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Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. In this study, we show that a snow fence experiment on High‐Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo‐erosion gully in the landscape. We estimate that 1.1–3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilized aquatic carbon. Our results show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss, highlighting the need for improved model representation. Plain Language Summary: Snow cover is steadily disappearing as a result of climate change, but in areas that remain below 0°C we can still expect an increase in snow depth in the middle of winter. Since snow acts akin to a blanket, this warms the soil and accelerates the thaw of permafrost—thereby potentially contributing to carbon release from these frozen soils. Ice wedges, which are typical for permafrost landscapes, are particularly vulnerable to thaw because they hold a large amount of ice. When this ice melts, the surface sinks down, and soil carbon may be lost. In this study, we show how experimentally raised snow cover triggered the collapse of several ice wedges, not only through a warming effect of the snow but also due to an increase in the flow of water through the ice wedge network. As a result, we estimate that 1.1–3.3 tons of carbon were removed from this location, of which a portion could have entered the atmosphere as CO2. We emphasize the importance of studying the interactions among snow, runoff, and permafrost thaw to better understand how this may affect the release of greenhouse gases to the atmosphere. Key Points: A decade of raised snow depths led to strong local thaw subsidence, while increased runoff triggered the downstream collapse of ice wedgesThe abrupt thaw process mobilized about 1.1–3.3 tons of soil organic carbon in total, and dissolved organic carbon was degraded in the thermo‐erosion gullyFeedbacks linking changes in snow cover, surface drainage, and permafrost thaw are important drivers of thermokarst and soil carbon loss [ABSTRACT FROM AUTHOR]

When nutrient level in the soil surpasses vegetation demand, nutrient losses due to surface runoff and subsurface leaching are the major reasons for the deterioration of water quality. The Lower Mississippi river basin (LMRB) is one of the sub-basins that deliver the highest nitrogen loads to the Gulf of Mexico. Potential changes in episodic events induced by hurricanes may exacerbate water quality issue in the future. However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. While the overall performance of the calibrated ELM is satisfactory, some discrepancies in WTD remain in slope areas with low precipitation due to the missing lateral flow process in ELM. Simulations including biogeochemistry performed using ELM with and without model calibration showed important influences of soil hydrology, precipitation intensity, and runoff parameterization on the magnitude of nitrogen runoff loss and leaching pathway. Despite such sensitivities, both ELM simulations produced reduced WTD and increased runoff and accelerated nitrate-nitrogen runoff loading during Hurricane Ida in August 2021, consistent with the observations. With observations suggesting more pronounced effects of Hurricane Ida on nitrogen runoff than the simulations, we identified factors for model improvement to provide a useful tool for studying hurricane-induced nutrient losses in the LMRB region. [ABSTRACT FROM AUTHOR]

Ethiopia, marked by diverse geography and rapid land use/land cover (LULC) changes, grapples with formidable water resource management challenges. Understanding the hydrological response to these changes is paramount for sustainability. This review paper provides an overview of studies that investigated the hydrological impacts of LULC changes in Ethiopian basins. The review discusses methodologies, key findings, implications for sustainable land and water resource management, research gaps, and future directions. Emphasizing results from diverse studies that utilized hydrological and geospatial modelling, LULC significantly affects surface runoff, streamflow patterns, and groundwater recharge. The observed consequences are increased surface runoff, modified streamflow timing, and reduced groundwater recharge. The review underscores the urgency for integrated land and water management strategies to alleviate adverse effects. Future research priorities include basin-specific studies, long-term monitoring, integrated modelling, consideration of socio-economic factors, and incorporating climate change scenarios to enhance understanding and guide sustainable water resource management in Ethiopian basins. [ABSTRACT FROM AUTHOR]

The restoration of native vegetation like pines helps mitigate soil erosion, but the effects of pine needle litter on runoff, erosion, and hydrology in karst hillslopes are unclear. This study investigated these factors during the rainy season from June to August 2021 under six treatments (flat, gentle, and steep slopes with and without pine needle coverings). The results showed that pine needle litter significantly increased the soil water content, particularly on steeper slopes, by improving the water-holding capacity. It also substantially reduced runoff (by 23.8%) and soil erosion (by 92.5%). The critical rainfall threshold for generating runoff did not occur in the pine needle covering treatment, whereas it was 19.3 ± 8.8 mm in the bare soil treatment under wet conditions. These findings highlight the efficacy of pine needle cover in reducing runoff and erosion in karst areas, promoting it as a sustainable method for restoring degraded soil. [ABSTRACT FROM AUTHOR]

The Himalayan water tower provides crucial water resources for Asia. Permafrost degradation is deemed to exert important impacts on streamflow in the Himalayan rivers. Yet, the magnitudes of such impacts remain poorly quantified. Here, we established a robust hydrological model that incorporated active layer deepening and ground ice melt for the drainage basin of the largest river in the northern Himalayas-the Yarlung Zangbo River (YZR). We estimated that permafrost degradation led to ∼0.65 km3/yr decrease in surface runoff and ∼0.35 km3/yr increase in baseflow and ground ice melt contributed ∼0.25% to the annual streamflow in the YZR for the period 2001–2022. The "fill-and-spill" mechanism helps explain the seeming contradiction of observed increasing versus decreasing baseflow in different permafrost regions worldwide. We propose that the dilution of riverine dissolved organic carbon (DOC) concentrations by baseflow may lead to the riverine DOC hysteresis patterns. This study not only lays solid scientific basis for water resources management in the Himalayas, but also yields new insights into how to interpret measured river discharge and nutrient flux in permafrost regions over the globe. [ABSTRACT FROM AUTHOR]

N‐(1,3‐dimethylbutyl)‐N′‐phenyl‐p‐phenylenediamine‐quinone (6PPD‐quinone) is a widespread contaminant of emerging concern resulting from oxidation of 6PPD, which is an antidegradant substance added to tires. The recent identification of 6PPD‐quinone as the cause of acute mortality in coho salmon has quickly motivated studies on 6PPD‐quinone toxicity to other species. Subsequent findings have shown that 6PPD‐quinone toxicity is highly species specific. Closely related species can differ widely in response to 6PPD‐quinone from extremely sensitive to tolerant. Hence toxicity testing is currently the only way to establish whether a species exhibits 6PPD‐quinone toxicity. We investigated the acute toxicity of 6PPD‐quinone in pink salmon alevins (sac fry). This species has is the only Pacific salmon that so far has not been tested for 6PPD‐quinone sensitivity. Fish were exposed in static water in eight treatments with initial concentrations ranging from 0.1 to 12.8 μg/L. Fish were observed for 48 h, and changes in concentrations of 6PPD‐quinone were monitored throughout the experiment. No mortalities or substantial changes in behavior were recorded. Environ Toxicol Chem 2024;43:1332–1338. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR]

Assessing runoff changes is of great importance especially its responses to the projected future climate change on local scale basins because such analyses are generally done on global and regional scales which may lead to generalized conclusions rather than specific ones. Climate change affected the runoff variation in the past in the upper Daqinghe Basin, however, the climate was mainly considered uncertain and still needs further studies, especially its future impacts on runoff for better water resources management and planning. Integrated with a set of climate simulations, a daily conceptual hydrological model (MIKE11-NAM) was applied to assess the impact of climate change on runoff conditions in the Daomaguan, Fuping and Zijingguan basins in the upper Daqinghe Basin. Historical hydrological data (2008–2017) were used to evaluate the applicability of the MIKE11-NAM model. After bias correction, future projected climate change and its impacts on runoff (2025–2054) were analysed and compared to the baseline period (1985–2014) under three shared social economic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios from Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The MIKE-11 NAM model was applicable in all three Basins, with both R2 and Nash-Sutcliffe Efficiency coefficients greater than 0.6 at daily scale for both calibration (2009–2011) and validation (2012–2017) periods, respectively. Although uncertainties remain, temperature and precipitation are projected to increase compared to the baseline where higher increases in precipitation and temperature are projected to occur under SSP2-4.5 and SSP5-8.5 scenarios, respectively in all the basins. Precipitation changes will range between 12%–19% whereas temperature change will be 2.0°C–2.5°C under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. In addition, higher warming is projected to occur in colder months than in warmer months. Overall, the runoff of these three basins is projected to respond to projected climate changes differently because runoff is projected to only increase in the Fuping basin under SSP2-4.5 whereas decreases in both Daomaguan and Zijingguan Basins under all scenarios. This study's findings could be important when setting mitigation strategies for climate change and water resources management. [ABSTRACT FROM AUTHOR]

The presentation of soil erosion on agricultural land, both to the expert and the public, frequently takes the form of pictures and descriptions of extreme events. These attention‐grabbing images are case studies of worst‐case scenarios and serve an important purpose of warning what may happen under certain circumstances; they also have a potential to mislead. On the other hand, long‐term studies of erosion are able to present extreme events in a more acceptable scientific context. Monitoring studies emphasise the importance of frequent, low‐magnitude runoff events and their ability to transport nutrients and pesticides to freshwater systems. Thus, the need for a balanced presentation of erosion which places extreme events in a broad context in space and time. Communicating with practitioners such as farmers requires the use of non‐conventional channels rather than the reliance on academic journals. [ABSTRACT FROM AUTHOR]

Background Patients with complex lower extremity (LE) wounds and single-vessel LE runoff (1-VRO) are often considered for amputation. While more challenging, free tissue transfer (FTT) is a means for limb salvage. This study aims to demonstrate the feasibility of limb salvage with FTT in patients with 1-VRO. Methods Patients undergoing FTT by a single surgeon between 2011 and 2021 were retrospectively reviewed. Data collected included demographics, wound characteristics, vascular status, and operative details. Patients were divided into cohorts based on 1- versus 3-VRO of tibial vessel inflow. Outcomes of interest included postoperative complications such as flap necrosis, flap success, limb salvage, and ambulatory status. Results A total of 188 patients underwent FTT to LE, with 25 patients (13.3%) having 1-VRO. Patients with 1-VRO had a comparable prevalence of diabetes (56.0% vs. 50.0%, p = 0.569) and end-stage renal disease (8.0% vs. 3.7%, p = 0.319). Osteomyelitis was more common in the 1-VRO group (80.0% vs. 60.1%, p = 0.056). FTT donor sites and flap composition were similar between cohorts. At mean follow-up of 21.2 months (interquartile range 24.5:5.6, 30.1 months), limb salvage rates were similar between cohorts (84.0% vs. 91.4%, p = 0.241), with no significant differences in ambulatory status or mortality. Higher complication rates occurred in the 1-VRO cohort (48.0% vs. 21.5%, p = 0.004), of which partial flap necrosis was more prevalent in the 1-VRO group (8.0% vs. 1.2%, p = 0.029). There was no difference in flap success rates between groups (p = 0.805). More postflap angiograms were performed in the 1-VRO group (32.0% vs. 9.2%, p = 0.001), but there was no difference in need for repeat percutaneous endovascular intervention between groups. Conclusion This study demonstrates that FTT reconstruction to the LE remains a reliable reconstruction option for limb salvage in patients with single-vessel supply to the LE. Reliance on advanced perioperative management and patient optimization is effective at reducing negative outcomes. [ABSTRACT FROM AUTHOR]

The Storm Water Management Model (SWMM) is a hydrological model for simulating and predicting runoff. Although powerful, SWMM can be computationally demanding. Therefore, we develop machine learning (ML) models to approximate the behavior of SWMM and expedite the task of predicting runoff. We perform a case study for the First Creek watershed in Knoxville, Tennessee, USA. We train ML models using rainfall data and subcatchment characteristics and apply feature engineering and clustering to objectively compare the outputs from SWMM and ML models. The results show that random forests can predict runoff volume accurately, with a Mean Absolute Error (MAE) of 0.006 (0.001) ${10^6}$ 10 6 gallons, where predictions are made almost instantaneously. Hence, our proposed ML-based approach can accurately predict runoff while greatly reducing computational requirements, filling a critical need in the field. [ABSTRACT FROM AUTHOR]

This study investigated the level of dissolved heavy metals in the water of Longhu Lake and focused on the speciation, mobility, and risks of heavy metals (Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb) in sediment. The levels of heavy metals in the water were found to be below the thresholds set by the Chinese safe drinking water standard. The highest average percentages of Cr, Ni, Zn, and As were bound with residual fractions, while Pb and Cu were bound with reducible fractions. Additionally, the highest percentage of Cd and Mn was bound to the acid-soluble fraction in the sediment. These findings suggest that Cd, Mn, Pb, and Cu likely originated from anthropogenic sources in the lake. There was a strong correlation between total organic carbon (TOC) and metal fractions in the sediment, indicating that TOC may play a role in transporting heavy metal fractions. Risk assessment code values for Cd and Mn indicated higher risks and mobility, while Ni, Cu, and Zn showed medium risks and mobility for aquatic biota. To mitigate heavy metal contamination, it is recommended to improve monitoring and regulation of urban runoff and inlet areas. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to examine the influence of forest vegetation changes on water-sediment dynamics, and to provide a scientific basis for formulating and adjusting ecological environment development policies in the Yellow River Basin. [Methods] Based on the runoff and sediment discharge data from the Dacun hydrological station in the Shiwangchuan River Basin from 1959 to 2018, as well as precipitation data within the basin, the hydrological changes over a span of 60 years were analyzed. By combining NDVI data from 1985 to 2018 and land use data from five periods, the vegetation underlying surface driving effects on water-sediment changes in the watershed were analyzed and discussed. [Results] The runoff and sediment discharge in the Shiwangchuan River Basin had shown a significant decreasing trend over the past 60 years（p＜0.01）, with abrupt changes occurring in 1988 and 1983, respectively. However, there had been no significant trends in land use and precipitation within the watershed during this period. The NDVI during the growing season showed little variation before 2000 but exhibited a significant increasing trend after 2000. [Conclusion] Before 2000, the implementation of various soil and water conservation measures was the main reason for the decrease of runoff and sediment transport in the basin; After entering the 21st century, the improvement of vegetation quality in the basin has further reduced runoff and sediment transport. [ABSTRACT FROM AUTHOR]

Increased stormwater runoff due to urbanization has highlighted the need for faster water removal. Porous concrete has high void content and porosity; however, it has poor mechanical properties. Its performance can be improved by changing parameters such as cement content, aggregate size, water-to-cement ratio, gravel-to-cement ratio, and additives. Contact surface area and aggregate interlock are the main factors to consider in the design of porous concrete. This study investigated the influence of varying aggregate size and adding polypropylene fiber on the density, mechanical properties, porosity, void ratio, and permeability of porous concrete. Twenty-six mixtures that combined different aggregate sizes and polypropylene fiber content were considered. The results revealed that increased contact surface area and void ratio using multiaggregate sizes provided adequate compressive strength and permeability performance. The best performance was achieved when aggregate sizes of 9.5–6.7 mm and 6.7–4.75 mm were combined at a 3∶1 ratio and 0.1% polypropylene fiber was added. Many applications in civil engineering might be both financially and environmentally beneficial. Compared with pond retention systems, porous concrete systems showed a major benefit in controlling stormwater runoff and avoiding soil degradation influenced by water when used as a trenching system for the upcoming water surrounding the building's foundations. Further benefits as the natural hydrological cycle restoration are achieved by infiltrating water back into the soil rather than pouring it into the sewage system. However, porous concrete relies on voids in its design, reducing its mechanical capacity, and thus cannot withstand heavy traffic. In addition, freezing and thawing cycles in cold weather countries might degrade its capacity. This article collected the aggregate sizes that could be susceptible to porous concrete production and combined at least two sizes to attain the best mechanical and permeability performance. Optimized mixes were discussed in both cases; single and combined aggregate sizes along with fiber additives to enhance the optimized mixes mechanical response and increase their bending capacity for improved traffic resistance. The optimized mixes also showed higher compressive strength than control porous concrete mixes. [ABSTRACT FROM AUTHOR]

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling. [ABSTRACT FROM AUTHOR]

Freshwater input from Greenland ice sheet melt has been increasing in the past decades from warming temperatures. To identify the impacts from enhanced meltwater input into the subpolar North Atlantic from 1997 to 2021, we use output from two nearly identical simulations in the eddy‐rich model VIKING20X (1/20°) only differing in the freshwater input from Greenland: one with realistic interannually varying runoff increasing in the early 2000s and the other with climatologically (1961–2000) continued runoff. The majority of the additional freshwater remains within the boundary current enhancing the density gradient toward the warm and salty interior waters yielding increased current velocities. The accelerated boundary current shows a tendency to enhanced, upstream shifted eddy shedding into the Labrador Sea interior. Further, the experiments allow to attribute higher stratification and shallower mixed layers southwest of Greenland and deeper mixed layers in the Irminger Sea, particularly in 2015–2018, to the runoff increase in the early 2000s. Plain Language Summary: Global warming has accelerated the melting of the Greenland ice sheet over the past few decades resulting in enhanced freshwater input into the North Atlantic. The additional freshwater can potentially inhibit deep water formation and have future implications on ocean circulation. To determine the influence from Greenland melt, we compare two high‐resolution model experiments all with the same forcing but differing input of Greenland freshwater fluxes from 1997 to 2021. We find that in the experiment with realistically increasing Greenland meltwater, the water becomes fresher and cooler along the continental shelf and boundary of the subpolar gyre. The density difference between the shelf and interior increases with more freshwater, resulting in faster West Greenland Current speeds and enhanced eddy formation. Deeper mixed layers are found in the eastern Irminger Sea, particularly in 2015–2018. From 2009 to 2013, there were shallower mixed layers in the Labrador Sea where less Greenland meltwater was mixed downwards and spread eastward, causing mixed layers to deepen in the Irminger Sea. Key Points: The West Greenland Current (WGC) freshens and cools with the observed recent increase in meltwater runoff from GreenlandThe density gradient across the boundary current intensifies, strengthening the WGC and increasing local eddy formationEnhanced meltwater runoff contributed to an eastward shift in deep convection towards the Irminger Sea (2015–2018) [ABSTRACT FROM AUTHOR]

Snowmelt runoff and soil freezing have been combined to seriously exert on the rill networks and soil erosion intensity in the black soil region of northeastern China. The dynamic development of rill networks can be used to quantitatively extract the parameters of erosion. Previous studies have focused primarily on the runoff erosion caused by rainfall. Only a few studies were to consider the combined effects of snowmelt runoff and freezing. Currently, available methods cannot fully meet the high precision of rill morphology. Therefore, it is very necessary to systematically investigate the rill erosion under combined effects using high-precision extraction. This study aims to simulate the snowmelt runoff scouring on the frozen (-20°C freezing) and non-frozen slopes (10°C), refilled with black soil from northeastern China. A 0°C ice-water mixture was used to simulate the snowmelt runoff scouring. Meanwhile, three-dimensional laser scanning was employed to obtain the point cloud data of the slope surface after different times of scouring, until the eroded surface stabilized. The Digital Surface Model (DSM) of Difference (DoD) with point cloud reverse engineering was used to detect and parameterize the micro-topographic pattern (e.g., development of rill networks and erosion area, erosion volume, rill length, and rill density). The results show that there was a significant impact of freezing-induced temperature on the development of rill networks: (1) The frozen slope was more prone to develop the rill networks. The erosion rate after each scouring was significantly higher than that on the nonfrozen slopes. Upon reaching stability, the erosion area, erosion volume, and rill length were 291%, 557%, and 437% of those on the non-frozen slopes, respectively; (2) The cumulative erosion volume of the frozen and non-frozen slopes showed similar patterns during the rill development. But there were noticeable differences along the cross-sectional direction of the rills. On the frozen slopes, the width-depth ratio (RW/D) of the rill cross-sections decreased rapidly at the rill intersections with accelerated downcutting, whereas, the width and depth of rills increased linearly and stably. On the non-frozen slopes, the RW/D at the midslope cross-sections increased with the number of scouring events, thus accelerating lateral erosion, while the RW/D at other sections decreased with the number of scouring events with accelerated downcutting; (3) The relative error range was -12.70% to 4.42% for the Differential Digital Surface Model (DoD) with ArcGIS hydrological processing and GeoMagic point cloud reverse engineering models for the frozen slopes, with an absolute mean relative error of 5.48%, and an extraction accuracy of over 94%. The combined extraction with GeoMagic and ArcGIS showed a great contribution to obtain the rill parameters on the freeze-treated slopes. The finding can serve as a promising potential to the rill development under complex eroding settings. [ABSTRACT FROM AUTHOR]

Mountain glaciers are considered natural indicators of warming and a device for climatic change. In addition, it is also a solid reservoir of freshwater resources. Along with climate change, clarifying the dynamic changes of glacier in the Aksu River Basin (ARB) are important for hydrological processes. The study examined the variations in glacier area, elevation, and their reaction to climate change in the ARB between 1990 and 2022. The glacier melt on the runoff is explored from 2003 to 2020. This investigation utilized Landsat and Sentinal-2 images, ICESat, CryoSat, meteorological and hydrological data. The findings suggest that: (1) The glacier area in the ARB retreated by 309.40 km2 (9.37%, 0.29%·a−1) from 1990 to 2022. From 2003 to 2021, the ARB glacier surface elevation retreat rate of 0.38 ± 0.12 m·a−1 (0.32 ± 0.10 m w.e.a−1). Comparison with 2003–2009, the retreat rate is faster from 2010 to 2021. (2) From 1990 to 2022, the Toxkan and the Kumalak River Basin's glacier area decreases between 61.28 km2 (0.28%·a−1) and 248.13 km2 (0.30%·a−1). Additionally, the rate of glacier surface elevation declined by −0.34 ± 0.11 m·a−1, −0.42 ± 0.14 m·a−1 from 2003 to 2021. (3) The mass balance sensitivities to cold season precipitation and ablation-phase accumulated temperatures are +0.27 ± 0.08 m w.e.a−1(10%)−1 and −0.33 ± 0.10 m w.e.a−1 °C−1, respectively. The mass loss is (962.55 ± 0.57) × 106 m3 w.e.a−1, (1087.50 ± 0.68) × 106 m3 w.e.a−1 during 2003–2009, 2010–2021 respectively. Warmer ablation-phase accumulated temperatures dominate glacier retreat in the ARB. (4) Glacier meltwater accounted for 34.57% and 41.56% of the Aksu River's runoff during the ablation-phase of 2003–2009 and 2010–2020, respectively. The research has important implications for maintaining the stability of water resource systems based on glacier meltwater. [ABSTRACT FROM AUTHOR]

Urban floods caused by extreme rainstorm events have increased in recent decades, particularly in concave-down bridge zones. To simulate urban flooding processes accurately, an integrated urban flood model (IUFM) was constructed by coupling a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM), a widely used pipe convergence module in the storm water management model (SWMM), with an inundation module that describes the overflow expansion process associated with terrain and land-cover. The IUFM was used in a case study of the Anhua Bridge (a typical concave-down overpass) study area in Beijing, China. The spatial-temporal variations in flood depth modeled by the IUFM were verified to be reliable by comparison with actual measurements and other simulations. The validated IUFM was used to obtain temporal variations in flood range, depth, and volume under four rainstorm scenarios (return periods of 3-year, 10-year, 50-year, and 100-year). The results showed that the surface runoff process, overflow from drainage networks, and overflow expansion process could affect the flooding status by changing the composition and spatial configuration of pervious or impervious patches, drainage capacity, and underlying surface characteristics (such as terrain and land-cover). Overall, although the simulation results from the IUFM contain uncertainties from the model structures and inputs, the IUFM is an effective tool that can provide accurate and timely information to prevent and control urban flood disasters and provide decision-making support for long-term storm water management and sponge city construction. [ABSTRACT FROM AUTHOR]

Underlying surface has led to the complex hydrological response of watersheds in small mountainous areas. Among them, the slope can be the basic unit of hydrological response. It is of great significance to explore the hydrodynamic features of runoff under different conditions of underlying surface on mountainous slopes. In this study, the underlying surface of the slope was selected in the typical mountain areas, the Guanshan River watershed. Multiple simulated rainfall experiments were conducted in the laboratory. A systematic investigation was made to clarify the runoff and erosion hydrodynamic features of the soil slope under different slope gradients, soil thickness distribution, and soil base permeability. The results showed that: 1) The Reynolds number, shear stress, and runoff power increased with the increase of rainfall intensity, whereas, the DarcyWeisbach resistance coefficient decreased. The rain intensity initiated a significant increase in the mean values of Reynolds number and runoff power (P<0.05). The mean values of Reynolds number and runoff power at the rain intensity of 60, 90, and 120 mm/h increased by 130%-435%, compared with the mean values of 30 mm/h. Slope gradients shared significant positive effects on the mean shear stress and mean runoff power (P<0.05). The mean shear stress for the 15 and 25 slope gradients treatments increased 135% and 187%, respectively, compared with the 5 slope gradients treatment; The mean runoff power increased 224% and 357%, respectively. The thickness of the soil layer had significant effects on the mean Darcy-Weisbach resistance coefficient, mean Reynolds number and mean runoff power under a permeable soil base. The permeability of the soil base also exhibited a significant effect on the mean Reynolds number and the mean shear stress, when the thickness of the soil layer was thin at the top. The hydrodynamic features depended mainly on the rainfall intensity. The variance contributions to the Reynolds number reached 83.11%. The dominant influencing factors on shear stress and runoff power were the rainfall intensity and slope gradients. The sum of both variance contributions to shear stress and runoff power reached 67.64%, and 80.58%, respectively. Slope and soil thickness were the main influencing factors on the hydrodynamic parameters under a single rainfall intensity, followed by the interactions between soil thickness and slope gradients. The bottom permeability and soil thickness also presented some influence on the hydrodynamic parameters. Therefore, it is very necessary to consider the soil bottom permeability, soil thickness, and their interaction on hydrodynamic features, rainfall intensity, and slope gradients. The findings can provide some theoretical support to determine the influence of runoff and sediment yields on the complex slope surface. Some parameters can also be offered to simulate the hydrological process in small mountainous watersheds. [ABSTRACT FROM AUTHOR]

A pneumatic spray delivery-based solid set canopy delivery system (PSD-SSCDS) has been successfully configured for efficient spray applications in modern apple orchards typical of the state of Washington state. This study investigates the spray performance of optimized PSD-SSCDS against an axial-fan airblast sprayer in terms of spray deposition, coverage, and off-target drift in a tall spindle architecture of a commercial apple orchard. For field evaluation, mylar cards and water sensitive paper samplers were placed in different canopy zones on either side of the leaf surface, and 500 ppm of biodegradable fluorescent tracer solution was sprayed with PSD-SSCDS and an airblast sprayer. Additionally, aerial and downwind samplers were installed to evaluate ground runoff and drift, as well as aerial drift. The spray deposited on the mylar cards was analyzed using fluorometry, and spray coverage on WSPs was evaluated using image processing techniques. PSD-SSCDS (645.0 ± 94.7 ng cm-2, mean ± standard error) and an airblast sprayer treatment (661.6 ± 26.9 ng cm-2) had comparable mean spray deposition with no significant difference in different canopy zones for both treatments. However, the spray coverage was significantly higher for the airblast sprayer (32.7 ± 2.4%) compared to PSD-SSCDS (22.5 ± 3.1%). These differences are likely due to air-assist in airblast spraying, which is absent in PSD-SSCDS. Evidently, the airblast sprayer had significantly higher losses to the ground and aerial drift compared to PSD-SSCDS, as the ground drift for the latter was mostly confined within 4.1 m downwind of the sprayed row and spray losses to air were minimal. [ABSTRACT FROM AUTHOR]

Arid regions have a strong lack of observational datasets, specifically in rainfall and runoff, which plays a crucial role in the effective management of water resources and the assessment of hydrological systems. Therefore, the objective of this study was to investigate the impact of different precipitation estimation methods on the hydrological response, specifically the runoff, in the Wadi Nu'man basin located in Saudi Arabia. To achieve this, rainfall data were collected over 13 years (2006–2018) from ground observations and satellite rainfall products. The remote sensing-based precipitation data utilized in this study includes Tropical Rainfall Measuring Mission (TRMM-3B42), TRMM-Realtime (TRMM-3B42RT), and Climate Hazards Group InfraRed Precipitation (CHRIPS) datasets. The performance of these datasets was evaluated using various statistical metrics. Additionally, a linear scaling bias correction method was performed to align the satellite and ground-based station data. Finally, the HEC-HMS model was utilized to simulate the runoff using all available bias-corrected precipitation datasets. This article primarily focuses on comparing and assessing two categories of data: rainfall and runoff. In terms of daily rainfall data, a lower correlation (R2 < 0.5) was observed when comparing the datasets. However, a moderate regression (R2 = 0.52) was found between the average values derived from the two gauges and the corresponding satellite data. Similarly, a moderate relationship (R2 ≥ 0.43) was found on a monthly scale between ground observations and satellite precipitation. The average gauge data, along with the corresponding satellite data, were utilized for runoff modeling in HEC-HMS. The runoff model showed a high regression (R2) value of 0.75 between peak discharges generated by the gauges and CHRIPS data. However, a lower (R2) value of 0.3 was observed between gauge rainfall and TRMM rainfall. This research emphasizes the importance of utilizing the satellite rainfall dataset for runoff estimation in arid regions of Saudi Arabia where gauge data may be unavailable. Furthermore, future studies should focus on implementing more sophisticated bias correction techniques, testing diverse satellite datasets, and integrating advanced modeling tools such as machine learning for enhanced predictions of the hydrologic response. [ABSTRACT FROM AUTHOR]

We investigate historical simulations of relevant components of the Arctic energy and water budgets for 39 Coupled Model Intercomparison Project Phase 6 (CMIP6) models and validate them against observation-based estimates. We look at simulated seasonal cycles, long-term averages and trends of lateral transports and storage rates in atmosphere and ocean as well as vertical fluxes at top-of-atmosphere and the surface. We find large inter-model spreads and systematic biases in the representation of annual cycles and long-term averages. Surface freshwater fluxes associated with precipitation and evaporation as well as runoff from Arctic lands tend to be overestimated by most CMIP6 models and about two thirds of the analysed models feature an early timing bias of one month in the runoff cycle phase, related to an early snow melt bias and the lack of realistic river routing schemes. Further, large biases are found for oceanic volume transports, partly because data required for accurate oceanic transport computations has not been archived. Biases are also present in the simulated energy budget components. The net vertical energy flux out of the ocean at the Arctic surface as well as poleward oceanic heat transports are systematically underestimated by all models. We find strong anti-correlation between average oceanic heat transports and mean sea ice cover, atmospheric heat transports, and also the long-term ocean warming rate. The latter strongly suggests that accurate depiction of the mean state is a prerequisite for realistic projections of future warming of the Arctic. Our diagnostics also provide useful process-based metrics for model selection to constrain projections. [ABSTRACT FROM AUTHOR]

Precise long-term runoff prediction holds crucial significance in water resource management. Although the long short-term memory (LSTM) model is widely adopted for long-term runoff prediction, it encounters challenges such as error accumulation and low computational efficiency. To address these challenges, we utilized a novel method to predict runoff based on a Transformer and the base flow separation approach (BS-Former) in the Ningxia section of the Yellow River Basin. To evaluate the effectiveness of the Transformer model and its responsiveness to the base flow separation technique, we constructed LSTM and artificial neural network (ANN) models as benchmarks for comparison. The results show that Transformer outperforms the other models in terms of predictive performance and that base flow separation significantly improves the performance of the Transformer model. Specifically, the performance of BS-Former in predicting runoff 7 days in advance is comparable to that of the BS-LSTM and BS-ANN models with lead times of 4 and 2 days, respectively. In general, the BS-Former model is a promising tool for long-term runoff prediction. [ABSTRACT FROM AUTHOR]

The impacts of the accelerated glacier retreat in recent decades on glacier runoff changes are still unknown in most Andean catchments, increasing uncertainties in estimating water availability. This particularly affects the outer tropics and Dry Andes, heavily impacted by prolonged droughts. Current global estimates overlook climatic and morphometric disparities, which significantly influence model parameters, among Andean glaciers. Meanwhile, local studies have used different approaches to estimate glacier runoff in a few catchments. Improving 21st-century glacier runoff projections relies on calibrating and validating models using corrected historical climate inputs and calibrated parameters across diverse glaciological zones. Here, we simulate glacier evolution and related runoff changes between the periods 2000–2009 and 2010–2019 across 786 Andean catchments (11 282 km2 of glacierized area, 11° N to 55° S) using the Open Global Glacier Model (OGGM). TerraClimate atmospheric variables were corrected using in situ data, getting a mean temperature bias by up to 2.1 °C and enhanced monthly precipitation. Glacier mass balance and volume were calibrated, where melt factor and the Glen A parameter exhibited significant alignment with varying environmental conditions. Simulation outcomes were validated against in situ data in three documented catchments (with a glacierized area > 8 %) and monitored glaciers. Our results at the Andes scale reveal an average reduction of 8.3 % in glacier volume and a decrease of 2.2 % in surface area between the periods 2000–2009 and 2010–2019. Comparing these two periods, glacier and climate variations have led to a 12 % increase in mean annual glacier melt (86.5 m3s-1) and a decrease in rainfall on glaciers of - 2 % (- 7.6 m3s-1) across the Andes, with both variables comprising the glacier runoff. We confirmed the utility of our corrected regional simulations of glacier runoff contribution at the catchment scale, where our estimations align with previous studies (e.g., Maipo 34° S, Chile) as well as provide new insights on the seasonal glaciers' largest contribution (e.g., La Paz 16° S, Bolivia) and new estimates of glacier runoff contribution (e.g., Baker 47° S, Chile). [ABSTRACT FROM AUTHOR]

The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very-high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation IceBridge over the 2011–2019 period to investigate our hypothesis that vertical heterogeneities in firn (i.e. ice layers) cause vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1 m is strongly related to the vertical heterogeneity of a firn profile and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles over a depth range of ∼ 4 m. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011–2013 covering an area of ∼ 350 815 km 2 , followed by firn replenishment over the years 2014–2019 spanning an area of ∼ 667 725 km 2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns. [ABSTRACT FROM AUTHOR]

Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct "single-peak" pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB. [ABSTRACT FROM AUTHOR]

Understanding the diagenetic processes of organic matter (OM) within mangrove forests is essential to grasp ecosystem dynamics and exchanges with adjacent ecosystems. This study investigated the influence of urban runoff on leaf litter degradation in a New Caledonian mangrove forest, subjected to urban rainwater for over 50 years. Focusing on Avicennia marina and Rhizophora stylosa , our objectives were to determine factors affecting leaf litter degradation, assess urban runoff effects on decay rates and element concentrations, and understand OM changes at the molecular level. Litterbags containing senescent leaves placed on the mangrove floor were collected after 7, 14, 28, 56, and 72 days. C and N along with their stable isotopes were evaluated during degradation as well as lignin and neutral carbohydrates contents. Despite lower initial N content, R. stylosa exhibited faster degradation (t 1/2 of 36 ± 3 and 28 ± 2 days) than A. marina (t 1/2 of 43 ± 9 and 33 ± 4 days), emphasizing the critical role of species position within the mangrove forest regarding tidal immersion. Urban runoff, submerging the urban site, intensified leaf litter degradation, influencing both mass loss and molecular changes in OM. After 72 days of leaf litter degradation, the loss of rhamnose and glucose was more pronounced at the control site (13 % and 75 % for rhamnose and 46 % and 53 % for glucose for A. marina and R. stylosa , respectively) compared to the urban site. The study also exposed molecular tendencies during mangrove leaf litter degradation. The content of ferulic acid in the leaf litter decreased at all stands after 72 days of degradation (between −25 % and −58 %), while total syringyl phenols increased (between + 64 % and + 232 %). This research exposed the global implications of urban runoff, indicating accelerated leaf litter degradation in mangrove forests, potentially disrupting C sequestration dynamics and threatening ecosystem services. [ABSTRACT FROM AUTHOR]

Two modules were designed for high school science students to investigate the performance of a rain garden installed on school property. The rain garden, a green infrastructure system which allows soil infiltration, was installed to reduce impacts to urban streams and can increase the community's resilience to flooding. By involving students in the analysis of this mitigation strategy, students learned new technical skills, gained varied experiences in collecting and analyzing data, were exposed to new STEM careers, and learned about local issues that impact their community while collaborating with local professionals. In each module, students used their authentic data to address research questions. In the first module, students conducted a land survey and calculated the volumetric capacity of the rain garden. In the second module, students collected rainfall data using rain gauges and analyzed various aspects of rainfall collection. Although these modules were focused on a rain garden already installed on school property, they can be implemented at schools without this mitigation strategy present. The surveying module can easily be applied to measure any land surface feature, and the rain gauge module can be implemented anywhere as it is focused on rainfall collection. [ABSTRACT FROM AUTHOR]

With global surge in reservoir construction over the past decades, river systems worldwide have been profoundly fragmented. Consequently, flow manipulation by reservoirs has altered the natural hydrological processes, resulting in extensive modifications of fluvial-marine ecosystems. Mitigating the adverse ecological consequences of reservoirs has become a global concern and has garnered increasing attention. The Yellow River, as one of the most extensively manipulated river systems globally, has experienced substantial changes in the amount and timing of water discharge due to the presence of numerous reservoirs scattered throughout its catchment area. These alterations have caused physicochemical changes in the estuary and subsequent modifications to the estuarine ecosystem. In recent years, the Yellow River Conservancy Committee initiated the release of water through the Xiaolangdi Dam during the major spawning period of fisheries, specifically in the spring, with the aim of improving the estuarine ecological environment. From 2011 to 2020, a total of 84.05 km3 of water was discharged from the Xiaolangdi Reservoir during spring seasons, of which 40% (33.16 km3) constituted water impounded within the reservoir during preceding months. Correspondingly, the spring water discharge from the Yellow River to the sea increased significantly from 1.50 km3/yr to 3.46 km3/yr in the past decade, leading to a decrease in estuarine salinity by 1.6 PSU. The estuarine fishery resources, such as fish eggs in the Yellow River estuary, have demonstrated evident improvement. The reservoir regulation in the Yellow River, which has successfully enhanced spring water discharge and subsequently restored estuarine fishery resources, presents an effective attempt for mitigating the adverse ecological effects associated with reservoirs. [ABSTRACT FROM AUTHOR]

Traditional rainfall-runoff modeling techniques require large datasets and often an exhaustive calibration process, which is challenging, especially in poorly-gauged basins and resource-limited settings. Therefore, it is necessary to examine new ways of constructing predictive models for runoff that can achieve satisfactory results, while also minimizing the data requirement and model construction time. In this study, the effectiveness of integrating the Random Forest (RF) as an important feature identifier with novel gradient boosted trees to achieve satisfactory results was examined for two adjacent catchments in Vietnam. Antecedent daily runoff in combination with daily and one-day antecedent rainfall was found to significantly influence the runoff at the outlet of the catchments. Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) were effective in predicting day-ahead runoff. For instance, CatBoost with NSE, d, r, and R2 values of 0.92, 0.98, 0.96, and 0.92, respectively, and XGBoost with NSE, d, r, and R2 values of 0.91, 0.98, 0.96, and 0.92, respectively, are well suited for predicting runoff. A comparative analysis of their results with previous studies revealed that the models were very effective since they were able to better reduce generalization errors at different calibration and validation phases. This study presents the integration of RF and gradient boosted trees as a simplified alternative to computationally expensive and data-intensive physically-based rainfall-runoff models. The practitioners can build upon the experimentation presented in this study to minimize the computational time requirement, construction process complexity, and data requirement, which are often serious constraints in physically-based rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

The Storm Water Management Model (SWMM) is a reliable software program for simulating stormwater runoff in combined drainage facilities. However, it faces challenges in efficiency, especially when executing real-time forecasting. This research explores an alternative machine learning (ML) approach to predict water levels in sewer and street drainage systems utilizing SWMM data for model training. The goal is to construct a hybrid ML model for urban flooding prediction, considering hydrological conditions, geomorphologic properties, and drainage facility features. A robust training method is introduced to deliberate complex flow conditions. The suggested approach achieves favourable performance in predictive accuracy and computational efficiency. Findings include: (1) similarity between ML and SWMM flooding depth on street nodes increases from 62.3% to 96.2% with the proposed training; (2) the proposed model is about 50 times faster than SWMM in urban flood simulations; (3) reliable predictions from the ML model are demonstrated through four accuracy metrics. [ABSTRACT FROM AUTHOR]

Rainfall serves as a significant factor contributing to slope stability challenges in mountainous areas, and simulating the process of slope rainwater movement is a crucial approach for analyzing the stability of slopes triggered by rainfall. By combining computer numerical simulation technology with traditional hydraulic and hydrological calculation theories, it is possible to create an efficient and precise rainwater movement model that can simulate and analyze the process of rainwater movement on slopes. Utilizing natural slopes as the focal point of our research, the cellular automaton method was applied to simulate rainfall runoff on slopes, and a Cellular Automata (CA) based model for rainwater movement process was developed. This model modified the Green-Ampt (G-A) infiltration model by adopting an elliptical water content curve and introducing a coefficient that quantifies the ratio of saturated to unsaturated depth. Additionally, we refined the rules governing runoff generation and convergence within the slope and on its surface, enabling a comprehensive simulation of the entire rainwater movement process on the slope. Furthermore, the effectiveness of the model was validated through analytical solutions derived from simplified assumptions, laboratory experiments on infiltration and runoff in the flume, and a case study of a natural slope. The results show that the infiltration calculation results of the rainwater movement model are closer to the experimental values, and their overall values are slightly higher than the measured values, which are basically consistent with the model test results; The runoff calculation results show a phenomenon of initially increasing and gradually approaching the measured values compared to the measured values. When applying the model to an actual slope, it was found that the model comprehensively accounts for the influence of slope seepage, infiltration and runoff process, has better performance compared to G-A modified model. The model can be used to describe the spatial distribution and temporal variation of infiltration and runoff processes. [ABSTRACT FROM AUTHOR]

In the recent past, low-lying areas close to riverbanks and urban agglomerations have witnessed frequent floods due to inadequate mapping of flood zones and rapidly growing impervious areas in cities/towns. This inadequacy is due to the use of design storms for run-off estimates, which do not account accurately for antecedent moisture conditions. Furthermore, when using daily rainfall data, capturing accurate run-off estimates becomes challenging as rainfall characteristics such as duration and intensity are not accounted for. These problems can be addressed with long-term hourly rainfall data. However, most gauging stations in India have long-term daily rainfall data and hourly data for the last few years. There are various global methods to overcome this data limitation by disaggregating daily data into hourly data. However, well-established, peer-reviewed research on this process is still in infancy in India. Therefore, a methodology has been developed to disaggregate long-term daily rainfall data into hourly rainfall using statistical and probabilistic principles based on sample hourly data. In this study, the run-off estimates derived from disaggregated data closely match those obtained from actual hourly data with similar characteristics when simulated over the Belagavi city catchment area in Karnataka, India. The methodology developed relies on sample hourly rainfall, making it scalable across various locations. It holds promise for resilient urban stormwater infrastructure planning in the absence of long-term hourly rainfall data. [ABSTRACT FROM AUTHOR]

Modelling surface runoff and infiltration in an agricultural watershed is essential for understanding the nutrient cycle and water quality management. This research evaluates the critical rainfall events causing maximum surface runoff and infiltration. Empirical models such as Soil Conservation Service (SCS) curve number and Explicit Green-Ampt (EGA) were analysed for different soil hydraulic conditions and storm events with specific return periods. The present study forecasted the maximum daily rainfall and intensity as 113.6 mm and 19.9 cm h–1, respectively, considering a 100-year return period storm. The deterministic part of the model calculates the daily maximum surface runoff as 5.51 cm (from the SCS model). The EGA model revealed that the rainfall intensity of 0.143, 0.163, 0.175, 0.179, 0.181, 0.182, and 0.182 cm h−1 (duration of 24 h) was found critical for 1, 2, 5, 10, 25, 50, 100 year return period of the storm, respectively in different soil texture classes (sand, loamy sand, sandy loam, loam, silt loam, sandy clay loam, clay loam, silty clay soils). Exceptions imply for three occasions where the intensity of 0.407 cm h−1 and duration of 18 h produced maximum infiltration for silty clay loam, and intensity of 0.352 and 0.354 cm h−1 with 9 h rainfall (each) produced maximum infiltration for clay. The probabilistic model revealed that a daily storm event probability of 2.74 × 10–5 is associated with a simulated mean runoff depth of 0.69 cm and infiltration of 20.7 cm. Critical rainfall probabilities calculated in this study can be multiplied with the overall microbial/pollutant human health risk assessment framework to calculate conditional probability and better estimate microbial risk through the drinking water pathway. This study is very relevant for the wet climatic condition of Ireland, where most land use is pastures. [ABSTRACT FROM AUTHOR]

A paired catchment study approach was used to quantify fluxes of Nitrogen (N), Phosphorus (P), sediment and Escherichia coli (E. coli) in surface runoff from fields where sheep were wintered on swede (Brassica napus) and kale (Brassica oleracea) crops. The effectiveness of a strategic grazing approach that protected critical source areas (CSAs) was examined to determine if these fluxes could be reduced. Averaged over two years, estimated fluxes of N, P and sediment in surface runoff recorded in winter and early spring (June-September) were up to 0.64, 0.22 and 51 kg ha−1, respectively, and up to 2 × 1011 MPN ha−1 for E. coli. With the exception of E. coli, these fluxes were low compared to those reported in other studies and largely attributed to the low amounts of surface runoff recorded. This, in turn, can be explained by relatively benign weather conditions recorded during the study period and the relatively light grazing pressures imposed by sheep. Compared to standard grazing practice, protection of CSAs reduced N, P, sediment, and E. coli fluxes by 38%, 48%, 55% and 63%, respectively. These reductions can largely be attributed to lower concentrations of contaminants in surface runoff when the CSA was protected. [ABSTRACT FROM AUTHOR]

One of the greatest challenges faced when using gravity settling is achieving efficient particle removal and retention rates under high inflow rates. This paper proposes a new method for enhancing sedimentation and protecting the settled particle. This is accomplished by placing one layer of the virtual bed above the real bed to divide the water body into hydraulically different upper and lower regions. The normal structure of the newly-proposed virtual bed is a flat plate with many small perforations. When the water flows along the top surface of the virtual bed, it creates two effects: (1) it isolates and protects water in the area below the virtual bed from being disturbed by the fast and turbulent flows above, which enhances sedimentation of the particles in the lower region and protects the sediment which could be eroded; and (2) the vertical vortex generated by the surficial flows passing over the openings helps the nearby suspended particles to enter the quiescent water region below. To assess the performance of the newly-proposed virtual bed, a rectangular settling tank was used to conduct comparison tests of particle removal with three particle size ranges, four inflow rates, five virtual bed structural designs, and multiple experimental conditions. The results clearly showed that, compared with a traditional settling tank (without the virtual bed), the addition of the proposed settling structure notably enhanced the particle settling rate by 10%–15% for the particles tested and experimental conditions assessed. The virtual bed method easily can be applied to various water treatment devices and facilities to enhance the suspended particle removal efficiencies in treatments of storm runoff, wastewater, and many other kinds of water without the need for chemical additions or energy-intensive processes. This paper proposes and tested an innovative method for increasing the removal of suspended particles using a virtual bed. A virtual bed is composed of a plate with numerous holes through it suspended above particle-settling facilities. It enhances particle settling by providing a less disturbed region for settling particles, protecting already-settled particles, and encouraging the suspended particles to move toward the real bed. Laboratory tests in a rectangular tank with different particle sizes and flow rates showed that the virtual bed effectively improved particle removal rates. Various possible virtual bed structure designs are proposed and were examined. The best-performing virtual bed was identified, and possible reasons for its performance were discussed based on hydraulics and physics principles. Experiments with different tank configurations and virtual bed position arrangements were conducted, and based on the experimental results, general suggestions for using the virtual bed in practice more effectively are given. In principle, the proposed virtual bed can be applied to any particle-separation facilities. [ABSTRACT FROM AUTHOR]

The occurrence of scour around bridge elements due to the transportation of bed material during flood events can cause serious structural damage and loss of life. Increased uncertainties in precipitation and runoff predictions due to climate change make this phenomenon more complex and dangerous. Bridge scour countermeasures should thus be more focused on decreasing scour formation around bridge elements. In this study, abutment scour under clear-water conditions with constant flow intensity was conducted and collars were tested as scour countermeasures around semi-circular end bridge abutments. The experimental study was performed in a rectangular channel with an almost uniform cohesionless bed material for 3 h with and without collars. Collars of various lengths located at different elevations around the abutments were tested to investigate the effect of collars on scour development. The results of the study showed that the scour depth decreased with increasing collar width and when the collar was placed below the bed level for a given abutment length. The results were compared with those of similar earlier studies to show the effect of abutment shape, size of the bed sediment and test durations on the development of scour depth around abutments. [ABSTRACT FROM AUTHOR]

The study of the effect of vegetation cover on water and sediment content is of great significance for the in-depth understanding of ecological and environmental effects in river basins and the formulation of corresponding management measures. Based on the monitoring data of rainfall and runoff of Panjiazhuang and Dangchengwan Hydrologic Stations in Shule River Basin from 2000 to 2020 and the sediment discharge of Changmabao, methods such as geographic information technology (GIS), landscape pattern analysis, land use transfer matrix, correlation analysis, principal component analysis, and linear regression analysis were used to study water and sediment change, land use pattern, vegetation change characteristics, and local water and sediment change in Shule River Basin and construct vegetation–topographic landscape factors. The main research results are as follows: (1) Vegetation coverage in the Shule River Basin increased year by year from 2000 to 2020, with a cumulative increase of 0.064 in 20 years. Vegetation cover has a significant effect on water and sediment content, and the correlation is −0.966. (2) The cultivated land area of the Shule River Basin increased by 604 km2 from 2000 to 2020, and the conversion rate was 67%. From 2000 to 2020, the water area increased by 442 km2, and the conversion rate was 51%. The area of grassland and forest increased by 198 km2 and 12 km2, respectively, and the conversion rate was 68% and 33%, respectively. Forest had the highest transfer rate (0.67). The lowest conversion rate was 0.32 for grassland. (3) The variation coefficient of water and sediment content in Shule River Basin during 1971–2020 was 45.21%, and the highest variation coefficient during 2001–2010 was 49.15%. The lowest variation coefficient was 39.73% during 2011–2020. The annual sediment transport in the Shule River Basin fluctuates greatly and has a high degree of dispersion during 1971–2020. (4) The results of the landscape index in Shule River Basin during 2000–2020 had a small difference, with a difference of less than 0.5. According to the principal component analysis of landscape index and water and sediment content, the maximum patch index (LPI) had the strongest positive correlation with water and sediment content (0.958). The diversity index SHDI had the strongest negative correlation with water and sediment content (−0.995). [ABSTRACT FROM AUTHOR]

Nature-based solutions (NbSs) are considered to form an innovative stormwater management approach that has living resolutions grounded in natural processes and structures. NbSs offer many other environmental benefits over traditional grey infrastructure, including reduced air pollution and climate change mitigation. This review predominantly centers on the hydrological aspect of NbSs and furnishes a condensed summary of the collective understanding about NbSs as an alternatives for stormwater management. In this study, which employed the CIMO (Context, Intervention, Mechanism, Outcome) framework, a corpus of 187 NbS-related publications (2000–2023) extracted from the Web of Science database were used, and we expounded upon the origins, objectives, and significance of NbSs in urban runoff and climate change, and the operational mechanisms of NbSs (including green roofs, permeable pavements, bioretention systems, and constructed wetlands), which are widely used in urban stormwater management, were also discussed. Additionally, the efficacy of NbSs in improving stormwater quality and quantity is discussed in depth in this study. In particular, the critical role of NbSs in reducing nutrients such as TSS, TN, TP, and COD and heavy metal pollutants such as Fe, Cu, Pb, and Zn is emphasized. Finally, the main barriers encountered in the promotion and application of NbSs in different countries and regions, including financial, technological and physical, regulatory, and public awareness, are listed, and future directions for improving and strategizing NbS implementation are proposed. This review gathered knowledge from diverse sources to provide an overview of NbSs, enhancing the comprehension of their mechanisms and applications. It underscores specific areas requiring future research attention. [ABSTRACT FROM AUTHOR]

Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries. [ABSTRACT FROM AUTHOR]

Rainfall is a fundamental input to water resources models. Data products are making rainfall data available in application ready formats. There is a temptation to adopt these datasets directly without undertaking thorough quality assurance checks. The SWTools R package is introduced to download, summarise, check and, if necessary, correct rainfall data from a commonly used data product in Australia, the SILO database. A case study is presented where interpolation to extend a dataset after a station closed introduced non-homogeneity when compared to adjacent sites. SWTools functions are used to identify this change and apply corrections to the data. Calibrating a rainfall–runoff model to the datasets with and without corrections had a substantial influence on the simulated water availability in the catchment, with a 30% reduction in average runoff after the rainfall corrections. This case study highlights the importance of undertaking data quality assurance checks, particularly when applying a model outside the calibration period. [ABSTRACT FROM AUTHOR]

The Yangtze River basin (YRB) and its southern region in China (20°–34°N, 104°–123°E, YRBSC) are highly susceptible to climate change and experience extreme hydrological events. To understand the spatial and temporal distribution of summer runoff in these regions, a statistical diagnosis method was applied using monthly mean runoff grid data, global Sea Surface Temperature (SST) data and meteorological reanalysis data from 1980 to 2022. The analysis revealed that variations in the isotropic phase within the YRBSC and the north–south inverse phase with the Yangtze River as the boundary are the main modes of summer runoff. Furthermore, a strong correlation was observed between winter SST anomalies (SSTAs) and late summer runoff in the YRBSC, as determined through singular value decomposition (SVD). In the first type of positive SSTA years, the eastward advance of the South Asian high pressure (SAH) and westward shift of the subtropical high pressure (SH) result in sufficient water vapour, strong upward movement and increased summer runoff. The second type of positive SSTA years exhibits a westward retreat of the SAH, upward movement north of 28°N, and downward movement between 20°N and 28°N. These conditions, combined with water vapour intermixing and dispersion, lead to a northward increase and southward decrease of summer runoff in the YRBSC, with the boundary at 28°N. Additionally, the study analysed the extreme drought situation observed in the YRB during the summer of 2022. The findings of this research provide valuable insights for ecological environmental protection, water resource planning and management in the region. [ABSTRACT FROM AUTHOR]

Straw mulching is a key method for controlling soil and water losses. Mulching costs may be reduced by applying it in strips rather than over entire areas. However, the effect of different straw mulching methods on the effectiveness of reducing soil erosion is unclear. In this study, the effects of straw mulching strip length (covering 1/4, 1/2, 3/4, and 4/4 of the slope length) and coverage rate (0.2, 0.5, and 0.8 kg m−2) on interception, infiltration, runoff, and soil erosion were investigated at the plot scale using rainfall simulation experiments. The further complex correlations between these variables were analyzed using structural equation modeling (SEM). Bare slopes were used as a control group. The rainfall intensity was chosen to be 60 mm h−1. The results showed that (1) the modified Merriam interception model can describe the change in interception with time under straw mulching conditions well (R2 > 0.91, NSE > 0.75). (2) A total of 35.39–78.79% of the rainwater is converted into infiltration on straw-covered slopes, while this proportion is 36.75% on bare slopes. The proportion of rainwater converted to infiltration was greatest (78.79%) when the straw covered 3/4 of the slope length at a coverage rate of 0.5 kg m−2, which was the most conducive to rainwater harvesting on the slope. (3) Straw mulching protects the topsoil from the impact of raindrops and directly affects the sediment yield (direct effect = −0.44). Straw mulching can also indirectly affect sediment yield by increasing interception, reducing runoff, and decreasing the sediment carrying capacity of runoff (indirect effect = −0.83). Compared with bare slopes, straw covering at least 1/2 of the slope length can significantly reduce runoff yield, but straw covering only 1/4 of the slope length can significantly reduce sediment yield. Moreover, once the straw mulch slope length reaches 3/4 and the coverage rate reaches 0.5 kg m−2, further increases in mulch slope length and coverage rate will not significantly reduce the runoff and sediment yields. These results assessed the effectiveness of different straw mulching methods in controlling soil and water losses on sloping farmland. [ABSTRACT FROM AUTHOR]

Deep learning models have a high application value in runoff forecasting, but their prediction mechanism is difficult to interpret and their computational cost is high when dealing with complex hydrological relationships, limiting their feasibility in hydrological process mechanism analysis. To address these concerns, the paper first introduces an attention mechanism (AM) for building a long short-term memory network (LSTM) model with AM in the hidden layer (AM-LSTM). The AM-LSTM model employs attention layers to improve information extraction from hidden layers, resulting in a more accurate representation of the relationships between runoff-related elements. Furthermore, in the hidden layers of the AM-LSTM model, interpretable spatiotemporal attention units are established, which not only improves the model's prediction accuracy but also provides interpretability to the forecasting process. Furthermore, parallelization techniques are used in the paper to address the issue of model runtime cost. Simultaneously, to address the accuracy degradation caused by parallelization, the paper employs wavelet denoising (WD) techniques and builds the WD-AM-LSTM model. This accomplishment enables the runoff forecasting model to predict runoff in real time and with high accuracy. Based on validation using ten-day runoff data from the Huanren Reservoir in the Hun River's middle reaches, the results show that, with two layers and an eight-batch size, the AM-LSTM model outperforms the LSTM model in capturing spatiotemporal runoff features. During the model testing phase, the AM-LSTM model improves the MAE, RMSE, and NSE performance metrics by 8.46%, 13%, and 3.82%, respectively. The WD-AM-LSTM model effectively mitigates the noise impact caused by data parallelization under the conditions of two layers and a batch size of 512, achieving the same level of prediction performance while reducing computational cost by 92.01%. By incorporating attention mechanisms and wavelet denoising techniques, this study obtains high-speed and accurate predictions with interpretable results. It expands the deep learning models' applicability in ten-day runoff forecasting work. [ABSTRACT FROM AUTHOR]

In the past decades, land use pattern and climate conditions of Shaying River Basin have changed significantly, which will inevitably have a significant impact on the river hydrological situation. Therefore, in order to study the response of the hydrological cycle process of the Shaying River Basin to land use and climate changes, this paper constructed the Soil and Water Assessment Tool (SWAT) hydrological model of the Shaying River Basin based on historical meteorological and hydrological data, and conducted parameter calibration and model verification to quantitatively explore the response of the runoff of the Shaying River Basin to different land use and climate change scenarios. The results showed that: (1) In calibration and verification periods, the determination coefficients (R2) were 0.80 and 0.83 respectively, the Nash–Sutcliffe efficiency coefficients (NSE) were 0.77 and 0.73 respectively, and the percentage deviation (PBIAS) was within ± 25%. (2) Setting different combinations of land use and climate changes into four scenarios S1, S2, S3, and S4, the simulated runoff depths were 257 mm, 298 mm, 259 mm, and 301 mm, respectively. The impacts of land use and climate changes on the annual runoff of Shaying River were 0.9% and 16.1% respectively. (3) In the scenario with 4 °C reduction and 20% precipitation increase and scenario 4 °C increase and 20% precipitation reduction, the maximum and minimum annual runoff were increased by 81.9% and decreased by 70.9% compared with the baseline period, respectively. (4) Under the seven scenarios, the precipitation, temperature and runoff in the middle and late 21st century showed an increasing trend, and precipitation will be the main controlling factor affecting runoff. The annual runoff depth showed an increasing trend, and the change of runoff depth in the lower reaches of the basin will be the most obvious. [ABSTRACT FROM AUTHOR]

• The driving mechanism of high NO 3 – level was revealed for first time in the LRYR. • The contribution of sources to NO 3 – was identified in the LRYR. • Combining multi-isotopic method can accurately identify nitrate sources. • The main sources of nitrate were SAM, CF and SN in the LRYR. • Nitrification was the most key process of nitrogen cycling. The continuous increase of nitrate (NO 3 –) level in rivers is a hot issue in the world. However, the driving mechanism of high NO 3 – level in large rivers is still lacking, which has limited the use of river water and increased the cost of water treatment. In this study, multiple isotopes and source resolution models are applied to identify the driving mechanism of high NO 3 – level and key processes of nitrogen cycling in the lower reaches of the Yellow River (LRYR). The major sources of NO 3 – were sewage and manure (SAM) in the low-flow season and soil nitrogen (SN) and chemical fertilizer (CF) in the high-flow season. Nitrification was the most key process of nitrogen cycling in the LRYR. However, in the biological removal processes, denitrification may not occur significantly. The temporal variation of contributions of NO 3 – sources were estimated by a source resolution model in the LRYR. The proportional contributions of SAM and CF to NO 3 – in the low-flow and high-flow season were 32.5%-52.3%, 44.2%-46.2% and 36.0%-40.8%, 54.9%-56.9%, respectively. The driving mechanisms of high NO 3 – level were unreasonable sewage discharge, intensity rainfall runoff, nitrification and lack of nitrate removal capacity. To control the NO 3 – concentration, targeted measures should be implemented to improve the capacity of sewage and wastewater treatment, increase the utilization efficiency of nitrogen fertilizer and construct ecological engineering. This study deepens the understanding of the driving mechanism of high nitrate level and provides a vital reference for nitrogen pollution control in rivers to other area of the world. [Display omitted] [ABSTRACT FROM AUTHOR]

The rainfall in the upper catchments associated with the hydro peaking events disturbs the regular flow regimes and is often responsible for downstream flooding in alluvial plains. The unregulated release from a hydropower dam abruptly alters the flow dynamics and inundates the adjacent floodplains. In this work, an integrated modeling framework is developed by linking a semi-distributed hydrological model, a two–dimensional hydrodynamic model with a multi-criteria decision approach, to investigate the downstream flooding of Beki River in Assam, India due to the storm runoff and release from the Kurichu Dam, Bhutan. A 48-h rainfall event and dam release flow from 16/06/2022–18/06/2022 is selected for the hydrological simulations. The computed hydrograph is used as an upstream boundary condition in the 2D model to generate the different flow scenarios up to the confluence point of the Brahmaputra River. The results indicate that a peak of 4970.86 m3/s is attained at downstream with an attenuation of 5.976%. The velocity in the stream ranges between 0.77 and 1.14 m/s, and near the meanders, it reaches up to 1.31 m/s. The computed hazard map indicates that some embankments and the road networks in the four revenue circles are significantly affected due to flooding. The proposed methodology and outcomes of the study will be useful for the effective management and monitoring of floods, especially in flood-prone regions. [ABSTRACT FROM AUTHOR]

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model’s performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R² ), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks. [ABSTRACT FROM AUTHOR]