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The inland river basin in Hexi, Gansu Province, is extremely short of water resources. There are certain differences in the changes of mountain runoff between the central and western and eastern water systems in the basin. Studying the changes in mountain runoff and the contribution rates of different factors is of great significance for ensuring the sustainable development and utilization of regional water resources. Based on the data of mountain runoff and precipitation and temperature in the main runoff producing areas from 1956 to 2021, various trends and mutation tests, cumulative slope change rate methods, and combined with the characteristics of runoff supply, the changes in mountain runoff and their responses to changes in precipitation and temperature in the three major water systems were analyzed. The results show that: 1 the average annual mountain runoff of the Shiyang River system was 1.36 billion m³, showing a non-significant decreasing trend over the years and a non-significant increasing trend in precipitation, The significant increase in temperature leads to an increase in evaporation, resulting in a decrease in runoff; 2 The average annual runoff from the Heihe River system has increased from 3.062 billion m³ before the mutation point in 2006 to 3.56 billion m³ after the mutation point. The contribution rate of precipitation to runoff changes is 16.7%, and the temperature has significantly increased, enhancing the impact on runoff changes; 3 The average annual runoff from the Shule River system has increased from 1.422 billion m3 before the mutation point in 1998 to 17.93 m3 after the mutation point. The impact of precipitation and temperature on runoff changes is similar, with contribution rates of 30.4% and 30.2%, respectively. There are certain differences in run-off supply characteristics and climate conditions among the three major inland river systems in Hexi, resulting in inconsistent responses of runoff to different driving factors. [ABSTRACT FROM AUTHOR]

Previously developed analytical stochastic models (ASMs) have the limitations in that they can only be individually applied for specific end-of-pipe or low impact development facilities. This paper proposed a framework of ASMs that can be used for easily analyzing different types of storm water control measures (SCMs). The effective storage capacity for either storage-based or infiltration-based control measures was defined and formulated. Various inflow and outflow patterns of specific SCMs were also considered and analyzed. This framework also provides better insights into the similarities and differences among the model structures of different SCMs. Case studies with six climatically different locations in China and the U.S., different soil properties and six types of SCMs demonstrated the effects of various factors on runoff reduction ratios. Despite the limitations of the proposed framework such as the assumption of exponential distribution for rainfall event characteristics and the unavailability to perform single rainfall event simulation, it can still be used as a convenient toolkit to make fast and comprehensive decisions in selecting different SCMs for a specific runoff control target in design practices. [ABSTRACT FROM AUTHOR]

Accurate forecasting of mid to long-term runoff is essential for water resources management. However, the traditional model cannot predict well and the precision of runoff forecast needs to be further improved. Here, we proposed a novel data-driven model aimed at enhancing the performance of the Gated Recurrent Unit (GRU) through the integration of Robust Local Mean Decomposition (RLMD) and the Slime Mould Algorithm (SMA). The objective is to improve mid to long-term runoff prediction in three hydrographic stations: Heishiguan, Baimasi, and Longmenzhen, located within the Yiluo River Watershed in central China. The model leverages monthly runoff data spanning from 2007 to 2022 to achieve this objective. The results indicated that (1) the new data-driven model (RLMD -SMA-GRU) had the highest monthly runoff prediction accuracy. Both RLMD and SMA can improve the accuracy of the model (NSE = 0.9466). (2) The precision of the models in wet season outperformed in dry season. (3) The hydrological stations with large discharge and stable runoff sequence have better forecasting effect. The RLMD-SMA-GRU model has good applicability and can be applied to the monthly runoff forecast at watershed scale. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN (WANN) and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale. [ABSTRACT FROM AUTHOR]

Runoff from glaciated catchments is an integrated process that includes glacier melt, snowmelt, rainfall and surface and subsurface runoff of meltwater from glacierized and non-glacierized areas. Monitoring and quantifying the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Himalayan basins is essential for decision-making in the water sector, particularly in water resources management and flood risk reduction in the region. An attempt has been made to characterize and hydrologically model streamflow (Bhagirathi River) for the Gangotri Glacier (Central Himalaya, India). A semi-distributed conceptual hydrological model is used for the streamflow modelling and assessing the major streamflow components (snow melt, glacier melt and rainfall runoff). Initially, the model was calibrated using the available in situ hydro-meteorological records for the ablation seasons of 2013–14 to 2015–16 (3 years), and further validated for the ablation seasons of 2016–17 to 2018–19 (3 years). The model performed well for all the studied years except for some months, where abrupt changes in the contrasting weather parameters (precipitation and temperature) were recorded. In the Gangotri Glacier Valley (upper Bhagirathi River catchment), snowmelt contributed the largest portion (55.5%) to total streamflow followed by glacier melt (29.7%) and rainfall runoff components (14.7%). [ABSTRACT FROM AUTHOR]

In rubber plantations, understory coverage is often disrupted by human activities, which increases the risk of soil erosion under intense rainfall typical of tropical islands. Evaluating the effectiveness of soil and water conservation measures (SWCMs) is crucial for effectively conserving subcanopy resources. This study focused on Hainan Island's rubber plantations, where nine different SWCMs were implemented, and the runoff and sediment yield were monitored during the rainy season using runoff plots. Through correlation analysis, we identified the primary rainfall characteristic factors leading to soil and water loss on rubber plantation slopes. Path analysis was then used to quantify the impacts of these characteristic factors. The results showed that the SWCMs were significantly more effective in erosion reduction (68.55%) than in runoff reduction (58.95%). Of all the measures, comprehensive SWCMs proved most effective in controlling runoff (71.34%), followed by engineering SWCMs (62.03%) and biological SWCMs (43.51%). Comprehensive SWCMs were also found to be effective in erosion reduction, with a rate of 77.84%, surpassing engineering and biological SWCMs by 7.23% and 20.66%, respectively. Notably, the combination of narrow terraces, contour trenches, and grass planting was the most effective, achieving runoff-reduction rates of 80.94% and erosion-reduction rates of 85.27%. This combination is recommended as a primary prevention method. Rainfall and maximum 30-min intensity (I30) were identified as key variables affecting the efficacy of SWCMs, with rainfall positively correlating with runoff yield and I30 being more closely linked to sediment production. This study provides valuable insights for developing erosion control strategies for sloping garden lands in similar regions and lays theoretical foundations for future ecological restoration projects. [ABSTRACT FROM AUTHOR]

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The identification of the onset and retreat dates of the monsoon season is a crucial and intricate phenomenon, given its annual spatiotemporal variability. The monsoon season contributes significantly to rainfall, replenishing water sources and hydrological systems but causes hydrological extremes, especially for the high‐altitude watersheds in Southeast Asia. Global Circulation Model (GCM)‐Coupled Model Intercomparison Project Phase 6 (CMIP6)‐based rainfall and temperature data are helpful for adequately representing present and future climate scenarios. However, the usability of uncorrected GCM‐CMIP6 datasets needs to be assessed regionally. This study focuses on identifying the best‐suited GCM‐CMIP6 based on the monsoon onset (MO) and retreat (MR) dates along with other climatological temporal parameters. A numerical definition for MO and MR has been formulated to find the best‐suited GCM‐CMIP6 (i.e., CMCC‐ESM2). In this context, runoff simulation is carried out using the meteorological inputs of the monsoon onset‐retreat (MOR)‐based best‐suited GCM to evaluate its usability. A multi‐model simulation approach has been carried out for runoff estimation based on observed datasets to find a better‐suited hydrological model. The proposed overall methodology is tested in a hydrological extreme‐prone ungauged watershed (i.e., Ranikhola). CMCC‐ESM2 and SSP2‐4.5 has been identified as best‐suited SSP based on statistical evolution (R2 [0.693], NSE [0.662] and RSR [0.581]) for future daily runoff prediction. Future hydrological analysis shows that the average monsoon peak runoff magnitude will increase from the calibrated period (2015–2020) by 18.01% in the coming years (2021–2049). [ABSTRACT FROM AUTHOR]

The article discusses the importance of setting expectations for hydrologic model performance using an ensemble of simple benchmarks. It highlights the limitations of traditional metrics and the need for objective benchmarks to assess model performance. The study demonstrates that relying on the mean annual flow as a benchmark is insufficient, and an ensemble of simple benchmarks provides a more accurate context for model performance evaluation. The use of benchmark models helps in understanding regional differences in model predictability and provides a basis for comparing model performance against simpler models. [Extracted from the article]

Flood forecasting in data‐scarce catchments is challenging for hydrologists. To address this issue, a regional long short‐term memory model (R‐LSTM) is proposed. Given the diverse physical characteristics of sub‐catchments, this model scalarises the runoff data based on catchment attributes including area, confluence path length, slope and minimum and maximum runoff values, thereby eliminating the local influence and producing a geomorphological‐runoff factor as the model input. To assess the effectiveness of R‐LSTMs for flood forecasting in data‐scarce basins, the Jiaodong Peninsula in China was selected as the study area. The proposed R‐LSTMs are compared with local LSTMs, regional LSTMs that do not use catchment attributes, or regional LSTMs that incorporate catchment attributes in different ways. The results show that R‐LSTMs outperform the benchmarking LSTM models, especially in the simulation of flood peaks. The study indicates the potential of regionalization and the benefit of building the scalarised inputs of runoff data for regional LSTM that consider catchment attributes meticulously. The research findings can provide a reference for flood forecasting in data‐scarce regions. [ABSTRACT FROM AUTHOR]

Topography is a key control on runoff generation, as topographic slope affects hydraulic gradients and curvature affects water flow paths. Simultaneously, runoff generation shapes topography through erosion, affecting landscape morphology over long timescales. Previous modeling efforts suggest that subsurface hydrological properties, relative to climate, are key mediators of this relationship. Specifically, when subsurface transmissivity and water storage capacity are low, (a) saturated areas and storm runoff should be larger and more variable, and (b) hillslopes shorter and lower relief, assuming other geomorphic factors are held constant. However, it remains uncertain whether subsurface properties can exert such strong controls on emergent properties in real landscapes. We compared emergent hydrological function and topography in two watersheds with very similar climatic and tectonic history, but very different subsurface properties due to contrasting bedrock lithology. We found that hillslopes were systematically shorter and saturated areas more dynamic at the lower transmissivity site. To test whether these features could be the result of coevolution between topography, hydrological function, and subsurface properties, we estimated all parameters of a coupled groundwater‐landscape evolution model for each site. Limitations were revealed in the model's ability to reproduce aspects of morphology and hydrologic behavior, however, model results suggested differences in hillslope length and variably saturated area between the sites could be explained by differences in subsurface properties, and not by differences in geomorphic process rates alone. This work demonstrates one way subsurface hydrology can profoundly affect landscape evolution. Plain Language Summary: In many humid landscapes, runoff is generated by water that flows through the shallow subsurface from ridges to valleys, eventually emerging and draining to rivers. When subsurface capacity to move water is greater, more water can be transported downslope before surface runoff begins. Surface water may cause erosion, which shapes topography over millions of years. We previously developed a computer model based on these principles and showed that subsurface capacity to store and transmit water affects both runoff generation and topographic evolution. Lower capacity results in more extensive surface runoff and shorter hillslopes, when all other factors are held constant. Here we tested this by comparing two watersheds that differ primarily in their bedrock composition, which affects subsurface water storage and transmissivity. We found that the low transmissivity site experienced more widespread surface runoff in response to precipitation, and had shorter hillslopes, supporting our predictions. We set up computer models for both sites, which suggested that subsurface differences are necessary to explain observed differences in runoff and topography. Finally, we discuss some key limitations of the model that could be improved upon in future attempts to understand how hydrology affects the long‐term evolution of Earth's surface. Key Points: We test theoretical predictions about the relationship between hillslope length and relief, saturated area, and transmissivityComparing two watersheds, we find lower transmissivity is associated with shorter hillslopes and larger variably saturated areasHydrogeomorphic modeling suggests that subsurface properties drive the coevolution of differences between the sites [ABSTRACT FROM AUTHOR]

Studying the centennial or millennial timescale response of large rivers to changing patterns in precipitation, discharge, flood intensity and recurrence, and associated sediment erosion is critical for understanding long‐term fluvial geomorphic adjustment to climate. Long hydrographs, maintaining reliable Flow Duration Curves (FDCs), are a fundamental input for such simulations; however, recorded discharge series rarely span more than a few decades. The absence of robust methodologies for generating representative long‐term hydrographs, especially those incorporating coarse temporal resolution or lacking continuous simulations, is therefore a fundamental challenge for climate resilience. We present a novel approach for constructing multi‐century hydrographs that successfully conserve the statistical, especially frequency analysis, and stochastic characteristics of observed hydrographs. This approach integrates a powerful combination of a weather generator with a fine disaggregation technique and a continuous rainfall‐runoff transformation model. We tested our approach to generate a statistically representative 300‐year hydrograph on the Ninnescah River Basin in Kansas, using a satellite precipitation data set to address the considerable gaps in the available hourly observed data sets. This approach emphasizes the similarities of FDCs between the observed and generated hydrographs, exhibiting a reasonably acceptable range of average absolute deviation between 6% and 18%. We extended this methodology to create projected high‐resolution hydrographs based on a range of climate change scenarios. The projected outcomes present pronounced increases in the FDCs compared to the current condition, especially for more distant futures, which necessitates more efficient adaptation strategies. This approach represents a paradigm shift in long‐term hydrologic modeling. Plain Language Summary: River hydrographs are key inputs for understanding long term Earth surface processes. Due to the limited lengths of observational streamflow records, various techniques were previously developed with limited capabilities to generate representative long hydrographs. Through a novel integrated approach, we are able to construct robust high‐resolution hydrographs on multi‐century timescales, based on developing a linkage between hydroclimatic forces and watershed characteristics within a stochastic framework. We used this methodology to generate a 300‐year high‐resolution hydrograph with satisfactory correlation with the observed FDC. Due to the stochastic background of this framework, the deviation between the observed and generated FDCs was estimated to fall within a reasonable range of 6% and 18%. This framework was extended to provide hourly runoff projections for several future climatic models. Median projections for the near‐term period 2040–2069 demonstrated less deviation from reference data set compared to those for the more distant future 2070–2099. This study represents a scientific shift for long‐term simulations through re‐constructing past, simulating present, or projecting future hydrographs. Key Points: Introducing a novel framework designed to generate statistically robust hydrographs on multi‐century timescales for long‐term simulationsIntegrating a weather generator and a disaggregation technique within a rainfall runoff model to achieve high‐temporal resolution hydrographsUtilizing multiple climate models to evaluate the impacts of climate change on hourly runoff responses [ABSTRACT FROM AUTHOR]

Adjuvants are included in many pesticide spray mixtures to enhance the performance of the applied chemical. Many adjuvants which modify the emulsion or extensional viscosity of the tank‐mixture have been found to offer benefits in drift management, primarily by eliminating or reducing the 'Fine' droplets included in the spray with diameters <100–200 μm that can move off‐target in unfavorable conditions during ground, airblast and aerial pesticide applications. Among wind tunnel and field studies conducted around the world, there is consensus that while some adjuvants are effective for drift management, the performance varies on a case‐by‐case basis, requiring verification for each adjuvant which could be achieved through a programme such as certification based on showing a reduction in Fine droplets and/or a reduction in airborne drift. These can be measured in wind tunnel studies according to international standards. This article provides a review of the current science in this subject area, from the approaches to data collection to a review of existing data and regulatory application for encouraging and rewarding the use of appropriate adjuvants that have been demonstrated to reduce airborne spray drift potential and therefore the size of no‐spray buffer zones appropriate to protect nontarget sensitive areas from drift exposure. Some adjuvants can offer the same reduction in drift as offered by hooded sprayer retrofits. A drift reduction programme based on adjuvant use could include testing candidate adjuvants for their effect on droplet size and reduction in Fine droplets when sprayed through reference nozzles and compared against sprays without the adjuvant. Testing could also be based alternatively on measurements of drift potential on collectors such as monofilament line in wind tunnel or field studies. Once shown to be effective in reducing 'Fines' or spray drift, adjuvants could be certified and then referenced on pesticide labels and/or regulatory or best management practice schemes to encourage their use and offer reductions in use restrictions or no‐spray buffer zone sizes based on drift management. Studies have shown that some adjuvants can reduce pesticide leaching into soils and contamination of groundwater, as well as runoff of active ingredients from plants into the environment. Performance depends on the adjuvant type, the pesticide with which it is used, the soil or plant type, the timing and mass of water input from rainfall and climatic factors. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Because low-impact development (LID) infrastructures are effective in capturing and utilizing urban stormwater runoff, real-time control (RTC) strategies can be viewed as soft infrastructures, offering an additional suite of opportunities to improve the stormwater drainage system performance. However, there is a paucity of analyses that disentangle the contributing sources of performance enhancement in these joint hard–soft stormwater infrastructures. This study evaluated the stormwater system performance with integrated LID infrastructures, in-sewer storage capacity, and RTC strategies. Our results showed that LID infrastructures play a major role in mitigating the flow peaks and high volumes, whereas adding RTC to the storage unit and in-sewer flow control mostly improves the performance under relatively small rainfall events, e.g., those with a 3-year return period. The critical contribution of RTC is realized by utilizing storage units as much as possible during the rainfall event. A downstream storage unit that is too close to the outfall may not be suitable for RTC application together with in-sewer capacity utilization. In the present case, the combination of a midstream storage unit and upstream control site at branch pipes worked better than those at other locations. The results suggest that the integration of LID and RTC on a simulation-system level can enhance the reduction of the peak runoff and total discharged volume only if the storage site and control locations are selected carefully. [ABSTRACT FROM AUTHOR]

Floods in a small river basin measuring a few square kilometers can sometimes cause the water levels to rise more than 1 m within 10 min. Therefore, the importance of runoff prediction within tens of minutes is greater in small rivers than in large ones. In this study, we developed a runoff prediction method that uses only observed rain by considering the lag time, representing the time from rainfall to runoff, as prediction lead time. The developed prediction method (DSG) employed the Storage Function model for the runoff simulation and a Genetic Algorithm (GA) for dynamically automatic parameter fitting that was performed in each time step of a rain event. The DSG was applied to six heavy rain events that caused flooding in the Kohatsu River in Japan from 2020 to 2022. The simulation results showed that the GA could effectively determine the optimal parameters to ensure that past simulation depths agreed with the observed ones at each time step of the simulation. Prediction results of the DSG were compared with those of the Storage Function Model with static pre-calibrated parameters (SSG), the simple persistence model (SP), and the gradient persistence model (GP) used as benchmarks. The prediction results of the developed model had the slightest total error of the water level hydrograph (Average (TE)=0.17) compared to the predictions of the SSG, SP, and GP models across all six heavy rain events. In addition, the coefficient of determination (R2) for the flow depth prediction using the developed method (DSG) was higher than 0.9 for each rain event. [ABSTRACT FROM AUTHOR]

Prediction of flood hydrographs in semiarid regions is a complex task due to limited rainfall-runoff observations. The application of complex loss models that have intensive input data requirements can be impractical for such regions. The performance of three loss models, namely, initial and constant rate (IC), Soil Conservation Service (SCS), and constant fraction (CF), in prediction of flood events in a 37.2-km2 semiarid watershed was evaluated using deterministic and probabilistic hydrologic models. We quantified the performance in terms of bias, error, and correlation via relative error (RE), Nash–Sutcliff efficiency (NSE), and percent bias (PBIAS) for 14 events with dry prestorm conditions and a range of rainfall properties (duration, depth, and temporal pattern) and runoff characteristics (peak, volume, and time to peak). The NSE values of the deterministic model ranged from 0.61 to 0.90 and −0.50 to 0.63 for calibration and validation, respectively, in the best model (IC). The results suggest that the performance of loss models was inconsistent in terms of hydrograph attributes. The IC model was best in terms of peak flow according to both deterministic and probabilistic models and best in terms of volume according to the deterministic model, but similar to SCS and better than the CF based on the probabilistic model. The CF model mostly underestimated the runoff volume and peak flow. The performances of the loss models were almost identical in the prediction of the time to peak. These results suggested that deterministic models may be insufficient for selecting the best loss models. Probabilistic models, incorporating the parametric uncertainty, are needed to further evaluate the performance of loss models. There was no correlation between the performance of models and the size of events. Rainfall temporal pattern was found to be an effective factor in the accuracy of flood hydrology predictions. The results can guide the selection of loss models in semiarid watersheds. [ABSTRACT FROM AUTHOR]

A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis. [ABSTRACT FROM AUTHOR]

The Northeast Greenland Ice Stream (NEGIS) has experienced substantial dynamic thinning in recent years. Here, we examine the evolving behavior of NEGIS, with focus on summer speedup at Zachariae Isstrøm, one of the NEGIS outlet glaciers, which has exhibited rapid retreat and acceleration, indicative of its vulnerability to changing climate conditions. Through a combination of Sentinel‐1 data, in‐situ GPS observations, and numerical ice flow modeling from 2007, we investigate the mechanisms driving short‐term changes. Our analysis reveals a summer speedup in ice flow both near the terminus and inland, with satellite data detecting changes up to 60 km inland, while GPS data capture changes up to 190 km inland along the glacier center line. We attribute this summer speedup to variations in subglacial hydrology, where surface meltwater runoff influences basal friction over the melt season. Incorporating subglacial hydrology into numerical models makes it possible to replicate observed ice velocity patterns. Plain Language Summary: The Northeast Greenland Ice Stream (NEGIS), a crucial part of the Greenland Ice Sheet, has been experiencing significant dynamic thinning recently. This study focuses on the summer speedup of Zachariae Isstrøm (ZI), one of NEGIS's outlet glaciers, which is rapidly retreating and accelerating, highlighting its sensitivity to climate change. Utilizing Sentinel‐1 satellite data, in‐situ GPS observations, and numerical ice flow modeling, we explore the mechanisms behind short‐term dynamic changes. We find that satellite data reveals short‐term summer (June to August) fluctuations in ice flow speed near the glacier terminus and up to 50–70 km inland. However, GPS data shows that this speedup extends further inland, up to at least 190 km along the main flow line. Only GPS data can detect the smaller‐scale summer speedups in these inland regions, providing critical observations for validating ice flow models. We determine that the seasonal acceleration of ice velocity at Zachariae Isstrøm is due to variations in subglacial hydrology, where surface meltwater runoff reduces basal friction by altering the subglacial hydrologic system during the melt season. Additionally, our study highlights that these findings are applicable beyond NEGIS, with similar speedup patterns observed in other Greenland glaciers. Key Points: GPS data reveal summer speed up at least 190 km inland along the main flowline of Zachariae IsstrømSubglacial hydrology is the main driver of the summer speedup near the terminus and deep inlandRecord high warming in 2019 led to a more intense and longer duration of the summer speedup [ABSTRACT FROM AUTHOR]

Quantifying suspended sediment concentration‐runoff (SSC‐Q) hysteresis can help to understand the source and transport mechanism of sediment. Due to the unique lithology, shallow soil, high infiltration capacity, complex geomorphology and heterogeneous landscape of karst watersheds in southwestern China, SSC‐Q hysteresis patterns remain unclear in this region. In the present study, the hysteresis index (HI) was used to quantify the SSC‐Q hysteresis pattern. The effects of rainfall and runoff on the HI were also evaluated from 2014 to 2019 in the Mahuangtian (2.26 km2) and Muzhaihe (0.09 km2) karst watersheds. The results showed that the clockwise loops accounted for more than 60% of the total hysteresis patterns in the two karst watersheds. In contrast, the counter‐clockwise and figure‐eight loops occupied no more than 30% and 10% of the total hysteresis patterns, respectively. These differences in the hysteresis patterns probably resulted from the spatial distribution of sediment sources and hydrological connectivity. The HI was significantly affected by the maximum discharge in the Mahuangtian watershed (P < 0.05) and by the flashiness index in the Muzhaihe watershed. This may be attributed to their different 3D hydrogeological structure, lithology and watershed size. This study can facilitate a better understanding of suspended sediment dynamics and provide important guidance for accurately implementing soil and water conservation strategies in karst areas. [ABSTRACT FROM AUTHOR]

Surface water pollution is a critical and urgent global issue that demands immediate attention. Surface water plays a crucial role in supporting and sustaining life on the earth, but unfortunately, till now, we have less understanding of its spatial and temporal dynamics of discharge and storage variations at a global level. The contamination of surface water arises from various sources, classified into point and non-point sources. Point sources are specific, identifiable origins of pollution that release pollutants directly into water bodies through pipes or channels, allowing for easier identification and management, e.g., industrial discharges, sewage treatment plants, and landfills. However, non-point sources originate from widespread activities across expansive areas and present challenges due to its diffuse nature and multiple pathways of contamination, e.g., agricultural runoff, urban storm water runoff, and atmospheric deposition. Excessive accumulation of heavy metals, persistent organic pollutants, pesticides, chlorination by-products, pharmaceutical products in surface water through different pathways threatens food quality and safety. As a result, there is an urgent need for developing and designing new tools for identifying and quantifying various environmental contaminants. In this context, chemical and biological sensors emerge as fascinating devices well-suited for various environmental applications. Numerous chemical and biological sensors, encompassing electrochemical, magnetic, microfluidic, and biosensors, have recently been invented by hydrological scientists for the detection of water pollutants. Furthermore, surface water contaminants are monitored through different sensors, proving their harmful effects on human health. [ABSTRACT FROM AUTHOR]

Estuaries and coasts runoff plays a crucial role in the dynamics of the ocean system. However, the current emphasis on synthetic aperture radar (SAR) retrieved current velocities has been on studying strong boundary currents and large-scale ocean circulation, resulting in relatively limited observational research on estuarine current velocities. This study aims to observe the current velocities in the Yangtze River estuary area using Sentinel-1A SAR data. The findings indicate a high agreement between the current velocities retrieved by Sentinel-1A and those observed at hydrographic stations, showcasing the characteristics of current velocity distribution associated with seafloor topography. This highlights the significant potential of SAR in observing estuarine current velocities, contributing to the analysis of benthic topography and the statistics of river runoff. [ABSTRACT FROM AUTHOR]

Flood occurred in the Melamchi River on June 15 and recurred on July 31, 2021 have led notorious impacts in Nepal. This study has investigated these extreme flood events and their nexus with climate i.e. precipitation and temperature. The available daily and hourly hydro-meteorological data were analyzed. The regional flow during both flood events were estimated using 1-D hydraulic HEC-HMS model and the correlation among rainfall -runoff, temperature with snow water equivalent were examined. Result show that the Melamchi region found an average annual rainfall of 2610 mm during 1992–2021. Specifically, the middle section i.e. Shermathang and Tarkeghang has observed the highest daily average rainfall of 26.8 mm and 39.2 mm during the first and 61.4 mm and 66.6 mm during the second flood event, respectively. The discharge found at the Melamchi town segment was at 2893 m3/s and 1105 m3/s in the first and second events respectively in which regional contribution into discharge were found significant. The Bhemathang, which is landslide damming area where The daily average discharge observed 357 m3/s and 76 m3/s on both events, respectively. At the Nakote, the rainfall and runoff are poorly correlated while snow water equivalent and temperature showed positive correlation during summer. The snowmelt contribution to discharge was found 9.68 m3/s in the Melamchi River during the Summer season. The regional contribution to discharge and spatial variations of precipitation during flooding events showed that precipitation is the main climatic driver while temporary damming and increasing temperature also contributes to the magnitude of the Melamchi flood. [ABSTRACT FROM AUTHOR]

The acceleration of urbanization has disrupted natural water cycles, resulting in increased impervious urban surfaces and non-point source pollution from stormwater runoff. Addressing urban stormwater recharge has become crucial. This study introduces a novel silica sand-based permeable filtration material, investigating its surface characteristics, pore structure, permeability, and pollutant interception capabilities. The results demonstrate that hydrophilic binder coating modification of the permeable surface sand aggregate, combined with hydrophilic inorganic additives, having a porous structure with an average pore size of less than 50 μm and a porosity between 15% and 35%, significantly enhances surface hydrophilicity, achieving a permeation rate of up to 6.8 mL/(min·cm²). Moreover, it shows exceptional filtration and anti-clogging properties, achieving over 98% suspended solids interception and strong resistance to fouling. Dynamic biofilm formation experiments using simulated rain and domestic wastewater explore biofilm morphology and function on silica sand filtration well surfaces. Mature biofilms sustain COD removal efficiency exceeding 70%, with levels consistently below 50 mg/L, NH4+ decreasing to 2 mg N/L, and total nitrogen maintained below 10 mg N/L. The system features anoxic, anoxic, and aerobic zones, fostering synergistic organic matter and nitrogen removal by diverse microorganisms, enhancing pollutant mitigation. Silica sand-based permeable filtration material effectively mitigates urban stormwater runoff pollutants—suspended solids, organic matter, and nitrogen—offering an innovative solution for sponge city development and rainwater resource management. [ABSTRACT FROM AUTHOR]

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), permanganate index (CODMn), and 5-day biochemical oxygen demand (BOD5)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH3-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River's water environment. [ABSTRACT FROM AUTHOR]

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation. [ABSTRACT FROM AUTHOR]

While the biogeochemical properties of the Israeli coastal shelf (ICS) are similar to adjacent pelagic waters, the external sources of inorganic nitrogen (N) are very different. The main source of 'new' N to the pelagic zone is deep winter mixing, with minor contributions from atmospheric deposition and eddy diffusion across the nutricline. For the ICS, major N sources include offshore water advection (260 × 10⁶ mol N y−¹), atmospheric input (115 × 10⁶ mol N y−¹), and riverine input (138 × 10⁶ mol N y−¹), which primarily consists of treated wastewater and stormwater runoff. Direct pollutant discharge from sewage outfalls and submarine groundwater discharge are relatively minor. Key N sinks are new production (420 × 10⁶ mol N y−¹) and sediment deposition and uptake (145 × 10⁶ mol N y−¹). Inputs of nitrate and ammonium were similar and dominant in winter. Unlike temperate shelves, where riverine input is dominant, here it was only slightly higher than atmospheric input, with net N advection onto the shelf being significant. External N inputs did not change net primary production (NPP) by more than ~30% or affect dominant pico and nanophytoplankton genera, except in localized patches. This study offers baseline values for future climate and environmental change assessments. [ABSTRACT FROM AUTHOR]

The Loess Plateau region features fragmented landforms, sparse vegetation, and short but intense summer rainstorms that can easily cause severe soil erosion. To evaluate its applicability to a typical Loess Plateau in northern China, an indoor laboratory experimental study was conducted for events with different slope lengths (3, 4, and 5 m) and rainfall intensities (50, 60, 70, 80, 90, 100, 110, and 120 mm/h). EUROSEM (European Soil Erosion Model) was used to simulate the soil erosion process and predict both total runoff and sediment yield. The EUROSEM modeling results were compared with the observed experimental values and analysed to determine the performance of EUROSEM. The results and analysis showed that EUROSEM could effectively simulate the runoff processes. It produced better predictions for the total runoff yield than for the sediment yield, with Nash–Sutcliffe efficiencies of 0.9003 and 0.1820, respectively. The qualified rates of the RE (relative error) between simulated and observed values of the total runoff and sediment yield were 87.5% and 37.5%, respectively. Compared to the sediment yield under rainfall intensities of 50–80 mm/h, those under 90–110 mm/h had lower RE and higher NSE, which increased from −3.9 to 0.5. However, the modeling results deviated more from the experimental data at a rainfall intensity of 120 mm/h. According to this study, EUROSEM is a good fit for simulating the runoff and partial sediment yield processes of the Loess Plateau region's slopes. However, the accurate prediction of sediment yield still requires more research to achieve complete effectiveness using EUROSEM for typical and wide scenarios in China. [ABSTRACT FROM AUTHOR]

Stormwater runoff is the main source of carbaryl in natural waters; bioretention cells can effectively retain and remove carbaryl from stormwater runoff. However, the accumulation of carbaryl in the bioretention cell impacts its stormwater purification ability, especially nitrogen removal performance. To investigate the mechanisms behind the influence of carbaryl in stormwater runoff on the nitrogen removal performance of bioretention cells, the purification of carbaryl in bioretention facilities was compared under four carbaryl concentrations (0, 0.5, 1.0, and 2.0 mg/L); the effects of carbaryl input on nitrogen removal and the microbial community structure inside the filler were analyzed. After entering the bioretention cell, carbaryl was mainly adsorbed within the filler at a depth of 10–30 cm, and the desorption-adsorption process continued during subsequent rainfall. Carbaryl input negatively affected the denitrification performance of the bioretention cell. The removal rate of nitrate nitrogen (NO3−-N) decreased by 61.13–100.09%, and that of total nitrogen (TN) was reduced by 24.82–38.95%. Carbaryl accumulation reduced the abundance and diversity of microorganisms in the bioretention cell. The relative abundance of some denitrifying bacteria genera (Terrimonas, Bdellovibrio, Aquabacterium, Ohtaekwangia, Sphingomonas, and SWB02) also decreased, which was the main reason for the decrease in the nitrogen removal performance. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of spatiotemporal runoff changes in the Yarlung Zangbo (YZ) basin in the southern Tibetan Plateau (TP) at a sub-basin scale, amidst varying climatic and cryospheric conditions, is imperative for effective water resources management. However, spatiotemporal differences of runoff composition and change and their attribution within the YZ basin have not been extensively explored, primarily due to the lack of hydrometeorological observations, especially in the downstream region. In this study, we investigated historical and future evolution of annual and seasonal total water availability, as well as glacier runoff and snowmelt contributions across six sub-basins of the YZ, with a particular focus on the comparison between the upstream Nuxia (NX) basin and the downstream Nuxia–Pasighat (NX-BXK) basin, based on a newly generated precipitation dataset and a well-validated model with streamflow, glacier mass, and snow cover observations. Our findings revealed that large spatiotemporal differences in changes exist within the YZ basin for 1971–2020. Firstly, runoff generation was dominated by rainfall runoff throughout the YZ basin, with glacier runoff playing a more important role in the annual total runoff (19 %) in the NX-BXK sub-basin compared to other sub-basins. Notably, glacier runoff contributed 52 % of the total runoff at the Pasighat outlet of the YZ basin. Secondly, annual runoff exhibited an increasing trend in the NX basin but a decreasing trend in the NX-BXK, primarily attributed to rainfall runoff changes influenced by atmospheric moisture. Glacier runoff enhanced water supply by offsetting the decreasing contribution from rainfall. Total runoff will consistently increase (27–100 mm (10 yr) -1) across the sub-basins through the 21st century, resulting from increased rainfall runoff and a minor effect of increased snowmelt and glacier runoff. [ABSTRACT FROM AUTHOR]

The Thoubal river catchment (TRB) and Iril river catchment (IRB), which are two major adjoining subbasins of the Manipur river basin in India, were selected for this study. A GIS-based semi-distributed hydrological model soil and water assessment tool (SWAT) was used to derive the hydroclimatic parameters such as surface run-off, evapotranspiration. The present study aims to calibrate and validate the SWAT model using streamflow and satellite soil moisture data, as well as to examine the hydroclimatological responses in the two adjacent basins, and compare the relative changes in treating the Iril-Thoubal river basin jointly and as two separate entities. The model was calibrated and validated using the SUFI-2 tool. During calibration and validation, both IRB and TRB showed good model performance. This study shows that in a poorly gauged river basin with scarce data, remotely sensed soil moisture data may be effectively applied for the calibration and validation of hydrological models. The novel aspect of this study is the efficient calibration and validation of a hydroclimatic model for subtropical river sub-basins with limited hydrological ground data reinforced with open-source satellite-derived soil moisture datasets. [ABSTRACT FROM AUTHOR]

Whether farmers should consider the role of arbuscular mycorrhizal fungi (AMF) in agriculture is a hotly debated topic. We aimed to investigate the role of indigenous AMF in reducing nitrogen (N) loss from paddy fields via runoff, leaching, NH3 volatilization, and N2O emission. We conducted a pot experiment employing a mycorrhiza‐defective rice mutant (non‐mycorrhizal) as the control, grown in soil containing indigenous AMF. The corresponding AMF treatment used the progenitor of this mutant with the same soil. The plants were fertilized with nitrogen, phosphorus and potassium 6 weeks after sowing. The root colonization was 23% in mycorrhizal rice, and no typical AMF structures were observed in the roots of non‐mycorrhizal rice. Our findings indicated that the mycorrhizal system exhibited lower N concentrations of runoff and leachate further compounded by reduced fluxes of N2O and NH3. This led to 14% decrease (mycorrhizal rice 111 kg N ha−1; the non‐mycorrhizal rice: 129 kg N ha−1) in cumulative N loss within 3 days post‐fertilization. While this AMF effect was consistent across the four tested N loss pathways, differences were observed between NH4+‐N and NO3−‐N in the runoff pathway. Notably, our results revealed no evidence of trade‐offs in AMF effect on N loss among the tested pathways. Additionally, mycorrhizal rice had larger shoots and roots than their non‐mycorrhizal counterparts. Our study underscores the potential benefits of indigenous AMF in paddy fields for mitigating water pollution and reducing greenhouse gas emission. [ABSTRACT FROM AUTHOR]

The impact of surface mining on surface runoff was reviewed in articles published from 2009 to 2020 indexed in Science Direct, Scopus, Web of Science, and Scielo databases. Measurement methods, quantities, influencing factors and trends of surface runoff in mining areas are presented and research gaps, challenges, and opportunities are discussed. A total of 10,274 articles were found, 39 of which were selected for qualitative and quantitative analysis. Among the methods, laboratory and field measurements using plots, and, to a greater extent, estimates are used worldwide to measure runoff in surface mining areas. Based on the reviewed articles, the surface runoff in the last decade ranged from 2.25 to 488 mm annually for rainfall between 386 and 2189 mm. Monthly values ranged from 0.5 to 83.3% of precipitation. Mine reclamation must primarily consider the vegetative cover, the soil structure, and the microrelief as factors that reduce surface runoff, which is the main trend when this is evaluated over time. The biggest challenges in monitoring surface runoff in mining areas are the long times required and the difficulty of collecting data in the field; in addition, when estimated by precipitation data, the values may have low precision. [ABSTRACT FROM AUTHOR]

Urban and agricultural runoffs can transport contaminants and pesticides into freshwater ecosystems, particularly in the developing tropics. For instance, organophosphate and pyrethroids pesticides, such as Roundup, Malathion, and Permethrin, have been found in tropical streams. The uncontrolled application of these pesticides has become a growing concern due to their adverse effects on various non-targeted organisms. Unfortunately, most studies have focused on a few selected model species, ignoring the effects on other non-target organisms, which may play an important role in tropical lotic ecosystems. In addition, the biological characteristics of aquatic crustaceans, including their morphology, physiology, and behavior, make them susceptible to toxic chemicals. For this reason, this study used the widely distributed freshwater shrimp Xiphocaris elongata as a model organism to determine the acute toxicity of Permethrin, Malathion, and Roundup. Our results show that the proportion of mortality of X. elongata in each concentration group became progressively higher as the concentration of exposure increased. We also found that the synthetic pyrethroid Permethrin was the most toxic pesticide tested, with a median lethal concentration (LC50) value for 96 h of 3.96 × 10−6 µg·L−1, followed by organophosphate Malathion (8.87 µg·L−1) and Roundup (748.92 µg·L−1). Experiments with this freshwater shrimp showed a good control performance and reproducibility for the tested pesticides. This study demonstrated that X. elongata is a suitable test organism that can be a representative bioindicator of pesticide toxicity in tropical streams. [ABSTRACT FROM AUTHOR]

Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing the complex relationships among water, soil, and the environment. Understanding its attributes and variations in space and time is essential for effective water resource management, erosion mitigation, and land-use planning. This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman's correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. The results indicate a year-by-year increase in rainfall erosivity in the basin, with a trend towards stabilization. The average annual rainfall erosivity over the years is 711 MJ · m m · h m − 2 · h − 1 , peaking at 1098 MJ · m m · h m − 2 · h − 1 in 2014. A significant 93.9% of rainfall erosivity is concentrated in the wet season, with a maximum of 191 MJ · m m · h m − 2 · h − 1 in July. The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. Rainfall factors predominantly influence erosivity, with erosive rainfall showing the strongest correlation (rho = 0.93), while temperature and elevation have relatively minor effects. This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use. [ABSTRACT FROM AUTHOR]

The present study analyzed sensitivity of flow parameters using SWAT and effect of climate change on surface water resources, considering uncertainty with Monte Carlo. For this purpose, output of nine-model related to fifth-climate change-report during baseline period 1971–2000 was weighted. Using Monte Carlo, 100 samples of probabilistic distribution of basin temperature and rainfall were generated. LARS-WG model was used for downscaling, then temperature and precipitation were calculated under RCP2.6 and RCP8.5 for future periods 2040–2069 and 2070–2099. SWAT was calibrated using observational-data and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) (NCEP CFSR) global climate datasets and sensitivity of parameters affecting flow was analyzed. Results showed that observed-data had better performance than NCEP CFSR. Finally, future runoff was calculated under RCP2.6 and RCP8.5 for 2040–2069 and 2070–2099. Results showed that average annual runoff decreased by 84, 80, 82 and 80%, for 2040–2069 (RCP2.6 and RCP8.5) and for 2070–2099 (RCP2.6 and RCP8.5) relative to baseline, respectively. [ABSTRACT FROM AUTHOR]

County level estimates of mean sheet and rill erosion from the Conservation Effects Assessment Project (CEAP) are useful for program development and evaluation. Since county sample sizes in the CEAP survey are insufficient to support reliable direct estimators, small area estimation procedures are needed. The quantity of water runoff is a useful covariate but is unavailable for the full population. We use an estimate of mean runoff from the CEAP survey as a covariate in a small area model with sheet and rill erosion as the response. As the runoff and sheet and rill erosion are estimators from the same survey, the measurement error in the covariate is important as is the correlation between the measurement error and the sampling error. We conduct a detailed investigation of small area estimation in the presence of a correlation between the measurement error in the covariate and the sampling error in the response. In simulations, the proposed predictor is superior to small area predictors that assume the response and covariate are uncorrelated or that ignore the measurement error entirely. [ABSTRACT FROM AUTHOR]

Valid simulation results from global hydrological models (GHMs), such as WaterGAP3, are essential for detecting hotspots or studying patterns in climate change impacts. However, the lack of worldwide monitoring data makes it challenging to adapt GHM parameters to enable such valid simulations globally. Therefore, regionalization is necessary to estimate parameters in ungauged basins. This study presents the results of regionalization methods for the first time applied to the GHM WaterGAP3. It aims to provide insights into (1) selecting a suitable regionalization method for a GHM and (2) evaluating its impact on runoff simulation. In this study, four new regionalization methods have been identified as appropriate for WaterGAP3. These methods span the full spectrum of methodologies, i.e., regression-based methods, physical similarity, and spatial proximity, using traditional and machine-learning-based approaches. Moreover, the methods differ in the descriptors used to achieve optimal results, although all utilize climatic and physiographic descriptors. This demonstrates (1) that different methods use descriptor sets with varying efficiency and (2) that combining climatic and physiographic descriptors is optimal for regionalizing worldwide basins. Additionally, our research indicates that regionalization leads to spatially and temporally varying uncertainty in ungauged regions. For example, regionalization highly affects southern South America, leading to high uncertainties in the flood simulation of the Río Deseado. The local impact of regionalization propagates through the water system, also affecting global estimates, as evidenced by a spread of 1500 km 3 yr -1 across an ensemble of five regionalization methods in simulated global runoff to the ocean. This discrepancy is even more pronounced when using a regionalization method deemed unsuitable for WaterGAP3, resulting in a spread of 4208 km 3 yr -1. This significant increase highlights the importance of carefully choosing regionalization methods. Further research is needed to enhance the predictor selection and the understanding of the robustness of the methods on a global scale. [ABSTRACT FROM AUTHOR]

Snowmelt erosion and rainfall erosion are important components of soil erosion in Northeast China. Studying how snowmelt and rainfall affect runoff and sediment yield is essential to control soil erosion in this region. Based on the soil and water loss observation data in small watershed during snowmelt and rainfall periods, snowmelt and rainfall erosion processes were studied. The runoff–sediment relationship was analyzed. The results indicate that 13 snowmelt runoff events and 14 rainfall runoff events were recorded. During the snowmelt period, runoff depth (RD) and sediment yield (SY) exhibited a slow increase (early stage), rapid increase (middle stage), and then rapid decrease (late stage) trend. RD and SY in the middle stage were higher than those in the early and late stages. Moreover, during the rainfall period, soil erosion mainly occurred from July to August; during these two months, the rainfall and rainfall erosivity accounted for 66.0 and 91.1% of the total rainfall and rainfall erosivity, respectively; and the RD and SY in this period accounted for 88.4 and 89.8% of total amounts in the whole rainfall period, respectively. In addition, the contributions of snowmelt and rainfall to RD and SY were 18.6 and 81.4%, and 3.8 and 96.2%, respectively. Hysteresis analysis suggested that clockwise hysteresis loop was the dominant pattern, followed by eight-shaped anticlockwise loop, and complex anticlockwise loop. [ABSTRACT FROM AUTHOR]

Maravanthe Beach has been witnessing erosion due to waves caused by tidal forces, destroying vegetation, and causing enormous damage to people's lives, livestock, and property. Hence, an attempt has been made to understand the shoreline change analysis using the automatic shoreline extraction method and determine the rate of change in terms of erosion and accretion rate before and after the construction of coastal structures during the period from 2014 to 2023. The SWAT model was applied to analyze the influence of surface runoff on shoreline changes near Maravanthe Beach for the periods 2004–2014 and 2012–2023. Also, runoff was chosen as one of the parameters for validation of the analysis. It was noticed that the shoreline stretch had experienced significant erosion prior to the construction of shore protection infrastructure. Soon after the deployment of certain shore structures, the shoreline progressed from a high erosion stretch to a low erosion stretch. This study reveals that Maravanthe Beach witnessed greater erosion during the period 2014–2018 than it did from 2019–2023 and also observed a decrease in erosion from 11% to 3% and an increase in the accretion zones from 2 to 9%. [ABSTRACT FROM AUTHOR]

Variable infiltration capacity model is used to quantitatively analyse the runoff reduction benefits of three basins on the Loess Plateau since the implementation of the 'Grain for Green' programme. The programme played an important role, contributing to >60% of the runoff variation in the different basins. Furthermore, for every 1000 km2 increase in cumulative forest area in basins, the rate of runoff coefficient reduction changed by −30%, −37.3% and 169% in the late stage of the programme compared to the early stage, respectively ('-' represents a decrease in reduction rate). This was closely related to the restoration of vegetation. [ABSTRACT FROM AUTHOR]

An understanding of turbulence and mixing is essential to the knowledge of turbulent dissipation, sediment transport, advection of nutrient-rich and organic effluents, and stormwater runoff in prototype water systems, as well as in laboratory channels used for physical modeling of geometrically-scaled full-scale water bodies and for validation of computational models. The acoustic Doppler velocimeter (ADV) system is a robust instrument well suited for such turbulence measurements in open channel flows. The application of acoustic Doppler velocimeter to transient free-surface flows is reviewed in this paper. Based upon field applications and laboratory experiments, the intricacy and inherent difficulties are discussed. The experience and expertise in transient flows highlighted the importance of the signal processing and precise synchronization. Finally, we stress a major benefit of the acoustic Doppler velocimetry for its capability to be used in both field and laboratory, in turn providing some level of confidence in the comparison between full-scale and laboratory data. [ABSTRACT FROM AUTHOR]

Rainfall erosion can cause environmental and economic damage by decreasing the storage capacity of water reservoirs because of the detachment of soil particles. The purpose of this study was to develop a one-dimensional physicomathematical model that can help predict the effects of rainfall erosion on the banks of water reservoirs. The model was developed using the Mein–Larson model to describe water infiltration, the kinematic wave approximation to represent overland flow generation, and the steady state sediment continuity equation to estimate sediment transport. The model was validated using rainfall simulator tests and lateritic soil samples with a bimodal soil–water retention curve. The results showed conformity with the experimental data, identifying a threshold in the models for discharge per unit area and sediment yield rate, as well as a linear increase in the models for total runoff and sediment load per unit area. However, the model failed to capture the peak in sediment yield rate owing to raindrop impact during the initial minutes of rainfall. Parametric analysis highlighted the impact of increasing the calibration constant of splash erosion, erodibility coefficient, and critical shear stress on the slope of the sediment load per unit area model. Despite its limitations, the model demonstrates satisfactory predictive capability for sediment load per unit area under high-intensity rainfalls, achieving an R2 greater than 0.92 in five of the six cases examined. [ABSTRACT FROM AUTHOR]

Low Impact Development (LID) measures have proven to be effective in reducing the runoff volume and the peak of runoff at small spatial scales. Assessment of the hydrological impacts of LID measures at river basin scales is currently limited by the absence of adequate physically-based process models that can simulate LID processes at such scales. In this study, an attempt has been made to develop LID modules to simulate the water balance and hydrological processes based on the Multi-Layer Green-Ampt (MLGA) infiltration method, which is an advancement to the existing LID modeling techniques. The performance of the MLGA-based LID modules was benchmarked with the process-intensive numerical tool HYDRUS 1D for about 5000 soil samples across the soil textural classes. The results indicated that the MLGA-based model simulates the hydrological processes in close agreement with HYDRUS for soils with moderate-to-high hydraulic conductivity values, which are amenable for LID implementation. [ABSTRACT FROM AUTHOR]

Seasonal variation in the quality of water from River Wupa, Abuja, Nigeria was studied by collecting sixty water samples in both dry and wet seasons which were analyzed using standard methods. The respective results for in dry and wet seasons were pH (7.13±0.09, 7.31±0.06), temperature (21.51±0.51, 27.66±0.49 °C), EC (247.19±46.21, 152.43±17.76 µS/cm), TDS (156.80±39.74, 75.39±9.23 mg/dm³), TSS (33.38±14.12, 333.85±43.45 mg/dm³), turbidity (26.20±13.04, 361.51±52.89 NTU), alkalinity (34.49±4.12, 29.31±3.78 mg/dm³), DO (7.73±0.29, 8.95±0.87 mg/dm³), BOD (17.00±4.90, 15.70±2.98 mg/dm³), COD (40.36±14.01, 28.28±8.18 mg/dm³), hardness (33.74±2.10, 34.82±3.82 mg/dm³), sulphate (41.09±1.01, 28.82±1.05 mg/dm³), nitrate (12.12±2.02, 12.23±1.78 mg/dm³), nitrite (0.08±0.02, 0.06±0.02 mg/dm³), phosphate (2.09±0.33, 2.15±0.35 mg/dm³) and chloride (40.96±3.53, 32.23±3.82 mg/dm³). Correlation results shows that during the dry season there is a strong and positive correlation between (EC and TDC, r =0.910), (TSS and Turbidity, r = 0.976), (TSS and BOD, r = 0.945), (TSS and COD, r = 0.912), (Turb and BOD, r = 0.868), (Turbidity and COD, r = 0.820), (BOD and COD, r = 0.979), (pH and nitrite, r = 0.750) (pH and Cl, r = 0.737), (TSS and Cl, r = 0.748) and (Turb and Cl, r = 0.761). During the wet season strong and positive correlation is between (EC and TDS, r = 0.879), (BOD and COD, r = 0.756) and (Turbidity and sulpate, r = 0.730). Results for all parameters were below the limits recommended by NSDWQ except for turbidity, DO and phosphate, also in both seasons there was no significant difference for all parameters except for turbidity and COD. It is concluded that the variation in these two parameters is attributable to runoffs from farmlands and direct discharge of domestic and industrial effluent during the wet season and the strong and positive correlation indicates that the pollutants that affect water quality parameters come from the same sources. [ABSTRACT FROM AUTHOR]

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When using runoff infiltration devices to remove nitrogen and phosphorus pollutants from urban runoff, the quality of the effluent is affected by the length of dry spells between rain events. This study presents a novel analysis of how these dry periods impact the device's effectiveness in removing pollutants and the resulting biological succession within the filter. Our analysis examines nitrogen and phosphorus removal in a rainwater filtration context, providing new insights into how dry period duration influences infiltration system performance. The results indicate that biological processes have a significant impact on reducing total nitrogen (TN) and total phosphorus (TP) contents under different drying periods. A 3-day drying period is most effective for reducing TN through biological processes, while a 7-day period is best for TP reduction. This suggests that moderately extending the drying period improves TP removal efficiency but does not enhance TN removal. The dominant bacterial phylum responsible for denitrification and phosphorus removal is Proteobacteria, with Pseudomonas and Acinetobacter as the leading genera. As the drying period lengthens, the dominant genera shift from Pseudomonas to Massilia. At a 3-day drying period, denitrification primarily occurs through Pseudomonas on the surfaces of maifanite and zeolite. At a 7-day dry-out period, Acinetobacter is mainly responsible for phosphate removal on maifanite surfaces. However, after a 14-day dry-out period, both biomass and bioactivity of Pseudomonas and Acinetobacter decrease, leading to reduced efficiency in removing nitrogen and phosphorus pollutants from runoff infiltration devices. These results aid in developing runoff infiltration devices for specific scenarios and offer crucial guidance for regulating runoff pollution control technologies. [ABSTRACT FROM AUTHOR]

Runoff gullies are among the most typical and common disorders in rammed earthen sites. However, only a few studies have effectively elucidated their developmental characteristics, influencing factors, and evolutionary mechanisms. Here, employing 15 sites in three climatic areas and five dynasties as the research objects and based on the investigation and statistics of runoff gullies, standard grading of gully erosions in single sites, classification of the ages of the gullies, and test for the refined disintegration processes of rammed earth using the H-FRA and AHP-EWM-TOPSIS methods, a preliminary study on the temporal and spatial characteristics of the development, as well as influencing factors of gullies, was conducted. The results indicated that the age development of the gullies in the 15 sites proceeded via alternating surface and bottom erosions and that the gullies were mainly at their early and mature stages at this time. The comprehensive degrees of gully erosion exhibited the following temporal and spatial patterns: semi-arid > extreme-arid > arid area and Ming > Han > Qing > Tang > Song Dynasties. Further, the AHP-EWM-TOPSIS model with the disintegration index positively affected the temporal and spatial variations of the comprehensive development degrees of gullies, with up to 93.33% accuracy. HIGHLIGHT: The temporal and spatial characteristics of runoff-gully development in rammed earthen sites are revealed. The runoff-gully was classified according to the age development order, and the comprehensive development degree of gully was proposed as a new index. The disintegration process tests were refined and the indexes of disintegration characteristics at different stages were obtained. AHP-EWM-TOPSIS comprehensive evaluation method was used to analyze the influence factors of the runoff-gully. [ABSTRACT FROM AUTHOR]

Frequent soil erosion disasters in the middle reaches of the Yellow River (MRYR) have a profound effect on the sediment load of the river. This paper addresses the intertwined effects of human activities and climate change on river runoff and sediment load. Therefore, runoff and sediment loads from hydrological stations along the main and tributary rivers within the MRYR were used. The Mann–Kendall (M–K) trend test and the double mass curve analysis, among other analytical tools, were used to examine the erosion patterns of these rivers from 1950 to 2022, as well as the main factors driving these changes. The results showed that the runoff depth of the Yan River tended to decrease, and there was a significant decrease in the mainstream and nine other tributaries, with a significant decrease in the sediment transport modulus for both the mainstream and tributaries. In the main river, human activities contributed between 69.99% and 94.69% to the runoff and between 88.52% and 98.49% to the sediment load, while in the tributaries, the contribution of human activities was greater. The annual runoff and annual sediment load in the MRYR showed a decreasing trend, with a discernible impact of human activities. The results of this research are of great significance for erosion control and the restoration of the ecological balance in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Coastal and riverine floods are major concerns worldwide as they can impact highly populated areas and result in significant economic losses. In a river mouth environment, interacting hydrological and oceanographical processes can enhance the severity of floods. The compound flood hazards from high sea levels and high river discharge are often estimated using copulas, among other methods. Here, we systematically investigate the influence of different data sources coming from observations and models as well as the choice of copula on extreme water level estimates. While we focus on the river mouth at the city of Halmstad (Sweden), the approach presented is easily transferable to other sites. Our results show that the choice of data sources can considerably impact the results up to 10 % and 15 % for the river time series and 3 % to 4.6 % for the sea level time series under the 5- and 30-year return periods, respectively. The choice of copula can also strongly influence the outcome of such analyses up to 13 % and 9.5 % for the 5-year and 30-year return periods. Each percentage refers to the normalized difference in return level results we can expect when choosing a certain copula or input dataset. The copulas found to statistically best fit our datasets are the Clayton, BB1, and Gaussian (once) ones. We also show that the compound occurrence of high sea levels and river runoff may lead to heightened flood risks as opposed to considering them independent processes and that, in the current study, this is dominated by the hydrological driver. Our findings contribute to framing existing studies, which typically only consider selected copulas and datasets, by demonstrating the importance of considering uncertainties. [ABSTRACT FROM AUTHOR]

The paper studies the influence of shrubs in a runoff-regulating forest belt of a combined (dense–blown–lattice) design on the natural factors of meltwater runoff formation: snow accumulation, freezing depth and soil moisture. The research was conducted at the Amphitheater runoff-erosion station in agroforestry landscapes of a dry-steppe natural zone (Volgograd) with the use of the water-balance method at runoff sites. During the field experiment with different placement of shrubs in three- or four-row forest belts (in the middle, on the upper edge, on the lower edge, and on both sides), a positive role in increasing the ameliorative effect of runoff-regulating forest belts was established: reduction of freezing depth, accumulation of snow reserves, and additional moisture in the soil. In terms of impact on natural factors of meltwater runoff, the placement of shrubs in the flow-regulating forest belt of combined design on the lower and upper edges was the most effective. This measure provided better snow accumulation inside the forest belt and increased soil moisture in it. Thus, when creating new erosion control measures, it is necessary to introduce shrubbery into the composition of runoff-regulating forest belts, which affects the natural factors of runoff formation—snow accumulation, freezing depth, and soil moisture—and contributes to the reduction of surface runoff of melt water. [ABSTRACT FROM AUTHOR]