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The UnCommons Project, as the name implies, introduces an alternative building model to traditional inefficient and environmentally destructive building stock, making it a beacon of sustainability. This state-of-the-art structure is a localized, bio-based structure reflecting concentric rings of impact from the site level, to regional, and to liberating materials from global supply chain constraints. This project’s approach integrates restorative renewable resources, water harvesting, recycling, and conservation measures to minimize its footprints. This 1,000 square foot structure, serving as a student common area at UCI, showcases high-tech, low-impact sustainable solutions through the seamless integration of AI for temperature regulation, structural wall-adjustments, and predictive maintenance systems. Given its proximity to the UCI Ecological Preserve, this structure aims to augment local biodiversity, cultivate ecological resilience, and enable students to blend with and foster an enhanced coexistence with nature. The UnCommons Project envisions buildings as living structures that harmonize with their environment.Industry Advisors: James Bucknam, Brett Kaufmann

The wettability properties of the porous components in a proton exchange membrane fuel cell (PEMFC) are a key factor in relation with the water management issue and a key information in order to predict the water retention curve, a crucial input data for the macro-homogeneous models. In this work, we propose an approach to construct the contact angle distribution in three-dimensional digital images of the cathode catalyst layer. Various reconstruction scales are considered, from the platinum particle scale to the pore scale and the scale of sub-blocks in the CCL. This leads to effective contact angle distributions depending on the scale of interest. The approach also allows to investigate the link between the pore size and the effective contact angle at the pore scale. The combined information of the pore size and the effective contact angle for each pore is used to get insights into the water retention curve. • Contact angle distributions are computed in the secondary pore space. • The impact of the ionomer coating thickness spatial variation is considered. • The effective contact angle is determined for each pore. • The water retention curve is obtained. [ABSTRACT FROM AUTHOR]

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments. [ABSTRACT FROM AUTHOR]

Introduction: Emerging contaminants in irrigation wastewater can cause bioaccumulation in crops, posing health risks to humans and other living organisms. The extent to which different emerging contaminants (ECs) are taken up by plants varies depending on the physicochemical properties of the contaminants and the type of crops grown. Methods: This study investigated the fate of emerging contaminants (ECs), particularly pharmaceuticals, in durum wheat grown on soils irrigated with treated wastewater in southern Italy. The experiment was conducted in lysimeters irrigated with wastewater during previous crop cycles, assessing the presence and distribution of ECs in both soil and plant tissues. Three different levels of exposure to ECs were compared: freshwater irrigation (FW), wastewater treated to European average contaminant levels (TWWx1), and a triple dose of contaminants (TWWx3). Results: The behavior of 12 different ECs in the plant-soil complex was analyzed and found variable. Fluconazole and carbamazepine were found to have higher uptake concentrations in the plant, with accumulation observed in the plant and grain, especially in the TWWx3 treatment. However, some ECs (such as Sulfamethoxazole, Trimethoprim, Ketoprofen, Diclofenac, Metoprolol, and Naproxen) showed high uncertainties in their fate, probably due to degradation in soil and influential crop parameters. Discussion: The results of this study contribute to the argument that the reuse of treated wastewater for irrigation, if properly monitored, can be a safe approach in agriculture and can support policymakers in developing future legislative frameworks for sustainable water management. [ABSTRACT FROM AUTHOR]

Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River. [ABSTRACT FROM AUTHOR]

Monsoon precipitation variability over the Indo‐China Peninsula (ICP) has become more complicated affected by global warming. In this study, the modulation of Pacific decadal oscillation (PDO) on the relationship between El Niño–Southern Oscillation (ENSO) and the rainy season onset over the ICP were investigated. The results showed that the ICP rainy season onset were predominantly correlated with winter sea surface temperature anomalies (SSTAs) in the East Pacific Ocean, with late and early onsets following El Niño and La Niña events, respectively. During the warm and cold PDO phase, the correlations tended to be substantially strengthened and weakened, respectively. Further analysis indicates that PDO significantly influenced the effects of ENSO on the ICP rainy season onset by modulating SSTAs and low‐level wind fields. During the El Niño events, abnormal easterlies over the Bay of Bengal (BoB) and southern ICP suppressed water vapour transporting to the ICP, which may be related to the zonal SST anomaly gradient between the Indian Ocean and the Northwest Pacific Ocean. When the El Niño occurred during the warm PDO phase, the rainy season onsets were later. The anomalous easterlies became stronger corresponds to the increasing zonal sea surface temperature anomaly (SSTA) gradient between the Indian Ocean and the Northwest Pacific Ocean. There was no significant anomaly on the rainy season onset during the cold PDO phase. During the La Niña events, the abnormal westerlies in BOB accelerated water vapour transport, and the rainy season onset were earlier during the warm and cold PDO phase. The modulating effects of PDO on La Niña were less than those on El Niño. These results suggest that the predictability of rainy season onset over the ICP can be improved through PDO and thus help agricultural planning and water resources management. [ABSTRACT FROM AUTHOR]

Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed. [ABSTRACT FROM AUTHOR]

Alongside the advancement of navigation, control, and communication technologies, which are fundamental aspects of unmanned underwater vehicles (UUVs), research & development on UUV energy system for long-term mission capabilities has been conducted actively since the 2000s. The hydrogen fuel cell energy system (FCES), which is characterized by low noise, air-independent propulsion, and high energy density, has attracted considerable attention as the energy system for UUVs. In this paper, the key features of the components of a hydrogen FCES meant for use in large-class UUVs are presented. In addition, the current technological development status of FCES-powered large-class UUVs is outlined. Finally, the factors that must be considered when designing large-class UUVs powered by hydrogen FCESs are discussed. • Hydrogen fuel cell systems offer high energy density for long-endurance unmanned underwater vehicles. • Differences between land-based and underwater FCESs include materials and byproduct handling. • Effective design must consider gravimetric and volumetric energy density, heat, and water management. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is suffering from a substantial decline in terrestrial water storage (TWS) in exorheic basins, threatening water resources that are critical for ∼2 billion people downstream. TWS changes are commonly estimated using gravity satellites through observations of the total terrestrial mass storage (TMS) change, with an implicit assumption of a negligible contribution from sediment transport. Through long‐term (2002–2017) sediment flux observations in seven headwater basins on the TP, we reveal that the gravity satellite‐derived TMS has decreased at a rate of 3.85 ± 0.23 Gt yr−1 in the seven basins, of which 0.35 ± 0.04 Gt yr−1 is contributed by sediment transport. Neglecting this contribution leads to an overestimation of the TWS loss by 10.1 ± 1.3%, equivalent to the annual water demand of an additional 0.62 million people in the surrounding nations. Regionally, the overestimation is surprisingly high in the Indus River and Yarkant River basins, reaching up to 50.8%–77.6%. Plain Language Summary: Accurate quantification of the change in the terrestrial water storage (TWS) on the Tibetan Plateau (TP) is imperative to improve the assessment of water availability that are critical for ∼2 billion people downstream. Through long‐term sediment flux observations, here we find that sediment transport makes a substantial contribution to gravity satellite‐derived TWS change in regions with a high erosion rate such as the TP, which has never been taken into consideration in previous studies. Neglecting this contribution leads to an overestimation of the TWS loss by about 10% on average for the seven headwater basins on the TP. The contribution is especially high in the Indus River and Yarkant River basins. Our findings improve the regional estimation of water availability and thus support climate adaptation and sustainable water resource management. Key Points: For the first time, we quantify the contribution of sediment transport to gravity satellite‐based estimation of TWS change on the TPNeglecting the contribution of sediment transport leads to an overestimation of the TWS loss by 10.1% on averageThe overestimated TWS loss is equivalent to the annual water demand of an additional 0.62 million people in surrounding nations [ABSTRACT FROM AUTHOR]

The growing number of threats facing continental-scale transboundary water treaties warrants contemporary evaluation of not only the political and climatological conditions under which they were constructed, but also of how different management strategies for accommodating changes in those conditions can lead to treaty success or failure. We assess these threats by highlighting key attributes and vulnerabilities of water treaties across North America that frame a diverse set of future water management priorities. While these threats are ubiquitous globally, they are particularly pronounced in North America where water-abundant basins along the border between the United States (US) and Canada contrast with arid basins along the border between the US and Mexico. We propose addressing these needs through a three-step call to action for management agencies, politicians, and the public at large to embrace a holistic perspective on transboundary water agreements. [ABSTRACT FROM AUTHOR]

As legal battles over surface water allocation in Klamath County, Oregon, USA have gained national attention, earning the name "Water Wars," authorities have been increasingly turning to groundwater to compensate for water shortfalls. This case study aims to identify the nuanced perspectives of household groundwater well users who are affected by groundwater extraction. Using Q methodology, we uncover groundwater well users' perceptions and to what extent they think water problems are dealt with fairly. We identify four water perspectives that differ in the degree to which respondents prioritize clean water accessibility, industry accountability, individual responsibility for water management, and trust in governments to manage water competently. This research contributes to the growing body of literature reshaping our understanding of human-water relations by exploring different water axiologies. [ABSTRACT FROM AUTHOR]

East Asian summer monsoon precipitation is projected to increase under greenhouse warming with strong intraseasonal variation. Using a 35-member CESM Large Ensemble and 30 CMIP6 models, this study reveals that in July and August, maximum rainfall changes in East Asia take place in the midlatitudes, influencing regions encompassing North and Northeast China, the Korean Peninsula, and Japan. The intensified precipitation is attributed to the combined effect of the thermodynamic and dynamic components. The former stems from the enriched low-level moisture, which peaks in continental East Asia in July and August, under global warming. The dynamic effect is due to the enhanced upward motion, associated with the enhanced southerlies throughout the troposphere over midlatitude East Asia. The southerlies also act to intensify the low-level monsoonal circulation, strengthening moisture transport from the tropical ocean to the midlatitudes. In addition to the mean-state changes, the precipitation interannual variability in this region also intensifies, partly due to the enhanced low-level moisture and partly associated with enhanced large-scale circulation anomalies, such as the northwestern Pacific anticyclone. The enhanced background precipitation, along with the intensified interannual variability, may lead to more rainy summers in a warmer climate, with instances where historically extreme precipitation events become more frequent, posing challenges for water resource management and agriculture in the region. [ABSTRACT FROM AUTHOR]

Hydrogen and oxygen-stable isotopes are widely used as tracers of the water cycle, and surface water is an integral part of the water cycle. Compared with other water bodies, surface water is more susceptible to different natural and anthropogenic factors, and an accurate understanding of surface water changes is of great significance in ensuring regional water security, maintaining ecological balance, and promoting sustainable economic and social development. Due to the influence of observation conditions and instrumental analysis, global surface water isotopes' spatial and temporal distribution could be more balanced worldwide. For this reason, we have compiled and analyzed the stable hydrogen and oxygen isotope data in surface water from 22432 sampling stations worldwide from 1956 to 2023, with 102862 data records. The results found that (1) surface water stable isotopes are gradually depleted from the equator to the poles and from the coast to the interior. However, there are significant differences in the spatial and temporal distributions of surface water isotopes in different regions. (2) The variation of stable isotopes in surface water is controlled by geographic location, topographic conditions, and meteorological factors (especially temperature), and its heterogeneity is considerable. The global stable isotope dataset of surface water provides vital information for an in-depth understanding of the water cycle and climate change. It can provide essential data references for global water resource management and research. The Global Surface Water Stable Isotope Dataset is available at https://doi.org/10.17632/fs7rwp7fpr.1 (Zhu, 2024). [ABSTRACT FROM AUTHOR]

Precipitation plays a crucial role in the hydrological cycle and is vital for water resources management, climate change research, and ecosystem conservation. Precipitation stable isotopes serve as the 'fingerprints' of precipitation, which can clearly trace the formation, transport, and subsequent processes of precipitation. However, due to the scarcity of precipitation stable isotope data, we face challenges of temporal discontinuity and spatial heterogeneity when studying it at large to medium scales. Therefore, we compiled precipitation hydrogen and oxygen stable isotope data (δ18O and δ2H) from 2059 global sites spanning from 1961 to 2023, totaling 141,624 records. Our study indicates significant variations of global precipitation stable isotopes both spatially and temporally. Spatially, the isotopic composition of precipitation in different regions varies significantly due to factors such as geographical location, underlying surface conditions, and atmospheric circulation. Temporally, δ18O and δ2H show decreasing trends, while d-excess shows an increasing trend, with the impact of global temperature rise being very apparent. This precipitation stable isotope dataset provides robust support for our understanding of global precipitation changes and climate change. Through further investigation of precipitation stable isotope data, we hope to uncover more mechanisms and influencing factors of precipitation processes, providing a more accurate basis for the assessment and prediction of climate and water resource changes. [ABSTRACT FROM AUTHOR]

Water scarcity is a global challenge, underscoring the importance of efficient water resource management. Solar stills offer a cost-effective method to convert brackish water into potable water but face productivity limitations. This study aims to enhance solar still productivity through modifications using different fin materials and water depth. Computational Fluid Dynamics (CFD) simulations were employed to evaluate thermal performance across four scenarios: copper and aluminum fins at water depths of 20 mm and 40 mm. Key parameters including temperature distributions, friction volume, and fluid velocity were analyzed for each configuration (MSS-I to MSS-IV). Energy and exergy efficiencies were also assessed. MSS-III, utilizing copper fins at a 20 mm depth, demonstrated the highest daily productivity (8.33 liters) compared to MSS-IV (8.02 liters), MSS-I (7.81 liters), and MSS-II (6.71 liters). Energy efficiencies were highest for MSS-III (60.10%), followed by MSS-IV (57.41%), MSS-I (55.22%), and MSS-II (52.18%). MSS-III also exhibited the highest exergy efficiency (21.50%), with MSS-I (17.15%), MSS-IV (16.43%), and MSS-II (14.12%) following. The study underscores significant improvements in thermal and energy efficiency achieved through specific design modifications of solar stills. MSS-III's higher performance, attributed to the use of copper fins and optimized depth, highlights the critical role of material selection and structural design in enhancing solar still productivity. These findings have important implications for sustainable water resource management, emphasizing the potential of optimized solar still designs to address water scarcity challenges. [ABSTRACT FROM AUTHOR]

Objective: This study delves into the impact of urban meteorological elements—specifically, air temperature, relative humidity, and atmospheric pressure—on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius), air pressure (in hectopascals), and relative humidity (in percent) were used for the statistical period 2017–2023. Various models, including the correlation coefficient, generalized additive models (GAM), generalized linear models (GLM), and support vector machines (SVM), were employed to scrutinize the data. Results: Water consumption increases due to the influence of relative humidity and air pressure when the temperature variable is controlled. Under specific air temperature conditions, elevated air pressure coupled with high relative humidity intensifies the response of water consumption to variations in these elements. Water consumption exhibits heightened sensitivity to high relative humidity and air pressure compared to low levels of these factors. During winter, when a western low-pressure air mass arrives and disrupts normal conditions, causing a decrease in pressure and temperature, urban water consumption also diminishes. The output from the models employed in this study holds significance for enhancing the prediction and management of water resource consumption. [ABSTRACT FROM AUTHOR]

Background: Populus spp. is a crucial fast-growing and productive tree species extensively cultivated in the mid-latitude plains of the world. However, the impact of intensive cultivation management on gene expression in plantation remains largely unexplored. Results: Precision water and fertilizer-intensive management substantially increased key enzyme activities of nitrogen transport, assimilation, and photosynthesis (1.12–2.63 times than CK) in Populus × euramericana 'Neva' plantation. Meanwhile, this management approach had a significant regulatory effect on the gene expression of poplar plantations. 1554 differential expression genes (DEGs)were identified in drip irrigation (ND) compared with conventional irrigation. Relative to ND, 2761–4116 DEGs, predominantly up-regulated, were identified under three drip fertilization combinations, among which 202 DEGs were mainly regulated by fertilization. Moreover, drip irrigation reduced the expression of cell wall synthesis-related genes to reduce unnecessary water transport. Precision drip and fertilizer-intensive management promotes the synergistic regulation of carbon and nitrogen metabolism and up-regulates the expression of major genes in nitrogen transport and assimilation processes (5 DEGs), photosynthesis (15 DEGs), and plant hormone signal transduction (11 DEGs). The incorporation of trace elements further enhanced the up-regulation of secondary metabolic process genes. In addition, the co-expression network identified nine hub genes regulated by precision water and fertilizer-intensive management, suggesting a pivotal role in regulating the growth of poplar. Conclusion: Precision water and fertilizer-intensive management demonstrated the ability to regulate the expression of key genes and transcription factor genes involved in carbon and nitrogen metabolism pathways, plant hormone signal transduction, and enhance the activity of key enzymes involved in related processes. This regulation facilitated nitrogen absorption and utilization, and photosynthetic abilities such as light capture, light transport, and electron transport, which faintly synergistically regulate the growth of poplar plantations. These results provide a reference for proposing highly efficient precision intensive management to optimize the expression of target genes. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to forecast the spatiotemporal water change trends in Urmia Lake through 2030. Three space–time cube-based models were applied. The forest-based forecast model, with a mean of 0.14 forecast root mean square error (RMSE) and 0.39 validation RMSE, had a better performance. According to the model’s results, the peripheral parts of the lake will mainly stay completely arid with a 42.5% extension in the entire period, 21.7% in spring, and a 27% reduction in winter. In the middle parts, the aridity will increase by 121% in summer, 93% in fall, and 38% in spring. Eventually, the centric areas will not be completely arid; however, the water patterns’ extent will decrease by 53.46% in fall, 34.6% in spring, and 28.5% in summer. Additionally, southern, eastern, western, and northern areas will experience drier conditions, respectively. The findings can be used for water resource management and water restoration plans for the lake. [ABSTRACT FROM AUTHOR]

The blue and green water resource distribution of the Jing River Basin, a cradle of Chinese civilization on the Loess Plateau, was studied using the Soil and Water Assessment Tool and the Sequential Uncertainty Fitting algorithm (vers. 2). Understanding these resources aids in the ecological preservation of the Yellow River Basin and its high-quality development. Future climate conditions were simulated using the Statistical Downscaling Model (SDSM). The data came from the Shared Socioeconomic Pathway (SSP) projections (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the Canadian Earth System Model (vers. 5), covering the 2030s, 2060s, and 2090s (based on 2015-2045, 2046-2075, and 2076-2100 climate projections, respectively). The SDSM accurately simulated temperature and precipitation trends, with its temperature predictions being more accurate. The results show that the maximum temperature, minimum temperature, and precipitation tend to increase under the three future climate scenarios, and the amount of blue and green water continues to increase in the future, with the SSP5-8.5 scenario showing the highest amount of blue and green water, and the SSP1-2.6 scenario showing the lowest amount of blue and green water, in terms of the climate scenarios. In terms of temporal distribution, 2090s has the most abundant blue and green water and 2030s has the least blue and green water content. Forecasting blue and green water changes due to climate change is vital for regional water management and risk assessment. [ABSTRACT FROM AUTHOR]

An international dispute between the Republic of Poland and Czechia over the Turów mine has recently resulted in infringement proceedings brought before the Court of Justice of the European Union concerning the implementation of the Water Framework Directive. This case and the judicial and extrajudicial processes surrounding it facilitate an assessment of the procedure provided for in Articles 258 to 260 of the Treaty on the Functioning of the European Union. The assessment of this procedure is undertaken both in terms of the achievement of the internal policy objectives of the Member State and in terms of the effectiveness of the implementation of European law. We argue that this procedure (under Articles 258–260 of the Treaty on the Functioning of the European Union) is effective in both connections, because it provides a wide range of opportunities for both the judicial authorities of the European Union and the Member States themselves to achieve their objectives. [ABSTRACT FROM AUTHOR]

Vadose zone soil moisture is often considered a pivotal intermediary water reservoir between surface and groundwater in semi-arid regions. Understanding its dynamics in response to changes in meteorologic forcing patterns is essential to enhance the climate resiliency of our ecological and agricultural system. However, the inability to observe high-resolution vadose zone soil moisture dynamics over large spatiotemporal scales hinders quantitative characterization. Here, utilizing pre-existing fiber-optic cables as seismic sensors, we demonstrate a fiber-optic seismic sensing principle to robustly capture vadose zone soil moisture dynamics. Our observations in Ridgecrest, California reveal sub-seasonal precipitation replenishments and a prolonged drought in the vadose zone, consistent with a zero-dimensional hydrological model. Our results suggest a significant water loss of 0.25 m/year through evapotranspiration at our field side, validated by nearby eddy-covariance based measurements. Yet, detailed discrepancies between our observations and modeling highlight the necessity for complementary in-situ validations. Given the escalated regional drought risk under climate change, our findings underscore the promise of fiber-optic seismic sensing to facilitate water resource management in semi-arid regions. A fiber-optic seismic sensing principle provides a spatiotemporal view of vadose zone soil moisture dynamics, offering a scalable solution for enhanced long-term, large-scale water resource management in face of climate variability. [ABSTRACT FROM AUTHOR]

Soil moisture plays a critical role in agricultural productivity and water resource management, especially in a diverse and populous country like India. Understanding variations in soil moisture across different regions and seasons is essential for adapting agricultural practices and water management strategies to local conditions. This study examines changes in soil moisture levels across India, comparing contemporary data from 2023 with historical averages from 2000 to 2005 using advanced remote sensing and GIS technologies. The primary objective of this study is to identify Soil Moisture Anomalies (SMA) across India, quantify their impacts on agriculture and water resources, and provide recommendations for targeted management strategies. By comparing recent soil moisture data against historical averages, the study aims to highlight trends and changes that could influence future water resource planning and agricultural practices. The research utilizes data from the Famine Early Warning Systems Network's (FEWS NET) i.e. Land Data Assimilation System (FLDAS), obtained from NASA's data archives. The study employs a systematic approach to analyze seasonal variations in soil moisture across different Indian states. Soil moisture levels were analyzed using zonal statistics in GIS to classify regions into categories based on the degree of anomaly observed. This classification helped in understanding the spatial distribution of soil moisture during the pre-monsoon, monsoon, and post-monsoon seasons. The study found significant regional and seasonal variations in soil moisture across India. During the monsoon period, areas such as Bihar and Jharkhand consistently showed significant moisture deficits, indicative of drought conditions, affecting agricultural output and necessitating urgent water conservation measures. Conversely, regions like Punjab benefited from positive soil moisture anomalies, enhancing agricultural productivity. The pre-monsoon and post-monsoon seasons also showed variations, with some areas experiencing deficits requiring careful water management while others had surpluses that increased the risk of flooding. The analysis of SMA in India underscores the need for region-specific agricultural and water management strategies that consider significant variability in soil moisture conditions. The study highlights the importance of integrating soil moisture monitoring into national policy frameworks to enhance climate resilience and sustainable agricultural practices. Future research should focus on updating soil moisture assessments with more recent data and refining predictive models to improve the accuracy and effectiveness of water management and agricultural interventions. [ABSTRACT FROM AUTHOR]

Dead-ended proton exchange membrane fuel cells (PEMFC) using pure hydrogen oxygen can improve fuel efficiency and simplify fuel cell systems have been wildly used for a closed space. But the dead-ended operation of the PEMFC will cause difficulties in water management, especially in the cathode side, resulting in deteriorating of fuel cell stability. For this reason, gravity assisted drainage method, static drainage method are designed to migrate the water out of the cell. However, even with these methods, the reliability of the water removing from the cell remains questionable. Therefore, this paper introduces a novel water removal method to solve these problems and visualisation techniques were used to a more comprehensive knowledge of water transport mechanisms in dead-ended PEMFCs. A pressure-swing operation is realized by controlling the inlet of PEMFC solenoid valve to remove water and recycle oxygen during purging. The dynamic response characteristics of this system under different current densities, pressure differences, cell temperature and purging intervals are experimentally investigated in detail. It found that the water removal rate of the cathode flow channel of dead-ended PEMFC was as high as 99.25 ​%, and the fuel utilisation of the cell was close to 100 ​% in this water management mode. [ABSTRACT FROM AUTHOR]

[Objective] This study aims to explore the variation characteristics of streamflow components and baseflow index (BFI) in the Tuweihe River Basin, to clarify the impact of environmental factors on their variations, to identify the dominant factors, and to provide scientific basis for watershed water resources management. [Methods] Tuweihe River Basin in the middle reaches of the Yellow River was taken as the study area. The changes of surface runoff, baseflow and baseflow index (BFI) during the period 1958-2017 were analyzed. The correlation analysis and elasticity coefficient methods were employed to qualitatively and quantitively analyze the influence of environmental factors on them. [Results] The surface runoff and baseflow significantly decreased at a rate of 4.55 and 9.73 mm/decade (p<0.01), respectively, while the BFI showed a significant upward trend (p<0.05). Surface runoff (or baseflow, BFI) was highly sensitive to the changes in potential evapotranspiration, normalized difference vegetation index (NDVI), and volumetric soil water. The changes in NDVI and potential evapotranspiration contributed to the majority of the changes in surface runoff (-34% and -29%), baseflow (-39% and -29%), and BFI (33% and 31%). [Conclusion] The direct and potential hydrological effects of vegetation restoration play an important role in reducing the streamflow components. The changes of streamflow components as well as their relations to environmental changes provide a theoretical basis for the sustainability of water resources and vegetation. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the change characteristics and driving factors of runoff and sediment load in the Weihe River Basin, and to provide scientific basis for water resource management and ecological environment construction in the Weihe River Basin. [Methods] Based on the measured precipitation, runoff and sediment load data of Huaxian and Zhuangtou hydrological stations in the Weihe River Basin from 1957 to 2019, the trend and the change-point year of annual precipitation, runoff and sediment load were analyzed by modern mathematical statistical. The contribution rates of runoff and sediment load reduction in Weihe River Basin were calculated. The main influencing factors of human activities on runoff and sediment load changes in the Weihe River were discussed. [Results] There was a strong decreasing trend in runoff and sediment load of Weihe River Basin from 1957 to 2019 (p<0.01), and the annual precipitation fluctuated randomly and had no obvious change trend. Abrupt year analyses showed that more rapid declines for runoff began in 1990 (p<0.01), and sediment load began in 1996 (p<0.01). The contribution rates of precipitation to the reduction of runoff and sediment load were 19% and 17%, respectively. The contribution rates of runoff and sediment load reduction caused by human activities were 81% and 83%, respectively. [Conclusion] Human activities are the main reason for the significant reduction of runoff and sediment load in the Weihe River Basin. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the runoff response of Zishui Basin under future climate change scenarios, and to provide scientific support for the sustainable development of the basin and the decision-making of flood control and drought control. [Methods] Based on three CMIP6 global climate models, the SDSM downscaling model and SWAT hydrological model were constructed to predict the changes of temperature and precipitation in the Zishui Basin during 2030—2089 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 climate scenarios, and to further explore the response of runoff to climate change. [Results] Zishui Basin will present a significant warming and humidification trend in the future, and this trend will increase with the increase of discharge scenario. Spatially, the temperature rise of the whole basin is the largest in the vicinity of Xinning and Shaoyang stations. The increasing areas of precipitation mainly concentrate in the middle and lower reaches with Lengshuijiang as the center, while the decreasing area is located west of Dongkou station. In this context, the future annual runoff of Taojiang station and Shaoyang station will have the same variation trend with the watershed precipitation, and the amount of available water resources in the upstream area above Shaoyang station is more likely to decrease. Water resources in the basin will face the risk of more shortage in the dry season, and the flood season tends to advance, and the evenness of water resources allocation in the main flood season will increase. [Conclusion] Under the background of climate warming and humidification, the management of water resources in Zishui Basin will encounter more severe challenges, and the protection of water resources and the integrated management of Zishui Basin should be strengthened. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to reveal the variation characteristics and future evolution trend of water surface evaporation in the Shule River Basin, to better understand the dynamic changes of water resources in the basin, so as to provide decision support for the planning and management of water resources utilization in the region. [Methods] Based on the monthly and yearly water surface evaporation data of four hydrology stations in Changmabao, Shuangtabao Reservoir, Dangcheng Bay and Danghe Reservoir from 1980 to 2020, linear dip rate, sliding t test, R/S analysis, BP neural network and other analysis methods were used to examine the characteristics of surface evaporation changes in the past 40 years, and the future evaporation and its change trend were predicted. [Results](1) The annual variation of water surface evaporation at all stations in the Shule River Basin showed a decreasing trend during 1980—2020, and the trend rate of each station was ranked as: Shuangtabao Reservoir Station(-187.10 mm/decade)>Danghe Reservoir Station(-178.20 mm/decade)>Changmabao Station(-33.00 mm/decade)>Dangchengwan Station(-30.00 mm/decade), among which the decrease trend of Shuangtabao Reservoir and Danghe Reservoir Station was very significant.(2) The water surface evaporation of Changmabao, Shuangtapao Reservoir and Danghe Reservoir Stations showed a decreasing trend in all seasons, while that of Dangchengwan Station showed an insignificant increasing trend in spring and a decreasing trend in other seasons.(3) The mutation years of Changmabao, Dangcheng Bay, Shuangtabao Reservoir and Danghe Reservoir were 2004, 2015, 2007 and 2007, respectively.(4) The uneven coefficient and concentration of evaporation on the surface of the basin showed a decreasing trend as a whole.(5) It is predicted that the evaporation of water surface will continue to show a downward trend in 2021—2026. [Conclusion] On the whole, the annual and seasonal water surface evaporation in the Shule River Basin shows a decreasing trend, and it also shows a decreasing trend in the future. [ABSTRACT FROM AUTHOR]

The study of the spatio-temporal distribution and evolution trends of water resources in large regions plays an important role in the study of regional water resource planning, regional economic and social development, and water disasters. In this study, a Landsat multi-index relationship and water probability thresholding method is developed based on the Google Earth Engine (GEE) platform, which can support the integration of multiple Landsat satellites. The algorithm jointly combines multiple remote sensing metrics along with the calculation of water probability to produce an interannual water body product for the Jianghan Plain on a 20-year time series. The results indicate that the Landsat multi-index relationship algorithm used in this study has high accuracy in extracting long-term water bodies in extensive, flat terrain areas such as the Jianghan Plain, with an overall accuracy (OA) of 97.23%. By analyzing the water body products and landscape patterns, we have identified the following features: (1) From 2002 to 2021, the changes in river water bodies in the Jianghan Plain were relatively small, and some lakes experienced a shrinkage in area. Overall, there is a strong correlation between water distribution and precipitation. (2) The complexity index of water bodies shows a strong negative correlation with effective irrigation area and population, indicating a strong mutual influence between water bodies and socio-economic activities. (3) Through the study of the distribution characteristics of built-up areas and the water system, it was found that for large rivers, the larger the size of the river, the more built-up areas are nearby. Most extensive built-up areas are located near large rivers. This study contributes to providing methods and data support for urban planning, water resource management, and disaster research in the Jianghan Plain. [ABSTRACT FROM AUTHOR]

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model's reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study's outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management. [ABSTRACT FROM AUTHOR]

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions. [ABSTRACT FROM AUTHOR]

Nowadays, using domestic sewage wastewater for agricultural irrigation is becoming increasingly important for sustainable water resources management. Several national and international programs have been implemented to mitigate wastewater. However, it is important to ensure that these measures are affordable, effective, and sustainable. The aim of the present study is to determine, after all these mitigations, whether these domestic sewage wastewaters can be used as a potential irrigation source and at the same time ensure environmental food security. With these objectives, we analyzed the consequences of domestic sewage effluent irrigation on soil physical–chemical properties and nutritive and nutraceutical properties of cultivated green vegetables, Luffa acutangula (L.) Roxb. and Lagenaria siceraria (Molina) Standl., in the semi‐urban ecosystem compared with well water irrigated crops in a rural area. The quality status of both well water irrigated and domestic sewage wastewater irrigated soil samples showed similar and/or comparable values, that is, domestic sewage wastewater irrigation does not harm the soil quality and both were comparable with the permissible limits of BIS (Bureau of Indian Standards) including heavy metal contents. And positively supporting the objective of our study, the nutrient status/proximate composition of both the vegetables in terms of protein, gross energy, carbohydrate, etc., were higher in domestic sewage wastewater irrigated crops compared with the well water irrigated crops. The antioxidant activity also exhibited a significant increase in the sewage wastewater irrigated crops rather than the well water irrigated ones. Hence the results of the present study state that due to the impact of the mitigation measures, domestic sewage wastewater is now pertinent to be used as an irrigation source and also a possible organic fertilizer without any ambiguity. Moreover, more field‐oriented studies may ensure the potential of wastewater irrigation for sustainable crop production without any pollution, particularly in a semi‐urban area. [ABSTRACT FROM AUTHOR]

Despite high pollution levels in Indian rivers, a comprehensive study on the water quality index (WQI) remains elusive. WQI values were computed, and their classes were determined using six water quality parameters from an available decennial dataset (n = 3595) on Indian Rivers. This study aims to assess the spatial distribution of WQI values and their classes across Indian River systems while exploring the application of machine learning (ML) based models in predicting WQI classes using a reduced number of input parameters. Modeling experiments were designed on five models- Decision Tree (DT), Random Forest (RF), Gradient Boosted Trees (GBT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) for predicting WQI classes. Each model was trained with input parameters and WQI classes with 2990 datasets. Testing of WQI classes by each model was made on 605 datasets under different framework sets. Models' performance metrics were evaluated by accuracy, weighted mean recall and precision, and F-score. Our study demonstrates that the two largest systems, Ganga and Brahmaputra, lie on the extremes of the WQI (mean) spectrum, reflecting the impact of contrasting population density, industrial activities, change in land-use-land-cover pattern, and agricultural use on the riverine WQI. Our modeling experiments underscore that with only three input parameters, GBT can predict WQI classes with > 80% of performance metrics. With only two input parameters, GBT, RF, and ANN, all can provide reliable estimates. Our study highlights that ML models can serve as decision-supporting tools for water resource policymakers and managers in making effective pollution control and water resource management decisions. [ABSTRACT FROM AUTHOR]

Anthropogenic factors such as over-pumping and natural events such as earthquakes impact coastal aquifers by reducing freshwater recharge, aquifer water budgets, and increasing saltwater intrusion (SWI). This study investigates the impact of hydrodynamic forces induced by earthquakes on SWI in one hypothetical case, namely, the Henry problem, and a real case of the Biscayne aquifer located in Florida, USA. The analysis was carried out using the analytical solution of estimating the earthquake's induced hydrodynamic pressure and applying the SEAWAT code to investigate the SWI for the base case and three scenarios, namely for the horizontal acceleration (αh) by 0.10 g, 0.20 g, and 0.30 g. The results show that earthquakes might considerably increase the SWI in coastal aquifers. Moreover, the rise in salinity across expansive land areas significantly threatens agricultural productivity and jeopardizes food security. Namely, in the case of Biscayne aquifer, salinity was increased by 12.10%, 21.90%, and 45.70% for the horizontal seismic acceleration of 0.1 g, 0.20 g, and 0.30 g, respectively. Hence, the conclusions drawn from this study underscore the need for carefull consideration of earthquake impacts in future planning and water management strategies for coastal regions. This proactive approach is crucial to preemptively address and mitigate the groundwater salinization hazard associated with SWI fluctuations due to earthquakes. [ABSTRACT FROM AUTHOR]

Upper and lower percentiles of Flow Duration Curves (FDCs) of daily streamflow data were investigated to develop frequency curves. Upper percentiles with exceedance probability of 1, 5 and 10% (Q1, Q5, Q10) were used for high flows, and lower percentiles with non-exceedance probability of 10, 5 and 1% (Q90, Q95, Q99) for low flows. Median value (Q50) was covered to represent the average conditions of streamflow. A mixed frequency analysis based on the total probability theorem taking zero values into account was applied for the lower percentiles of FDC. Case studies were performed for three intermittent Streamflow Gauging Stations (SGSs) from Kucuk Menderes River Basin in western Turkey. An overall assessment of results shows that the best-fit probability distribution function does not change from one SGS to another considerably for low flows while each SGS has its own probability distribution function for high flows. Upper and lower percentiles, and median value were calculated at various return periods by using the identified probability distribution functions. The calculated values were plotted in the form of frequency curves of high flow percentiles and low flow percentiles. The frequency curves have a practically significant potential use in hydrological analysis, water resources management and hydraulic design under high and low flow conditions. They are yet open to further development for regionalization and their applicability can be extended to ungauged sites in river basins. [ABSTRACT FROM AUTHOR]

Accurate streamflow prediction is significant for water resources management. However, due to the impact of climate change and human activities, accurately identifying the input factors of the streamflow prediction model and achieving high-precision results presents a significant challenge. In this study, past streamflow, meteorological, and climate factors were utilized as inputs to develop a predictive scenario for the bi-decomposition of input factors and streamflow series, i.e. Scenario 3 (S3). Mutual information (MI) was applied to recognize the input factors prediction potential. Based on the predictive potentials, factors were progressively incorporated into the kernel extreme learning machine (KELM) and hybrid kernel extreme learning machine (HKELM) models optimized by the gazelle optimization algorithm (GOA) to ascertain the optimal input configuration for each sub-series. The prediction results of S3-KELM and S3-HKELM models were obtained by reconstructing the optimal prediction results of each sub-series. The monthly streamflow of the upper Fenhe River Basin, which is in the semi-humid and semi-arid climate zone, was selected as a case study. The results indicate that in comparison to both undecomposed and singly decomposed scenarios, the input–output bi-decomposed scenario more accurately identifies the input factors and constructs high-precision prediction models. The Nash–Sutcliffe efficiency (NSE) of both the S3-KELM and S3-HKELM models exceeds 0.85. Specifically, the S3-HKELM model demonstrates superior performance, capable of handling more complex inputs, with its NSE reaching up to 0.93. Importantly, meteorological and climate factors contribute to the accuracy of streamflow predictions across different scenarios. [ABSTRACT FROM AUTHOR]

Rainfall is an important phenomenon for various aspects of human life and the environment. Accurate prediction of rainfall is crucial for a wide range of sectors, including agriculture, water resources management, energy production, disaster management and many more. The ability to predict rainfall in an accurate fashion enables stakeholders to make informed decisions and take necessary actions to mitigate the impacts of natural disasters, water scarcity and other issues related to rainfall. In addition, advances in rainfall prediction technologies have the potential to contribute to sustainable water management and the preservation of water resources by providing the necessary information for decision‐makers to plan and implement effective water management strategies. Hence, it is important to continuously improve the accuracy of rainfall prediction. In this paper, the integration of the maximum overlap discrete wavelet transform (MODWT) and machine learning algorithms for daily rainfall prediction is proposed. The main objective of this study is to investigate the potential of combining MODWT with various machine‐learning algorithms to increase the accuracy of rainfall prediction and extend the forecast time horizon to 3 days. In addition, the performances of the proposed hybrid models are contrasted with the models hybridized with commonly used discrete wavelet transform (DWT) algorithms in the literature. For this, daily rainfall raw data from three rainfall observation stations located in Turkey are used. The results show that the proposed hybrid MODWT models can effectively improve the accuracy of precipitation forecasting, based on model evaluation measures such as mean square error (MSE) and Nash‐Sutcliffe coefficient of efficiency (CE). Accordingly, it can be concluded that the integration of MODWT and machine learning algorithms have the potential to revolutionize the field of daily rainfall prediction. [ABSTRACT FROM AUTHOR]

Central Asia (CA) is among the world's most vulnerable regions to climate change. Increasing anthropogenic greenhouse gas concentrations (GHGs) are the primary forcing of the current and future climate system for the time scale of a century. By analysing observation datasets, we show that a warming of 1.2°C led to a decrease of 20% in snow‐depth CA during the last 70 years, especially over the mountains. In recent decades, longer summer times and fewer icing days (more than 20 days·year−1) have exposed unprecedented shock to CA's climate system's components. Furthermore, we analyse 442 model simulations from Coupled Model Inter‐comparison Project Phase 5 and 6 (CMIP5, CMIP6) and show that CMIP6 simulations are generally warmer and wetter than the CMIP5 ones in CA. For instance, under the highest emission scenarios (RCP8.5 and SSP5‐8.5), CMIP6 projects a 6.1°C increase, while CMIP5 projects a 5.3°C increase, suggesting CMIP6 anticipates greater warming with high emissions. In contrast to CMIP6, the CMIP5 precipitation trends suggest a potential nonlinear relationship between increased greenhouse gas emissions and changes in precipitation, though the impact is much less pronounced than the temperature changes. Our analysis shows that CMIP6 models are more sensitive to temperature rise than CMIP5 ones. Both simulation sets' ensemble means capture well the observed warming trend. The imposed snow‐melting leads to an increase in the run‐off in the vicinity of glaciers. Such climatic shifts lead to more flooding events in CA. Given the projected warming range of 2–6°C in CA at the end of the century in various scenarios and models, such warming trends might be catastrophic in this region. The seasonal cycle of the temperature change indicates an extension of the glacier's melting period under future scenarios with fossil‐fueled development. The models' uncertainty increases for the far‐future time‐slice, and warming larger than 4°C in CA is very likely among all the models and during all the seasons if no sustainable action is taken. This study also incorporates a detailed Köppen climate classification analysis, revealing significant shifts towards warmer climate categories in Central Asia, which may have profound implications for regional hydrological cycles and water resource management, particularly in the Amu Darya and Syr Darya river basins under warmer scenario by the end of the century. The Tundra and ice cap climate categories will lose more than 60% of their coverage at the end of the century compared to the historical period in the Amu Darya and Syr Darya river basins. [ABSTRACT FROM AUTHOR]

Water resource management is an important issue in a region where water resources are far from residential areas. Pipelines can provide a water connection between water resources and residential areas. Control valves are useful in controlling the supply of water based on a time table, faciliting effective manangement of the amount of water supplied. A control valve system with an independent electric energy source (CVS-IEES) can minimize maintenance costs in desert or jungle areas, where maintenance by human efforts may be insufficient. This study suggests that two types of tests, namely, repeatability and flow coefficient tests, can provide the impetus for designing the CVS-IEES. The repeatability test measures the number of repeatable operations of opening and closing an on/off valve. Test result showed that a fluid volume of 15 L at a pressure difference of 5 bar (gage) was feasible for operating a pneumatic on/off valve with a pipe size of 200 mm within 1 s, and the torque must be 7500 N·m. The flow coefficient test result was a function of the valve opening ratio (or aperture rate). The flow coefficient of a control valve with a pipe size of 50 mm was Kv = (2.48 ± 0.20) m3/bar0.5·h at q = 3.94 m3/h. The measurement uncertainty of 0.20 m3/bar0.5·h enables the CVS-IEES designers to estimate the accuracy when controlling the amount of supplied water. [ABSTRACT FROM AUTHOR]

To advance current environmental flow standards in Texas, we developed field, laboratory, and analytical methods to model recruitment dynamics of indicator species selected to cover a range of population responses to flow variation. Preliminary investigations were conducted in three central Texas river basins to evaluate the feasibility of these methods for formulating and evaluating the state's environmental flow standards. Age analysis allowed back‐calculation of dates of fish hatching, mussel recruitment, and tree germination for estimation of temporal variation in recruitment. Recruitment data were then analyzed in relation to flow regime components. Statistical models were derived for flow–recruitment relationships of Shoal Chub Macrhybopsis hyostoma, Smallmouth Buffalo Ictiobus bubalus, threeridge mussel Amblema plicata, and boxelder Acer negundo. The Shoal Chub model was used to hindcast recruitment dynamics in the Brazos River, revealing a decline in recruitment after dam construction. The Smallmouth Buffalo model revealed that strong year‐classes were positively associated with springtime pulses of long duration but negatively associated with frequent short‐duration pulses. Results for the threeridge mussel were not as easily interpreted, but for all three river basins generally showed weaker recruitment during periods with extreme low and high flows. The boxelder model revealed positive associations between recruitment and high flow components and was used to forecast the probability of germination under five simulated flow regime scenarios, thereby providing an additional means for evaluating the suitability of environmental flow standards. Assessment of how hydrology influences recruitment provides an evidence‐based method to hindcast responses to past flow regimes and forecast responses to future flow scenarios, thereby providing a basis for evaluating environmental flow standards and water management options. [ABSTRACT FROM AUTHOR]

This paper demonstrates an automated workflow for extracting network data from policy documents. We use natural language processing tools, part‐of‐speech tagging, and syntactic dependency parsing, to represent relationships between real‐world entities based on how they are described in text. Using a corpus of regional groundwater management plans, we demonstrate unique graph motifs created through parsing syntactic relationships and how document‐level syntax can be aggregated to develop large‐scale graphs. This approach complements and extends existing methods in public management and governance research by (1) expanding the feasible geographic and temporal scope of data collection and (2) allowing for customized representations of governance systems to fit different research applications, particularly by creating graphs with many different node and edge types. We conclude by reflecting on the challenges, limitations, and future directions of automated, text‐based methods for governance research. [ABSTRACT FROM AUTHOR]

The diverse climates, distribution of snow and glaciers, and geographic locations directly affect the runoff response to climate change in the upper basins of the Third Pole. At present, a comprehensive analysis of runoff variations and their distinct responses to climate change in the westerlies- and monsoon-dominated upper basins is still lacking. This study comprehensively analyzed annual runoff variations in westerlies-dominated basins (the upper basins of the Aksu (UAKS), Syr Darya (USRD), Yarkant (UYK), Hotan (UHT), Amu Darya (UAMD), and Indus (UI)) and monsoon-dominated basins (the upper basins of the Yangtze (UYA), Yellow (UYE), Lancang (ULC), Nujiang (UNJ), and Yarlung Zangbo (UYZ)) of the Third Pole from 1961 to 2015. Using multi-source meteorological data and large-scale circulation factors, this study investigated the divergent responses of runoff in the upper basins to climate change, and explored the large-scale circulation mechanisms underlying runoff variations in these upper basins. The results showed that: (1) The annual runoff in the majority of upper basins (except for the UYE and UYZ) exhibited an increasing trend, and the annual runoff in the UAKS, UYK, and UI showed a significant increasing trend from 1961 to 2015. The annual runoff in the upper basins of the Third Pole changed abruptly from decreasing to increasing between the 1980s and 2000s, with the exception of the UYE. (2) The runoff in the monsoon-dominated upper basins has been controlled primarily by changes in precipitation over the past 55 years. In contrast, the runoff in the westerlies-dominated upper basins exhibited three distinct long-term responses to climate change: temperature-dominated (UYK and UHT), precipitation-dominated (USRD and UAMD), and the combined influence of precipitation and temperature (UAKS and UI). Since the 1960s, the sensitivity of runoff to warm season temperature changes in the most westerlies-dominated upper basins has decreased, while the response of runoff to precipitation changes has intensified. (3) The study revealed the connection between large-scale circulation, climate, and runoff in the upper basins of the Third Pole. The Atlantic Multidecadal Oscillation, the Westerly Index, and the El Niño-Southern Oscillation predominantly impact the precipitation or temperature in the upper basins of the Third Pole, which in turn affect the runoff variations in the upper basins dominated by either the westerlies or the monsoon. This study will be a valuable scientific reference for water resource management and climate change adaptation for both the westerlies- and monsoon-dominated upper basins in the Third Pole. [ABSTRACT FROM AUTHOR]

California contains a broad geography over which climate conditions can be suitable for cultivating multiple varieties of winegrapes. However, climate change is projected to make winegrape cultivation more challenging across many of California's winegrowing regions. In order to understand the potential effects of climate change on winegrapes, this study models variety-specific phenology for six winegrape varieties and quantifies the change in phenology and viticulturally-important agroclimate metrics over 12 of California's American Viticultural Areas (AVAs) by the mid-21st century. Results show more rapid development for winegrapes with earlier budburst, flowering, veraison, and maturation across all varieties and AVAs. Cabernet Sauvignon shows the greatest change in phenology timing, while Chardonnay shows the least change. Likewise, the West Sonoma Coast AVA shows the greatest average change in phenology timing across varieties and development stages and Lodi AVA shows the least. Projected changes in agroclimatic metrics include an additional month of potentially damaging heat days (above 35 °C) in some AVAs, and decreases in frost days. These results have implications for numerous factors related to viticultural production, including water resources management and crop yield and quality, and underscore the need for California winegrape growers to improve their resilience to climate change by adopting strategies such as increasing soil health and water use efficiency and selecting cultivars suited for future climate conditions. By conducting climate effects analyses at the variety-specific and AVA scale, important information is provided to the winegrowing industry at a resolution that can support decision-making towards resilience. [ABSTRACT FROM AUTHOR]

Alternate wetting and drying (AWD) is an effective water-saving practice for rice cultivation that may however promote nitrogen (N) losses compared to continuous flooding (CF). The interaction between water, crop residue and N fertilization management can influence the contribution of different N sources to plant uptake. We hypothesized that microbial processes driving the source-differentiated N supply for rice uptake during the early growth stages will depend on the interaction between water management, the timing of straw incorporation with respect to flooding and the temporal distribution of mineral N application. Rice was grown for 60 days in mesocosm experiment involving a factorial design with (i) two water regimes (CF vs. AWD) and (ii) three straw and fertilizer managements, during which soil N, porewater chemistry, plant growth and N uptake were evaluated. Source partitioning of plant N between fertilizer-, straw- and soil-derived N was achieved by means of a dual-stable isotope 15N tracing approach. Although AWD reduced total N uptake by about 4–25% with respect to CF, this could only be partly attributed to a lower uptake of fertilizer-N (and lower fertilizer-N use efficiency), suggesting that other N sources were affected by water management. Our findings evidence how the interaction between soil redox conditions and the availability of labile C and inorganic N strongly determined the supply of soil-derived N through microbial feedback and priming responses. Although incorporated straw contributed only minimally to rice N, it represented the primary driver controlling plant N nutrition through these microbial responses. These insights may contribute to identify suitable fertilization practices that favour plant N uptake during the early stages of rice growth under AWD. [ABSTRACT FROM AUTHOR]

The accurate assessment and monitoring of crop water status is a critical component of precision agriculture. Over the recent decade, unmanned aerial vehicles (UAVs) integrated with high-resolution thermal technologies, have pioneered a new era in remote sensing, enabling the acquisition of near-real-time data essential for the efficient management of water resources within crop farming systems. With the application of UAV-based thermal sensors, a diverse array of key biophysical and biochemical indicators that serve as critical proxies for crop water status have been evaluated. Therefore, this study adopted a systematic approach to review the progress, challenges, and opportunities in utilizing UAV thermal remote sensing to assess and map the water status within crop farming systems. In particular, this study sought to explore the current state of the literature and identify existing research gaps by analysing bibliometric data and recent trends in the literature. Based on the findings of this study, it was observed that even though UAV thermal remote sensings hold great potential, there currently exists significant research gaps in their application for assessing and monitoring crop water status, particularly in the global south. Furthermore, the results of this study concluded that the majority of the reviewed literature focused on assessing canopy temperature, potentially resulting in the application of UAV thermal sensors in characterizing other crop water-related indicators remaining relatively underexplored in scientific research. Overall, the findings of the review highlight UAV thermal remote sensing as an innovative tool capable of providing high-resolution, robust, and near-real-time datasets for crop water status assessments, therefore, revolutionizing agricultural farming practices.Therefore, this study serves as a stepping stone towards integrating UAV thermal technologies into agricultural practices and achieving robust and spatially explicit information on crop water status for the improved management of farming landscapes. [ABSTRACT FROM AUTHOR]

Governments worldwide invest in developing and diffusing innovations to deal with wicked problems. While experiments and pilots flourish, governments struggle to successfully scale innovations. Public sector scaling remains understudied, and scholarly suggestions for scaling trajectories are lacking. Following a design approach, this research develops an academically grounded, practice-oriented scaling instrument for planning and reflecting on the scaling of public sector innovations. We design this instrument based on the academic literature, an empirical analysis of three scaling projects at the Dutch Ministry of Infrastructure and Water Management, and six focus groups with practitioners. This research proposes a context-specific and iterative understanding of scaling processes and contributes a typology of scaling barriers and an additional scaling strategy to the literature. The presented instrument increases our academic understanding of scaling and enables teams of policymakers, in cooperation with stakeholders, to plan and reflect on a context-specific scaling pathway for public sector innovations. [ABSTRACT FROM AUTHOR]