This paper presents a water resources sustainability index that makes it possible to evaluate and compare different water management policies with respect to their sustainability. The sustainability index identifies policies that preserve or improve the desired water management characteristics of the basin in the future. This index is based on a previous sustainability index with improvements in its structure, scale, and content to make it more flexible and adjustable to the requirements of each water user, type of use, and basin. The Rio Grande transboundary basin is used as a case study demonstrating the use of the index. Tailor-made sustainability indexes are defined for water users in Mexico, the United States, the environment, and for meeting system requirements (international treaty obligations). Group sustainability indexes are calculated to summarize the results for groups of water users of each country, the environment, and the basin as a whole. Sustainability indexes by subbasins are calculated ...

Environmental flows (e‐flows) are powerful tools for sustaining freshwater biodiversity and ecosystem services, but their widespread implementation faces numerous social, political, and economic barriers. These barriers are amplified in water‐limited systems where strong trade‐offs exist between human water needs and freshwater ecosystem protection. We synthesize the complex, multidisciplinary challenges that exist in these systems to help identify targeted solutions to accelerate the adoption and implementation of environmental flows initiatives. We present case studies from three water‐limited systems in North America and synthesize the major barriers to implementing environmental flows. We identify four common barriers: (a) lack of authority to implement e‐flows in water governance structures, (b) fragmented water governance in transboundary water systems, (c) declining water availability and increasing variability under climate change, and (d) lack of consideration of non‐biophysical factors. We then formulate actionable recommendations for decision makers facing these barriers when working towards implementing environmental flows: (a) modify or establish a water governance framework to recognize or allow e‐flows, (b) strive for collaboration across political jurisdictions and social, economic, and environmental sectors, and (c) manage adaptively for climate change in e‐flows planning and recommendations.This article is categorized under: Water and Life > Conservation, Management, and Awareness Human Water > Water Governance Engineering Water > Planning Water

A new cooperative watershed management methodology is designed for developing an equitable and efficient Best Management Practice cost allocation among landowners in a watershed. The approach intends to control the total sediment yield in the watershed, considering landowners’ conflicting interests. Wet detention ponds, are considered as the only available options to the landowners. The quality of the storm water is evaluated by the Total Suspended Solid loading from the watersheds. The proposed methodology combines a watershed simulation model, named Soil Water Accounting Tool (SWAT), with an Ant Colony Optimization (ACO) module and the cooperative game theory approach. Integration of SWAT and ACO modules provide the best set of designs for any constraints on target sediment removal set forth by non-cooperative and cooperative behaviors of the stakeholders to participate in the coalition to minimize the total cost of management practice. Nash Bargaining Theory is used to investigate how the maximum saving on cost of the participating players in a coalition can be fairly allocated. The proposed method is illustrated by a hypothetical example. The results demonstrate the applicability of the methodology. For the hypothetical case example, the proposed methodology with grand coalition leads to approximately 48 percent cost saving.

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin‐wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach‐scale geomorphic channel types using publicly available geospatial data. A bottom‐up machine learning approach selects the most accurate and stable model among 20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three‐tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred‐meter reaches. Performance in statistical learning is reasonable with a 61% median cross‐validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large‐scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy‐based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors.

Understanding the impact of human‐made structures on groundwater levels is essential, with structures like dams or weirs presenting unique challenges and opportunities for study. The Baekje weir in South Korea presents an interesting case as the weir has undergone full gate opening, which is generally not the case for weirs and reservoirs, providing valuable opportunity for simulating weir removal conditions. The main objectives are investigation of groundwater level fluctuations under various weir operations, distances from the weir, and seasonal variations. The study utilizes observed data that simulates conditions with and without the weir, including scenarios of full gate opening. Multiple machine learning algorithms—Random Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. Results indicate that XGBoost outperforms other models in all three groups during both training and testing phases. Specifically, XGBoost surpasses RF by 2.09% (R²), 5.66% (RMSE), and 10.1% (MAE) in training, and outperforms SVR by 11.2% (R²), 42.0% (RMSE), and 129.2% (MAE) in testing. Additionally, the study generates groundwater level maps, providing a practical tool for managing groundwater systems and informing decision‐making in weir operations. This study not only sheds light on the dynamic relationship between weir operations and groundwater levels but also provides actionable insights for effective water management in similar hydrological settings.