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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.

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.

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.

Roughness is paramount in Earth sciences, and landscapes, laboratory alluvial fans, river bed elevation, bedload transport and the friction laws of fluid mechanics all exhibit a fractal behavior described by a scale-persistent roughness. Yet, for a given landscape, the exact meaning of statistical roughness, or fractal dimension, remains unclear. The fractal dimension of topography is mainly understood as two end-members: at large spatial scales, it describes tectonic processes; at small spatial scales it describes erosion processes. In this study, we nuance this description by identifying the spatial scale at which erosion processes are inadequately described by fractal dimension and provide quantitative bounds on the meaning of the statistical roughness of topography at scales from 0.25 km to 100 km using three lines of evidence. First, we leverage spatial statistics to evaluate the auto-correlation structure of topographic statistical roughness across the physiographically diverse state of California, USA. Second, we identify the down-slope and across-slope directions using two-dimensional Fourier analysis, and measure the anisotropy of topography by evaluating statistical roughness in each direction. Third, we perform a spatial correlation analysis between statistical roughness and the Péclet number which describes the balance between diffusion and incision processes. Our preliminary results indicate that correlation between statistical roughness and Péclet number fades at scales greater than 4.6 km. In addition, auto-correlation saturation occurs for statistical roughness at scales greater than 16.5 km. Hence our analysis provides a more nuanced description of the statistical roughness of topography: it represents erosion processes at scales up to 4.6 km while being dominated by tectonics at scales greater than 16.5 km.

While hydropower in California is one the main sources of renewable energy, population growth has continuously increased demand for energy. In addition, recent droughts reduced the amount of available water behind the hydropower dams to provide the water head needed to run the turbines in hydropower plants. A more sustainable alternative, instead of developing new infrastructure, is to enhance the daily operation of reservoirs to support hydropower generation. This study suggests a new optimal operation policy for Folsom Reservoir in California and hydropower plants, which maximizes hydropower generation and reduces flood risk. This study demonstrates the application of the cellular automata (CeA) approach to optimize the daily hydropower operation of Folsom Reservoir. The reservoir operation is a nonlinear problem, where the hydropower generation and elevation-area-storage functions are the main nonlinearity to accurately represent the daily operation of the system. Moreover, the performance of the CeA approach under two extreme climate conditions, wet and dry, was evaluated and compared to the operation during normal conditions. Results showed that the CeA approach provides more efficient solutions in comparison to the commonly used evolutionary optimization algorithms. For the size of the non-linear optimization problem designed in this study, CeA outperformed genetic algorithm for finding optimal solutions for different climate conditions. Results of CeA showed that although the annual average in-flow to the reservoir during the dry period was about 30% less than the normal condition, CeA offered about a 20% reduction in average hydropower generation. The new operation policy offered by CeA can partly compensate for the loss of the snowpack in California’s Sierra Nevada under a warming climate. The approach and its outcomes support an informed decision-making process and provide practical reservoir operational guideline to remediate the adverse effects of hydroclimatic changes in the future.

The uncertainty of water resources availability is a growing problem in California as agricultural industrialization, population growth, and climate change affect water resources. The intense manipulation of the hydrological regime has led to the depletion of the water resources in the state and the subsequent use of various adaptive management strategies to cope with environmental conditions and social concerns. The historical imbalance between water pumping and replenishment in Pajaro Valley has led to overdrafted aquifers, seawater intrusion, and salinization. The objective of this study is to estimate the sustainable carrying capacity of agricultural land in Pajaro Valley while preventing groundwater overdraft. A groundwater box model was built and calibrated using historical data to represent current and future hydrology and water management strategies. An optimization model maximized the economic profit using the agricultural acreage as the decision variable with a set of constraints aimed at determining the sustainable carrying capacity of the groundwater basin. Model constraints include total land and water availability, crop acreage, agricultural water use, and historical demand. In the Pajaro Valley, agricultural operations must use less water more efficiently, which means changes in crop types, size of activities, and fallowing land in parts of the basin. Results of the optimal scenario over 25 years show a 15% reduction of total agricultural acreage, 8.5% reduction in food production, average profit loss of 4%, and a 79% reduction in aquifer depletion. This study provides an overall vision of what can be accomplished with coordinated land use planning using strategies that harmonize individual decisions and shared natural resources.

Study region: Rio Verde Grande Basin, Brazil. Study Focus: Extensive regional development, mainly expansion of irrigated areas and urban population, has resulted in low water availability and caused water conflicts since the 1980s. Therefore, it was necessary to enact the Water Resources Plan of the Rio Verde Grande in 2011. The plan provided actions to improve water availability and meet increased water demand but there have been no studies on the sustainability of these policies. It was evaluated the future of the water management and calculate the sustainability index for water resources in the Rio Verde Grande Basin (RVGB), Brazil. New hydrological insights for the region: The water demand and available water have been compared and evaluated for activities in the Water Resources Plan (WRP) based on three different improvement of available water scenarios. These scenarios include water imports and the construction of channels and reservoirs. A sustainability index was used to evaluate and compare alternative plans for future water availability and water supply in these scenarios, considering measures of reliability, resilience, vulnerability and maximum deficit. The SI has identified the best scenario foreseen in the WRP for the RVGB that will improve the availability of water through 2030 having positive impact to water users of the basin. The results also indicate that the increase in available water will not result in significant improvements of sustainability of water resources by the implementation of the policies proposed in the WRP for the RVGB.

Balancing ecological and human water needs often requires characterizing key aspects of the natural flow regime and then predicting ecological response to flow alterations. Flow metrics are generally relied upon to characterize long-term average statistical properties of the natural flow regime (hydrologic baseline conditions). However, some key aspects of hydrologic baseline conditions may be better understood through more complete consideration of continuous patterns of daily, seasonal, and inter-annual variability than through summary metrics. Here we propose the additional use of high-resolution dimensionless archetypes of regional stream classes to improve understanding of baseline hydrologic conditions and inform regional environmental flows assessments. In an application to California, we describe the development and analysis of hydrologic baseline archetypes to characterize patterns of flow variability within and between stream classes. We then assess the utility of archetypes to provide context for common flow metrics and improve understanding of linkages between aquatic patterns and processes and their hydrologic controls. Results indicate that these archetypes may offer a distinct and complementary tool for researching mechanistic flow-ecology relationships, assessing regional patterns for streamflow management, or understanding impacts of changing climate.

The US and Mexico share a common history in many areas, including language and culture. They face ecological changes due to the increased frequency and severity of droughts and rising energy demands; trends that entail economic costs for both nations and major implications for human wellbeing. We describe an ongoing effort by the Environment Working Group (EWG), created by The University of California's UC-Mexico initiative in 2015, to promote binational research, teaching, and outreach collaborations on the implications of climate change for Mexico and California. We synthesize current knowledge about the most pressing issues related to climate change in the US-Mexico border region and provide examples of cross-border discoveries and research initiatives, highlighting the need to move forward in six broad rubrics. This and similar binational cooperation efforts can lead to improved living standards, generate a collaborative mindset among participating universities, and create an international network to address urgent sustainability challenges affecting both countries.

It is imperative for cities to develop sustainable water management and planning strategies in order to best serve urban communities that are currently facing increasing population and water demand. Water resources managers are often chastened by experiencing failures attributed to natural extreme droughts and floods. However, recent changes in water management systems have been responding to these uncertain conditions.Water managers have become thoughtful about the adverse effects of uncertain extreme events on the performance of water supply systems. Natural hydrologic variability and inherent uncertainties associated with the future climate variations make the simulation and management of water supplies a greater challenge. The hydrologic simulation process is one of the main components in integrated water resources management. Hydrologic simulations incorporate uncertain input values, model parameters, and a model structure. Therefore, stochastic streamflow simulation and prediction, and consideration of uncertainty propagation on performance of water supply systems (WSSs) are essential phases for efficient management of these systems. The proposed integrated framework in this study models a WSS by taking into account the dynamic nature of the system and utilizing a Markov chain Monte Carlo (MCMC) algorithm to capture the uncertainties associated with hydrologic simulation. Hydrologic responses from the results of a rainfall-runoff model for three watersheds of Karaj, Latyan, and Lar in Tehran, Iran, as the case study are used as inputs to the reservoirs. Results confirm that uncertainties associated with the hydrologic model's parameters propagate through the simulation and lead to a wide variation in reservoir storage and WSS performance metrics such as vulnerability and reliability. For example, water storage simulation in the Karaj Reservoir can vary up to 70% compared with the observed values. This causes contradiction and conflict in the management of reservoirs and water systems and decision making. The results emphasize the importance of analyzing WSS performance under uncertain conditions to improve the simulation of natural processes and support water managers for a more efficient decision-making process.

To date, subreach-scale variations in flow width and bed elevation have rarely been included in channel classifications. Variability in topographic features of rivers, however, in conjunction with sediment supply and discharge produces a mosaic of channel forms that provides unique habitats for sensitive aquatic species. In this study we investigated the utility of topographic variability attributes (TVAs) in distinguishing channel types and dominant channel formation and maintenance processes in montane and lowland streams of the Sacramento River basin, California, USA. A stratified random survey of 161 stream sites was performed to ensure balanced sampling across groups of stream reaches with expected similar geomorphic settings. For each site surveyed, width and depth variability were measured at baseflow and bankfull stages, and then incorporated in a channel classification framework alongside traditional reach-averaged geomorphic attributes (e.g., channel slope, width-to-depth, confinement, and dominant substrate) to evaluate the significance of TVAs in differentiating channel types. In contrast to more traditional attributes such as slope and contributing area, which are often touted as the key indicators of hydrogeomorphic processes, bankfull width variance emerged as a first-order attribute for distinguishing channel types. A total of nine channel types were distinguished for the Sacramento Basin consisting of both previously identified and new channel types. The results indicate that incorporating TVAs in channel classification provides a quantitative basis for interpreting nonuniform as well as uniform geomorphic processes, which can improve our ability to distinguish linked channel forms and processes of geomorphic and ecological significance.

Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management.

Background: Climate-smart agriculture (CSA) addresses the challenge of meeting the growing demand for food, fibre and fuel, despite the changing climate and fewer opportunities for agricultural expansion on additional lands. CSA focuses on contributing to economic development, poverty reduction and food security; maintaining and enhancing the productivity and resilience of natural and agricultural ecosystem functions, thus building natural capital; and reducing trade-offs involved in meeting these goals. Current gaps in knowledge, work within CSA, and agendas for interdisciplinary research and science-based actions identified at the 2013 Global Science Conference on Climate-Smart Agriculture (Davis, CA, USA) are described here within three themes: (1) farm and food systems, (2) landscape and regional issues and (3) institutional and policy aspects. The first two themes comprise crop physiology and genetics, mitigation and adaptation for livestock and agriculture, barriers to adoption of CSA practices, climate risk management and energy and biofuels (theme 1); and modelling adaptation and uncertainty, achieving multifunctionality, food and fishery systems, forest biodiversity and ecosystem services, rural migration from climate change and metrics (theme 2). Theme 3 comprises designing research that bridges disciplines, integrating stakeholder input to directly link science, action and governance. Outcomes: In addition to interdisciplinary research among these themes, imperatives include developing (1) models that include adaptation and transformation at either the farm or landscape level; (2) capacity approaches to examine multifunctional solutions for agronomic, ecological and socioeconomic challenges; (3) scenarios that are validated by direct evidence and metrics to support behaviours that foster resilience and natural capital; (4) reductions in the risk that can present formidable barriers for farmers during adoption of new technology and practices; and (5) an understanding of how climate affects the rural labour force, land tenure and cultural integrity, and thus the stability of food production. Effective work in CSA will involve stakeholders, address governance issues, examine uncertainties, incorporate social benefits with technological change, and establish climate finance within a green development framework. Here, the socioecological approach is intended to reduce development controversies associated with CSA and to identify technologies, policies and approaches leading to sustainable food production and consumption patterns in a changing climate.

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. Key Points: Predict daily groundwater level changes under different weir management policies, including the condition of fully opening the weir gatesApply machine learning algorithms to build the groundwater level prediction models and produce groundwater level map as the final productConclude that weir management policies (full and partial openings, normal), distance from the weir, and seasons impact groundwater level [ABSTRACT FROM AUTHOR]