Assessing the effects of water restrictions on socio-hydrologic resilience for shared groundwater systems

Al-Amin, Shams; Berglund, Emily Zechman; Mahinthakumar, G.; Larson, Kelli L.

doi:10.1016/j.jhydrol.2018.08.045

Cited by 31 publications

(16 citation statements)

References 28 publications

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“…For example, Al‐Amin et al. (2018) assessed the effectiveness of water conservation programs using an ABM with two types of agents, the municipals and households, to simulate the dynamic interactions between the agents. Our future work will consider incorporating multiple agent types, such as municipal and industrial water users.…”

Section: Discussionmentioning

confidence: 99%

“…Originating from the Artificial Intelligence community, agent‐based modeling (ABM) was introduced to the water resources field in the late 2000s (Berglund, 2015). Since then, ABMs have emerged for assessing human systems dynamics and impacts on water systems (e.g., Al‐Amin et al., 2018; Berglund, 2015; Ng et al., 2011; Yang et al., 2009). ABM is a distributed, bottom‐up planning approach for understanding human impacts on system performance.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach

Hung

Yang

2021

Water Resources Research

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One major challenge in water resource management is to balance the uncertain and nonstationary water demands and supplies caused by the changing anthropogenic and hydroclimate conditions. To address this issue, we developed a reinforcement learning agent‐based modeling (RL‐ABM) framework where agents (agriculture water users) are able to learn and adjust water demands based on their interactions with the water systems. The intelligent agents are created by a reinforcement learning algorithm adapted from the Q‐learning algorithm. We illustrated this framework in a case study where the RL‐ABM is two‐way coupled with the Colorado River Simulation System (CRSS), a long‐term planning model used for the administration of the Colorado River Basin, for assessing agriculture water uses impacts on water scarcity. Seventy‐eight intelligent agents are simulated, which can be grouped into three categories based on their parameter values: the “aggressive” (swift actions; low regrets), the “forward‐looking conservative” (mild actions; high regrets; fast learning), and the “myopic conservative” (mild actions; median regrets; slow learning). The ABM‐CRSS results showed that the major reservoirs in the Upper Colorado Basin might experience more frequent water shortages due to the increasing water uses compared to the original CRSS results. If the drought continues, the case study also demonstrates that agents can learn and adjust their demands.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach

Hung

Yang

2021

Water Resources Research

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“…Farming and irrigation management Nüsser et al, 2012Nüsser et al, , 2019Bury et al, 2013;Ertsen et al, 2014;Liu et al, 2014;Pande and Ertsen, 2014;Schumann and Nijssen, 2014;Purdue and Berger, 2015;Den Besten et al, 2016;Giuliani et al, 2016;Jeong and Adamowski, 2016;Martin et al, 2016;Bouziotas and Ertsen, 2017;Han et al, 2017;Noël and Cai, 2017;Nüsser and Schmidt, 2017;Roobavannan et al, 2017;Sanderson et al, 2017;Srinivasan et al, 2017;Essenfelder et al, 2018;Gunda et al, 2018;Iwanaga et al, 2018;Kuil et al, 2018;O'Keeffe et al, 2018O'Keeffe et al, , 2020Ogilvie et al, 2019;Pouladi et al, 2019;Sun et al, 2019;Khalifa et al, 2020;Hanus, 2021;Lu et al, 202131 20% Groundwater Elshafei et al, 2015Hu et al, 2015;Bakarji et al, 2017;Han et al, 2017;Noël and Cai, 2017;Re et al, 2017;Srinivasan et al, 2017;Al-Amin et al, 2018;Hough et al, 2018;Iwanaga et al, 2018;O'Keeffe et al, 2018;…”

Section: Water Components Used In Primary Studiesmentioning

confidence: 99%

A Systematic Review of Spatial-Temporal Scale Issues in Sociohydrology

et al. 2021

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Sociohydrology is a recent effort to integrate coupled human-water systems to understand the dynamics and co-evolution of the system in a holistic sense. However, due to the complexity and uncertainty involved in coupled human-water systems, the feedbacks and interactions are inherently difficult to model. Part of this complexity is due to the multi-scale nature across space and time at which different hydrologic and social processes occur and the varying scale at which data is available. This systematic review seeks to comprehensively collect those documents that conduct analysis within the sociohydrology framework to quantify the spatial-temporal scale(s) and the types of variables and datasets that were used. Overall, a majority of sociohydrology studies reviewed were primarily published in hydrological journals and contain more established hydrological, rather than social, models. The spatial extents varied by political and natural boundaries with the most common being cities and watersheds. Temporal extents also varied from event-based to millennial timescales where decadal and yearly were the most common. In addition to this, current limitations of sociohydrology research, notably the absence of an interdisciplinary unity, future directions, and implications for scholars doing sociohydrology are discussed.

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“…In recent years, groundwater shortage has drawn much concern in these regions under the stress of climate change and anthropogenic activities [4][5][6][7][8][9], such as the large aquifers in Eastern and Northern Africa, the California's Central Valley, and Southern Plains in North America, south-eastern Spain, North China Plain, and Middle East [10][11][12][13][14][15][16][17]. Strategies such as introducing substitute water, artificial recharge, water saving and reuse, and groundwater management are applied in order to ensure the sustainable use of groundwater [18][19][20][21][22]. In some arid or semi-arid areas unavailable for additional surface water resources, applying groundwater management to control pumping and water level is a more realistic choice.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Groundwater Exploitation in an Irrigation Area in the Arid Upper Peacock River, NW China: Implications for Sustainable Agriculture and Ecology

Yang

Chen

et al. 2021

Sustainability

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Groundwater is the main irrigation water source in the Upper Peacock River. As fast enlargement of irrigation areas continues in recent years, the groundwater level declines continuously and has posed a threat to the sustainability of local agriculture and ecology. A numerical model was established with the code MODFLOW–2000 in order to predict the declining trend of groundwater level and formulate measures to counter the overexploitation, in which the river–aquifer interaction was elaborated and characterized by field survey. The results show that under current intensity of groundwater withdrawal, the levels of both unconfined and confined waters would decline continuously in 7 years from 2015. To stop the groundwater level from declining on the regional scale, the withdrawal rate should be compressed by 45% with respect to that in 2015. Moreover, taking consideration of the constraint of maintaining the ecological water level in the vicinity of the Euphrates Poplar forest in the study area, the withdrawal rate should be compressed 70% for seven towns around the forest.

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Assessing the effects of water restrictions on socio-hydrologic resilience for shared groundwater systems

Cited by 31 publications

References 28 publications

Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach

Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach

A Systematic Review of Spatial-Temporal Scale Issues in Sociohydrology

Optimization of Groundwater Exploitation in an Irrigation Area in the Arid Upper Peacock River, NW China: Implications for Sustainable Agriculture and Ecology

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