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Modelling the hydrological characteristics of watershed is a method of understanding behavior and simulating the water balance components of watershed for planning and development of integrated water resources management. The soil and water assessment tool (SWAT) physically based hydrological modelling was used for modelling hydrologic characteristics of the Hangar watershed. The data used for this study were digital elevation model (DEM), land use land cover data, soil map, climatological and hydrological data. The model calibrated and validated using measured streamflow data of 13 years (1990-2002) and 9 years (2003-2011) respectively including warm-up period. The SWAT model performs well for both calibration (R2 = 0.87, NSE = 0.82 and PBIAS = +1.4) and validation (R2 = 0.89, NSE = 0.88 and PBIAS = +1.2). The sensitivity analysis, which was carried out using 18 SWAT parameters, identified the 13 most sensitive parameters controlling the output variable and with which goodness-of-fit was reached. The analysis results indicated that the watershed receives around, 9.6%, 59.9%, and 30.5% precipitation during dry, wet and short rainy seasons respectively. The received precipitation was lost by 9.6 %, 40.5%, and 41.3% in the form of evapotranspiration for each seasons correspondingly. The surface runoff contribution to the Watershed were 3.8%, and 79.2% during dry and wet seasons respectively, whereas, it contributes by 17.0% during short rainy seasons.

A multiple scenario-based ensemble prediction (MSEP) method is developed for exploring the impacts of climate and land-use changes on runoff in the Naryn River Basin. MSEP incorporates multiple global climate models, Cellular Automata–Markov and Soil and Water Assessment Tool (SWAT) within a general framework. MSEP can simultaneously analyze the effects of climate and land-use changes on runoff, as well as providing multiple climate and land-use scenarios to reflect the associated uncertainties in runoff simulation and prediction. A total of 96 scenarios are considered to analyze the trend and range of future runoff. Ensemble prediction results reveal that (i) climate change plays a leading role in runoff variation; (ii) compared to the baseline values, peak flow would increase 36.6% and low flow would reduce 36.8% by the 2080s, which would result in flooding and drought risks in the future and (iii) every additional hectare of arable land would increase the water deficit by an average of 10.9 × 103 m3, implying that the arable land should be carefully expanded in the future. Results suggest that, to mitigate the impact of climate change, the rational control of arable land and the active promotion of irrigation efficiency are beneficial for water resources management and ecological environmental recovery.

A hydrodynamic model is developed for rainfall–runoff on asphalt pavement using two-dimensional shallow water equations. A simple yet precise expression is presented to compute flow velocity in order to alleviate the problems associated with numerical instabilities due to small water depths of thin sheet flow. The developed model performed well against measured data and numerical results in two segments. Then, the model was applied to study the influence of highway horizontal alignment, drainage manner, rainfall pattern, surface roughness and geometric parameters on pavement runoff. The results demonstrate that: (i) the influence of highway horizontal alignment on pavement runoff is nonsignificant, while that of drainage manner and the pavement surface roughness is significant. Great differences are observed in flow depth under concentrated drainage and overflow drainage conditions, especially in the area beyond 6 m away from the highway center axis; (ii) remarkable differences in maximum flow depth and peak runoff are presented under uneven and even rainfall conditions, while no great differences are found under three uneven rainfall conditions (front type, center front type and back front type); (iii) the sensitivity of the geometric parameters to the maximum flow depth from strong to weak is cross slope, width, slope length, and longitudinal slope under overflow drainage condition; while that is width, slope length, longitudinal slope and cross slope under concentrated drainage condition. HIGHLIGHTS A hydrodynamic model was developed for simulating rainfall–runoff on asphalt pavement.; A new solution of flow velocity was presented for thin sheet flow.; The influences of highway horizontal alignments, drainage manner, rainfall pattern, surface roughness and geometric parameters on pavement runoff were studied.

Pollutants discharged by roads may impact water bodies and soils. The best method to characterise road runoff is by monitoring, which is not always possible due to human or material constraints. Therefore, prediction tools can be a valuable method to manage road runoff discharges and protect the environment. The present work reviewed and evaluated international tools for road runoff quality prediction, in order to assess if an existing tool could be suitable for wide usage by stakeholders in Europe. Four tools from the USA and Europe were selected and tested at 22 road sites located in regions with annual precipitation values ranging from 500 to 1,000 mm, from seven European countries. The results for the site median concentration (SMC) of total suspended solids (TSS), Zn, Cu, Pb and Cd showed coefficients of determination (R2) from 0.0004 to 0.2890 for the different pollutants and tools. It was concluded that none of the tools could predict the road runoff pollutant concentrations, except for the country where it had been calibrated. The findings support practitioners and researchers all over the world, pointing out directions, and gaps to be filled, regarding the management of road runoff discharges and use of prediction tools. HIGHLIGHTS Tools for road runoff quality prediction could support environmental management.; Evaluation of tools in seven European countries with monitoring data sets.; Site median concentrations of road runoff change over time and region.; It is not feasible that a tool accounts for the whole phenomena of road runoff.; Need for innovative approaches in water management under climate change.

This study aims to investigate the effectiveness of the low impact development (LID) practices on sustainable urban flood storm water management. We applied three LID techniques, i.e. green roof, permeable pavements and bioretention cells, on a highly urbanized watershed in Istanbul, Turkey. The EPA-SWMM was used as a hydrologic-hydraulic model and the model calibration was performed by the well-known Parameter ESTimation (PEST) tool. The rainfall-runoff events occurred between 2012 and 2020. A sensitivity analysis on the parameter selection was applied to reduce the computational cost. The Nash-Sutcliffe efficiency coefficient (NSE) was used as the objective function and it was calculated as 0.809 in the model calibration. The simulations were conducted for six different return periods of a storm event, i.e. 2, 5, 10, 25, 50 and 100 years, in which the synthetic storm event hyetographs were produced by means of the alternating block method. The results revealed that the combination of green roof and permeable pavements have the major impact on both the peak flood reduction and runoff volume reduction compared to the single LIDs. The maximum runoff reduction percentage was obtained as 56.02% for a 10 years return period of a storm event in the combination scenario. HIGHLIGHTS The PEST software is used for the auto-calibration of US EPA-SWMM and the calibration results are statistically significant.; The green roof application has more impact on both the peak discharge reduction and the volume reduction than both the permeable pavements and bioretention cells in the highly urbanized Ayamama watershed.; The combination of green roof and permeable pavements has substantial effect on flood mitigation.

Climate change is a global issue and causes great uncertainties in runoff and streamflow projections, especially in high-altitude basins. The quantification of climatic indicators remains a tedious job for the scarcely gauged mountainous basin. This study investigated climate change by incorporating GCM (CCSM4) using the SDSM method for RCPs in the Jhelum river basin. Historical climatic data were coupled with Aphrodite data to cope with the scarcity of weather stations. SDSM was calibrated for the period 1976–2005 and validated for the period 2006–2015 using R2 and RMSE. Future climatic indicators were downscaled and debiased using the MB-BC method. The de-biased downscaled data and MODIS data were used to simulate discharge of Jhelum river basin using SRM. Simulated discharge was compared with measured discharge by using Dv% and NSE. The R2 and RMSE for SDSM range between 0.89–0.95 and 0.8–1.02 for temperature and 0.86–0.96 and 0.57–1.02 for precipitation. Projections depicted a rising trend of 1.5 °C to 3.8 °C in temperature, 2–7% in mean annual precipitation and 3.3–7.4% in discharge for 2100 as compared to the baseline period. Results depicted an increasing trend for climatic indicators and discharge due to climate change for the basin. HIGHLIGHTS Climate change has great impacts on the hydrological cycle of the river basin.; Statistical downscaling is the more efficient technique to project climate change using GCMs.; Hydrology of the mountainous river basin has been severely impacted by temperature rise and glacier/snow melting.; The climatic and hydrologic projections for the mountainous river Jhelum basin were made for two RCPs (RCP-4.5 and RCP-8.5).

Scientific and accurate prediction of river runoff is important for river flood control and sustainable use of water resources. This study evaluates the ability of a Nonlinear Auto Regressive model (NAR) in predicting runoff volume. Using the Cuntan Hydrological Station in the upper reaches of the Yangtze River as the research object, the model was established based on the runoff characteristics from 1951 to 2020 and tested by NAR. To improve the prediction efficiency, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) preprocessing technique is used to decompose the data. The results show that the coupled CEEMDAN-NAR model has better predictive ability than the single model, with a coupled model deterministic coefficient (DC) of 0.93 and a prediction accuracy of Class A. HIGHLIGHTS Proposed a runoff prediction model using CEEMDAN-NAR hybrid approach.; Decomposition of runoff data using CEEMDAN preprocessing techniques to improve model prediction accuracy.; Using models to predict runoff in the upper Yangtze River from 2005 to 2020 and verifying their accuracy; The CEEMDAN-NAR model provides better predictions than the GRU, NAR and EEMD-NAR prediction models.

Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. The rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash–Sutcliffe efficiency), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93–0.95 and 0.96–0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2–3%.

The present study aims to investigate the homogeneity of runoff time series and also to review the existence of trends in Tale Zang hydrometric station (the hydrometric station at the inflow into the Dez reservoir) runoff, using 61 years (1956–2016) daily recorded data. The Pettit test, which is a common method in investigating the homogeneity of time series, was used to identify change points. Both Mann-Kendall and auto-correlated Mann-Kendall tests were applied to analyze the existence or non-existence of trends in each annual, seasonal, and monthly time series observed in a runoff. In time series, significant trends at the 95% level of confidence were recognized, upper and lower limit values were presented for Sen's slope and it was tested for the increasing or decreasing trends in nature. Based on the results of this study, the significant change point at the 95% level of confidence was recognized in the annual, spring, summer, autumn, March, May, June, July, August, September, and October data sets in 1997,1997, 1999, 1997, 1999, 1999, 1999, 1997, 2000, 2007 and 2008, respectively. Analyzing the existence of a trend at the 95% level of confidence indicated that in spring, and in March, September, and October, for both Mann-Kendall and auto-correlated Mann-Kendall tests, the trend is significant and additive until the change point.

Quantifying the contributions of climatic variables to runoff variance is still a great challenge to water resource management. This study adopted an extended Budyko framework to investigate the effects of terrestrial water storage changes (ΔS) on runoff variance across the source region of the Yellow River, China, during the period of 2003–2014. A new decomposition framework based on the extended Budyko framework was proposed to effectively quantify the contributions of different climatic variables including precipitation, PET and ΔS to runoff variance. The results demonstrated that the extended Budyko framework showed a better performance in presenting the water and energy balance than the original Budyko framework, especially at fine time scales. Meanwhile, the variance in runoff estimated by the new decomposition framework was close to that of runoff observations, indicating that this framework can effectively capture the variation in runoff during 2003–2014. It was also found that precipitation was the most important factor that contributed to runoff changes, while PET made a slightly smaller contribution compared to precipitation. Notably, the results also emphasized the important effects of ΔS on runoff variance at fine time scales, which was useful to better understand the interactions between atmospheric and hydrological processes for regions. HIGHLIGHTS A new decomposition framework was proposed to effectively quantify the contributions of different climatic variables to runoff variance.; Precipitation was the most important factor that contributed to runoff changes, while PET made a slightly smaller contribution compared to precipitation.; The important effects of ΔS on runoff variance at fine time scales can not be neglected.

The contamination of water catchments by nonpoint source faecal pollution is a major issue affecting the microbial quality of receiving waters and is associated with the occurrence of a range of enteric illnesses in humans. The potential sources of faecal pollution in surface waters are diverse, including urban sewage leaks, surface runoff and wildlife contamination originating from a range of hosts. The major contributing hosts require identification to allow targeted management of this public health concern. In this study, two high-performing Microbial Source Tracking (MST) assays, HF183/Bac242 and BacCan-UCDmodif, were used for their ability to detect host-specific Bacteroides 16Sr RNA markers for faecal pollution in a 12-month study on an urban coastal lagoon in Sydney, Australia. The lagoon was found to contain year-round high numbers of human and canine faecal markers, as well as faecal indicator bacteria counts, suggesting considerable human and animal faecal pollution. The high sensitivity and specificity of the HF183/Bac242 and BacCan-UCDmodif assays, together with the manageable levels of PCR inhibition and high level DNA extraction efficiency obtained from lagoon water samples make these markers candidates for inclusion in an MST ‘toolbox’ for investigating host origins of faecal pollution in urban surface waters. HIGHLIGHTS This long-term study (twelve months with bi-monthly sampling) validated the use of HF183/BacCan242 and BacCan-UCDmodif to quantify host-associated faecal inputs in urban surface waters.; Correlations between MST markers, traditional FIB measurements, and physiochemical parameters indicate that the latter may be used to predict the likelihood of human- and canine-associated faecal inputs at this site.

Rainfall infiltration, rainwater retention, runoff and evapotranspiration (ET) are important components of the water balance in green roofs. These components are expected to be influenced by variations in the structural configurations (i.e., substrate layers) of green roofs. This study explores the influence of layered soil and green roof configurations on the rainwater retention capacity (RRC) of the roofs as compared to conventional improvements (i.e., soil conditioning and enhanced substrate depth). Ten different extensive green roof modules were designed by varying the substrate materials, substrate depths, storage/drainage layers and vegetation layers. For all modules, the RRCs ranged from 34 to 59%. The RRCs of layered soil were 1–4% higher than that for single-layer soil. The RRC increased by 13% in the presence of a water storage module. It can be concluded that highest RRC corresponds to a combination of high-permeability soil in the upper layer along with a relatively large water holding capacity in the deep layer. Water storage layer and layered soil could significantly delay the water stress in vegetation. The importance of wick irrigation, vegetation types, back-to-back rain events and the ET rate on the RRC were also discussed. HIGHLIGHTS The capacity of rainwater management and substrate moisture replenishment of green roof with water storage layer was pointed out.; Layered soil could significantly improve the substrate rainwater retention capacity compared with those obtained in single-layer substrates.; The increase rate of rainwater retention declined with an increase in depth beyond a certain point.

The effects of long-term natural climate change and human activities on runoff generation mechanism in the middle Yellow River Basin are long-standing concerns. This study analyzed the characteristics of hydro-climatic variables in the meso-scale Tuweihe catchment based on the observed data for the period 1956–2016 and a climate elastic method. The spatial distribution of dominant runoff processes (DRP) following land use changes in case of rainfall was identified. The results show significant decreasing trends in annual runoff, whereas slightly downward trends are identified for annual precipitation and potential evapotranspiration, 1984 is detected as the mutation year of the study period. The average contributions of climate change and human activities to the runoff reduction in the Tuweihe catchment were 33.2% and 66.8%, respectively. In general, the influences of human activities on runoff are applied mostly through the alteration of the catchment characteristics. The dominant runoff processes changes between 1980 and 2015 show significant effects of large-scale soil and water conservation measures in the Tuweihe catchment. We found that Hortonian overland flow (HOF) and fast subsurface flow (SSF1) were the two main processes in 1980 (30.3% and 34.4% respectively), but the proportion of HOF decreased by 9.6% in 2015. The proportions of saturation overland flow (SOF) and SSF have increased to varying degrees, which means that the catchment is more prone to generate subsurface flow processes. Consequently, under similar rainfall conditions, the runoff yield of flood events decreases in the second period. HIGHLIGHTS Based on GIS to identify the spatial distribution map of the dominant runoff process in the watershed, it can be used as a starting point for the construction of a refined hydrological model in the future.; Attribution analysis of the covariant phenomenon of the runoff law of storms and floods under changing conditions from the mechanism level.

Simulation and forecasting of runoff play an important role in the early warning and prevention of drought and flood disasters. To improve the accuracy of spring runoff simulations, it is important to identify spring runoff production patterns under the combined effect of snow and frozen soil. Based on the theory of the hydrological cycle, three important parameters, which include surface and subsurface runoff, precipitation and temperature, were selected for this study. The trend analysis, statistical analysis and Eckhardt's recursive numerical filtering method were used to qualitatively identify the production patterns of spring runoff, the start and end dates and stage periods of the production patterns. Based on the qualitative identification results, the contribution of each production runoff to the total annual runoff and the total annual spring runoff is quantitatively assessed. The results of the study show that the spring runoff production patterns in the Second Songhua River Basin can be divided into snowmelt runoff, frozen soil conditions of snowmelt–rainfall runoff and rainfall runoff under frozen soil conditions; the snowmelt production is from 21 March, the frozen soil conditions production is from 21 April and the frozen soil ablation ended on 15 June; the shortest phases of each production pattern last 28, 20 and 18 days and the longest last 31, 26 and 24 days. This research provides the basis for improving the principles of production runoff calculation in spring runoff simulation methods. HIGHLIGHTS Determine the main water source of spring runoff in cold regions.; Classify the runoff generation patterns of spring runoff based on the characteristics of runoff generation mechanism in high-latitude mid-temperate regions.; Identify the duration of the runoff according to the characteristics of each runoff pattern.

In South Africa, urban activities contribute high levels of pollution to rivers and groundwater via stormwater runoff. In reducing urban stormwater loads of engineered plant biofiltration, an effective and self-sustaining component of green infrastructure is a treatment option. The country's extensive natural biodiversity offers untapped potential of indigenous species' use in plant biofilters. This paper presents the findings of a plant biofilter column experiment, which investigated the performance of nine indigenous plant species under varied urban stormwater pollutant load strengths. Average significant loads of dissolved Cd (>98%), Cu (>84%), Pb (>99%) and Zn (>95%), as well as NH3-N (>93%), were removed by the plant biofilters, whereas the removal of -N (−37 to 79%) and -P (−81 to 63%) was more variable. Biofilters equipped with indigenous plant species were on average at least 11% more efficient than unvegetated soil in the removal of urban nutrient and metal pollutants. Over time, planted biofilters improved nutrient and metal removal efficiencies. The results support the inclusion of indigenous plants in biofilters within urban stormwater green infrastructure initiatives. Further research to inform plant biofilter design practicalities and assess plant biofilter performance in the field is warranted. HIGHLIGHTS This is the first study investigating nutrient and metal removal by indigenous South African plant species.; Assessment of low, typically observed and high strength pollutant load removal.; Consistently greater removal by vegetation as opposed to unvegetated soil.; -P and dissolved Cu removal influenced by compost.

Runoff processes are the basis for maintaining the safety of river ecosystems. The Yarlung Zangbo River (YZR) faces changes in flow regimes due to the impacts of human activities and climate change, which may threaten its fragile ecosystem. In this study, a new comprehensive system for evaluating runoff variation was constructed to investigate the degree of runoff alternation in the YZR. Based on the data from the primary hydrological stations in the YZR from 1956 to 2000, the assessment indicators of runoff variation were selected by considering the flow, sediment, and water temperature processes. Furthermore, a comprehensive evaluation system for runoff variation was constructed via multiple hydrological analysis methods and vague sets. The results showed that the variation index of the YZR from 2010 to 2013 was 0.15–0.20 compared with the flow regimes of the YZR before 2000, which were within a reasonable range, indicating that the comprehensive runoff conditions of the YZR were not greatly disturbed by human activities such as reservoir construction and river regulation during this period. These results provide a tool for evaluating the runoff change in the YZR and new references for researching runoff variation in other similar watersheds.

Land disturbance and slope length play key roles in affecting runoff-associated nitrogen (N) and phosphorus (P) losses in different forms under natural rainfall. Field monitoring was conducted in nine plots located parallel on a 15° purple slope in southwest China. Three slope lengths (20-, 40-, 60-m) combined with measures of artificial disturbance and natural restoration were implemented. The highest N concentration was observed in soft rainfall events across all plots. The highest P concentration was recorded in heavy rainfall events for the artificially disturbed plots and in soft rainfall events for the naturally restored plots. Land disturbance caused orthophosphate concentration to differ in the 20-m plot, and affected N and P loss amounts in different forms. Slope length caused total dissolved phosphorus concentration to differ in naturally restored plots, and also caused the loss amounts of total dissolved nitrogen and orthophosphate to differ in artificially disturbed plots. Natural restoration reduced loss amounts of total nitrogen and total phosphorus by 62.14–79.05% and 79.28–83.43% relative to artificial disturbance, respectively. Concentrations of nitrate-nitrogen, total phosphorus and dissolved phosphorus were closely correlated with rainfall and runoff variables, respectively, in artificially disturbed plots. Our results highlight the dominant role of natural restoration in reducing erosion and nutrient loss in sloping land.

Global warming influencing regional climate is playing a significant role in triggering recurrent drought. The current study demonstrates a PCA (Principal Component Analysis) driven watershed prioritization in a part of Koel river basin by runoff computation during monsoon season along with assessment of Vegetation Health Index (VHI) derived from MODIS satellite data during the period from 2000 to 2017. Koel river catchment area of 7,261 sq km was divided into 82 sub-watersheds based on drainage networks derived from a Survey of India (SOI) topographical map at scale 1: 50,000. High resolution satellite image of Sentinel-2 was used to prepare a land use land cover map. Soil conservation service curve number method (SCS CN) was used to estimate runoff. The result obtained from runoff estimation of 82 sub watersheds shows high runoff (50 to 60% of rainfall) with 290,000 m3 total runoff volume in the upper and middle parts of the catchment dominated by agricultural/fallow and barren lands, whereas low runoff was estimated (20 to 30%) with 29,467 m3 in the lower catchments where a large area is covered with forests. The value of satellite based VHI ranges between 23 to 53 with major parts of the area exhibiting values less than 30, reflecting poor vegetation health. Most of the sub-watersheds in parts of Ranchi, Lohardaga, Gumla and Khunti districts experienced high total runoff, with poor vegetation health index reflecting more proneness to drought. Watershed prioritization was done based on correlation among four parameters viz., rainfall, drought zones, direct runoff and total runoff through PCA. Strong correlation between total runoff volume and drought areas was used for watershed prioritization, which indicated 42 sub-watersheds (4,703 sq km) in the upper catchment required high prioritization. The outcomes of the study would help proper planning of water resources and soil moisture management to overcome the recurrent drought conditions at watershed level.

With the implementation of low impact development (LID) in urban areas, it is necessary to quantify the actual effectiveness of LID facilities. In this study, a coupled hydrology-hydrodynamic numerical model was utilized to investigate the runoff control effectiveness of permeable pavements in the city centre of Shijiazhuang, China. Two groups of designed rainfall events with the same duration but different rainfall amounts and peak rainfall intensity locations were presented, and the effectiveness of permeable pavement was demonstrated by the reduction in the total runoff volume, water depth, and inundated area. The results indicate that the rainfall amount is the main factor affecting the runoff control of permeable pavements, and their effectiveness decreases with increasing rainfall amounts and peak intensity coefficients. Moreover, permeable pavements are more effective in reducing the residential waterlogging area, and the proportion of the inundated area above a depth of 0.2 m is considerably diminished. This study reveals the response of the runoff control of permeable pavements to different rainfall patterns, which is essential for supporting the design and practical operation of permeable pavements. HIGHLIGHTS Simulation scenarios with different rainfall patterns were presented to investigate the runoff control effectiveness of permeable pavement.; A series of indicators, including runoff volume, water depth, and inundated area, were presented to illustrate the runoff control effectiveness.; The effect of the relationship between rainfall intensity and infiltration rate on runoff generation was analysed.

The water and sediment regimes of the Yellow River are the basis of decision-making of major projects of the Yellow River. Based on water and sediment data at the Huayuankou station, Gaocun station, Aishan station, and Lijin station in the lower reach of the Yellow River, the Mann–Kendall test, the T-test for differences, wavelet analysis, slope change ratio method and the double cumulative curve method were applied to analyze the runoff and sediment regime alteration. The results show that the water and sediment of the lower Yellow River have a significant downward trend, and the annual sediment decreases significantly compared with the annual runoff. The annual runoff and sediment of the four hydrological stations changed around the 1980s and 1990s, respectively. The water and sediment of hydrological stations have periodic variations on multiple time-scales, but the variation scales are different. Precipitation, human activities and other factors lead to the decreasing trend of water and sediment in the lower Yellow River, and their contribution rates to the change of water and sediment are also different. Precipitation contributed 0.15%–8.71% and 0.06%–22.32% to the reduction of runoff and sediment load at the hydrological stations, while human activities contributed 91.29%–99.85% and 77.68%–102.21% to the reduction of runoff and sediment load, respectively. Human activity is the main factor in runoff and sediment reduction. HIGHLIGHTS To statistically detect the trend, change-points and periodic relationship of the annual runoff and sediment discharge of the four main hydrological stations on the lower Yellow River.; To analyze the contribution of climate change and human activities to runoff and sediment in the lower reaches of the Yellow River basin.

Due to the nonlinear characteristics of runoff data and the poor performance of the single prediction model, a weighted integrated modified complementary ensemble empirical mode decomposition (MCEEMD)-based model was proposed to predict the monthly runoff of three hydrological stations in the lower reaches of the Yellow River. In this model, particle swarm optimization (PSO) was used to optimize the parameters of support vector regression (SVR), back propagation neural network (BP), long short-term memory neural network (LSTM) that constitute it. The weight coefficients and frequency terms decomposed by MCEEMD were used to obtain the final prediction results. Results indicated that this model performs better than other models, with the Nash–Sutcliffe efficiency (NSE) reaching above 0.92, qualification rate (QR) reaching above 75% and all error indicators being minimal. In addition, considering the influence of extreme weather and climate anomalies, the integrated model combined the atmospheric circulation anomalies factors and the results can still be improved. It can be verified that this weighted integrated model can be used for the stable and accurate predication of medium- and long-term runoff. HIGHLIGHTS Decomposition of runoff into smooth sequences using MCEEMD reduces the accumulation of errors associated with the CEEMD.; A weighted integrated method was used to develop predictive models based on the MCEEMD decomposition.; In view of the influence of extreme weather and abnormal climate on runoff prediction accuracy, the atmospheric circulation anomaly factors were used as the input data of the model to predict.

Low impact development practices (LID) as alternative measures of urban drainage can be used within the approach of resources recycling and co-management. This study evaluates the potential contribution of a bioretention system to flood control, non-potable water demands (NPD) and resources co-management. Bioretention setups were tested experimentally under variable conditions to identify operational key-factors to multiple purposes. Additionally, the efficiencies obtained for laboratory scale were extrapolated for household and watershed scale, quantifying the indicators of water demand reduction (WDR), energy demand reduction (EDR) and carbon emission reduction (CER) for hybrid systems with LID. The laboratory results indicated that the use of a bioretention with a submerged zone can improve the quality of the water recovered for reuse, while maintaining the efficiency of runoff retention and peak flow attenuation. Comparing the bioretention effluent quality with the Brazilian standards for stormwater reuse, the parameters color, turbidity, E. coli and metals were above the limits, indicating the necessity of a better treatment for solids particles and disinfection. Expanding the analysis to watershed scale, the bioretention helped to reduce NPD demands up to 45%, leading to a reduction in energy demand and carbon emission from the centralized water supply system. HIGHLIGHTS Bioretention prototype is evaluated for flood resilience and non-potable water demands.; Bioretention setups are tested under variable rainfall, soil saturation and head losses.; Water reuse for non-potable demands require better pollutant removal rates.; Stormwater harvesting decreases water stress, energy demands and carbon emission.; Monetary savings through stormwater harvesting were obtained.

Floods are often caused by short-term heavy rainfall. An Integrated Flood Analysis System (IFAS) model is good at runoff simulation and a Long Short-Term Memory (LSTM) model is good at learning massive data and realizing rainfall forecast. In this paper, the applicability of the IFAS model to runoff simulation in the Tokachi River basin and the LSTM model to forecast hourly rainfall was studied, and the accuracy of flood prediction was also studied by inputting the optimal rainfall data forecasted by the LSTM model into the IFAS model. The research results show that the IFAS model can accurately simulate the runoff process in the Tokachi River basin. In the calibration period and the verification period, the Nash–Sutcliffe Efficiency coefficient (NSE) of all simulation results are above 0.75; the LSTM model can achieve forecast hourly rainfall with high precision, the NSE of best forecast results is 0.86; the IFAS model can achieve flood prediction with high precision by using the optimal rainfall data forecasted by the LSTM model, the NSE of simulation result is 0.81. The above conclusions show that it is of great significance to combine the hourly rainfall forecasted by the LSTM model with the IFAS model for flood prediction. HIGHLIGHTS The Integrated Flood Analysis System (IFAS) model can accurately simulate runoff in the Tokachi River basin.; The Long Short-Term Memory (LSTM) model can achieve high-precision hourly rainfall forecast.; The combination of the optimal hourly rainfall predicted by the LSTM model and the calibrated IFAS model can achieve high-precision runoff simulation, which makes a beneficial exploration for the realization of flood forecasting.

Due to the difficulty in monitoring subsurface runoff and sediment migration, their loss loads are still not clear and need further study. This study monitored water and soil loss occurring within experimental field plots for two calendar years under natural rainfall events. The sediment loss load was quantified by considering the corresponding water flow flux and its sediment concentration. The results showed that 60.04% of the runoff and 2.83% of the sediment were lost underground. The annual underground sediment loss reached up to 54.6 kg*ha−1*yr−1. A total of 69.68% of the runoff yield and 67.25% of the sediment yield were produced during the corn planting stage (CPS: March–July). Heavy rain and torrential rain events produced 94.45%, 65.46% of the annual runoff and 94.45%, 76.21% of the sediment yields during the corn-planting stage and summer fallow period (SFP: August–September). The rain frequency, rainfall, and rainfall duration of each planting stage significantly affected the resulting runoff and sediment yield. Measures aimed at the prevention and control of water-soil loss from purple soil sloping land should focus heavily on torrential rain and heavy rain events during the CPS and SFP. This paper aims to provide a practical reference for quantifying the water and soil loss from purple soil sloping cropland. HIGHLIGHTS Subsurface loss was more than 60% of total water loss of purple soil sloping cropland.; Annual accumulated underground sediment loss was considerable and noteworthy.; Surface and subsurface water and soil loss mainly happened in the corn planting stage.; Rainfall pattern controls the surface and subsurface loss of water and soil.

Climate change affects the water cycle in different regions. The response of annual runoff and seasonal distribution to climate change in the upper reaches of the Minjiang River during 2021–2050 was studied by coupling the Statistical Downscaling Model (SDSM) and the Soil and Water Assessment Tool (SWAT). This model was driven by the second-generation Canadian Earth System Model (CanESM2) under RCP2.6, RCP4.5, and RCP8.5 scenarios. The results show that the runoff in the upper reaches of the Minjiang River has a unique response to climate change. The maximum and minimum temperatures will increase with the increase in emissions, especially in December–January. The daily precipitation shows an upward trend, especially in July–August in the RCP4.5 scenario. The annual runoff shows an upward trend with the increase in emissions. Compared with the current increase of 13–26%, the most prominent period is November–April. Because the study area covers high mountains and gorge landforms, the altitude difference is great, and the influence of evapotranspiration and snow melting processes is more prominent, causing the monthly runoff to decrease in June–July with an increase in precipitation. From April to May, precipitation decreased while runoff increased. HIGHLIGHTS The first study on annual runoff, seasonal distribution, and primary tributaries of the upper Yangtze River response to climate change in the Minjiang River upper reaches.; Analysis of differences among the Minjiang upper reaches and other upper reaches of the Yangtze River.; Runoff variation causes about seasons and three typical concentration paths.; Coupling SDSM and SWAT analyzes application.

The acceleration of urbanization has brought significant changes to the urban underlying surface. As a result, the flood disaster caused by stormwater runoff has become increasingly prominent. The infiltration function of the permeable area can lead to flood disasters, but the extent and depth of the effect are still unclear. Therefore, based on the storm water management model (SWMM) and Green-Ampt infiltration model, this paper discussed the effect of improving soil saturated hydraulic conductivity (SSHC) and soil capillary suction head (SCSH) on the stormwater runoff process. The results show that the increase in SSHC and SCSH can significantly reduce runoff and increase infiltration. However, the improvement of SSHC can more effectively alleviate flood disasters compared with the improvement of SCSH. The change of SSHC has a significant effect on the stormwater runoff with a critical SSHC value while the effect can be ignored. In addition, there is a cross value; when the value of SSHC and SCSH is larger than the cross value, the difference between SSHC and SCSH in reducing runoff duration no longer exists. The critical value and cross value are not constant but change with the change of rainfall intensity. HIGHLIGHTS The effect of an urban underlying surface change on stormwater runoff is notionally studied.; Improving SSHC can reduce total runoff and increase total infiltration more effectively than improving SCSH.; There is an effect of rainfall intensity and urban IR in SSHC and SCSH on soil infiltration.; There exists a critical SSHC value, below which the change of SSHC has a significant effect on the stormwater runoff.

First flush is a common phenomenon in urban runoff pollution. Typical cement roof and asphalt road runoff in Beijing, China were monitored for 2 years. Based on the M(v) curve, the suspended solids (SS), chemical oxygen demand (COD), total phosphorus (TP) and particulate phosphorus in cement roof runoff presented a stronger first flush than those in asphalt road runoff. The first flush volume (VFF) of SS, COD, total nitrogen (TN) and TP in asphalt road runoff differed slightly from the cement roof. There were also differences in the first flush assessment depending on which method was used. We proposed a new method based on the runoff depth versus pollutant cumulative mass curve. According to the national standards in China (VFF = 3 mm), various masses of different pollutants, such as 91.42 ± 9.80% (cement roof) and 78.49 ± 19.41% (asphalt road) of SS and 86.85 ± 13.54% (cement roof) and 72.80 ± 25.79% (asphalt road) of COD, can be effectively controlled, but our mass control efficiencies were 55.91%–66.65% when VFF = 1 mm. The new method proposed in this study provides an alternative approach for assessing runoff pollution control efficiency of different VFF. HIGHLIGHTS SS, COD, TP and PP exhibited a stronger first flush in cement roof runoff than in asphalt road runoff by M(v) method.; VFF of SS, COD, TN and TP in asphalt road runoff differed slightly from the cement roof.; Differences were found in the first flush assessment results between M(v) and VFF methods.; A new method based on the runoff depth versus pollutant cumulative mass curve was proposed.

Based on the scenario hypothesis method, this paper applied a Soil and Water Assessment Tool (SWAT) to analyze the sensitivity of runoff to climate and land-use changes in the Longtan basin, China. Results indicated that (1) for every 1 °C increase in temperature, the average annual runoff decreased by 9.9 mm, and the average annual evaporation increased by 9.3 mm. However, for every 10% increase in rainfall, the average annual runoff and evapotranspiration increased by 96.3 mm and 11.53 mm, respectively. Obviously, runoff was more sensitive to the change in rainfall than temperature in the Longtan basin. Meanwhile, (2) forestland could conserve water resources, but its water consumption was larger. Although grassland played a relatively small role in water conservation, it consumed less water. At the same time, increasing the area of forestland and grassland could weaken peak floods, and the water retention function of vegetation could prevent runoff from increasing and decreasing steeply. Therefore, it is worth improving vegetation coverage. HIGHLIGHTS Constructing 25 climate change scenarios based on CMIP5 simulation results and local temporal and spatial variation characteristics.; Constructing 4 land-use scenarios based on its variation characteristics and local development plan.; Simulating and quantifying runoff response to different climate and land-use change scenarios.; Identifying the major impact factors for runoff variability.; Experiment in the karstic basin, where there is a lack of related research.

Many studies have focused on analyzing variation characteristics of the watershed resilience based on different indicators, while few efforts have been made to quantificationally evaluate contributions of climatic and anthropogenic factors to the varied resilience. In this study, we investigate changes in the seasonal runoff resilience of the entire Yangtze River basin during 1961–2014 by using a convex model and a resilience indicator (Pi). The MIKE 11HD model and the regression method were adopted to further differentiate effects of climate variations and human activities. Results show that climate variation (especially droughts and floods) and human activities exert negative and positive effects, respectively, and become primary reasons for falling and increasing trends in entire watershed resilience. These impacts grow with time under the gradually intensified climate variability and human activity. HIGHTLIGHTS Effects of climatic and anthropogenic factors on the varied watershed runoff resilience are quantificationally estimated. Investigating the changes in the watershed resilience in the entire Yangtze River.

In cold region, conceptual models assigned precipitation phase, liquid (rain) or solid (snow), cause vastly different atmospheric, hydrological, and ecological responses, along with significant differences in evaporation, runoff, and infiltration fates for measured precipitation mass. A set air temperature threshold (ATT) applied to the over 30% annual precipitation events occurring with surface air temperatures between −3 and 5 °C resulted in 11.0 and 9.8% misclassified precipitation in Norway and Sweden, respectively. Surface air temperatures do not account for atmospheric properties causing precipitation phase changes as snow falls toward the ground. However, cloud base height and relative humidity (RH) measured from the surface can adjust ATT for expected hydrometeor-atmosphere interactions. Applying calibrated cloud base height ATTs or a linear RH function for Norway (Sweden) reduced misclassified precipitation by 4.3% (2.8%) and 14.6% (8.9%) misclassified precipitation, respectively. Cloud base height ATTs had lower miss-rates with low cloud bases, 100 m in Norway and 300 m in Sweden. Combining the RH method with cloud base ATT in low cloud conditions resulted in 16.1 and 10.8% reduction in misclassified precipitation in Norway and Sweden, respectively. Therefore, the conceptual model output should improve through the addition of available surface data without coupling to an atmospheric model. HIGHLIGHTS This paper lays out two new methods to decrease misclassified precipitation in conceptual surface-based models using air temperature thresholds (ATTs) to distinguish between rain and snow, common in hydrological modeling.; The cloud base height method has not been written about in scientific publications and gives another simple solution to help reduce misclassified precipitation phase in surface-based models.; The linear relative humidity ATT formula suggested in this paper simplifies earlier attempts to include relative humidity in surface-based precipitation phase determination.; The information needed for both methods is widely available in surface meteorological reports and improves surface-based models without coupling to an atmospheric model for precipitation phase.; In this paper, misclassified precipitation between −3 and 5 °C was reduced by 4.3% (2.8%) using cloud base height ATTs, 14.6% (8.9%) using a linear relative humidity formula, and 16.1% (10.8%) using a combination of both methods in Norway (Sweden), showing great potential for reducing model uncertainties when applying this work.

The purpose of this study was to investigate the relationship between stream water quality and the surface runoff rate defined as the ratio of annual surface runoff to annual average precipitation. The surface runoff rate was first estimated in the Han River basin located in South Korea using the calibrated and validated HSPF model. Then a linear regression analysis was performed to investigate the correlation between the computed surface runoff rate and the observed water quality. It was found that there were statistically significant relationships between the surface runoff rate and concentrations of BOD, COD, and T-P and higher surface runoff rate led to the deterioration of water quality in streams. Finally, the applicability of the surface runoff rate as an indicator to measure the impact of land development on stream water quality was evaluated using a receiver operating characteristic (ROC) curve analysis. The ROC curve analysis indicated that the surface runoff rate could be utilized as a useful indicator to illustrate the degradation of stream water quality at the watershed scale. The results from this study also suggest that the surface runoff rate needs to be managed and controlled within about 15% to prevent the degradation of stream water quality. HIGHLIGHTS The HSPF model was applied for simulating hydrological components.; The linear regression analysis and receiver operating characteristic (ROC) curve analysis were utilized to analyze the impact of surface runoff rate on water quality.; There were significant relationships between the surface runoff rate and water quality.; Controlling the surface runoff rate within 15% is suggested to prevent the degradation of water quality.

Throughout Europe, there is a considerable need for investment in the upgrade of sewer systems – due to three main factors: ageing infrastructure, climate change and urban population growth. The need for investments is expected to grow significantly in the years ahead. Trenchless rehabilitation (no-dig) of sewer pipelines is a cost-efficient and environmental friendly method for upgrading existing pipelines with sufficient capacity. This study examines the possibility of applying no-dig to combined sewer systems (CS) with insufficient capacity. In this study, a concept assessment methodology that combines the analytical approaches from stormwater and sewer system assessments is presented. The methodology was tested on a case area that was part of an environmental project in Oslo, Norway. Three alternative concepts were examined; A0: no-dig and low impact development (LID), A1: no-dig, LID and green infrastructure (GI), and A2: CS up-sizing using open-cut methods. The study concludes that CS with insufficient capacity can be rehabilitated using no-dig if LID and GI. The combination of no-dig and LID reduces costs considerably but does involve the risk of damages from uncontrolled surface runoff. The main risk-reduction measure is the development of GI as an integrated stormwater management system that requires cross-sector collaboration within municipalities. HIGHLIGHTS Trenchless rehabilitation of combined sewer systems (CS) in combination with low impact development (LID) can give a 60–90% cost reduction per meter compared to open-cut methods.; An integrated stormwater management system (ISMS), including green infrastructure, should be implemented to avoid damage from uncontrolled stormwater runoff from LID.; Cross-sector collaboration can contribute to intensifying rehabilitation of CS.

Along the coast of the State of São Paulo, Brazil, urban drainage channels introduce a complex mixture of pollutants into the South Atlantic Ocean, that may cause deleterious effects to the aquatic biota. The objective of this study was to analyse, for the first time, the mutagenicity (Ames Salmonella/microsome test) and ecotoxicity (acute and chronic tests, with Daphnia simillis and Ceriodaphnia dubia, respectively) exerted by the diffuse loads discharged in Guarujá, São Paulo coast, Brazil. Water sampling occurred bimonthly between January and July 2018 (rainy season: January through March; dry season: May through July) at four beaches with different profiles of use and land occupation: Tombo (Blue Flag certification), Enseada (high use by tourists), Perequê (fishing community) and Iporanga (conservation unit). No mutagenic potential was detected in the complex mixtures flowing to the study beaches. However, 30 and 80% of the analyses showed acute and chronic toxicities, respectively, mainly in the Enseada and Perequê channels during the rainy season. To improve the environmental quality of these coastal waters and to reduce the ecological risks posed to the aquatic organisms and public health, several actions are imperative, such as the amelioration of the basic sanitation facilities and land regularisation actions. HIGHLIGHTS Evaluation of the genotoxicity and ecotoxicity of urban surface runoff of Guarujá (State of São Paulo, Brazil).; No mutagenicity (Ames Salmonella) was detected in the water samples.; Acute (Daphnia simillis) and chronic (Ceriodaphnia dubia) toxicities were however recorded.; Results suggest potential risk to the environmental and public health.

Highly populated coastal environments receive large quantities of treated and untreated wastewater from human and industrial sources. Bivalve molluscs accumulate and retain contaminants, and their analysis provides evidence of past contamination. Rivers and precipitation are major routes of bacteriological pollution from surface or sub-surface runoff flowing into coastal areas. However, relationships between runoff, precipitation, and bacterial contamination are site-specific and dependent on the physiographical characteristics of each catchment. In this work, we evaluated the influence of precipitation and river discharge on molluscs' Escherichia coli concentrations at three sites in Central Italy, aiming at quantifying how hydrometeorological conditions affect bacteriological contamination of selected bivalve production areas. Rank-order correlation analysis indicated a stronger association between E. coli concentrations and the modelled Pescara River discharge maxima (r = 0.69) than between E. coli concentration and rainfall maxima (r = 0.35). Discharge peaks from the Pescara River caused an increase in E. coli concentration in bivalves in 87% of cases, provided that the runoff peak occurred 1–6 days prior to the sampling date. Precipitation in coastal area was linked to almost 60% of cases of E. coli high concentrations and may enhance bacterial transportation offshore, when associated with a larger-scale weather system, which causes overflow occurrence. HIGHLIGHTS Using a grid-distributed hydrological model to assess hydrometeorological conditions in the absence of direct measurements.; Analysis of a previous uninvestigated area.; New discharge threshold determining Escherichia coli concentration peaks found in Central Italy in order to allow the development of an early warning system for risk assessment.