, Tanzania, Water Int., 45, 497–511, https://doi.org/10.1080/02508060.2020.1768724, 2020. a
Oruc, S.: Performance of bias corrected monthly CMIP6 climate projections with different reference period data in Turkey, Acta Geophys., 70, 777–789, https://doi.org/10.1007/s11600-022-00731-9, 2022. a
Pardo-Igúzquiza, E., Collados-Lara, A. J., and Pulido-Velazquez, D.: Potential future impact of climate change on recharge in the Sierra de las Nieves (southern Spain) high-relief karst aquifer using regional climate models and statistical corrections, Environ. Earth Sci., 78, 598, https://doi.org/10.1007/s12665-019-8594-4, 2019.
Patle, G. T., Singh, D. K., and Sarangi, A.: Modelling of climate-induced groundwater recharge for assessing carbon emission from groundwater irrigation, Curr. Sci. India, 115, 64–73, https://www.jstor.org/stable/26978149 (last access: 19 June 2024), 2018. a
Pravitasari, A. E., Rustiadi, E., Mulya, S. P., Setiawan, Y., Fuadina, L. N., and Murtadho, A.: Identifying the driving forces of urban expansion and its environmental impact in Jakarta-Bandung mega urban region, IOP C. Ser. Earth Env., 149, 012044, https://doi.org/10.1088/1755-1315/149/1/012044, 2018. a
Rahiem, M. A.: Hydrogeological Information of Bandung Basin, Indonesia, https://malikarrahiem.shinyapps.io/BandungBasin/ (last access: 19 June 2024), 2020. a
Rahimi, R., Tavakol-Davani, H., and Nasseri, M.: An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation, Water Resour. Manag., 35, 2503–2518, https://doi.org/10.1007/s11269-021-02844-0, 2021. a
Rajczak, J. and Schär, C.: Projections of Future Precipitation Extremes Over Europe: A Multimodel Assessment of Climate Simulations, J. Geophys. Res.-Atmos., 122, 10773–10800, https://doi.org/10.1002/2017JD027176, 2017. a
Rusli, S., Weerts, A., Taufiq, A., and Bense, V.: Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin, J. Hydrol. Regional Studies, 37, 100911, https://doi.org/10.1016/j.ejrh.2021.100911, 2021. a, b, c, d, e
Rusli, S., Bense, V., Taufiq, A., and Weerts, A.: Quantifying basin-scale changes in groundwater storage using GRACE and one-way coupled hydrological and groundwater flow model in the data-scarce Bandung groundwater Basin, Indonesia, Groundwater for Sustainable Development, 22, 100953, https://doi.org/10.1016/j.gsd.2023.100953, 2023a. a, b, c, d, e, f, g, h, i, j
Rusli, S., Weerts, A., Mustafa, S., Irawan, D., Taufiq, A., and Bense, V.: Quantifying aquifer interaction using numerical groundwater flow model evaluated by environmental water tracer data: Application to the data-scarce area of Bandung groundwater basin, West Java, Indonesia, J. Hydrol. Regional Studies, 50, 101585, https://doi.org/10.1016/j.ejrh.2023.101585, 2023b. a, b, c, d, e, f, g, h, i, j, k
Rusli, S., Bense, V., Mustafa, S., and Weerts, A.: Data and models used for paper 'The impact of future climate projections and anthropogenic activities on basin-scale groundwater availability, 4TU.ResearchData [data set], https://doi.org/10.4121/d9706a2a-b77b-412f-a3aa-6e22bd8ddf4a, 2024. a
Shahid, S., Wang, X.-J., Moshiur Rahman, M., Hasan, R., Harun, S. B., and Shamsudin, S.: Spatial assessment of groundwater over-exploitation in northwestern districts of Bangladesh, J. Geol. Soc. India, 85, 463–470, https://doi.org/10.1007/s12594-015-0238-z, 2015. a
Siarkos, I., Sevastas, S., Mallios, Z., Theodossiou, N., and Ifadis, I.: Investigating groundwater vulnerability variation under future abstraction scenarios to estimate optimal pumping reduction rates, J. Hydrol., 598, 126297, https://doi.org/10.1016/j.jhydrol.2021.126297, 2021. a
Silva Jr., G. and Pizani, T.: Vulnerability assessment in coastal aquifers between Niterói and Rio das Ostras, Rio de Janeiro State, Brazil, Rev. Lat. Am. Hidrogeol., 3, 93–99, 2003. a
Smerdon, B. D.: A synopsis of climate change effects on groundwater recharge, J. Hydrol., 555, 125–128, https://doi.org/10.1016/j.jhydrol.2017.09.047, 2017. a
Soundala, P. and Saraphirom, P.: Impact of climate change on groundwater recharge and salinity distribution in the Vientiane basin, Lao PDR, J. Water Clim. Change, 13, 3812–3829, https://doi.org/10.2166/wcc.2022.161, 2022. a
Stevenazzi, S., Bonfanti, M., Masetti, M., Nghiem, S. V., and Sorichetta, A.: A versatile method for groundwater vulnerability projections in future scenarios, J. Environ. Manage., 187, 365–374, https://doi.org/10.1016/j.jenvman.2016.10.057, 2017. a
Taie Semiromi, M. and Koch, M.: How Do Gaining and Losing Streams React to the Combined Effects of Climate Change and Pumping in the Gharehsoo River Basin, Iran?, Water Resour. Res., 56, e2019WR025388, https://doi.org/10.1029/2019WR025388, 2020. a
Tillman, F. D., Gangopadhyay, S., and Pruitt, T.: Changes in groundwater recharge under projected climate in the upper Colorado River basin, Geophys. Res. Lett., 43, 6968–6974, https://doi.org/10.1002/2016GL069714, 2016. a
Trásy-Havril, T., Szkolnikovics-Simon, S., and Mádl-Szőnyi, J.: How Complex Groundwater Flow Systems Respond to Climate Change Induced Recharge Reduction?, Water, 14, 3026, https://doi.org/10.3390/w14193026, 2022. a
van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications, Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, 2024. a, b
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Climatic Change, 109, 1, https://doi.org/10.1007/s10584-011-0148-z, 2011. a
Varouchakis, E. A., Karatzas, G. P., and Giannopoulos, G. P.: Impact of irrigation scenarios and precipitation projections on the groundwater resources of Viannos Basin at the island of Crete, Greece, Environ. Earth Sci., 73, 7359–7374, https://doi.org/10.1007/s12665-014-3913-2, 2015. a
Wada, Y., van Beek, L. P. H., van Kempen, C. M., Reckman, J. W. T. M., Vasak, S., and Bierkens, M. F. P.: Global depletion of groundwater resources, Geophys. Res. Lett., 37, L20402, https://doi.org/10.1029/2010GL044571, 2010. a
Wang, S.-J., Lee, C.-H., Yeh, C.-F., Choo, Y. F., and Tseng, H.-W.: Evaluation of Climate Change Impact on Groundwater Recharge in Groundwater Regions in Taiwan, Water, 13, 1153, https://doi.org/10.3390/w13091153, 2021. a
Wannasin, C., Brauer, C., Uijlenhoet, R., van Verseveld, W., and Weerts, A.: Daily flow simulation in Thailand Part I: Testing a distributed hydrological model with seamless parameter maps based on global data, J. Hydrol. Regional Studies, 34, 100794, https://doi.org/10.1016/j.ejrh.2021.100794, 2021. a
Wu, T., Lin, H., Zhang, H., Ye, F., Wang, Y., Liu, M., Yi, J., and Tian, P.: Effects of Climatic Change on Soil Hydraulic Properties during the Last Interglacial Period: Two Case Studies of the Southern Chinese Loess Plateau, Water, 12, 511, https://doi.org/10.3390/w12020511, 2020a. a
Wu, W.-Y., Lo, M.-H., Wada, Y., Famiglietti, J. S., Reager, J. T., Yeh, P. J.-F., Ducharne, A., and Yang, Z.-L.: Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers, Nat. Commun., 11, 3710, https://doi.org/10.1038/s41467-020-17581-y, 2020b. a
Wu, Y., Miao, C., Fan, X., Gou, J., Zhang, Q., and Zheng, H.: Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties With Bias Correction Techniques, Earth's Future, 10, e2022EF002963, https://doi.org/10.1029/2022EF002963, 2022. a, b
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017gl072874, 2017. a
Yawson, D., Adu, M., Mulholland, B., Ball, T., Frimpong, K., Mohan, S., and White, P.: Regional variations in potential groundwater recharge from spring barley crop fields in the UK under projected climate change, Groundwater for Sustainable Development, 8, 332–345, https://doi.org/10.1016/j.gsd.2018.12.005, 2019. a
Yuan, F., Tung, Y.-K., and Ren, L.: Projection of future streamflow changes of the Pearl River basin in China using two delta-change methods, Hydrol. Res., 47, 217–238, https://doi.org/10.2166/nh.2015.159, 2015. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.621, 2019. a, b
Zhang, L. and Wang, J.: Prediction of the soil saturated hydraulic conductivity in a mining area based on CT scanning technology, J. Clean. Prod., 383, 135364, https://doi.org/10.1016/j.jclepro.2022.135364, 2023. a
Zhao, Y., Dong, N., Li, Z., Zhang, W., Yang, M., and Wang, H.: Future precipitation, hydrology and hydropower generation in the Yalong River Basin: Projections and analysis, J. Hydrol., 602, 126738, https://doi.org/10.1016/j.jhydrol.2021.126738, 2021. a
Short summary
In this paper, we investigate the impact of climatic and anthropogenic factors on future groundwater availability. The changes are simulated using hydrological and groundwater flow models. We find that future groundwater status is influenced more by anthropogenic factors than climatic factors. The results are beneficial for informing responsible parties in operational water management about achieving future (ground)water governance.
In this paper, we investigate the impact of climatic and anthropogenic factors on future...