Water is essential for human development and is an indispensable resource for economic activity and a country's growth. However, current water practices, along with increasing land-use change, climate change, and agricultural practices, have significantly altered the hydrological cycle and water availability. This study introduces the concept of a resilient flow regime-a flow regime that can absorb certain human-induced perturbations while preserving ecologically beneficial characteristics of the natural flow regime-and explores its implications for sustainable water management. Using the Rio Grande/Bravo (RGB) basin, a transboundary watershed shared by the U.S. and Mexico, as a case study, the research evaluates the similarities and differences among natural, resilient, and regulated flow regimes. The RGB faces significant water resource challenges due to extensive infrastructure development, overuse, and climate variability. The study identifies three natural streamflow classes in the basin (snowmelt-driven, monsoon-driven, and bimodal) and evaluates functional flow metrics across 16 gage stations. Results reveal strong correlations between natural and resilient flow metrics, particularly in magnitude components, while regulated flows deviate more significantly from natural conditions and the statistical analyses show that resilient flow regimes maintain ecological functionality and hydrological integrity, balancing human water needs and ecosystem health. By maintaining or restoring resilient flow conditions, water management strategies can mitigate the adverse impacts of human activities, preserve biodiversity, and promote the long-term sustainability of riparian ecosystems. This research provides a framework for integrating ecological considerations into water management practices, addressing the challenges of climate change, population growth, and increasing water demands.

Harmful algal blooms (HABs) have increased in severity and abundance over the last several decades, threatening water quality for an increasing number of water purveyors. Using Clear Lake, California as a case study, we performed three multidisciplinary analyses to evaluate the socioeconomic impact of HAB water treatment in vulnerable communities. First, a water rate analysis was conducted to determine if there is a relationship between source water quality and water rates. Second, a chemical cost analysis was performed to determine if water treatment costs change with seasonal HABs. Third, a regulatory analysis was performed to determine if adequate funding mechanisms exist to alleviate the financial strain caused by HABs in vulnerable communities. Through these analyses, we found that (1) residents served by Clear Lake water purveyors contributed an average of 3% of their gross monthly income (GMI) toward water bills during the study period, double the threshold for affordable water expenditures; residents of three Clear Lake purveyors paid more than triple the threshold; (2) the chemical cost of water treatment at the Golden State Water Company-Clearlake system (GSWC-CS) increased upward of 34% during HAB season; and (3) current funding mechanisms can be improved to address ongoing operations and maintenance (O&M) costs for contaminants of emerging concern, including cyanotoxins.

In an increasingly unstable climate, it is critical to optimize water needed for crop irrigation to secure food production and livelihoods while reducing environmental impacts. Here, we focus on water use for almonds — a crop that occupies roughly 20% of the irrigated agricultural land in California and has long been the focus of scrutiny. Regenerative agriculture, a term used to describe system designs that increase soil health, biodiversity, resilience to climate, and profitability while reducing greenhouse gas emissions, water use, and pollution, offers a potential way forward. We used eddy covariance, micrometeorological, and soil moisture measurements from 2022 and 2023 to quantify the evapotranspiration of California almond orchards under different soil and plant management practices and produce comprehensive estimates of the water footprint of different management systems. In five almond orchards, we find that there is little difference between evapotranspiration at regenerative and conventional sites in winter months, and that regenerative sites have similar or slightly lower evapotranspiration during the growing season. Orchards with cover crops had higher infiltration rates of winter precipitation than those without; however, soil moisture did not differ between management types. This case study demonstrates that regenerative management in almond orchards leads to improvements in soil moisture retention without guaranteeing increased evapotranspiration.

The Rio Grande-Bravo basin shared by the United States and Mexico is experiencing a severe water crisis demanding urgent attention. In recent decades, water storage reservoirs, aquifers, and annual streamflow volumes have been substantially depleted, leaving little buffer for continued over-consumption of renewable water supplies. Despite the great scarcity of water and intensifying water shortages in this basin, a full accounting of the river’s consumptive uses and losses has never been undertaken. In this study we assemble detailed water consumption estimates from a broad array of sources to describe how surface and ground water were consumed for both direct uses (agricultural, municipal, commercial, thermoelectric power generation) and indirect uses (reservoir evaporation and riparian evapotranspiration) in each of 14 sub-basins during recent decades. We estimate that only half (48

There is not enough water in California to support current water uses and preserve healthy environments. California aquifers have been chronically depleted over decades, causing household water insecurity, degrading groundwater-dependent ecosystems, affecting small and medium farmers, and inducing subsidence. The California government enacted the Sustainable Groundwater Management Act more than a decade ago to prevent declining aquifer levels to continue causing undesirable results, which has driven the necessity to reduce irrigated agriculture by about half million hectares. If this change is left to market forces alone, cropland retirement could disrupt local economies and vulnerable communities, increasing the levels of injustice for local residents and threatening farmer and farmworker livelihoods. However, when cropland repurposing is strategically organized and managed in collaboration among all the involved groups, it can enhance quality of life in agricultural disadvantaged communities, diversify regional economies, generate local socioeconomic opportunities, and improve environmental health while simultaneously fostering food and nutrition security and advancing water sustainability. In this study, we present a systems-level, coproduced Framework of best practices in cropland repurposing to achieve socioenvironmental and economic benefits for all. The Framework is informed and supported by peer-reviewed science, authors' first-hand experiences, and public engagement about the topic for several years. Our team includes scientists, community leaders, and other experts in cropland repurposing, socioenvironmental justice, agriculture, climate change, land trusts, disadvantaged communities, energy, nonprofit work, Indigenous knowledge, and ecosystems. The Framework includes guiding objectives, best practices, and implementation strategies to overcome co-occurring challenges. We conduct an extensive literature review of the current status quo to support the best practices identified in our Framework. This review and coproduced Framework aim to provide best practices for developing new solutions without causing new problems, while fully considering the impacts on all groups affected firsthand by cropland repurposing.

Riparian corridors in arid climates sustain life in otherwise inhospitable environments, creating zones of ecological and cultural importance. However, rivers in arid climates are often managed to provide water for human populations at the expense of a river's freshwater biodiversity. In this study, ecosystem response to river flow management is assessed using mature cottonwood tree-ring growth and carbon isotope composition as bio-indicator proxies for river ecosystem health. We examine the ecological impacts of flow management on the Lower Truckee River in Nevada, USA, which runs through an arid-climate basin that has been subject to decades of heavy flow diversion and management. Particular attention is given to the effects of major lawsuits in 1973 and 1982 that restored spring and summer flows to the river following progressive dewatering since 1905. Most mature trees (>30 years old) downstream of diversions responded strongly to restored flows, with average annual tree-ring growth increases of 160%. Among tested streamflow metrics, average annual flow had the strongest positive influence on cottonwood growth, and aspects of the spring snowmelt recession were also influential. Precipitation was also linked with cottonwood growth, primarily during the period of management before 1973 when dry season flows were severely limited. Not all floodplain trees responded similarly to changes in flow metrics, suggesting that individual tree attributes and heterogeneity in floodplain soils are highly important to tree growth. Results offer promising evidence that flow restoration can lead to measurable improvement in riparian forest productivity, although site-specific considerations including channel form and location on the floodplain are important in determining response to changes in flow patterns.

During the last decade, meter-resolution topo-bathymetric digital elevation models (DEMs) have become increasingly utilized within fluvial geomorphology, but most meter-scale geomorphic analyses are done on just one to a few rivers. While such analyses have contributed greatly to our collective understanding of river discharge-topography interactions, which is applicable in both river restoration design and environmental flow regulation contexts, their generalizability across a range of river types remains largely unevaluated. This study assessed the dominance of a single hydro-morphodynamic mechanism, flow convergence routing, in 35 ephemeral rivers divided among five river types in California's South Coast region by answering five questions. Geomorphic covariance structure (GCS) analysis was performed on longitudinal standardized width and standardized, detrended bed elevation spatial series from meter-resolution DEMs. All river types had coherent, multi-scalar structures of longitudinal fluvial topography, implicating a process-morphology link. GCS metrics revealed that landform patterning was consistent with the requirements of the morphodynamic mechanism of flow convergence routing. Thus, that process was found to be a broadly relevant channel altering mechanism among sites, but its relationship with water stage differed between river types. Specifically, river types in unconfined valleys exhibited a strong bankfull width control over base flow bed undulations, with no obvious flood-stage control over bankfull landform patterning. River types in partially confined valleys also exhibited strong bankfull width control over base flow bed undulations, but their bankfull landform patterns appear to have coalesced with coherent width and bed elevation undulations during flood flows. Finally, metrics for confined river types showed that it takes higher magnitude, less frequent floods to set their coherent width and bed elevation undulations, but even these channels do exhibit flow convergence routing when given enough discharge for sufficient duration. How does flow convergence routing (FCR) vary among ephemeral river types? FCR was a relevant channel altering mechanism in 4 of 5 ephemeral river types. Baseflow nozzles are preferentially nested within bankfull wide bars. Wide bars are preferentially nested within oversized cross-sections. image

Clustering and machine learning-based predictions are increasingly used for environmental data analysis and management. In fluvial geomorphology, examples include predicting channel types throughout a river network and segmenting river networks into a series of channel types, or groups of channel forms. However, when relevant information is unevenly distributed throughout a river network, the discrepancy between data-rich and data-poor locations creates an information gap. Combining clustering and predictions addresses this information gap, but challenges and limitations remain poorly documented. This is especially true when considering that predictions are often achieved with two approaches that are meaningfully different in terms of information processing: decision trees (e.g., RF: random forest) and deep learning (e.g., DNNs: deep neural networks). This presents challenges for downstream management decisions and when comparing clusters and predictions within or across study areas. To address this, we investigate the performance of RF and DNN with respect to the information gap between clustering data and prediction data. We use nine regional examples of clustering and predicting river channel types, stemming from a single clustering methodology applied in California, USA. Our results show that prediction performance decreases when the information gap between field-measured data and geospatial predictors increases. Furthermore, RF outperforms DNN, and their difference in performance decreases when the information gap between field-measured and geospatial data decreases. This suggests that mismatched scales between field-derived channel types and geospatial predictors hinder sequential information processing in DNN. Finally, our results highlight a sampling trade-off between uniformly capturing geomorphic variability and ensuring robust generalisation. Combining machine learning clustering and prediction is valuable when unevenly distributed information leads to an information gap between data-rich and data-poor locations. Clustering at data-rich locations impacts subsequent predictions at data-poor predictions, and this effect is stronger for deep neural networks than for random forest. There is a trade-off between collecting the minimum number of observations to uniformly capture natural variability and collecting enough data to ensure that a generalisable pattern is learned. image

AbstractPersistent overuse of water supplies from the Colorado River during recent decades has substantially depleted large storage reservoirs and triggered mandatory cutbacks in water use. The river holds critical importance to more than 40 million people and more than two million hectares of cropland. Therefore, a full accounting of where the river’s water goes en route to its delta is necessary. Detailed knowledge of how and where the river’s water is used can aid design of strategies and plans for bringing water use into balance with available supplies. Here we apply authoritative primary data sources and modeled crop and riparian/wetland evapotranspiration estimates to compile a water budget based on average consumptive water use during 2000–2019. Overall water consumption includes both direct human uses in the municipal, commercial, industrial, and agricultural sectors, as well as indirect water losses to reservoir evaporation and water consumed through riparian/wetland evapotranspiration. Irrigated agriculture is responsible for 74% of direct human uses and 52% of overall water consumption. Water consumed for agriculture amounts to three times all other direct uses combined. Cattle feed crops including alfalfa and other grass hays account for 46% of all direct water consumption.

There is not enough water in California to support current water uses and preserve healthy environments. California aquifers have been systematically depleted over decades, causing household water insecurity, degrading groundwater-dependent ecosystems, affecting small and medium farmers, and inducing subsidence. The California government enacted the Sustainable Groundwater Management Act a decade ago to prevent declining aquifer levels from continuing to cause undesirable results. This law has indirectly driven the necessity to reduce irrigated agriculture by about half a million hectares. If this change is left to market forces alone, cropland retirement could disrupt local economies and vulnerable communities, increasing the levels of injustice for local residents and threatening farmer and farmworker livelihoods. However, if cropland repurposing is organized and managed correctly and collaboratively among the stakeholders involved, it could improve quality of life in disadvantaged agricultural communities, diversify the economy, create more local socioeconomic opportunities, and increase environmental health while promoting food and nutrition security and advancing water sustainability. In this study, we present a systems-level, coproduced Framework of best practices in cropland repurposing to achieve socioenvironmental and economic benefits for all. The Framework is informed and supported by peer-reviewed science, authors’ first-hand experiences, and public engagement about the topic for several years. Our team includes scientists, community leaders, and other experts in cropland repurposing, socioenvironmental justice, agriculture, climate change, land trusts, disadvantaged communities, energy, Indigenous knowledge, and ecosystems. The Framework includes guiding objectives and best practices to overcome co-occurring challenges that prioritize public health, justice, equitable development, sustainable agriculture, green economies, protection to vulnerable groups, education, grassroots leadership, and cultural preservation. We conduct an extensive literature review of the current status quo and to support the best practices identified in our Framework. This review and coproduced Framework aim to ensure that anyone following these best practices can develop new solutions without causing new problems, while fully considering the impacts on all groups affected firsthand by cropland repurposing.

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. Predict daily groundwater level changes under different weir management policies, including the condition of fully opening the weir gates Apply machine learning algorithms to build the groundwater level prediction models and produce groundwater level map as the final product Conclude that weir management policies (full and partial openings, normal), distance from the weir, and seasons impact groundwater level

Understanding the relationship between droughts and drought awareness is vital towards decision making and policy for water management and conservation strategies, and socioeconomic outcomes. We used computer vision (UNet models) to analyze nonlinear, heterogeneous, lagged correlations between Standardized Precipitation Evapotranspiration Index (SPEI) and Google Trends Search Interest within the Continental United States (CONUS). The most important drivers of the relationship between drought occurrence and drought awareness are the variability and ranges of drought trends and severity, as well as extreme drought conditions. This relationship was the strongest for Western states, followed by Northeastern, Southeastern, and Central regions. Search interest tends to lag droughts by a period of 1-3 months. We also found evidence that reductionist linear approaches, such as a Principal Component Analysis, might not be as effective as UNet models in capturing the nuanced relationship between droughts and drought awareness at various dimensions and scales.

The Rio Grande-Rio Bravo's flow regime has been highly altered for more than 130 years, yet the river ecosystem still supports important biodiversity including numerous endangered species. More than 80% of water consumed in the basin goes to irrigating farms, but in recent decades, farmers have repeatedly experienced severe water shortages. Given this water-scarce condition, any plans for enhancing environmental flows must be carefully designed to minimize impacts or provide benefits to agriculture. This study describes the development of the Rio Grande-Rio Bravo's first whole-basin hydrologic model-representing both the United States and Mexico portions of the basin-to enable exploration of environmental flow restoration needs and options for meeting these needs. We then demonstrate an analytical process in which environmental flow needs are compared to existing flow conditions to quantify gaps, and then evaluate how those gaps can be filled by reducing farm irrigation needs by shifting to less water-intensive crops and fallowing a portion of existing farmland while maintaining or improving net revenues. In our pilot assessment we find that an improvement of 2.2 m3/s would fill the environmental flow gap for late-summer low-flow conditions at Albuquerque, New Mexico. This flow enhancement is attainable by fallowing 18%-26% of cropland and shifting to more profitable and less water-intensive crops to sustain overall farm revenues.

Almond (Prunus amygdalus) orchard systems are highly productive and widespread in Mediterranean climates and dominate the California agricultural landscape. However, current intensive monocultural bare soil production practices limit the potential to support nonproduction functions (i.e., multifunctionality) and long-term sustainability of the orchard system (Aizen et al. 2019; Fenster et al. 2021). Managing orchards for multifunctional benefits includes maintaining ecologically and economically viable yields while prioritizing water quality, soil health, reduced input use, and support for biodiversity. Recent studies in almond demonstrate that diversification, including planted or spontaneous (resident) vegetation in orchard alleys, can improve multifunctionality by enhancing nonproduction functions in the orchard without reducing crop yield, thereby providing opportunities to enhance sustainability and resilience (Fenster et al. 2021; Morugán-Coronado et al. 2020).

The present study applied a multi-criteria analysis to evaluate the best approach among six theoretical frameworks related to the integrated management of water–environmental resources, analyzing the frequency of multiple management criteria. The literature review covers the period from 1990 to 2015, with a notable presence of the theoretical frameworks of Integrated Water Resources Management (IWRM), Ecohealth, Ecosystem Approach (EA), Water Framework Directive (WFD), and, to a lesser extent, the Watershed Governance Prism (WGP) and the Sustainability Wheel (SW). The multi-criteria decision-making (MCDM) methods applied include AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations). Twenty-five criteria were analyzed, such as governance, participation, sustainability, decentralization, and health and well-being, among others. We started with five criteria for evaluating the hierarchy of the six theoretical frameworks using the AHP method. Subsequently, we again evaluated the five criteria using the TOPSIS and PROMETHEE methods to calibrate the results with the AHP. Then, using word counting, we evaluated the best approach, applying 10, 15, 20, and 25 more criteria. Our results indicate that the best integrated management alternative was the WFD, which fulfilled 47% of the management criteria. Second, with 45%, was the WGP, and third was IWRM, with 41%; less successful approaches to the criteria were demonstrated by the EA, SW, and Ecohealth methods. By applying this methodology, we demonstrated an excellent structured tool that can aid in the selection of the most important issue within a given sector.

California farmers who use reduced-disturbance tillage and winter cover cropping can boost production and improve soil health. However, some farmers are hesitant to try these conservation practices due to uncertainty about whether planting winter cover crops will deplete soil moisture in already drought-stricken regions. Our study addresses these concerns by looking at how long-term reduced-disturbance tillage and winter cover cropping, compared to fallowed soils with standard tillage, affected soil moisture. Although we found a statistical difference in total soil water content, the difference was only about 0.3 inches of water per foot of soil. On average, the soil water content of the top 0–96 inches was highest for the reduced-disturbance fields with winter cover crops. This was especially evident during our driest field season, from November 1, 2017, to March 15, 2018, when cumulative rainfall was only 1.9 inches. Our findings show that winter cover cropping and reduced-disturbance tillage can improve soil without depleting soil water levels in row crops.

Hydro-economic modeling (HEM) addresses research and policy questions from socioeconomic and biophysical perspectives under a broad range of water-related topics. Applications of HEM include economic evaluations of existing and new water projects, alternative water management actions or policies, risk assessments from hydro-climatic uncertainty (e.g., climate change), and the costs and benefits of mitigation and/or adaptation to such events. This paper reviews applications of HEM in five different categories: (1) climate change impacts and adaptation, (2) water–food–energy–ecosystems nexus management, (3) capability to link to other models, (4) innovative water management options, and (5) the ability to address and integrate uncertainty. We find that (i) the increasing complexity and heterogeneity of water resource management problems due to the growing demand and competition for water across economic sectors, (ii) limited availability and high costs of developing additional supplies, and (iii) emerging recognition and consideration of environmental water demands and value, have inspired new integrated hydro-economic problems and models to address issues of water–food–energy nexus sustainability, resilience, reliability through water (re)allocation based on the relative “value” of water uses. In the past decade, the field of HEM has improved the integration of ecosystem needs, but their representation is still insufficient and mostly ineffective. HEM studies address how to sustainably manage water resources, including groundwater which has become an area of particular interest in climate change adaptation. The current most used spatial and temporal resolutions (basin-scale and yearly time-step) are appropriate for planning but not for operational decisions and could be underestimating impacts from extreme events (e.g., flood risk) captured only by sub-monthly time scales. In addition, HEM primarily focuses on biophysical and economic indicators but often overlooks preferences and perspectives of stakeholders. Lastly, HEM has been widely used to analyze transboundary cooperation, showing benefits for increasing water security and economic development, particularly as climate change develops. We conclude that the field of HEM would benefit from developing more operational models and enhancing the integration of commonly neglected variables, such as social equity components, ecosystem requirements, and water quality.

The water management of the Colorado River is at a tipping point. This paper describes water management strategies in the Mexican portion of the Colorado River Basin considering water scarcity scenarios. A water allocation model was constructed representing current and future water demands and supply. The Colorado River system in Mexican territory is used as a case study, and all its water demands are characterized [Irrigation District Rio Colorado (DR-014), Mexicali, San Luis Rio Colorado, Tecate, Tijuana-Rosarito, and Ensenada]. Individual strategies were run by subsystem and then their impact was analyzed systemwide. Performance criteria and a performance-based sustainability index were evaluated to identify water stressors and management strategies to improve water supply for agricultural, urban, and environmental users. Analysis of results shows that the irrigation district (DR-014) is the most affected user due to water cuts because it has the lowest priority and, thus, any reduction in Colorado River allocations affects them directly. A range of water management strategies was investigated, including a no-action scenario. The current system depends on the long-term aquifer overdraft to supply water demand. The reduction of the cultivated area was the strategy that increased the sustainability index the most for DR-014. Agricultural to urban transfers, water use efficiency, wastewater reuse, and desalination are prime possibilities to improve the current water supply in the coastal zone (Tijuana, Rosarito, Ensenada). This research shows the spectrum of possible outcomes that could be expected, ranging from systemwide effects of inaction to the implementation of a portfolio of water management strategies.

We used computer vision (U-Net) model to leverage Standardized Precipitation Evapotranspiration Index (SPEI), Google Trends Search Interest (SI), and Twitter data to understand patterns with which people in Continental United States (CONUS) indicate awareness of and interest in droughts. We found significant statistical relationships between the occurrence of meteorological droughts (MD), as measured by SPEI, and SI on drought topics over CONUS. SI tends to lag MD by a period of 2-3 months, however relationships between MD and corresponding SI varies significantly over the CONUS in both space and time. People in states with increasingly dry conditions have become increasingly interested in drought topics. However, with worsening drought conditions in California, public SI on drought topics in the state has not increased significantly between 2016 and 2020, despite the overall SI being high. We additionally applied sentiment analysis on 5 million tweets related to droughts and found that public emotions towards drought have become more polarized.

Advances have been made in water resource investigation due to the implementation of mathematical models, the development of theoretical frameworks, and the evaluation of sustainability indices. Together, they improve and make integrated water resource management more efficient. In this paper, in the study area of the Duero River Basin, located in Michoacan, Mexico, we schematize a series of numerical indices of the Watershed Governance Prism to determine the quantitative status of water governance in a watershed. The results, presented as axes, perspectives, and prisms in the Axis Index, Water Governance Index, and Watershed Governance Prism Index, provide the conclusion that it is possible to establish and evaluate the Watershed Governance Prism Index using our numerical implementation of the Watershed Governance Prism theoretical framework. Thus, it is possible to define a quantitative status and evoke how water governance is being designed and implemented in a watershed.