Designing Watersheds for Integrated Development (DWID): A stochastic dynamic optimization approach for understanding expected land use changes to meet potential water quality regulations

Huang, Yu-Kai; Bawa, Ranjit; Mullen, John D.; Hoghooghi, Nahal; Kalin, Latif; Dwivedi, Puneet

doi:10.1016/j.agwat.2022.107799

Cited by 6 publications

(2 citation statements)

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“…This logic has been a useful approach for several aspects of WRM research and practice, with various applications [8,9], including water resources allocation [10][11][12], water infrastructure, irrigation networks, dams and reservoirs, hydropower works, etc. [13][14][15], hydrology and hydraulics [16][17][18], disaster analysis and management [19][20][21], water quality management [22][23][24][25], transboundary water management [26][27][28][29], policy/governance/development [30][31][32][33][34][35], Water-Energy-Food Nexus [36][37][38], and other cross-disciplinary fields such as hydro-economics, socio-hydrology, ecohydrology, etc. [39][40][41][42][43][44][45][46].…”

Section: Integrated Water Resources Management Optimization Applicationsmentioning

confidence: 99%

“…Dynamic optimization uses differential and algebraic relations that consider the changing nature of the variables over time, by breaking the problem in smaller and simpler sub-problems, so spanning several points in time. The optimum solutions can involve parameter estimation and be also found based on predictions of future outcomes [25].…”

Section: Dynamic Problem Formulationmentioning

confidence: 99%

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Optimization Examples for Water Allocation, Energy, Carbon Emissions, and Costs

Alamanos,

Garcia

2024

Encyclopedia

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The field of Water Resources Management (WRM) is becoming increasingly interdisciplinary, realizing its direct connections with energy, food, and social and economic sciences, among others. Computationally, this leads to more complex models, wherein the achievement of multiple goals is sought. Optimization processes have found various applications in such complex WRM problems. This entry considers the main factors involved in modern WRM, and puts them in a single optimization problem, including water allocation from different sources to different uses and non-renewable and renewable energy supplies, with their associated carbon emissions and costs. The entry explores the problem mathematically by presenting different optimization approaches, such as linear, fuzzy, dynamic, goal, and non-linear programming models. Furthermore, codes for each model are provided in Python, an open-source language. This entry has an educational character, and the examples presented are easily reproducible, so this is expected to be a useful resource for students, modelers, researchers, and water managers.

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