Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1737-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1737-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1
Joint Global Change Research Institute, Pacific Northwest National Laboratory (JGCRI–PNNL), College Park, MD, USA
Stephen B. Ferencz
Earth System Science Division, Pacific Northwest National Laboratory (PNNL), Richland, WA, USA
Neal T. Graham
Joint Global Change Research Institute, Pacific Northwest National Laboratory (JGCRI–PNNL), College Park, MD, USA
Earth System Science Division, Pacific Northwest National Laboratory (PNNL), Richland, WA, USA
Thomas B. Wild
Joint Global Change Research Institute, Pacific Northwest National Laboratory (JGCRI–PNNL), College Park, MD, USA
Mohamad Hejazi
King Abdullah Petroleum Studies and Research Center (KAPSARC), Riyadh, Saudi Arabia
David J. Watson
Washington River Protection Solutions, Richland, WA, USA
Chris R. Vernon
Earth System Science Division, Pacific Northwest National Laboratory (PNNL), Richland, WA, USA
Related authors
Mengqi Zhao, Thomas B. Wild, Neal T. Graham, Son H. Kim, Matthew Binsted, A. F. M. Kamal Chowdhury, Siwa Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, and Guta W. Abeshu
Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024, https://doi.org/10.5194/gmd-17-5587-2024, 2024
Short summary
Short summary
The Global Change Analysis Model (GCAM) simulates the world’s climate–land–energy–water system interactions , but its reservoir representation is limited. We developed the GLObal Reservoir Yield (GLORY) model to provide GCAM with information on the cost of supplying water based on reservoir construction costs, climate and demand conditions, and reservoir expansion potential. GLORY enhances our understanding of future reservoir capacity needs to meet human demands in a changing climate.
Mengqi Zhao, Thomas B. Wild, Neal T. Graham, Son H. Kim, Matthew Binsted, A. F. M. Kamal Chowdhury, Siwa Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, and Guta W. Abeshu
Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024, https://doi.org/10.5194/gmd-17-5587-2024, 2024
Short summary
Short summary
The Global Change Analysis Model (GCAM) simulates the world’s climate–land–energy–water system interactions , but its reservoir representation is limited. We developed the GLObal Reservoir Yield (GLORY) model to provide GCAM with information on the cost of supplying water based on reservoir construction costs, climate and demand conditions, and reservoir expansion potential. GLORY enhances our understanding of future reservoir capacity needs to meet human demands in a changing climate.
Stephen B. Ferencz, Ning Sun, Sean W. D. Turner, Brian A. Smith, and Jennie S. Rice
Nat. Hazards Earth Syst. Sci., 24, 1871–1896, https://doi.org/10.5194/nhess-24-1871-2024, https://doi.org/10.5194/nhess-24-1871-2024, 2024
Short summary
Short summary
Drought has long posed an existential threat to society. Population growth, economic development, and the potential for more extreme and prolonged droughts due to climate change pose significant water security challenges. Better understanding the impacts and adaptive responses resulting from extreme drought can aid adaptive planning. The 2008–2015 record drought in the Colorado Basin, Texas, United States, is used as a case study to assess impacts and responses to severe drought.
Jim Yoon, Nathalie Voisin, Christian Klassert, Travis Thurber, and Wenwei Xu
Hydrol. Earth Syst. Sci., 28, 899–916, https://doi.org/10.5194/hess-28-899-2024, https://doi.org/10.5194/hess-28-899-2024, 2024
Short summary
Short summary
Global and regional models used to evaluate water shortages typically neglect the possibility that irrigated crop areas may change in response to future hydrological conditions, such as the fallowing of crops in response to drought. Here, we enhance a model used for water shortage analysis with farmer agents that dynamically adapt their irrigated crop areas based on simulated hydrological conditions. Results indicate that such cropping adaptation can strongly alter simulated water shortages.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
Short summary
Short summary
Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
Short summary
Short summary
GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Min Chen, Chris R. Vernon, Maoyi Huang, Katherine V. Calvin, and Ian P. Kraucunas
Geosci. Model Dev., 12, 1753–1764, https://doi.org/10.5194/gmd-12-1753-2019, https://doi.org/10.5194/gmd-12-1753-2019, 2019
Short summary
Short summary
Demeter is a community spatial downscaling model that disaggregates land use and land cover changes projected by integrated human–Earth system models. However, Demeter has not been intensively calibrated, and we still lack good knowledge about its sensitivity to key parameters and parameter uncertainties. This paper aims to solve this problem.
Zhongwei Huang, Mohamad Hejazi, Xinya Li, Qiuhong Tang, Chris Vernon, Guoyong Leng, Yaling Liu, Petra Döll, Stephanie Eisner, Dieter Gerten, Naota Hanasaki, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 22, 2117–2133, https://doi.org/10.5194/hess-22-2117-2018, https://doi.org/10.5194/hess-22-2117-2018, 2018
Short summary
Short summary
This study generate a historical global monthly gridded water withdrawal data (0.5 × 0.5 degrees) for the period 1971–2010, distinguishing six water use sectors (irrigation, domestic, electricity generation, livestock, mining, and manufacturing). This dataset is the first reconstructed global water withdrawal data product at sub-annual and gridded resolution that is derived from different models and data sources, and was generated by spatially and temporally downscaling country-scale estimates.
Related subject area
Integrated assessment modeling
emIAM v1.0: an emulator for integrated assessment models using marginal abatement cost curves
pathways-ensemble-analysis v1.1.0: an open-source library for systematic and robust analysis of pathway ensembles
MESSAGEix-Materials v1.1.0: representation of material flows and stocks in an integrated assessment model
GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model
CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests
Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation
Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China
MESSAGEix-GLOBIOM nexus module: integrating water sector and climate impacts
Minimum-variance-based outlier detection method using forward-search model error in geodetic networks
Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)
Bidirectional coupling of the long-term integrated assessment model REgional Model of INvestments and Development (REMIND) v3.0.0 with the hourly power sector model Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2
GCAM-CDR v1.0: enhancing the representation of carbon dioxide removal technologies and policies in an integrated assessment model
The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures
Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data
A tool for air pollution scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies
Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland
Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data
Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information
TIM: modelling pathways to meet Ireland's long-term energy system challenges with the TIMES-Ireland Model (v1.0)
ANEMI_Yangtze v1.0: a coupled human–natural systems model for the Yangtze Economic Belt – model description
Nested leave-two-out cross-validation for the optimal crop yield model selection
GCAM-USA v5.3_water_dispatch: integrated modeling of subnational US energy, water, and land systems within a global framework
GOBLIN version 1.0: a land balance model to identify national agriculture and land use pathways to climate neutrality via backcasting
Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2
REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits
Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF
Estimating global land system impacts of timber plantations using MAgPIE 4.3.5
Weiwei Xiong, Katsumasa Tanaka, Philippe Ciais, Daniel J. A. Johansson, and Mariliis Lehtveer
Geosci. Model Dev., 18, 1575–1612, https://doi.org/10.5194/gmd-18-1575-2025, https://doi.org/10.5194/gmd-18-1575-2025, 2025
Short summary
Short summary
emIAM v1.0 is a new emulator for integrated assessment models (IAMs) using global and regional time-independent marginal abatement cost (MAC) curves for multiple greenhouse gases, optionally with time-dependent MAC curves for enhanced accuracy. Combined with climate emulators, emIAM enables development of cost-effective, multi-IAM emission pathways directly linked to temperature targets at low computational cost.
Lara Welder, Neil Grant, and Matthew J. Gidden
Geosci. Model Dev., 18, 239–252, https://doi.org/10.5194/gmd-18-239-2025, https://doi.org/10.5194/gmd-18-239-2025, 2025
Short summary
Short summary
Pathways investigating the link between emissions and global warming have been continuously used to inform climate policy. We have developed a tool that can facilitate the systematic and robust analysis of ensembles of such pathways. We describe the structure of this tool and then show an illustrative application of it. The application indicates the usefulness of the tool to the research community and shows how it can be used to establish best practices.
Gamze Ünlü, Florian Maczek, Jihoon Min, Stefan Frank, Fridolin Glatter, Paul Natsuo Kishimoto, Jan Streeck, Nina Eisenmenger, Dominik Wiedenhofer, and Volker Krey
Geosci. Model Dev., 17, 8321–8352, https://doi.org/10.5194/gmd-17-8321-2024, https://doi.org/10.5194/gmd-17-8321-2024, 2024
Short summary
Short summary
Extraction and processing of raw materials constitute a significant source of CO2 emissions in industry and so are contributors to climate change. We develop an open-source tool to assess different industry decarbonization pathways in integrated assessment models (IAMs) with a representation of material flows and stocks. We highlight the importance of expanding the scope of climate change mitigation options to include circular-economy and material efficiency measures in IAM scenario analysis.
Mengqi Zhao, Thomas B. Wild, Neal T. Graham, Son H. Kim, Matthew Binsted, A. F. M. Kamal Chowdhury, Siwa Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, and Guta W. Abeshu
Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024, https://doi.org/10.5194/gmd-17-5587-2024, 2024
Short summary
Short summary
The Global Change Analysis Model (GCAM) simulates the world’s climate–land–energy–water system interactions , but its reservoir representation is limited. We developed the GLObal Reservoir Yield (GLORY) model to provide GCAM with information on the cost of supplying water based on reservoir construction costs, climate and demand conditions, and reservoir expansion potential. GLORY enhances our understanding of future reservoir capacity needs to meet human demands in a changing climate.
Tommi Ekholm, Nadine-Cyra Freistetter, Aapo Rautiainen, and Laura Thölix
Geosci. Model Dev., 17, 3041–3062, https://doi.org/10.5194/gmd-17-3041-2024, https://doi.org/10.5194/gmd-17-3041-2024, 2024
Short summary
Short summary
CLASH is a numerical model that portrays land allocation between different uses, land carbon stocks, and agricultural and forestry production globally. CLASH can help in examining the role of land use in mitigating climate change, providing food and biogenic raw materials for the economy, and conserving primary ecosystems. Our demonstration with CLASH confirms that reduction of animal-based food, shifting croplands and storing carbon in forests are effective ways to mitigate climate change.
Léna Gurriaran, Yannig Goude, Katsumasa Tanaka, Biqing Zhu, Zhu Deng, Xuanren Song, and Philippe Ciais
Geosci. Model Dev., 17, 2663–2682, https://doi.org/10.5194/gmd-17-2663-2024, https://doi.org/10.5194/gmd-17-2663-2024, 2024
Short summary
Short summary
We developed a data-driven model simulating daily regional power demand based on climate and socioeconomic variables. Our model was applied to eight countries or regions (Australia, Brazil, China, EU, India, Russia, South Africa, US), identifying influential factors and their relationship with power demand. Our findings highlight the significance of economic indicators in addition to temperature, showcasing country-specific variations. This research aids energy planning and emission reduction.
Chao Gao, Xuelei Zhang, Aijun Xiu, Qingqing Tong, Hongmei Zhao, Shichun Zhang, Guangyi Yang, Mengduo Zhang, and Shengjin Xie
Geosci. Model Dev., 17, 2471–2492, https://doi.org/10.5194/gmd-17-2471-2024, https://doi.org/10.5194/gmd-17-2471-2024, 2024
Short summary
Short summary
A comprehensive comparison study is conducted targeting the performances of three two-way coupled meteorology and air quality models (WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) for eastern China during 2017. The impacts of aerosol–radiation–cloud interactions on these models’ results are evaluated against satellite and surface observations. Further improvements to the calculation of aerosol–cloud interactions in these models are crucial to ensure more accurate and timely air quality forecasts.
Muhammad Awais, Adriano Vinca, Edward Byers, Stefan Frank, Oliver Fricko, Esther Boere, Peter Burek, Miguel Poblete Cazenave, Paul Natsuo Kishimoto, Alessio Mastrucci, Yusuke Satoh, Amanda Palazzo, Madeleine McPherson, Keywan Riahi, and Volker Krey
Geosci. Model Dev., 17, 2447–2469, https://doi.org/10.5194/gmd-17-2447-2024, https://doi.org/10.5194/gmd-17-2447-2024, 2024
Short summary
Short summary
Climate change, population growth, and depletion of natural resources all pose complex and interconnected challenges. Our research offers a novel model that can help in understanding the interplay of these aspects, providing policymakers with a more robust tool for making informed future decisions. The study highlights the significance of incorporating climate impacts within large-scale global integrated assessments, which can help us in generating more climate-resilient scenarios.
Utkan M. Durdağ
Geosci. Model Dev., 17, 2187–2196, https://doi.org/10.5194/gmd-17-2187-2024, https://doi.org/10.5194/gmd-17-2187-2024, 2024
Short summary
Short summary
This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss–Markov model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The mean success rate (MSR) comparisons highlight its effectiveness, improving the MSR by 40–45 % for multiple outliers.
Michaja Pehl, Felix Schreyer, and Gunnar Luderer
Geosci. Model Dev., 17, 2015–2038, https://doi.org/10.5194/gmd-17-2015-2024, https://doi.org/10.5194/gmd-17-2015-2024, 2024
Short summary
Short summary
We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemical, steel, and other industries. We also present a method for deriving scenarios of industry subsector activity and energy demand, consistent with established socioeconomic scenarios, allowing us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of the entire energy–economy–climate system.
Chen Chris Gong, Falko Ueckerdt, Robert Pietzcker, Adrian Odenweller, Wolf-Peter Schill, Martin Kittel, and Gunnar Luderer
Geosci. Model Dev., 16, 4977–5033, https://doi.org/10.5194/gmd-16-4977-2023, https://doi.org/10.5194/gmd-16-4977-2023, 2023
Short summary
Short summary
To mitigate climate change, the global economy must drastically reduce its greenhouse gas emissions, for which the power sector plays a key role. Until now, long-term models which simulate this transformation cannot always accurately depict the power sector due to a lack of resolution. Our work bridges this gap by linking a long-term model to an hourly model. The result is an almost full harmonization of the models in generating a power sector mix until 2100 with hourly resolution.
David R. Morrow, Raphael Apeaning, and Garrett Guard
Geosci. Model Dev., 16, 1105–1118, https://doi.org/10.5194/gmd-16-1105-2023, https://doi.org/10.5194/gmd-16-1105-2023, 2023
Short summary
Short summary
GCAM-CDR is a variant of the Global Change Analysis Model that makes it easier to study the roles that carbon dioxide removal (CDR) might play in climate policy. Building on GCAM 5.4, GCAM-CDR adds several extra technologies to permanently remove carbon dioxide from the air and enables users to simulate a wider range of CDR-related policies and controls.
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
Short summary
Short summary
Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
climate-assessmentworkflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.
Théo Le Guenedal, Philippe Drobinski, and Peter Tankov
Geosci. Model Dev., 15, 8001–8039, https://doi.org/10.5194/gmd-15-8001-2022, https://doi.org/10.5194/gmd-15-8001-2022, 2022
Short summary
Short summary
The CATHERINA model produces simulations of cyclone-related annualized damage costs at a country level from climate data and open-source socioeconomic indicators. The framework couples statistical and physical modeling of tropical cyclones to bridge the gap between general circulation and integrated assessment models providing a precise description of tropical-cyclone-related damages.
William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, and Noelle E. Selin
Geosci. Model Dev., 15, 7767–7789, https://doi.org/10.5194/gmd-15-7767-2022, https://doi.org/10.5194/gmd-15-7767-2022, 2022
Short summary
Short summary
Understanding policy effects on human-caused air pollutant emissions is key for assessing related health impacts. We develop a flexible scenario tool that combines updated emissions data sets, long-term economic modeling, and comprehensive technology pathways to clarify the impacts of climate and air quality policies. Results show the importance of both policy levers in the future to prevent long-term emission increases from offsetting near-term air quality improvements from existing policies.
Chengyong Wu, Kelong Chen, Chongyi E, Xiaoni You, Dongcai He, Liangbai Hu, Baokang Liu, Runke Wang, Yaya Shi, Chengxiu Li, and Fumei Liu
Geosci. Model Dev., 15, 6919–6933, https://doi.org/10.5194/gmd-15-6919-2022, https://doi.org/10.5194/gmd-15-6919-2022, 2022
Short summary
Short summary
The traditional Carnegie–Ames–Stanford Approach (CASA) model driven by multisource data such as meteorology, soil, and remote sensing (RS) has notable disadvantages. We drove the CASA using RS data and conducted a case study of the Qinghai Lake basin alpine grassland. The simulated result is similar to published and measured net primary productivity (NPP). It may provide a reference for simulating vegetation NPP to satisfy the requirements of accounting carbon stocks and other applications.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
Short summary
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté, Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie
Geosci. Model Dev., 15, 6115–6142, https://doi.org/10.5194/gmd-15-6115-2022, https://doi.org/10.5194/gmd-15-6115-2022, 2022
Short summary
Short summary
CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.
Olexandr Balyk, James Glynn, Vahid Aryanpur, Ankita Gaur, Jason McGuire, Andrew Smith, Xiufeng Yue, and Hannah Daly
Geosci. Model Dev., 15, 4991–5019, https://doi.org/10.5194/gmd-15-4991-2022, https://doi.org/10.5194/gmd-15-4991-2022, 2022
Short summary
Short summary
Ireland has significantly increased its climate mitigation ambition, with a recent commitment to reduce greenhouse gases by an average of 7 % yr-1 in the period to 2030 and a net-zero target for 2050. This article describes the TIMES-Ireland model (TIM) developed to inform Ireland's energy system decarbonisation challenge. The paper also outlines a priority list of future model developments to better meet the challenge, taking into account equity, cost-effectiveness, and technical feasibility.
Haiyan Jiang, Slobodan P. Simonovic, and Zhongbo Yu
Geosci. Model Dev., 15, 4503–4528, https://doi.org/10.5194/gmd-15-4503-2022, https://doi.org/10.5194/gmd-15-4503-2022, 2022
Short summary
Short summary
The Yangtze Economic Belt is one of the most dynamic regions of China. The fast urbanization and strong economic growth in the region pose severe challenges for its sustainable development. To improve our understanding of the interactions among coupled human–natural systems in the Belt and to provide the foundation for science-based policy-making for the sustainable development of the Belt, we developed an integrated system-dynamics-based simulation model (ANEMI_Yangtze) for the Belt.
Thi Lan Anh Dinh and Filipe Aires
Geosci. Model Dev., 15, 3519–3535, https://doi.org/10.5194/gmd-15-3519-2022, https://doi.org/10.5194/gmd-15-3519-2022, 2022
Short summary
Short summary
We proposed the leave-two-out method (i.e. one particular implementation of the nested cross-validation) to determine the optimal statistical crop model (using the validation dataset) and estimate its true generalization ability (using the testing dataset). This approach is applied to two examples (robusta coffee in Cu M'gar and grain maize in France). The results suggested that the simple models are more suitable in crop modelling where a limited number of samples is available.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
Short summary
Short summary
GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Colm Duffy, Remi Prudhomme, Brian Duffy, James Gibbons, Cathal O'Donoghue, Mary Ryan, and David Styles
Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, https://doi.org/10.5194/gmd-15-2239-2022, 2022
Short summary
Short summary
The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
bottom-upbiophysical land use model capable of identifying broad pathways towards climate neutrality in the agriculture, forestry, and other land use (AFOLU) sector. The model is intended to bridge the gap between hindsight representations of national emissions and much larger globally integrated assessment models.
Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
Short summary
Short summary
In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
Short summary
Short summary
This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
Short summary
Short summary
This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
Short summary
Short summary
Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Cited articles
Abeshu, G. W., Tian, F., Wild, T., Zhao, M., Turner, S., Chowdhury, A. F. M. K., Vernon, C. R., Hu, H., Zhuang, Y., Hejazi, M., and Li, H.-Y.: Enhancing the representation of water management in global hydrological models, Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, 2023. a
Advisor, H.: Average Costs per Foot of Well Drilling & Digging, https://www.homeadvisor.com/cost/landscape/drill-a-well/#costs (last access: August 2018), 2018. a
Alam, M. F.: Evaluating the benefit-cost ratio of groundwater abstraction for additional irrigation water on global scale, Student thesis, http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199089 (last access: 17 February 2025), 2016-12-28T13:41:19.323+01:00, 2016. a
Alam, M. F., McClain, M., Sikka, A., and Pande, S.: Understanding human–water feedbacks of interventions in agricultural systems with agent based models: a review, Environ. Res. Lett., 17, 103003, https://doi.org/10.1088/1748-9326/ac91e1, 2022. a, b
Balasubramanya, S., Garrick, D., Brozović, N., Ringler, C., Zaveri, E., Rodella, A.-S., Buisson, M.-C., Schmitter, P., Durga, N., Kishore, A., Minh, T. T., Kafle, K., Stifel, D., Balasubramanya, S., Chandra, A., and Hope, L.: Risks from solar-powered groundwater irrigation, Science, 383, 256–258, https://doi.org/10.1126/science.adi9497, 2024. a
Bierkens, M., De Graaf, I. E., Lips, S., Perrone, D., Reinhard, A. S., Jasechko, S., van der Himst, T., and van Beek, R.: Global Economic Limits of Groundwater When Used as a Last Resort for Irrigation, Research Square [preprint], https://doi.org/10.21203/rs.3.rs-1874539/v1, 2022. a, b
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
Burek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., Zhao, F., and Wada, Y.: Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management, Geosci. Model Dev., 13, 3267–3298, https://doi.org/10.5194/gmd-13-3267-2020, 2020. a
Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019. a, b, c
Canales, M., Castilla-Rho, J., Rojas, R., Vicuña, S., and Ball, J.: Agent-based models of groundwater systems: A review of an emerging approach to simulate the interactions between groundwater and society, Environ. Model. Softw., 175, 105980, https://doi.org/10.1016/j.envsoft.2024.105980, 2024. a
Castilla-Rho, J. C., Rojas, R., Andersen, M. S., Holley, C., and Mariethoz, G.: Social tipping points in global groundwater management, Nat. Hum. Behav., 1, 640–649, https://doi.org/10.1038/s41562-017-0181-7, 2017. a, b, c
Davidsen, C., Liu, S., Mo, X., Rosbjerg, D., and Bauer-Gottwein, P.: The cost of ending groundwater overdraft on the North China Plain, Hydrol. Earth Syst. Sci., 20, 771–785, https://doi.org/10.5194/hess-20-771-2016, 2016. a, b
de Graaf, I. E. M. and Stahl, K.: A model comparison assessing the importance of lateral groundwater flows at the global scale, Environ. Res. Lett., 17, 044020, https://doi.org/10.1088/1748-9326/ac50d2, 2022. a
de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., and Bierkens, M. F. P.: A high-resolution global-scale groundwater model, Hydrol. Earth Syst. Sci., 19, 823–837, https://doi.org/10.5194/hess-19-823-2015, 2015. a, b, c
de Graaf, I. E. M., van Beek, R. L. P. H., Gleeson, T., Moosdorf, N., Schmitz, O., Sutanudjaja, E. H., and Bierkens, M. F. P.: A global-scale two-layer transient groundwater model: Development and application to groundwater depletion, Adv. Water Resour., 102, 53–67, https://doi.org/10.1016/j.advwatres.2017.01.011, 2017. a
Dolan, F., Lamontagne, J., Link, R., Hejazi, M., Reed, P., and Edmonds, J.: Evaluating the economic impact of water scarcity in a changing world, Nat. Commun., 12, 1915, https://doi.org/10.1038/s41467-021-22194-0, 2021. a
Döll, P. and Fiedler, K.: Global-scale modeling of groundwater recharge, Hydrol. Earth Syst. Sci., 12, 863–885, https://doi.org/10.5194/hess-12-863-2008, 2008. a, b, c
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, https://doi.org/10.1126/science.1229881, 2013. a, b
Fenichel, E. P., Abbott, J. K., Bayham, J., Boone, W., Haacker, E. M. K., and Pfeiffer, L.: Measuring the value of groundwater and other forms of natural capital, P. Natl. Acad. Sci. USA, 113, 2382–2387, https://doi.org/10.1073/pnas.1513779113, 2016. a
Fisher-Vanden, K. and Weyant, J.: The Evolution of Integrated Assessment: Developing the Next Generation of Use-Inspired Integrated Assessment Tools, Annu. Rev. Resour. Econ., 12, 471–487, https://doi.org/10.1146/annurev-resource-110119-030314, 2020. a
Foster, T., Brozović, N., and Butler, A. P.: Analysis of the impacts of well yield and groundwater depth on irrigated agriculture, J. Hydrol., 523, 86–96, https://doi.org/10.1016/j.jhydrol.2015.01.032, 2015. a
Foster, T., Brozović, N., and Speir, C.: The buffer value of groundwater when well yield is limited, J. Hydrol., 547, 638–649, https://doi.org/10.1016/j.jhydrol.2017.02.034, 2017. a
Gleeson, T., Wada, Y., Bierkens, M. F. P., and van Beek, L. P. H.: Water balance of global aquifers revealed by groundwater footprint, Nature, 488, 197–200, https://doi.org/10.1038/nature11295, 2012. a
Gleeson, T., Moosdorf, N., Hartmann, J., and van Beek, L. P. H.: A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity, Geophys. Res. Lett., 41, 3891–3898, https://doi.org/10.1002/2014GL059856, 2014. a, b
Gleeson, T., Wagener, T., Döll, P., Zipper, S. C., West, C., Wada, Y., Taylor, R., Scanlon, B., Rosolem, R., Rahman, S., Oshinlaja, N., Maxwell, R., Lo, M.-H., Kim, H., Hill, M., Hartmann, A., Fogg, G., Famiglietti, J. S., Ducharne, A., de Graaf, I., Cuthbert, M., Condon, L., Bresciani, E., and Bierkens, M. F. P.: GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models, Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, 2021. a, b
Glotfelty, M. F. (Ed.): The art of water wells: technical and economic considerations for water well siting, design, and installation, NGWA Press, National Ground Water Association, ISBN 1-56034-048-7, 2019. a
Gorelick, S. M. and Zheng, C.: Global change and the groundwater management challenge, Water Resour. Res., 51, 3031–3051, https://doi.org/10.1002/2014WR016825, 2015. a
Grogan, D. S., Wisser, D., Prusevich, A., Lammers, R. B., and Frolking, S.: The use and re-use of unsustainable groundwater for irrigation: a global budget, Environ. Res. Lett., 12, 034017, https://doi.org/10.1088/1748-9326/aa5fb2, 2017. a
Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., Shen, Y., and Tanaka, K.: An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing, Hydrol. Earth Syst. Sci., 12, 1007–1025, https://doi.org/10.5194/hess-12-1007-2008, 2008. a
Harou, J. J., Pulido-Velazquez, M., Rosenberg, D. E., Medellín-Azuara, J., Lund, J. R., and Howitt, R. E.: Hydro-economic models: Concepts, design, applications, and future prospects, J. Hydrol., 375, 627–643, https://doi.org/10.1016/j.jhydrol.2009.06.037, 2009. a, b
Hejazi, M., Santos Da Silva, S. R., Miralles-Wilhelm, F., Kim, S., Kyle, P., Liu, Y., Vernon, C., Delgado, A., Edmonds, J., and Clarke, L.: Impacts of water scarcity on agricultural production and electricity generation in the Middle East and North Africa, Front. Environ. Sci., 11, 1082930, https://doi.org/10.3389/fenvs.2023.1082930, 2023. a, b, c
Howitt, R. E.: Positive mathematical programming, Am. J. Agric. Econ., 77, 329–342, https://doi.org/10.2307/1243543, 1995. a
IEA, I. E. A.: Energy Prices and Taxes, 2016, https://doi.org/10.1787/energy_tax-v2016-3-en, 2016. a, b
Jacob, C. E.: Drawdown Test to Determine Effective Radius of Artesian Well, T. Am. Soc. Civ. Eng., 112, 1047–1064, https://doi.org/10.1061/TACEAT.0006033, 1947. a
Jasechko, S. and Perrone, D.: Global groundwater wells at risk of running dry, Science, 372, 418–421, https://doi.org/10.1126/science.abc2755, 2021. a
Jasechko, S., Seybold, H., Perrone, D., Fan, Y., Shamsudduha, M., Taylor, R. G., Fallatah, O., and Kirchner, J. W.: Rapid groundwater decline and some cases of recovery in aquifers globally, Nature, 625, 715–721, https://doi.org/10.1038/s41586-023-06879-8, 2024. a
Kahil, T., Albiac, J., Fischer, G., Strokal, M., Tramberend, S., Greve, P., Tang, T., Burek, P., Burtscher, R., and Wada, Y.: A nexus modeling framework for assessing water scarcity solutions, Curr. Opin. Env. Sust., 40, 72–80, https://doi.org/10.1016/j.cosust.2019.09.009, 2019. a
Kanazawa, M. T.: Econometric estimation of groundwater pumping costs: A simultaneous equations approach, Water Resour. Res., 28, 1507–1516, https://doi.org/10.1029/92WR00198, 1992. a
Katsifarakis, K. L.: Groundwater Pumping Cost Minimization – an Analytical Approach, Water Resour. Manage., 22, 1089–1099, https://doi.org/10.1007/s11269-007-9212-x, 2008. a
Katsifarakis, K. L., Nikoletos, I. A., and Stavridis, C.: Minimization of Transient Groundwater Pumping Cost – Analytical and Practical Solutions, Water Resour. Manage., 32, 1053–1069, https://doi.org/10.1007/s11269-017-1854-8, 2018. a
Keppo, I., Butnar, I., Bauer, N., Caspani, M., Edelenbosch, O., Emmerling, J., Fragkos, P., Guivarch, C., Harmsen, M., Lefèvre, J., Le Gallic, T., Leimbach, M., McDowall, W., Mercure, J. F., Schaeffer, R., Trutnevyte, E., and Wagner, F.: Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models, Environ. Res. Lett., 16, 053006, https://doi.org/10.1088/1748-9326/abe5d8, 2021. a, b, c
Khan, Z., Thompson, I., Vernon, C. R., Graham, N. T., Wild, T. B., and Chen, M.: Global monthly sectoral water use for 2010–2100 at 0.5° resolution across alternative futures, Sci. Data, 10, 201, https://doi.org/10.1038/s41597-023-02086-2, 2023. a
Kim, S. H., Hejazi, M., Liu, L., Calvin, K., Clarke, L., Edmonds, J., Kyle, P., Patel, P., Wise, M., and Davies, E.: Balancing global water availability and use at basin scale in an integrated assessment model, Clim. Change, 136, 217–231, https://doi.org/10.1007/s10584-016-1604-6, 2016. a, b
Klassert, C., Yoon, J., Sigel, K., Klauer, B., Talozi, S., Lachaut, T., Selby, P., Knox, S., Avisse, N., Tilmant, A., Harou, J. J., Mustafa, D., Medellín-Azuara, J., Bataineh, B., Zhang, H., Gawel, E., and Gorelick, S. M.: Unexpected growth of an illegal water market, Nat. Sustain., 6, 1406–1417, https://doi.org/10.1038/s41893-023-01177-7, 2023. a, b
Konikow, L. F. and Kendy, E.: Groundwater depletion: A global problem, Hydrogeol. J., 13, 317–320, https://doi.org/10.1007/s10040-004-0411-8, 2005. a
Kyle, P., Ollenburger, M., Zhang, X., Niazi, H., Durga, S., and Ou, Y.: Assessing Multi-Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix Into the Future, Earth's Future, 11, e2022EF003323, https://doi.org/10.1029/2022EF003323, 2023. a
Lall, U., Josset, L., and Russo, T.: A Snapshot of the World's Groundwater Challenges, Annu. Rev. Environ. Resour., 45, 171–194, https://doi.org/10.1146/annurev-environ-102017-025800, 2020. a
Liu, Y., Hejazi, M., Li, H., Zhang, X., and Leng, G.: A hydrological emulator for global applications – HE v1.0.0, Geosci. Model Dev., 11, 1077–1092, https://doi.org/10.5194/gmd-11-1077-2018, 2018. a
McGuire, V. L. and Strauch, K. R.: Water-level and recoverable water in storage changes, High Plains Aquifer, predevelopment to 2019 and 2017 to 2019, USGS, https://doi.org/10.3133/sir20235143, 2024. a
McGuire, V. L., Johnson, M., Schieffer, R., Stanton, J., Sebree, S., and Verstraeten, I. M.: Water in storage and approaches to ground-water management, High Plains aquifer, 2000, vol. 1243, US Geological Survey Reston, VA, USA, https://pubs.usgs.gov/circ/2003/circ1243/pdf/C1243_v1.pdf (last access: 17 February 2025), 2003. a
Medellin-Azuara, J., MacEwan, D., Howitt, R. E., Koruakos, G., Dogrul, E. C., Brush, C. F., Kadir, T. N., Harter, T., Melton, F., and Lund, J. R.: Hydro-economic analysis of groundwater pumping for irrigated agriculture in California's Central Valley, USA, Hydrogeol. J., 23, 1205, https://doi.org/10.1007/s10040-015-1283-9, 2015. a
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016. a, b, c
Mora, M., Vera, J., Rocamora, C., and Abadia, R.: Energy efficiency and maintenance costs of pumping systems for groundwater extraction, Water Resour. Manage., 27, 4395–4408, https://doi.org/10.1080/07900627.2014.935302, 2013. a
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A., Popat, E., Portmann, F. T., Reinecke, R., Schumacher, M., Shadkam, S., Telteu, C.-E., Trautmann, T., and Döll, P.: The global water resources and use model WaterGAP v2.2d: model description and evaluation, Geosci. Model Dev., 14, 1037–1079, https://doi.org/10.5194/gmd-14-1037-2021, 2021. a, b
Narayanamoorthy, A.: Groundwater depletion and water extraction cost: some evidence from South India, Int. J. Water Resour. Dev., 31, 604–617, https://doi.org/10.1080/07900627.2014.935302, 2015. a
Niazi, H., Ferencz, S., Yoon, J., Graham, N., Wild, T., Hejazi, M., Watson, D., and Vernon, C.: Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets, MSD Live [data set], https://doi.org/10.57931/2307832, 2024a. a, b, c, d
Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., and Yoon, J.: Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins, MSD Live [data set], https://doi.org/10.57931/2484226, 2024b. a, b, c, d
Reinecke, R., Gnann, S., Stein, L., Bierkens, M., de Graaf, I., Gleeson, T., OudeEssink, G., Sutanudjaja, E., Ruz-Vargas, C., Verkaik, J., and Wagener, T.: Considerable gaps in our global knowledge of potential groundwater accessibility, Earth ArXiv, https://doi.org/10.31223/X5SM0R, 2023. a, b, c
Richts, A., Struckmeier, W. F., and Zaepke, M.: WHYMAP and the Groundwater Resources Map of the World 1:25,000,000, pp. 159–173, Springer Netherlands, Dordrecht, ISBN 978-90-481-3426-7, https://doi.org/10.1007/978-90-481-3426-7_10, 2011. a, b, c
Rodríguez-Flores, J. M., Valero Fandiño, J. A., Cole, S. A., Malek, K., Karimi, T., Zeff, H. B., Reed, P. M., Escriva-Bou, A., and Medellín-Azuara, J.: Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment, Water Resour. Manage., 36, 6115–6130, https://doi.org/10.1007/s11269-022-03344-5, 2022. a
Salem, G. S. A., Kazama, S., Shahid, S., and Dey, N. C.: Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region, Agric. Water Manage., 208, 33–42, https://doi.org/10.1016/j.agwat.2018.06.011, 2018. a
Scanlon, B. R., Fakhreddine, S., Rateb, A., de Graaf, I., Famiglietti, J., Gleeson, T., Grafton, R. Q., Jobbagy, E., Kebede, S., Kolusu, S. R., Konikow, L. F., Long, D., Mekonnen, M., Schmied, H. M., Mukherjee, A., MacDonald, A., Reedy, R. C., Shamsudduha, M., Simmons, C. T., Sun, A., Taylor, R. G., Villholth, K. G., Vörösmarty, C. J., and Zheng, C.: Global water resources and the role of groundwater in a resilient water future, Nat. Rev. Earth Environ., 4, 87–101, https://doi.org/10.1038/s43017-022-00378-6, 2023. a
Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell Nigel, W., Clark Douglas, B., Dankers, R., Eisner, S., Fekete Balázs, M., Colón-González Felipe, J., Gosling Simon, N., Kim, H., Liu, X., Masaki, Y., Portmann Felix, T., Satoh, Y., Stacke, T., Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K., Piontek, F., Warszawski, L., and Kabat, P.: Multimodel assessment of water scarcity under climate change, P. Natl. Acad. Sci. USA, 111, 3245–3250, https://doi.org/10.1073/pnas.1222460110, 2014. a
Siebert, S., Burke, J., Faures, J. M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T.: Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, 1863–1880, https://doi.org/10.5194/hess-14-1863-2010, 2010. a
Sophocleous, M.: The origin and evolution of safe-yield policies in the Kansas Groundwater Management Districts, Nat. Resour. Res., 9, 99–110, https://doi.org/10.1023/A:1010139325667, 2000. a
Srikrishnan, V., Lafferty, D. C., Wong, T. E., Lamontagne, J. R., Quinn, J. D., Sharma, S., Molla, N. J., Herman, J. D., Sriver, R. L., Morris, J. F., and Lee, B. S.: Uncertainty Analysis in Multi-Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems, Earth's Future, 10, e2021EF002644, https://doi.org/10.1029/2021EF002644, 2022. a
Steward, D. R., Bruss, P. J., Yang, X., Staggenborg, S. A., Welch, S. M., and Apley, M. D.: Tapping unsustainable groundwater stores for agricultural production in the High Plains Aquifer of Kansas, projections to 2110, P. Natl. Acad. Sci. USA, 110, E3477–E3486, https://doi.org/10.1073/pnas.1220351110, 2013. a
Strand, J.: The full economic cost of groundwater extraction (English). Policy Research working paper, no. WPS 5494 Washington, DC, World Bank, http://documents.worldbank.org/curated/en/592401468314702740 (last access: 17 February 2025), 2010. a
Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018. a
Suter, J. F., Rouhi Rad, M., Manning, D. T., Goemans, C., and Sanderson, M. R.: Depletion, climate, and the incremental value of groundwater, Resour. Energ. Econ., 63, 101143, https://doi.org/10.1016/j.reseneeco.2019.101143, 2021. a
Theis, C. V.: The relation between the lowering of the Piezometric surface and the rate and duration of discharge of a well using ground-water storage, Eos, Transactions American Geophysical Union, 16, 519–524, https://doi.org/10.1029/TR016i002p00519, 1935. a, b, c
Thurber, T., Vernon, C. R., Sun, N., Turner, S. W., Yoon, J., and Voisin, N.: mosartwmpy: A Python implementation of the MOSART-WM coupled hydrologic routing and water management model, J. Open Source Softw., 6, 3221, https://doi.org/10.21105/joss.03221, 2021. a
Turner, S. W. D., Hejazi, M., Calvin, K., Kyle, P., and Kim, S.: A pathway of global food supply adaptation in a world with increasingly constrained groundwater, Sci. Total Environ., 673, 165–176, https://doi.org/10.1016/j.scitotenv.2019.04.070, 2019a. a, b
Turner, S. W. D., Hejazi, M., Yonkofski, C., Kim, S. H., and Kyle, P.: Influence of Groundwater Extraction Costs and Resource Depletion Limits on Simulated Global Nonrenewable Water Withdrawals Over the Twenty-First Century, Earth's Future, 7, 123–135, https://doi.org/10.1029/2018EF001105, 2019b. a, b, c, d, e
Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H. P., Lin, H. X., and Bierkens, M. F. P.: GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model, Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, 2024. a
Vinca, A., Parkinson, S., Byers, E., Burek, P., Khan, Z., Krey, V., Diuana, F. A., Wang, Y., Ilyas, A., Köberle, A. C., Staffell, I., Pfenninger, S., Muhammad, A., Rowe, A., Schaeffer, R., Rao, N. D., Wada, Y., Djilali, N., and Riahi, K.: The NExus Solutions Tool (NEST) v1.0: an open platform for optimizing multi-scale energy–water–land system transformations, Geosci. Model Dev., 13, 1095–1121, https://doi.org/10.5194/gmd-13-1095-2020, 2020. a, b
Weyant, J.: Some Contributions of Integrated Assessment Models of Global Climate Change, Rev. Env. Econ. Policy, 11, 115–137, https://doi.org/10.1093/reep/rew018, 2017. a
Wild, T. B., Niazi, H., Graham, N. T., Birnbaum, A. N., Zhao, M., Lamontagne, J., Kim, S. H., Chowdhury, A., Msangi, S., and Zhang, Y.: Water and Global Change: An Integrated Modeling Perspective, in: AGU Fall Meeting Abstracts, 2023, H24B–1, https://ui.adsabs.harvard.edu/abs/2023AGUFM.H24B...1W/abstract (last access: 17 February 2025), 2023. a
Yoon, J., Klassert, C., Selby, P., Lachaut, T., Knox, S., Avisse, N., Harou, J., Tilmant, A., Klauer, B., Mustafa, D., Sigel, K., Talozi, S., Gawel, E., Medellín-Azuara, J., Bataineh, B., Zhang, H., and Gorelick, S. M.: A coupled human–natural system analysis of freshwater security under climate and population change, P. Natl. Acad. Sci. USA, 118, e2020431118, https://doi.org/10.1073/pnas.2020431118, 2021. a, b
Yoon, J., Romero-Lankao, P., Yang, Y. C. E., Klassert, C., Urban, N., Kaiser, K., Keller, K., Yarlagadda, B., Voisin, N., Reed, P. M., and Moss, R.: A Typology for Characterizing Human Action in MultiSector Dynamics Models, Earth's Future, 10, e2021EF002641, https://doi.org/10.1029/2021EF002641, 2022. a
Yoon, J., Voisin, N., Klassert, C., Thurber, T., and Xu, W.: Representing farmer irrigated crop area adaptation in a large-scale hydrological model, Hydrol. Earth Syst. Sci., 28, 899–916, https://doi.org/10.5194/hess-28-899-2024, 2024. a
Zhang, X., Sabo, R., Rosa, L., Niazi, H., Kyle, P., Byun, J. S., Wang, Y., Yan, X., Gu, B., and Davidson, E. A.: Nitrogen management during decarbonization, Nat. Rev. Earth Environ., 5, 717–731, https://doi.org/10.1038/s43017-024-00586-2, 2024. a
Zhao, M., Wild, T. B., Graham, N. T., Kim, S. H., Binsted, M., Chowdhury, A. F. M. K., Msangi, S., Patel, P. L., Vernon, C. R., Niazi, H., Li, H.-Y., and Abeshu, G. W.: GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model, Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024, 2024. a, b
Short summary
Superwell is a physics-based hydro-economic model that estimates the production costs and availability of groundwater worldwide. It calculates how much groundwater can be extracted and at what cost, using detailed maps and data of the Earth's properties. Through these estimates, and by using them with other models, Superwell facilitates exploration of coupled human–environmental system challenges, such as future water supply sustainability or multi-sectoral energy–water–land feedbacks.
Superwell is a physics-based hydro-economic model that estimates the production costs and...