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Models are widely used to research hydrological change and risk. However, the power embedded in the modelling process and outcomes is often concealed by claiming their neutrality. Our review shows that in the scientific literature relatively little attention is given to the influence of models on development processes and outcomes in water governance. At the same time, an emerging body of work offering critical insights into the political implications of hydrological models and a nuanced understanding of their application in context has begun to flourish. Drawing on this work, we call for power-sensitive modelling which includes the following considerations: take a holistic approach to modelling beyond programming and coding; foster accountability; work towards just and equitable water distributions; be transparent about the expectations and choices made; and democratise modelling by giving space to and being mindful of representations of multiple bodies of knowledge and multiple stakeholders and by incorporating marginalised people and nature into the modelling process. Our call should not be understood as a suggestion to do away with modelling altogether, but rather as an invitation to interrogate how quantitative models may help to foster transformative pathways towards more just and equitable water distributions. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to determine the appropriate DEM resolution for geomorphic hydrology research in a small watershed of the Loess Plateau, explore the applicability of scale effect curve and information entropy method, and provide a new perspective and approach for determining the appropriate resolution. [Methods] Taking Yanhe River Basin in the loess hilly region as the research area, ArcGIS was used to resample the original resolution DEM, obtain multiple groups of DEM with different resolutions, extract the terrain attribute values at corresponding scales, and draw two different scale effect curves (‘average slope-resolution ’and‘ local variance mean-resolution ’curves). Information entropy and mutual information entropy are introduced to distinguish suitable DEM resolution based on actual terrain. [Results] (1) In the Yanhe River Basin with complex terrain, the results obtained by different determination methods are different, while the results of local variance mean, information entropy and mutual information entropy are similar. (2) The results of determining the appropriate resolution by different terrain attributes are different. The appropriate resolution obtained by profile curvature and plane curvature is similar (the local mean variance is 15～20 m, and the information entropy and mutual information entropy are both 15 m), and the difference of the appropriate resolution obtained by slope is greater than that obtained by curvature (the local mean variance is 20～35 m). The results of information entropy and mutual information entropy are both 20 m. [Conclusion] Both local variance mean and information entropy method are suitable for DEM resolution determination in this area, and the appropriate DEM resolution in Yanhe River Basin is 15～20 m. [ABSTRACT FROM AUTHOR]

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research. Plain Language Summary: The use of satellites to collect information from far away helps us to understand how water behaves on the continents. But combining all this data with uncertain computer models is complicated. This study introduces a new method called multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) to combine many different kinds of data at once. We tested MoLEnKF in the Amazon River basin, using satellite data on water levels, terrestrial water storage, flood extent and soil moisture. MoLEnKF by using all these observations at the same time obtained better results holistically than the individual experiments, improving our ability to predict aspects such as the amount of discharge, water level and evapotranspiration. This study is a step forward and could be really useful for understanding and predicting water‐related phenomena worldwide, especially in a context of scarce or no availability of in‐situ observations. Key Points: Multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) advances multi‐observation and multi‐scale assimilation, overcoming holistically univariate experimentsMoLEnKF improves the simulation of large‐scale hydrologic‐hydrodynamic uncertain models using only remotely sensed dataMoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission [ABSTRACT FROM AUTHOR]

Comprehensive large sample hydrological datasets, particularly the CAMELS datasets (Catchment Attributes and Meteorology for Large-sample Studies), have advanced hydrological research and education in recent years. These datasets integrate extensive hydrometeorological observations with landscape features, such as geology and land use, across numerous catchments within a national framework. They provide harmonised large sample data for various purposes, such as assessing the impacts of climate change or testing hydrological models on a large number of catchments. Furthermore, these datasets are essential for the rapid progress of data-driven models in hydrology in recent years. Despite Germany's extensive hydrometeorological measurement infrastructure, it has lacked a consistent, nationwide hydrological dataset, largely due to its decentralised management across different federal states. This fragmentation has hindered cross-state studies and made the preparation of hydrological data labour-intensive. The introduction of CAMELS-DE represents a step forward in bridging this gap. CAMELS-DE includes 1555 streamflow gauges with hydro-meteorological time series data covering up to 70 years (median length of 46 years and a minimum length of 10 years), from January 1951 to December 2020. It includes consistent catchment boundaries with areas ranging from 5 to 15,000 km 2 along with detailed catchment attributes covering soil, land cover, hydrogeologic properties and data about human influences. Furthermore, it includes a regionally trained Long-Short Term Memory (LSTM) network and a locally trained conceptual model that were used as quality control and that can be used to fill gaps in discharge data or act as baseline models for the development and testing of new hydrological models. Given the large number of catchments, including numerous relatively small ones (617 catchments < 100 km 2 ), and the time series length of up to 70 years (156 catchments), CAMELS-DE is one of the most comprehensive national CAMELS datasets available and offers new opportunities for research, particularly in studying long-term trends, runoff formation in small catchments and in analysing catchments with strong human influences. [ABSTRACT FROM AUTHOR]

The Oceania region has often been overlooked in fundamental hydrological research owing to assumptions that cultural complexities hinder such efforts. However, this perspective is somewhat naïve. Substantial evidence demonstrates that Pacific Islanders actively engage in research, contributing both local Indigenous knowledge and expert capabilities in "island hydrology." Numerous projects and publications by Pacific Island scholars showcase this expertise. To enhance self-reliance in smaller Pacific nations, it is crucial to improve access to advanced hydrology education and secure funding to support it. Overcoming these challenges is essential to unlocking the full potential of future hydrological research in the Pacific Islands. [ABSTRACT FROM AUTHOR]

Fushun, located in Northeast China, is prone to debris flow disasters due to its complex topographical and geological conditions. In 2013, large-scale debris flow disasters triggered by a rainstorm broke out, causing hundreds of casualties and serious economic losses. Therefore, it is significant to evaluate the susceptibility and hazard of debris flows in Fushun. Instead of adopting grid units, this research adopts the hydrological response units as the evaluation units and conducts debris flow susceptibility assessment for Fushun area with analytical hierarchical process. By combining the susceptibility with two different precipitation data, hazard assessment is further conducted. Comparison between the two hazard maps is conducted to explore the influence of precipitation on debris flow hazard assessment. Under different precipitation conditions, the debris flow hazard of the same area changes. Statistics on the accuracy of the susceptibility and hazard assessment results is conducted and further compared with the local existing debris flow disaster records, demonstrating that the evaluation results are generally in good consistency with the actual situation in Fushun. [ABSTRACT FROM AUTHOR]

The utilization of deuterium (δ2H) and oxygen (δ18O) isotope ratios in cryogenically extracted water from soil samples is a widely employed method in hydrological and ecological research. Nevertheless, an increasing body of research indicates that cryogenic water extraction (CWE) leads to δ2H depletion in soil water. To investigate the widespread existence of this phenomenon, samples from eight physicochemically distinct soils in China underwent rehydration with a reference water at five different water contents and were subsequently extracted using CWE. In comparison to the reference water, significant and inconsistent δ2H depletion was observed in all eight soil samples. The δ18O bias also exhibited variation, ranging from enrichment to depletion. Generally, Z score assessments indicated unacceptable results for all soils. Water content emerged as the most influential variable affecting both δ2H and δ18O biases, while soil properties had different impacts on these biases. Source water, as calculated by a linear regression model, revealed that the isotopic composition of extracted soil water differed from that of the reference water. The cryogenic extraction error in soil water could not solely attributed to fractionation processes during the extraction but resulted from the release of tightly bound soil water into the reference water. Using the influencing factors, correction models for δ2H and δ18O biases by CWE were developed. By these models, the δ2H and δ18O biases were mostly successful corrected. High soil water extraction efficiency (e.g., 99%) was recommended to minimize isotopic biases. These efforts necessitate further testing, particularly in ecohydrological studies involving isotope measurements of soil water through CWE. [ABSTRACT FROM AUTHOR]

Recent developments in tree-ring research offer great potential for reconstructing past climate changes; determining the frequencies of natural hazards; and assessing the availability of freshwater resources over timescales that extend well into the pre-instrumental period. Here, we review the state of dendrochronological research in the Himalaya and outline future directions for tree-ring-based hydrological reconstructions in a region that has a pressing societal need to understand the causes and consequences of past, present and future changes in the hydrological cycle. We used 'tree ring' and 'Himalaya' as keywords to identify scholarly articles from the Web of Science that were published between 1994 and 2022. The resulting 173 publications were separated by their spatial coverage into the western, central and eastern Himalaya, as well as their scientific purpose (e.g. reconstructing growth-climate relationships, temperature, precipitation, streamflow, floods, droughts, etc.). Our analysis shows that dendrochronological research in the Himalaya primarily focused on understanding growth-climate relationships using annual tree-ring widths measurements obtained for coniferous species, and their application in climate reconstructions. Reconstructions of hydrological processes such as streamflows, and extremes such as glacial and landslide lake outburst floods, have received less attention. Recent advances in dendrochronology, including blue intensity (BI), quantitative wood anatomy (QWA), and tree-ring stable isotopes (TRSI) should be combined to improve the resolution and accuracy of hydrological reconstructions in all parts of the Himalaya. Such studies may allow us to better understand the effects of climate change and the Himalayan water resources for its lowland surroundings. They may also facilitate decision-making processes for mitigating the impacts of climate change on natural hazards, and for better managing water resources in the region. [ABSTRACT FROM AUTHOR]

This study innovatively assesses the Mun River flow components in Thailand, integrating artificial neural networks (ANNs) and isotopic (δ18O) end-member mixing analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. The ANN method predicts that the UMR and CR contribute approximately 70.5% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings emphasize the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning. [ABSTRACT FROM AUTHOR]

Hydrology plays a crucial role in understanding Earth's intricate water system and addressing water‐related problems, including against the backdrop of ongoing climate change. A retrospective review of the evolution of hydrology up to the current state of research is of great importance for understanding this role. While there have been some quantitative reviews of large numbers of hydrological publications, there still remains a lack of overarching hydrological research assessment, particularly with the focus on hydrological basins as fundamental spatial‐geographic units of hydrological analysis. Large language models, represented by OpenAI's ChatGPT, have demonstrated powerful textual understanding capabilities, making it possible to extract such overarching and basin information from hydrological publications. Here, we considered publications related to hydrology from Web of Science spanning January 1980 to October 2023, and parsed the information from this extensive body of literature by integrating a large language model and geocoding. These techniques enable quantitative analysis of research characteristics across different spatio‐temporal scales, focusing on hotspot topics, collaboration networks, and various basins worldwide. Our study revealed an increase in hydrological research since the 1990 s, with shifts in research priorities from groundwater and nutrients to climate change and ecohydrology. Some basins in North America and Europe have consistently been hotspots for hydrological research. Since the 2010s, there has been a noteworthy increase in interest toward basins in China and South Asia, but attention to many regions with frequent extreme rainfall remains insufficient. Geographical patterns show different preferred research topics for different basins, but climate change has emerged as the most prominent topic across all regions in the last decade. In conclusion, our study provides an effective approach to quantitative analysis of research trends, offering a fresh view on the evolution of hydrology as a research field, its focus on various hydrological basins around the world, and the emergence of overarching and basin‐specific hot topics over time. Key Points: We explore the literature review capability of a large language model combined with geocoding over the whole field of hydrological researchWe quantify the evolution of hydrological research, hotspot topics, collaboration networks, and basins in focus, worldwide over timeHydrological research publication exhibits major and accelerating growth with WRR emerging as the top influential journal over time [ABSTRACT FROM AUTHOR]

Accurate snow phenology detection, including snow cover days (SCD), snow start date (SSD), and snow end date (SED), is increasingly important for understanding mountain hydrology such as snow heterogeneity and snowmelt seasonality. Multiple cloud‐free daily snow cover products have recently been developed in China, employing diverse retrieval algorithms and cloud‐gap‐filling methods, resulting in varying accuracy levels. However, comprehensive analysis of differences among products and their impact on snow phenology detection is lacking. This study systematically evaluates eight state‐of‐the‐art snow cover products in China, focusing on the challenging Tibetan Plateau (TP). We introduce a novel metric, the consistency‐weighted correlation coefficient (CWR), customized for SSD and SED detection, and propose product‐combining schemes like "ensemble voting" and "sensor preference" to enhance reliability. Our findings highlight the prime influence of retrieval algorithms under clear‐sky conditions on accuracy, surpassing the importance of cloud‐gap‐filling methods. Specifically, a product optimizing normalized difference snow index thresholds for diverse landcover types consistently outperforms others in detecting all three snow phenology parameters, with correlation coefficients for SCD of 0.82 and 0.69, and CWR values for SSD of 0.54 and 0.40, and for SED of 0.53 and 0.37 in both China and the TP, respectively. Moreover, our proposed scheme combining three high‐accuracy products significantly enhances snow cover identification and SCD detection, especially when the best‐performing product alone faces substantial uncertainty. These findings provide immediate, crucial implications for optimizing the use of multiple cloud‐free products to enhance snow phenology detection, ultimately advancing the applicability of derived snow parameters in mountain hydrology research. Key Points: Performances of eight state‐of‐the‐art cloud‐free products and their combinations in detecting snow phenology parameters are evaluatedImpacts of retrieval algorithm, cloud‐gap‐filling method, and sensor differences are considered for analyzing products' accuracy differencesProposed scheme combining three high‐accuracy products notably enhances snow cover days detection but including less accurate ones degrades [ABSTRACT FROM AUTHOR]

We thank the commentators for discussing their viewpoints on the subject; the majority converge with our ideas, while a few others need clarity and which we have responded to. We also applaud the works of the commentators in Oceania Region (OR), especially the decades of work by Professors Terry and Weber, which surely has improved scientific and societal research education in the OR. Here we respond to the commentary. [ABSTRACT FROM AUTHOR]

We thank the commentators for discussing their viewpoints on the subject; the majority converge with our ideas, while a few others need clarity and which we have responded to. We also applaud the works of the commentators in Oceania Region (OR), especially the decades of work by Professors Terry and Weber, which surely has improved scientific and societal research education in the OR. Here we respond to the commentary. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive analysis of groundwater pollution in the Lower Anayari Catchment (LAC) through δ2H and δ18O isotopic analysis, along with positive matrix factorization (PMF) and PCS-MLR receptor models. Forty groundwater samples were collected from hand-dug wells and equipped boreholes across the LAC. Flame photometry for Na+ and K+, complexometric titration for Ca2+, ion chromatography for Cl−, F−, NO3−, SO42−, and PO43−, and atomic absorption spectrometry for Mg2+, Fe, Pb, Cd, As, and Ni were analytical techniques/instruments employed. In regard to cations, Na+ has the highest average concentration of 63.0 mg/L, while Mg2+ has the lowest at 2.58 mg/L. Concerning the anions and nutrients, Cl− has the highest mean concentration of 18.7 mg/L, and Fl− has the lowest at 0.50 mg/L. Metalloids were detected in trace amount with Fe displaying the highest mean concentration of 0.077 mg/L whereas Cd and As recorded lowest (0.001 mg/L). The average values for groundwater δ18O and δ2H were − 3.64‰ and − 20.7‰, respectively; the average values for rainwater isotopic composition were − 3.41‰ for δ18O and − 17.4‰ for δ2H. It is believed that natural geological features, particularly biotite granitoid and volcanic flow/subvolcanic rocks from the Birimian Supergroup, significantly influence groundwater mineralisation. Additionally, the impact of anthropogenic activities on water quality, with urban development and agricultural practices, may be attributed to increasing levels of certain contaminants such as Fe, Ni, NO3−, and PO43−. This research contributes to the broader field of hydrological study and provides practical implications for managing and conserving water resources in similar contexts. The innovative combination of isotopic and statistical analyses sets a new standard for future studies in groundwater quality assessment, emphasising the need for comprehensive approaches that consider both geological characteristics and human impacts for sustainable water resource management. [ABSTRACT FROM AUTHOR]

The Source Region of the Yellow River (SRYR), renowned as the "Water Tower of the Yellow River", serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model's proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m3/m3. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography. [ABSTRACT FROM AUTHOR]

Background and Objectives: Runoff is the main variable for the hydrological analysis of the watershed, and due to its importance, for several decades, hydrological research has focused on the simulation of rainfall-runoff relationships, which has led to the presentation of many models. Due to the multiplicity of hydrological models, choosing an optimal model among various models is not a simple process. For this purpose, in the present research, after selecting the Galikash watershed from the most flood-prone basins in Golestan province, the performance of three hydrological models AWBM, Tank, and IHACRES were evaluated. The parameters of the models were also analyzed for sensitivity, and finally, the efficiency of the models in wet and dry periods was examined. Materials and Methods: The amount of daily runoff from the watershed for a period of 30 years (1989-2019) was simulated using each of the mentioned models and using four criteria Nash-Sutcliffe evaluation coefficient, root mean square error, coefficient of determination, and mean absolute percentage error, the performance of each model has been checked in two periods of calibration and validation. After optimizing the values of all the parameters, the sensitivity of the parameters of each model has been analyzed. Finally, after specifying the drought condition with the SPI index, the performance of each model for two wet and dry periods has been investigated and evaluated. Results: The results indicate that two rainfall-runoff models, IHACRES and AWBM, have almost similar performance. IHACRES model with Nash-Sutcliffe coefficients of 0.73 and 0.75 and RMSE of 2.97 and 2.94, respectively, in two calibration and validation periods and the AWBM model with Nash-Sutcliffe coefficients of 0.74 and 0.69 and RMSE of 2.92 and 3.24 for the calibration and validation periods have shown good performance. Still the Tank model was not successful in simulating the watershed runoff and its performance is lower than the two other models. The sensitivity analysis of the model parameters also showed that Kbase, H11, and f parameters are the most sensitive to the change of their values in AWBM, Tank, and IHACRES models, respectively. Finally, the comparison of the performance of the models in wet and dry periods showed that all the models have succeeded in simulating the watershed runoff with high accuracy in the wet period, so that the Nash-Sutcliffe coefficient is 0.79, 0.74 and 0.78 for the three AWBM, Tank and IHACRES models, respectively, shows the acceptable performance of the models in simulating the runoff in the wet period. The evaluation of the results has shown the poor performance of all models in the dry period, and the Nash-Sutcliffe coefficient obtained for the models is -0.05, -0.45, and 0.12, respectively, which shows the weakness of the models in simulation of the low flow. Conclusion: In the evaluation of the three hydrological models AWBM, Tank, and IHACRES in daily runoff simulation, it was found that in general, with a small difference, the IHACRES model shows better results than the AWBM model. Also, in wet periods, according to the evaluations, the AWBM model led to good accuracy, while the IHACRES model showed better performance than other models in dry periods. Considering this issue, it can be said that the models performed weaker in simulating low flows that occur during dry periods, while the knowledge of streamflow conditions during dry periods can play an effective role in managing water resources. Therefore, to increase their accuracy, a solution should be found. [ABSTRACT FROM AUTHOR]

This study assesses the impact of different flux parameterizations and model parameters on simulations of snow depth. Through a sensitivity analysis in a process-based snow model based on the SUMMA framework, various options for parametrizing snow processes and adjusting parameter values were evaluated to identify optimal modeling approaches, understand sources of uncertainty, and determine reasons for model weaknesses. The study focused on model parameterizations of precipitation partitioning, liquid water flow, snow albedo, atmospheric stability, and thermal conductivity. In this study, sensitivity analysis (SA) is performed using the one-at-a-time (OAT) SA method as well as the Morris Method to estimate Elementary Effects, aiming to further explore the magnitudes and patterns of sensitivities. The sensitivity analyses in this study are used to evaluate process parameterizations, model parameter values, and model configurations. Performance metrics such as the Nash-Sutcliffe Efficiency (NSE), the Kling-Gupta Efficiency (KGE), the root mean squared log error (RMSLE), and mean are used to assess the similarity between simulated and observed data. Bootstrapping is employed to estimate the variability of mean Elementary Effects and establish confidence bounds. The key findings of this research indicate that sensitivity analysis of snow modelling parameters plays a crucial role in understanding their impact on decision outcomes. The study identified the most sensitive parameters, such as critical temperature and thermal conductivity of snow, as well as liquid water drainage parameters. It was observed that water balance fluxes exhibited higher sensitivity than energy balance fluxes in simulating snow processes. The analysis also highlighted the importance of accurately representing water balance processes in snow models for improved accuracy and reliability. A key finding in this study is that the sensitivity of performance metrics to model parameters is contaminated by equifinality (i.e., parameter perturbations lead to similar performance metrics for quite different snow depth time series), and hence many published parameter sensitivity studies may provide misleading results. These findings have implications for snow hydrology research and water resource management, providing valuable insights for optimizing snow modelling and enhancing decision-making. [ABSTRACT FROM AUTHOR]

Long-term gridded precipitation products (GPPs) are crucial for climatology and hydrological research to overcome the limitations of gauge observations. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) provides long-term daily precipitation data over the globe from 1981 to near-present, but its reliability varies across regions. This review aims to summarize the performance of CHIRPS from 123 research articles that mainly published between 2015 and 2021.The findings show that the number of CHIRPS validation studies has been increased dramatically in the past few years. These studies were primarily conducted in China, Ethiopia, Kenya, Uganda, and India, while a relatively few studies in North America, Central Asia, and Europe. The performance of CHIRPS varied depending on geographical location and climate condition, with greater performance in Africa. In contrast to other GPPs, CHIRPS is always not the best product, but it is considerably well in capturing monthly precipitation and is suitable for assessing drought. But, there are some common limitations such as less effectiveness across regions with sparse gauges and complex terrain and difficulty in detecting extremely high precipitation events. Future research directions on this topic should focus on (1) enhancing CHIPRS through bias correction and downscaling, (2) validating CHIRPS for extreme indices' calculations and relate to large-scale atmospheric circulations like ENSO, (3) evaluating the capability of CHIRPS in hydrological modelling, and (4) further validating CHIRPS under various topographical and climate conditions as well as other precipitation products. This review can act as a reference to scientists who wish to apply CHIRPS in their climatology analysis and hydroclimatic modelling as well as the developers to further improve the product. [ABSTRACT FROM AUTHOR]

The article offers information on the second edition of "Día de los Montes" celebrated in Castilla y León to highlight the role and significance of forest engineers. Topics include activities in various provinces such as hydrological-forest restoration, exploration of local fauna, historical forest recovery, and current timber and resin exploitation, along with a photography contest capturing the day's events.

Systematic literature reviews can provide an objective global overview of background research on hydrological questions. This study presents a methodology to ensure rigor, traceability, and replicability in evaluating state-of-the-art hydrological topics. The proposed methodology involved systematic, objective, and explicit steps. Strategies with defined selection criteria were used to search the relevant literature comprehensively and accurately on hydrological uncertainty as a case study. The most pertinent documents were filtered to build a critical state-of-the-art synthesis for evaluating their quality and relevance. This methodology allows systematic literature analysis to provide an objective summary of the evidence, with a structured procedure for state-of-the-art reviews, which promotes transparency in the search, unlike typical review papers that lack reproducible methodologies that may lead to obtaining a potentially subjective reference selection. Consequently, the proposed methodology improves reference reliability and study reproducibility while generalizing the methodology. This procedure has proven effective and practical for building state-of-the-art research in the study case (State-of-the-art Uncertainty in Hydrological forecasting from 2017 to 2023) and can be considered a relevant tool in hydrology and other STEM disciplines, providing a rigorous and transparent approach that enhances evidence quality and reliability. [ABSTRACT FROM AUTHOR]

In hydrological research, data assimilation (DA) is widely used to fuse the information contained in process‐based models and observational data to reduce simulation uncertainty. However, many popular DA methods are limited by low computational efficiency or their reliance on the Gaussian assumption. To address these limitations, we propose a novel DA method called DA(DL), which leverages the capabilities of DL to model non‐linear relationships and recognize complex patterns. DA(DL) first generates a large volume of training data from the prior ensemble, and then trains a DL model to update the system knowledge (e.g., model parameters in this study) from multiple predictors. For highly non‐linear models, an iterative form of DA(DL) can be implemented. Additionally, strategies of data augmentation and local updating are proposed to enhance DA(DL) for problems involving small ensemble size and the equifinality issue, respectively. In two hydrological DA cases involving Gaussian and non‐Gaussian distributions, DA(DL) shows promising performance compared to two ensemble smoother methods, that is, ES(K) and ES(DL), which respectively apply the Kalman‐ and DL‐based updates. Potential improvements to DA(DL) can be made by designing better DL model architectures, imposing physical constraints to the training of the DL model, and further updating other important variables like model states, forcings and error terms. Key Points: Many popular data assimilation (DA) methods are constrained by the Gaussian assumption or suffer from low computational efficiencyWe propose a novel DA method called DA(DL) based on deep learning (DL)DA(DL) shows promising performance in problems involving non‐linearity, high‐dimensionality and non‐Gaussianity [ABSTRACT FROM AUTHOR]

Against the backdrop of global climate warming, the issue of flash flood disasters in small watersheds triggered by heavy rainfall is gradually becoming more prominent. Selecting an appropriate hydrological model is crucial for flash flood disaster defense. This article focuses on the Daxi Water Basin in Lianping County, Guangdong Province, as the research area. Firstly, organize the data and subject it to standardization processing. Subsequently, establish the topological relationships within the basin, construct a hydrological model for simulating flood processes in Chinese mountainous regions, and obtain a set of model parameters applicable to the specific basin. The results indicated that: ① the relative errors of flood runoff depth were all less than 7%, with an average of 4.5%; ② the relative errors of peak flow for all events were less than 6%, with an average of 4.2%; ③ peak time errors were all within ±2 h, either earlier or later than the actual peak by 1 h; ④ the Nash-Sutcliffe efficiency coefficient for floods were all greater than 0.8, with an average of 0.86. The research results above will serve as a reference and guidance for flood defense management in the Daxi Water Basin. [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn , last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

So far, research with the hydrological model BlueM.Sim has been focused on reservoir management and integrated river basin modeling. BlueM.Sim is part of the official toolset for estimating immissions into rivers in Hesse (Germany) via long-term continuous modeling. Dynamic runoff modeling from rural catchments is permitted within the Hessian guidelines, but in practice, a constant flow or low flow is used. However, due to increasing water stress in the region caused by climate change, the dynamic modeling of runoff from rural catchments will become necessary. Therefore, dynamic baseflow modeling with BlueM.Sim is of the greatest importance. This study evaluated baseflow modeling with BlueM.Sim in a representative hard-rock aquifer in the German Low Mountain range. Two model setups (Factor Approach (FA): CN method + monthly baseflow; Soil Moisture Approach (SMA): physical soil moisture simulation) were calibrated (validated) for a 9-year (5-year) period. The FA achieved an NSE of 0.62 (0.44) and an LnNSE of 0.64 (0.60) for the calibration and validation periods. The selection of a solution for the successful validation of the FA was challenging and required a selection that overestimated baseflow in the calibration period. This is due to the major disadvantage of the FA, namely, that baseflow can only vary according to an estimated yearly pattern of monthly baseflow factors. However, the data requirements are low, and the estimation of monthly baseflow factors is simple and could potentially be regionalized for Hesse, leading to a better representation of baseflow than in current practice. The SMA achieved better results with an NSE of 0.78 (0.75) and an LnNSE of 0.72 (0.78). The data requirements and model setup are extensive and require the estimation of many parameters, which are limitations to its application in practice. Furthermore, a literature review has shown that a single linear reservoir, as in BlueM.Sim, is not optimal for modeling baseflow in hard-rock aquifers. However, for detailed climate change impact studies in the region with BlueM.Sim, the SMA should be preferred over the FA. It is expected that BlueM.Sim would benefit from implementing a more suitable model structure for baseflow in hard-rock aquifers, resulting in improved water balance and water quality outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Water Resources Journal / Revue Canadienne des Ressources Hydriques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Water Resources Journal / Revue Canadienne des Ressources Hydriques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences. [ABSTRACT FROM AUTHOR]

Accurate computation of peak flow in rivers is vital in preventing environmental degradation. This study developed stochastic models for computing annual peak flows in the Mkhomazi River, South Africa. Six curves of selected functions (Normal, Log–Normal, Pearson III, Log–Pearson III, Gumbel and Log–Gumbel) were fitted to the annual duration series of the at–site annual peak flow of the river. Chi–square tests for goodness–of–fit were performed at 10% significance level to determine the appropriateness of all models in describing the observed data. The non–parametric significant differences in the performances of applicable models found was examined by applying the Kruskal–Wallis (H) test. A system of ranking based on aggregate of scores from statistical performance measures was adopted to suggest a most suitable model. The Normal, Pearson, Log–Pearson and Gumbel models were found suitable for application in the study area. These models are recommended for use in modeling peak flow in the river as they were found appropriate based on standard statistical test. Thus, the outcome of this work provides additional inputs for the improvement of hydrological researches in South Africa. [ABSTRACT FROM AUTHOR]

Coupling hydrological modelling systems (HMS) with a geographic information system (GIS) can significantly enhance hydrological research and expand its applications. The calculation for HMS requires geographic information data; however, the current GIS data structure is not equipped to support the object-oriented hydrological modelling. Due to different objectives and design concepts, the differences between HMS and GIS have been profound, especially in their data structures from the perspective of object-oriented programming (OOP). This research introduces a novel approach to extend ArcGIS data structures for HMS, facilitating seamless coupling. This approach employs Microsoft Component Object Model (COM) technology to construct custom data structures that align with hydrological OOP principles. These can then be integrated into ArcGIS through a custom ArcGIS layer as an add-on. As a result, the HMS can leverage the full functionality of ArcGIS without the need for re-coding existing modelling systems. Moreover, HMS can be readily developed by using COM compatible computer languages, enabling the easy adaptation of this coupling approach to other HMS to ensure computational efficiency and to maximise the benefits of ArcGIS features. This new approach has been successfully implemented with the Xin'anjiang model, and the results validate its effectiveness in coastal areas. [ABSTRACT FROM AUTHOR]

This paper provides an assessment of hydrological research activity conducted in the Oceania Region. Literature from three databases (Web of Science (WOS), Scopus, and Google Scholar) was collected and analysed. Results show large gaps in the variety of themes and poor collaborative efforts between regional water specialists. Results also suggest that local specialists potentially lack the ability to conduct quality hydrological research or that, due to the region's developing status, more focus has been placed on project-based objectives rather than building a body of research. A solution could be the development of a self-reliant base for conducting hydrological research by empowering and improving capabilities of local citizens. Firstly, an overhaul of organization policies regarding the introduction of research and performance monitoring is needed. Secondly, education in hydrology must be improved to suit local capabilities while addressing the complex socio-cultural environment with transdisciplinary measures. This assessment could benefit the future management of hydrological services. [ABSTRACT FROM AUTHOR]

This study examines all of the equations necessary to derive the parameters for seven probability distributions of three parameters typically used in flood frequency research, namely the Pearson III (PE3), the generalized extreme value (GEV), the Weibull (W3), the log-normal (LN3), the generalized Pareto Type II (PG), the Rayleigh (RY) and the log-logistic (LL3) distributions, using the higher-order linear moments method (LH-moments). The analysis represents the expansion of previous research whose results were presented in previous materials, and is part of hydrological research aimed at developing a standard for calculating maximum flows based on L-moments and LH-moments. The given methods for calculating the parameters of the examined distributions are used to calculate the maximum flows on Romania's Prigor River. For both methods, the criterion for selecting the most suitable distribution is represented by the diagram of the L-skewness–L-kurtosis and LH-skewness–LH-kurtosis. The results for Prigor River show that the PG distribution is the best model for the L-moments method, the theoretical values of the statistical indicators being 0.399 and 0.221. The RY distribution is the best model for the LH-moments technique, with values of 0.398 and 0.192 for the two statistical indicators. [ABSTRACT FROM AUTHOR]

The impact of agricultural activities on water quality has become a growing concern in recent years, with diffused pollution from agricultural runoff emerging as a significant contributor to surface water degradation. This comprehensive study delves into the evaluation of diffused pollution in surface waters resulting from agricultural runoff, an assessment of its impact on water quality, identification of pollution sources, and the evaluation of mitigation measures. The study unfolds through meticulous data collection, laboratory analysis, hydrological modelling, ecological impact assessment, and human health implications analysis. Key findings include seasonally varying water quality parameters, favourable soil health indicators, and the effectiveness of land management strategies in reducing pollution. The analysis also underscores the need for ongoing monitoring and proactive measures to protect water resources. This research offers a holistic perspective on the multifaceted challenges posed by diffused pollution in agricultural areas. [ABSTRACT FROM AUTHOR]

Based on deep learning, precipitation prediction has been a hot topic in hydrological research in recent years. Monthly precipitation data is a typical small sample data that cannot meet the demands of deep learning for large datasets. Therefore, this paper proposes an improved method for predicting monthly precipitation using a Long Short-term Memory Network (LSTM) by integrating the ideas of signal decomposition and boundary correction. First, to address the " end effect" of sequence decomposition, this paper uses the waveform feature matching extension method to expand the boundaries of the original sequence. Then, the original sequence is decomposed twice by using Extreme-point Symmetric Mode Decomposition (ESMD) and Variational Mode Decomposition (VMD). ESMD is used to extract different sorts of scale information of the monthly precipitation sequence, obtaining several model components with decreasing frequencies and one residual component. After cutting off the most severe extension parts that "pollute" the internal data of each sub-sequence, VMD further smooths the high-frequency components. Finally, LSTM is applied to each sub-sequence for prediction, and the predicted results are reconstructed to obtain the final prediction. The monthly precipitation in Badong County, Hubei Province is selected as an example for verification. Through model comparison analysis, the results show that compared to traditional single SVM and LSTM models, the prediction model combining signal decomposition algorithm has more advantages in monthly precipitation prediction; The method of boundary correction is incorporated into the combined model by using the ESMD algorithm improves the overall prediction accuracy of the model. The prediction effect of high-frequency components is a key factor determining the accuracy of combined model prediction. The method proposed in this paper not only performs the best on the selected evaluation indicators but also significantly improves the fitting effect of extreme data points. In particular, even when facing a small sample dataset, the Nash-Sutcliffe Efficiency coefficient (NSE) of monthly precipitation prediction can reach 0.960 0 by using this model. [ABSTRACT FROM AUTHOR]

With high resolution and wide coverage, satellite precipitation products like the Multi-Source Weighted Ensemble Precipitation (MSWEP) could support hydrological research in arid regions, where the spatial heterogeneity of precipitation is high, but rain gauges are sparse, but these products are inevitably subject to errors. In this study, we integrated measured precipitation and topographic data, and improved MSWEP by interpolation and an optimal regression model. The Tianshan Mountain area was taken as a typical arid area, and spatial stratified heterogeneity of precipitation was analyzed. The results show that: (1) The optimal regression model combined the advantages of site interpolation and MSWEP and improved the underestimation of precipitation in mountainous areas; (2) The precipitation decreased from West to East, and from North to South, and the precipitation in West and East subregions increased linearly with the elevation. The maximum precipitation height zone in the North is located at 4300 m. There are two maximum precipitation height zones in the South; (3) The accuracy of spatial interpolation of station data was limited by the uneven distribution of the stations in the East subregion, and the inaccuracy of MSWEP was due to the land cover and topography of the North and South subregions. The improved MSWEP (IMMSWEP) not only resolved the problem of lack of rainfall stations in arid area, but also resulted in an inversion of regional spatial precipitation differentiation with high resolution and high precision, providing a reference for meteorological researchers. [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface-subsurface hydrological model that employs a finite volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma Watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://shuddata.com), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

The gravity recovery and climate experiment (GRACE) has emerged as a crucial source of land water storage information in hydrological analysis and research. Numerous factors contribute to regional terrestrial water storage (TWS), resulting in a complex mechanism. In the Loess Plateau region, the continuous alteration of natural conditions and profound impact of human activities have posed a serious threat to the natural ecosystem, leading to an escalating trend of TWS reduction. Addressing the specific analysis of how natural conditions and human activities affect TWS represents a pressing issue. This study employed the residual analysis method to discern the contribution rates of natural conditions and human activities, elucidated the spatial and temporal changes associated with each factor, and ascertained their individual influence. The findings indicated that TWS on the Loess Plateau exhibited a downward trend of −4.89 mm·a−1 from 2003 to 2017. The combined effects of climate change and human activities accounted for alterations in water resource reserves across most areas of the Loess Plateau, with human activities predominantly driving these changes. Precipitation emerged as the primary natural factor influencing TWS variations, and NDVI demonstrated a positive feedback effect on TWS at approximately 30%. Substantial spatial disparities in TWS existed within the Loess Plateau, with human activities identified as the primary cause for the decreasing trend. Vegetation restoration plays a positive role in saving water resources in the Loess Plateau to some extent, and vegetation growth exceeding the regional load will lead to water shortage. [ABSTRACT FROM AUTHOR]

The need to increase understanding of the impacts of changing natural and anthropogenic drivers on hydrological processes on local and regional scales is an essential prerequisite for advancing hydrology and a precondition for solving water resources management tasks. Slovakia exhibits abundant spatial and temporal variability of hydrological processes complicating the generalisation of runoff regimes. Changing climate and recent extreme floods and droughts put additional pressure on improving the observing, monitoring, describing, and modelling of hydrological processes. This paper reviews the response of hydrologic research in Slovakia to these challenges published in international journals from 2019 to 2022. It continues the practice of publishing the review part of the Slovak National Reports to IUGG on behalf of the IAHS (Szolgay, 2003; 2007; 2011; 2015; 2019), which follows the custom of National IUGG Committees to prepare a Quadrennial Report for the IUGG General Assemblies containing comprehensive summaries of national activities in geodesy and geophysics (Szolgay, et al., 2023). [ABSTRACT FROM AUTHOR]

Tipping bucket rain gauges (TBRs) continue to be one of the most widely used pieces of equipment for rainfall monitoring; they are frequently used for the calibration, validation, and downscaling of radar and remote sensing data, due to their major advantages—low cost, simplicity and low-energy consumption. Thus, many works have focused and continue to focus on their main disadvantage—measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks' operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques, summarizing TBR measurement uncertainties, focusing on calibration and error reduction strategies, discussing the state of the art and providing future perspectives of the technology. [ABSTRACT FROM AUTHOR]

Research in hydrological sciences is constantly evolving to provide adequate answers to address various water-related issues. Methodological approaches inspired by mathematical and physical sciences have shaped hydrological sciences from its inceptions to the present day. Nowadays, as a better understanding of the social consequences of extreme meteorological events and of the population's ability to adapt to these becomes increasingly necessary, hydrological sciences have begun to integrate knowledge from social sciences. Such knowledge allows for the study of complex social-ecological realities surrounding hydrological phenomena, such as citizens' perception of water resources, as well as individual and collective behaviors related to water management. Using a mixed methods approach to combine quantitative and qualitative approaches has thus become necessary to understand the complexity of hydrological phenomena and propose adequate solutions for their management. In this paper, we detail how mixed methods can be used to research flood hydrology and low-flow conditions, as well as in the management of these hydrological extremes, through the analysis of case studies. We frame our analysis within the three paradigms (positivism, post-positivism, and constructivism) and four research designs (triangulation, complementary, explanatory, and exploratory) that guide research in hydrology. We show that mixed methods can notably contribute to the densification of data on extreme flood events to help reduce forecasting uncertainties, to the production of knowledge on low-flow hydrological states that are insufficiently documented, and to improving participatory decision making in water management and in handling extreme hydrological events. [ABSTRACT FROM AUTHOR]

This paper aims to address the challenges of short optical communication distances and high Bit Error Rates (BERs) caused by seawater absorption, scattering, and underwater turbulence. This paper establishes an optical communication channel model based on the Monte Carlo method and simulates and analyzes the process of received optical power attenuation when light is transmitted underwater due to absorption and scattering. The simulation model focuses on the influence of seawater type, transmission distance, receiver position, and angle on the received optical power attenuation. The results of this simulation will provide theoretical support for the development of prototype underwater optical communication systems and experiments in complex environments. To solve the long-range weak signal reception and processing issues in underwater optical wireless communication systems, this paper proposes the design and implementation of an underwater optical wireless communication system using optimized on-off keying high-power light-emitting diode/laser diode modulation technology and avalanche photodiode (APD)/photomultiplier (PMT) receiver modules. In addition, this paper describes the design of the underwater optical communication host computer software, which provides functions such as mode selection, BER monitoring, and file transfer. To test the performance of the proposed system, this paper conducts experiments in a 35 m long pool, fixed platform, and underwater mobile platform, as well as cross-air-water interface optical communication performance test experiments in a complex hydrological environment. The results show that the system achieved a 35 m underwater medium communication distance, transmission rate of 5–20 Mbps, and cross-domain wireless optical communication function under the condition of a BER of less than 10−6. The experiments also revealed that the communication distance decreases as the turbidity of the water body increases and the PMT receiver module selected at the receiver end has a longer communication distance than the APD receiver module. Overall, this paper demonstrates the feasibility of the proposed technology for underwater optical communication systems in complex waters, providing theoretical and data support for the next engineering practice. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The spatiotemporal patterns of key hydrological variables across China were illustrated based on the developed Water and Energy Transfer Processes model in China (WEP-CN model). Time series of four key hydrological variables, namely, precipitation (P), runoff (R), infiltration (Inf), and actual evapotranspiration (ETa), were obtained over 60 years. Then, the temporal trends and spatial differences of these variables were analyzed using the Mann-Kendall and linear methods on a national scale and on the water resource regional scale. Moreover, we explored the drivers and constraints for changes in R, Inf, and ETa. The results showed: (1) Based on the coefficient of variations of P (5.24%), R (11.80%), Inf (2.57%), and ETa (3.77%), R was more fluctuating than the other variables. (2) These variables followed a similar trend of gradually decreasing from the southeast coast to the northwest inland. (3) Changes in R and Inf were caused mainly by P, having correlation coefficients with precipitation of 0.74 and 0.73, respectively. The ETa was constrained by a combination of P and energy. The results improved the refined and quantitative research on hydrological processes in China, identified the differences in hydrological variables between water-resource regions, and provided a useful supplement to the research of the large-scale hydrological process. [ABSTRACT FROM AUTHOR]

THE REMARKABLE RISE OF PROCESS HYDROLOGY IN CHINA Gu was at the leading edge of process hydrology in China during its remarkable ascent in the past several decades. Through the 1980s and 1990s, Gu authored a number of papers in the Chinese and English literature (Gu, [4]; Gu, [5]; Gu, [6]; Gu, [7]; Gu, [8]; Gu, [9], [10] - summarized in an important review paper in Gu et al., [11]) that showed - in many cases for the first time - the controls on the spatial and temporal evolution of soil water, groundwater and streamflow from the catchment. WEI-ZU GU AND HYDROHILL Gu was a research professor at the Nanjing Institute of Hydrology and Water Resources when he set up the Chuzhou Hydrology Laboratory and now-famous Hydrohill catchment. Following the USGS delegation to China, the USGS team reciprocated, and Gu came to the United States about a year later and toured the Mattole watershed, the Panola watershed in Georgia and other USGS research sites. [Extracted from the article]

An important decision in soil hydrological research is whether to conduct experiments outdoors or indoors. Both approaches have their advantages and trade-offs. Using undisturbed soil monoliths combines some of the advantages of outdoor and indoor experiments; however, there are often size limitations. Acquiring large monoliths necessitates heavy machinery, which is time-, cost-, and labor-intensive. Small- to medium-sized soil blocks, however, can be obtained using less demanding methods. A promising approach is the combination of smaller blocks to form a single large monolith, thereby optimizing cost and labor efficiency as well as representativity and upscaling potential. To this end, we compared the runoff properties of medium-sized (1×0.5×0.35 m) grassland soil monoliths cut in half and recombined with uncut blocks. We conducted artificial runoff experiments and analyzed the chemical composition and amount of outflow from four flow pathways (surface runoff, subsurface interflow, percolating water, lateral flow). Furthermore, we studied surface runoff velocity parameters using a salt tracer. Our results suggest that the effects of the recombination procedure are negligible compared to the variation in the data caused by the inherent soil heterogeneity. We propose that the benefits of combining soil monoliths outweigh the potential disadvantages. [ABSTRACT FROM AUTHOR]

High-quality gridded data on industrial water use is vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, namely, the China industrial water withdrawal dataset (CIWW), which spans a 56-year period from 1965 to 2020 at a spatial resolution of 0.1° and 0.25°. We utilized >400,000 records of industrial enterprises, monthly industrial product output data, continuous statistical IWW records from 1965 to 2020, to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in the developing the dataset. The CIWW dataset presented significant improvement in characterizing the spatial and seasonal patterns of IWW dynamics in China, with a much higher accuracy at fine scale while ensuring consistency with statistical records. The CIWW dataset, together with its methodology, and auxiliary data, is useful for water resource management and for research in hydrology, geography, environment, and sustainability sciences. This new dataset is now available at https://doi.org/10.6084/m9.figshare.21901074.v1 (Hou and Li, 2023). [ABSTRACT FROM AUTHOR]

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: "Tipping points and thresholds in hydrology," "Intensification of the water cycle," and "Water services under pressure." Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement. [ABSTRACT FROM AUTHOR]