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Abstract Understanding and anticipating the impacts of climate change on hydrological processes is crucial for sustainable water resource management. This study investigates the projected alterations in streamflow within the Tamor River Basin, Nepal, under changing climatic conditions, utilizing the soil and water assessment tool (SWAT). Future climatic variables, including precipitation, maximum, and minimum temperature, were assessed for the near (2022–2047), mid (2048–2073), and far future (2074–2100) periods under two shared socioeconomic pathways (SSPs): SSP245 and SSP585. Bias-corrected outputs from coupled model intercomparison project, phase 6 (CMIP6) models were integrated into the SWAT model to simulate the basin's hydrological response. Results indicate that, under the SSP245 scenario, annual average maximum and minimum temperatures are expected to rise by ~ 0.046 °C and 0.050 °C, respectively, with a 12.70% increase in precipitation. Similarly, the SSP585 scenario predicts temperature increases of 0.063 °C and 0.085 °C, alongside an 11.90% rise in precipitation. These climatic changes are projected to result in a significant increase in streamflow, with an estimated rise up to 20% by the end of the twenty-first century. The findings of this research provide valuable insights for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover usually affect the hydrological processes of river flows, sediment transport, and water quality on a global scale. Hence, the objective of this paper was to investigate the effect of Land Use Land Cover (LULCC) on the Hydrology of Rwizi River Catchment located in South-western Uganda using appropriate standard techniques and procedures including Soil and Water Assessment Tool (SWAT). Model calibration and validation demonstrated good agreement (R² = 0.76-0.86, NSE = 0.75-0.78). LULC analysis revealed significant increases in built-up areas and forestland, with declines in grazing land and wetlands. These changes were driven by socio-economic factors and hydro-climatic influences. The study found strong correlations between LULC types and sediment yield, highlighting the implications for water quality and erosion control. The results show that urban growth raises surface runoff and peak flows as a result of a rise in impervious surfaces, whereas agricultural intensification raises water demand and lowers base flow, especially during dry spells. Deforestation has led to increased sedimentation rates, degrading water quality. These findings underscore the significant anthropogenic and climatic impacts on LULCC dynamics and hydrological processes in the catchment. The study emphasizes the critical need for integrated watershed management strategies to mitigate these impacts and ensure sustainable water management practices. By advancing understanding in hydrological sciences, this research informs policy and management decisions for sustainable development in tropical river basins globally. [ABSTRACT FROM AUTHOR]

Context: In implementing the Murray–Darling Basin Plan, jurisdictions have defined environmental water requirements (EWRs) for sites along Basin rivers. EWRs are the flows (frequency, magnitude, duration, and timing) required to achieve environmental outcomes; they are derived from flow-ecology relationships by using best available scientific knowledge. Aims: To assess EWR achievement across the Murray–Darling Basin over a 43.5-year period (1 July 1979 – 31 December 2022) that included periods of drought as well as widespread flooding. Methods: By using a published EWR assessment tool, we analysed the achievement of EWRs for small fresh, large fresh, bankfull and overbank flows for 23 sites. Key results: At 65% of stream gauge sites assessed, most EWRs evaluated had not been met. We also compared analyses of different time periods, namely, a 43.5-year period, and a 10-year period since the Basin Plan was legislated. This highlighted some improvement in EWR achievement for the small fresh EWR. Conclusions and implications: Despite some improvements, the continued lack of achievement in meeting EWRs is likely to be a major contributing factor to the ongoing poor health of channel, wetland (including Ramsar wetlands) and floodplain ecosystems across most of the rivers of the Murray–Darling Basin. Environmental water requirements (EWRs) are the flows required to keep aquatic ecosystems healthy. We explored whether EWRs had been met across aquatic ecosystems in the Murray–Darling Basin over the past four decades, finding that at 65% of assessed sites they had not been achieved. The most recent decade since Basin Plan legislation has seen some improvement in EWR achievement for small freshes, possibly reflecting active environmental watering. However, across most rivers of the Murray–Darling Basin, poor health of channel, wetland (including Ramsar wetlands) and floodplain ecosystems remain. [ABSTRACT FROM AUTHOR]

Predicting streamflow for an ungauged river is essential for effective water resource management, flood risk mitigation, ecological protection, and infrastructure planning, providing critical insights despite the absence of direct measurement data. Therefore, the objective of this paper is to assess the use of a rainfall-runoff model for predicting streamflow in the ungauged Awun River Watershed, Kwara State, Nigeria. Due to the absence of measured data, regional calibration techniques were employed, utilizing data from nearby gauged river with similar hydrological characteristics. The hydrologic model predicted a peak discharge of 2164 m³/s and a total runoff volume of 19967.78 m³ during the modeling period, providing valuable insights for flood forecasting and water resource planning. Performance evaluation metrics indicated a Nash-Sutcliffe Efficiency (NSE) of 0.54 and a Mean Error (ME) of 0.33, reflecting moderate agreement between observed and simulated runoff data. The Percent Bias (PBIAS) of 49.25% highlighted a tendency towards overestimation. Furthermore, a high R-squared (R²) value of 0.89 demonstrated that the model successfully explained 89% of the variance in observed runoff, effectively capturing the key hydrological characteristics of the Awun River watershed. This modeling framework is valuable for land-use planning, water resource management, decision-making, and flood risk assessments in the Awun River region.

Changes in land use and land cover usually affect the hydrological processes of river flows, sediment transport, and water quality on a global scale. Hence, the objective of this paper was to investigate the effect of Land Use Land Cover (LULCC) on the Hydrology of Rwizi River Catchment located in South-western Uganda using appropriate standard techniques and procedures including Soil and Water Assessment Tool (SWAT). Model calibration and validation demonstrated good agreement (R² = 0.76-0.86, NSE = 0.75-0.78). LULC analysis revealed significant increases in built-up areas and forestland, with declines in grazing land and wetlands. These changes were driven by socio-economic factors and hydro-climatic influences. The study found strong correlations between LULC types and sediment yield, highlighting the implications for water quality and erosion control. The results show that urban growth raises surface runoff and peak flows as a result of a rise in impervious surfaces, whereas agricultural intensification raises water demand and lowers base flow, especially during dry spells. Deforestation has led to increased sedimentation rates, degrading water quality. These findings underscore the significant anthropogenic and climatic impacts on LULCC dynamics and hydrological processes in the catchment. The study emphasizes the critical need for integrated watershed management strategies to mitigate these impacts and ensure sustainable water management practices. By advancing understanding in hydrological sciences, this research informs policy and management decisions for sustainable development in tropical river basins globally.

Climate change poses significant challenges to water resources and streamflow in Ethiopia, a country highly dependent on agriculture and vulnerable to environmental shifts. This paper reviews the current state of knowledge on climate change impacts on streamflow in Ethiopia, emphasizing factors driving these changes and drawing insights from relevant studies. The analysis encompasses hydrological responses to climate change, including alterations in precipitation patterns, temperature fluctuations, and changes in water availability. Additionally, the study examines the impact of land use changes on streamflow dynamics. Comparative insights from neighboring countries and river basins further illuminate the broader regional implications of climate change on water resources. According to the previous research reviewed in this paper, climate change, land use change, and increment in extreme events (drought) have affected the stream flow over the last decades. The findings underscore the urgent need for adaptive strategies and sustainable water management practices to mitigate the adverse effects of climate change on streamflow and ensure water security in Ethiopia and beyond. HIGHLIGHTS The review paper provides localized insights into climate change impacts.; It contributes valuable information for water resource managers and policymakers.; It assesses the risks posed by climate change to Ethiopia's hydro-meteorological conditions.; It explores the socioeconomic consequences of hydro-meteorological changes induced by climate change.; The review contributes to the scientific knowledge base.;

ABSTRACT The Box Jenkins methodology for modeling and forecasting was used to identify suitable models and forecast future stream flow values for average and maximum discharge of Kaduna River at Wuya gauging station in Niger state and to predict extreme events of flooding. The average and maximum monthly stream flow data used for the analysis were obtained from the Nigerian Hydrological Serves Agency from January 1988 to September 2021 (33 years). Autoregressive Integrated Moving Average ARIMA (0,1,4)(0,1,4) and Autoregressive Integrated Moving Average ARIMA (0,1,3)(0,1,3) models were the most suitable models found adequate to forecast the average and maximum stream flow of Kaduna River at Wuya gauging station.. Both models achieved the lowest normalized Bayesian Information Criterion (BIC) which were the criteria for chosen the best model after prognosis. The selected models also have the best Ljung-Box Q statistical significance P Value of all the models identified. Diagnostic checks of the models revealed that the models selected have a residual that was white noise. The models used for forecasting had the highest Coefficient of determination (R2) of 0.795 and 0.813 with the least Mean absolute percentage error (MAPE) of 28.785%, 34.461% respectively for ARIMA (0,1,4) (0,1,4) and ARIMA (0,1,3) (0,1,3). Both model forecast was reasonable having their MAPE between 21% to 50%.

The importance of the optimal and efficient use of all available water resources becomes noticeable when today due to successive droughts and a decrease in rainfall, the surface water resources are running out. Runoff and surface water resources are some of the primary and vital available water resources, and hence, modeling and predicting their behavior are especially critical. In the current research, the aim was to model the stream flow of the Chehel Chai watershed in Golestan province, Iran, using the data of the stream flow and precipitation for a period of 45 years. For this reason, 4 machine learning algorithms namely, Extreme Learning Machine (ELM), Random Forest (RF), Gaussian Process Regression (GPR), and Gene Expression Programming (GEP) were used. The data were entered into the modeling in the form of different scenarios consisting of the stream flow and precipitation with varying lags of time. The results showed that scenario M2 (using only stream flow data with two time lags) in the ELM (extreme learning machine) model with the values of RMSE (root mean square error) =0.984 (m3/s) and R2=0.613 had the most accurate performance and predictions among all the models and scenarios.

Competition between aquatic organisms is heavily influenced by abiotic factors in the environment, specifically flow regime in aquatic systems. Flow regime has been shown to significantly affect the way in which a species uses the environmental resources and alterations in flow can exasperate competitive advantages by congenerics. However, little work has concentrated on the competitive outcome between native and invasive organisms as a function of flow regime. Here, we sought to uncover how competition between two crayfish species (the native virile crayfish [Faxonius virilis] and invasive rusty crayfish [Faxonius rusticus]) was affected by varying flow rates. To do this, we size-matched crayfish and quantified three separate behaviors (food use, shelter use, and fights) of crayfish in four different discharge levels (no discharge—0 cm3/s; low discharge—116 cm3/s; intermediate discharge—345 cm3/s; high discharge—450 cm3/s). We found that the number of foraging bouts was significantly (p < 0.001) influenced by discharge levels for both species, where rusty crayfish foraged more often in no and discharge. The number of times crayfish sheltered also varied between species and was significantly (p < 0.001) altered by discharge level, where rusty crayfish sheltered more often in no discharge and virile crayfish sheltered more in low and intermediate discharge levels. Similarly, the number of fights that crayfish engaged in also significantly (p < 0.001) depended upon species and discharge levels. Rusty crayfish engaged in more agonistic activities in no and high discharge levels while virile crayfish participated most often in low and intermediate discharge levels. [ABSTRACT FROM AUTHOR]

Rivers are the main source of fresh water in mountainous and downstream areas. It is crucial to investigate the possible threats of climate change and understand their impact on river watersheds. In this research, climate change's impact on the mountainous watershed of the Jajrood River, upstream of Latyan Dam in Iran, was assessed by using a multivariate recursive quantile-matching nesting bias correction (MRQNBC) and the soil and water assessment tool (SWAT). Also, this study considered ten global circulation models (GCMs) from the coupled model intercomparison project phase VI (CMIP6). With a higher correlation coefficient, the MIROC6 model was selected among other models. For the future period of 2031–2060, the large-scale outputs of the MIROC6 model, corresponding to the observational data were extracted under four common socioeconomic path scenarios (SSPs 1–2.6, 2–4.5, 3–7.0, 5–8.5). The bias was corrected and downscaled by the MRQNBC method. The downscale outputs were given to the hydrological model to predict future flow. The results show that, in the period 2031–2060, the flow will be increased significantly compared to the base period (2005–2019). This increase can be seen in all scenarios. In general, changes in future flow are caused by an increase in precipitation intensity, as a result of an increase in temperature. The findings indicate that, although the results show an increase in the risk of flooding, considering the combined effects of three components, i.e., increased precipitation concentration, temperature, and reduced precipitation, climate change is intensifying the problem of water scarcity. [ABSTRACT FROM AUTHOR]

AbstractThe shrinking of water resources along with the frequent droughts plays an effective role in intensifying the water crisis. Climate change adversely affects the hydrological cycle at the basin level. These are often reflected in the water and food insecurity, frequent hydrologic extremes, frequent drought, flooding, and deteriorating ecosystem health. This study aims to calibrate and validate the stream flows in Awash River basin using a distributed hydrological Water Evaluation And Planning (WEAP) model. Mean monthly Precipitation; temperature and stream flow are used to run the model. The model was calibrated and validated using mean stream flow. The trends of climate over the study basin were also investigated using Mann Kendall trend tests. The results revealed that the value of R2 ranged from 0.73 to 0.91 during calibration periods. On the other hand, the value of Nash—Sutcliffe efficiency (NSE) is ranged from 0.65 to 0.83. However, the values of coefficient of determination (R2) during validation periods ranged from 0.54 to 0.91 and the values of Nash—Sutcliffe efficiency (NSE), ranges from 0.50 to 0.84. Thus, The results at two important parts of the main river (upstream and downstream) gauges showed good agreement between the simulated and observed stream flow. The basin receives a significant amount of water during the rainy season (June to September). The mean annual rainfall of the basin ranges from 100 to 1700 mm with great spatiotemporal variation. The annual stream flow of the Awash River basin shows a sharp decreasing trend (Z = −0.120 during the study period. Thus, the findings of this study could provide insights for concerned bodies to implement effective water resources management techniques. The study also proposed that, to ensure the sustainability of water resources, better long-term management policies are required to be implemented in the basin and to meet future downstream water needs.

Abstract The environmental impacts caused by fish farming can lead to changes in aquatic ecosystems, especially in fish communities. In this study, we investigated possible changes in water quality, in the number of species and individuals of the same species caused by the construction of fish nurseries in dammed Amazonian streams. For this purpose, four streams located in the municipalities of Novo Airão and Presidente Figueiredo were selected. Samples were collected in streams without fish farming and in streams with stretches used for fish farming, where collections took place both downstream and upstream. The fish were captured, identified, quantified and the water was collected for physical and chemical analyses, in addition to the environmental characterization that was carried out. Comparisons were made using fish richness and abundance indices between the sampled points and correspondence analysis of the species identified at the collection sites, using the Mantel test and principal component analysis (PCA) for the environmental characteristics of the water and variables physical and chemical, and permutational multivariate analysis of variance (PERMANOVA) to verify the possible effects of species composition on the sampled conditions and on the analyzed environmental variables. In total, 2,302 fish belonging to 53 species, 15 families and six orders were found. We conclude that the Amazonian streams dammed for fish farming, under the analyzed conditions, can be characterized as environments with different and specific levels of richness and abundance, however, the data set analyzed in this study did not show that these characteristics are directly related to productive activity. We recommend that further studies be carried out following the current research.

Abstract Catchments characteristics, such as geomorphology, geology, soil, land use, and climatic variables, play an important role in total stream flow responses, a critical resource for people and the environment. Most of the previous literatures were applied a conventional statistical regression model to assess the relationship between landscape-climate descriptors, and streamflow and PET. However, a conventional statistical regression model didn’t consider dependence of explanatory variables that were collected or extracted across both space and time. This paper investigated the impacts of landscape attributes and climate variables on catchment scale temporal variation of total streamflow and spatio-temporal variation of potential evapotranspiration (PET) in the Mille catchment using multiple linear regression techniques, and the importance of this study was to test spatial autocorrelation in the spatial regression model which is required to properly assess and quantify the relationship between hydrological regime response components and Landscape-climate descriptors in a catchment with topographically complex, and high spatio-temporal climatic variation like in our case study area, the Mille catchment. Statistical regression analysis revealed significant relationships between streamflow and climate variables, especially with rainfall. Mean maximum temperature is the most dominant factor controlling temporal variation of potential evapotranspiration at a monthly scale, whereas NDVI is the most significant factor that controls the spatial variability of PET. The multiple regression model shows that 91.1% of temporal variation in streamflow was accounted for rainfall, whereas, 96.6% and 78.4% of temporal and spatial variation in potential evapotranspiration was accounted for in maximum temperature and NDVI, respectively. Methods also can be applied to catchments with similar landscape attributes and climate variables.

This study implemented a coupled SWAT-FEM simulation model to evaluate the impacts of land use land cover and climate change scenarios (LCS) on the water resources of river catchments in Chennai, India. The land use land cover data were obtained by merging the source data from National Remote Sensing Centre (NRSC) and International Water Management Institute (IWMI). Climate change simulations were obtained from four global climate models (GCM), including GFDL Baseline Scenario (1981–2000), GFDL A1B Scenario (2081–2100), CCSM4 Baseline Scenario (1986–2005), and CCSM4 A1B Scenario (2081–2100). The LCS predicted temperature increases of 2.32 °C and 1.74 °C for GFDL and CCSM4, respectively, by the end of the century. The water use predictions suggested increases above 20% in the utilization of water by 2100, inferring the noticeable dynamics of inter-annual as well as inter-month variability in water resources in the river basins of Chennai soon. The study is novel through its implementation of a coupled modeling approach to improve the practicality of the SWAT-FEM model and to deliver useful projections of land and climate change impacts on hydrological responses. The results provide useful insights into how the variability in climate conditions alters the spatiotemporal water responses in catchment scales. [ABSTRACT FROM AUTHOR]

Catchments characteristics, such as geomorphology, geology, soil, land use, and climatic variables, play an important role in total stream flow responses, a critical resource for people and the environment. Most of the previous literatures were applied a conventional statistical regression model to assess the relationship between landscape-climate descriptors, and streamflow and PET. However, a conventional statistical regression model didn't consider dependence of explanatory variables that were collected or extracted across both space and time. This paper investigated the impacts of landscape attributes and climate variables on catchment scale temporal variation of total streamflow and spatio-temporal variation of potential evapotranspiration (PET) in the Mille catchment using multiple linear regression techniques, and the importance of this study was to test spatial autocorrelation in the spatial regression model which is required to properly assess and quantify the relationship between hydrological regime response components and Landscape-climate descriptors in a catchment with topographically complex, and high spatio-temporal climatic variation like in our case study area, the Mille catchment. Statistical regression analysis revealed significant relationships between streamflow and climate variables, especially with rainfall. Mean maximum temperature is the most dominant factor controlling temporal variation of potential evapotranspiration at a monthly scale, whereas NDVI is the most significant factor that controls the spatial variability of PET. The multiple regression model shows that 91.1% of temporal variation in streamflow was accounted for rainfall, whereas, 96.6% and 78.4% of temporal and spatial variation in potential evapotranspiration was accounted for in maximum temperature and NDVI, respectively. Methods also can be applied to catchments with similar landscape attributes and climate variables. [ABSTRACT FROM AUTHOR]

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This study evaluates the use of different time series decomposition methods in association with echo state networks (ESNs), deep echo state networks (DeepESNs), and next generation reservoir computing (NGRC) models to predict the natural flow of water in two hydropower reservoirs with horizons of one, seven, fourteen, and twenty-one steps ahead. The Coyote optimization algorithm is used to adjust the hyperparameters of the ESNs. The use of reservoir computing (RC) methods with Coyote optimization algorithm, a swarm intelligence approach, is compared with the use of variational mode decomposition (VMD), empirical wavelet transform, and empirical mode decomposition. Analysis of the results shows that the use of signal decomposition methods in association with RC can improve the accuracy and performance of the prediction model. The results are compared using different statistical metrics, showing that, in most cases, decomposition methods can improve the forecasting performance of RC. Overall, NGRC outperformed ESN and DeepESN in most of evaluated cases. The best results were obtained using VMD for the forecast horizons of 7, 14, and 21 days ahead, with a decrease in mean absolute error in a range from 43.70% to 88.88% compared to all the other models. Using a Diebold–Mariano test, the results show that the use of VMD is statistically significant in all the cases, with a significant value of 1% in all tested cases of ESN forecasting. • NGRC outperforms ESN and DeepESN in hydropower forecasts 7, 14, and 21 days ahead. • Decomposition methods enhance forecasting accuracy. • VMD is the most effective decomposition for 7, 14 and 21-day horizons forecasts. • VMD's forecast improvements are statistically confirmed via the DM test. [ABSTRACT FROM AUTHOR]

As air temperature increases, it has been suggested that smaller individual body size may be a general response to climate warming. However, for ectotherms inhabiting cold, highly seasonal environments, warming temperatures may increase the scope for growth and result in larger body size.In a long‐term study of individual brook trout Salvelinus fontinalis and brown trout Salmo trutta inhabiting a small stream network, individual lengths increased over the course of 15 years. As size‐selective gains and losses to the population acted to reduce body sizes and mean body size at first tagging in the autumn (<60 mm) were not observed to change substantially over time, the increase in body size was best explained by higher individual growth rates.For brook trout, increasing water temperatures during the spring (when both trout species accomplish most of their total annual growth) was the primary driver of growth rate for juvenile fish and the environmental factor which best explained increases in individual body size over time.For brown trout, by contrast, reduction in and subsequent elimination of juvenile Atlantic salmon Salmo salar midway through the study period explained most of the increases in juvenile growth and body size.In addition to these major trends, a considerable amount of interannual variation in trout growth and body size was explained by other abiotic (stream flow) and biotic (population density) factors with the direction and magnitude of these effects differing by season, age‐class and species. For example, stream flow was the dominant growth rate driver for adult fish with strong positive effects in the summer and autumn, but flow variation could not explain increases in body size as we observed no trend in flow.Overall, our work supports the general contention that for high‐latitude ectotherms, increasing spring temperatures associated with a warming climate can result in increased growth and individual body size (up to a point), but context‐dependent change in other factors can substantially contribute to both interannual variation and longer‐term effects. [ABSTRACT FROM AUTHOR]

Estimation of dependable flow at different levels is considered one of the important parameters for planning, designing, and managing water-related projects, including hydropower generation, irrigation systems, water use, and river and reservoir sedimentation. For this purpose, the Flow Duration Curve (FDC) is developed by analyzing the available stream flow data at the site when adequate length of observed data are not available. The FDC is a valuable tool for determining the flow patterns of a river and estimating dependable flow. The FDC provides the percentage of time duration during which a stream flow (monthly, seasonal, or annual) is exceeded over a recorded period for a particular river or stream, which can be constructed by adopting the empirical method. By analyzing the FDC, the flow patterns of the Baspa river at the Kuppa barrage were determined, and the dependable flow was estimated. This paper presents a study on the assessment of monthly, seasonal, and annual dependable flow through the FDC by adopting an empirical method for the river Baspa at Kuppa barrage. The study suggests the dependable flow at different percentage levels like 75%, 90%, and 100% obtained from FDCs could be used for planning irrigation, hydropower, and drinking water projects in the study area. [ABSTRACT FROM AUTHOR]

The shrinking of water resources along with the frequent droughts plays an effective role in intensifying the water crisis. Climate change adversely affects the hydrological cycle at the basin level. These are often reflected in the water and food insecurity, frequent hydrologic extremes, frequent drought, flooding, and deteriorating ecosystem health. This study aims to calibrate and validate the stream flows in Awash River basin using a distributed hydrological Water Evaluation And Planning (WEAP) model. Mean monthly Precipitation; temperature and stream flow are used to run the model. The model was calibrated and validated using mean stream flow. The trends of climate over the study basin were also investigated using Mann Kendall trend tests. The results revealed that the value of R² ranged from 0.73 to 0.91 during calibration periods. On the other hand, the value of Nash--Sutcliffe efficiency (NSE) is ranged from 0.65 to 0.83. However, the values of coefficient of determination (R²) during validation periods ranged from 0.54 to 0.91 and the values of Nash--Sutcliffe efficiency (NSE), ranges from 0.50 to 0.84. Thus, The results at two important parts of the main river (upstream and downstream) gauges showed good agreement between the simulated and observed stream flow. The basin receives a significant amount of water during the rainy season (June to September). The mean annual rainfall of the basin ranges from 100 to 1700 mm with great spatiotemporal variation. The annual stream flow of the Awash River basin shows a sharp decreasing trend (Z = -0.120 during the study period. Thus, the findings of this study could provide insights for concerned bodies to implement effective water resources management techniques. The study also proposed that, to ensure the sustainability of water resources, better long-term management policies are required to be implemented in the basin and to meet future downstream water needs. [ABSTRACT FROM AUTHOR]

Estimation of stream flows is very important in basin and flood management. It is a very difficult topic to predict hydrological events that do not show linearity and depend on many parameters. In this study, streamflow modeling has been made and compared by using Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Deep Learning (DL) optimizations using monthly average streamflow data of Mersin Lamas River. When the results are examined, it has been observed that the best method in solving the problem is ANN and SVM methods, which give more unsuccessful results. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Nash Sutcliffe model Efficiency coefficient (NSE), Mean Absolute Relative Error (MARE) and Coefficient of Determination (R2) were used as comparison criteria. 1 input model of DL gave the best result (RMSE= 0.7647, MAE=0.7370, NSE=0.9724, MARE=27.9326, R²=0.9962). [ABSTRACT FROM AUTHOR]

Study region: Lake Tana sub-basin, Upper Blue Nile basin, Ethiopia. Study focuses: This study evaluated the degree to which climate is changing in the region, and its impact on stream flow of watersheds simulated by Soil Water Assessment Tool (SWAT) under the Representative Concentration Pathway (RCP8.5) emission scenario using six climate models including CanESM2, EC-EARTH, CNRM-CM5, HadGEM2- ES, NORESM1-M, and CSIRO-Mk3–6–0 by comparing the last thirty years of the past century (1971–2000) and the same years of this century (2071–2100). Bias correction for maximum temperature, minimum temperature, and rainfall data obtained from all climate models have been done using CMhyd software. The SWAT model is calibrated and validated using eleven sensitive hydrological parameters. New hydrological insights: The result revealed that the change in maximum temperature ranges from 2.93 °C (November) and 5.17 °C (March), and the change in minimum temperature also ranges from 3.08 °C to 4.79 °C on a monthly basis. Rainfall is expected to increase up to 29.75% (November) and decrease up to 9.26% (March) in different seasons. Due to the change in climate, a flow is predicted to increase up to 27.82%, 27.47%, 26.47%, and 24.97% in Ribb, Gilgel Abay, Gumara, and Megech watersheds, respectively, and it is also decreasing in winter and spring seasons. On average, the streamflow is expected to increase by 5.89%, 5.63%, 4.92%, and 4.87% in Ribb, Gumara, Megech, and Gilgel Abay watersheds, respectively.

Henan province is located in four major watersheds of the Haihe River, the Yellow River, the Yangtze River and the Huaihe River with a prominent water resources issue. It is of great significant to analyze runoff evolution to water resources management. Characteristics of river runoff variation in the Henan Province were investigated at annual and monthly scales by using recorded runoff gauged at six representative hydrometric stations and the Mann-Kendall rank correlation test method. The results show that the measured stream flow in the Henan Province presentes a decreasing trend over the recent 60 years with a slight increasing after 2000. The linear decreasing rate tends to decrease from west to east. The magnitude of runoff decrease after abrupt change year is over 15%• relative to that before mutation year for all six hydrometric station s with earlier abrupt change occurring in the northern rivers and higher change occurring in the southern rivers. The monthly average flow shows an overall trend of decrease, and the Henan section of Haihe River Basin and the Henan section of the Yellow River Basin are decreased significantly, reaching the significance level of 5%-. Except for the south sect ion of the Yellow River Basin, the monthly average flow in the whole province has little change in the annual distribution. [ABSTRACT FROM AUTHOR]

The aim of the present study was to detect land cover change for the last three decades and estimate its impact on stream flow dynamics under the current and future scenarios. Landsat satellite imageries were used for land cover classification for selected years (1987, 2002 and 2017). The effect of land cover change on stream flow was evaluated using SWAT model, and its performance was tested. The findings indicated significant land cover changes in the last three decades. Coverage of cultivated land (17%) and bare land (1%) in 1987 increased rapidly to 43 and 17% in 2017. Furthermore, there was 70% agreement between observed and simulated stream flow in both the calibration and validation phases. The stream flow of the watershed was changing significantly in response to land cover dynamics. The evaluation of hydrological response due to land cover change showed a monthly mean stream flow decrease by 12.7 m3/s (−38%) in 1987 and 2017 in dry months. Nevertheless, it showed a monthly mean stream flow increase by 53.06 m3/s (23%) in wet months. Similarly, between the years 2017 and 2047, the stream flow was estimated to increase by 42.84 m3/s (15%) for wet months and a decrease by 13.52 m3/s (−66%) for dry months. Generally, it can be concluded that land cover changes have significant impact on stream flow. Hence, establishing strong land use and water resource policies is an essential means for better evaluation and monitoring of water resource in the study area. [ABSTRACT FROM AUTHOR]

Background. Efforts are needed to evaluate heavy metal concentrations in aquatic sediments which serve as repositories and as sources of contamination of other habitats. Objectives. The present study assessed temporal changes in the pH, particle size and concentration of metals in sediments of a mining-affected river in Zambales, Philippines. Methods. Sediment samples were collected at different periods in four stations along the river using a modified Van Veen grab. The samples were subjected to quartering, air-drying, and sieved through a mesh of 40 mm prior to X-ray fluorescence spectroscopy analysis using Thermo Scientific Niton XL3t to determine metal concentrations. The sediment particle size was analyzed by the sieve method and soil pH by the electrode method. Results. Measured metal concentrations in the sediment were as follows: iron (Fe)> calcium (Ca)> chromium (Cr)> nickel (Ni)> manganese (Mn) with averages of 174.6 mg/g, 7.89 mg/g, 6.54 mg/g, 4.82 mg/g, and 2.75 mg/g dry matter (DM), respectively. The mean pH of riverine sediments was generally neutral, except for Station 4. In terms of particle size, silt and clay fractions increased in the upstream station but decreased in the midstream and downstream stations across the sampling periods. The flooding brought by Typhoon Koppu resulted in lowered concentrations of Fe, Mn, Cr, and Ni and an upsurge in Ca and potassium levels. Discussion. Most of the heavy metals (Fe, Ni, Cr, Mn) exceeded the probable effect level (PEL) for inorganics in sediments, suggesting that the adverse effects of these metals on the environment and aquatic organisms are expected to occur frequently. In comparison with Station 4, which was less affected by siltation, there was a sign of metal enrichment in the area. This indicates that soil erosion and runoff, which might have been triggered by vegetation loss, mineral extraction, and agricultural activities, had a significant impact on the quality of river sediments. Conclusions. The findings of the study point to the need for the formulation and implementation of appropriate regulatory measures for the protection and rehabilitation of the heavy metal-loaded river. Competing Interests. The authors declare no competing financial interests.

Abstract Freshwater organisms are threatened by changes in stream flow and water temperature regimes due to global climate change and anthropogenic activities. Threats include the disappearance of narrow‐tolerance species and loss of favorable thermal conditions for cold‐adapted organisms. Mayflies are an abundant and diverse indicator of river health that performs important functional roles. The relative importance of key hydro‐environmental factors such as water temperature and flow volumes in structuring these communities has rarely been explored in the tropical regions of Africa. Here, we investigate the response of mayfly species diversity to these factors in the Luvuvhu catchment, a strategic water source area in the arid northeastern region of South Africa. Mayfly larvae were sampled monthly in stones‐in‐current biotopes across 23 sites over a one‐year period. The relationship between these environmental drivers and mayfly diversity was modeled using linear mixed effects models (LMMs) and a model‐based multivariate approach. Threshold Indicator Taxa Analysis (TITAN) was used to model the response of mayfly species to important gradients and identify thresholds of change. Site‐specific characteristic were the most important predictor of mayfly diversity, and there was considerable variation over time, with mayfly diversity peaking during winter. Along this, gradient temperature was the best predictor of assemblage structure, with five out of six reliable indicator species being cold‐adapted, and a community threshold response at 19°C. Results support laboratory‐based thresholds of temperature for mayfly species survival and development, extending empirical evidence to include field‐based observations. Increased global (climate change) and local (riparian vegetation removal, impoundments) changes are predicted to have negative impacts on mayfly diversity and ultimately on ecosystem function.

With the development of landscape ecology and hydrology, the research in relationship between landscape pattern changes and hydrological process is pushed into a further level, and also to proposed a higher request to the development of related models. The SWAT-VRR model (Soil and Water Assessment Tool with Vegetation Runoff Regulation) is a distributed hydrological model to better show the effect of land use landscape change on hydrological processes. The Qihe watershed in Danjiangkou reservoir area was selected in a case study, and the applicability of SWAT-VRR was verified firstly, then set up two typical water-soil conservation scenario simulations to analyze the response to landscape pattern changes under SWAT-VRR. The results indicate that, the SWAT-VRR model achieved a notable improvement in monthly and daily hydrological simulation comparing to the SWAT model (Soil and Water Assessment Tool) simulation during 2003 to 2006. Compared with the two land use scenarios, the 300 m forest riparian along the Qihe River can adjust the runoff more effectively than the area greater than 15° is forest, and show a strong stability in water-soil conservation effect during different rainfall. The SWAT-VRR provides a more accurate scientific basis to estimate landscape pattern change on stream flow.

This study implemented a coupled SWAT-FEM simulation model to evaluate the impacts of land use land cover and climate change scenarios (LCS) on the water resources of river catchments in Chennai, India. The land use land cover data were obtained by merging the source data from National Remote Sensing Centre (NRSC) and International Water Management Institute (IWMI). Climate change simulations were obtained from four global climate models (GCM), including GFDL Baseline Scenario (1981–2000), GFDL A1B Scenario (2081–2100), CCSM4 Baseline Scenario (1986–2005), and CCSM4 A1B Scenario (2081–2100). The LCS predicted temperature increases of 2.32 °C and 1.74 °C for GFDL and CCSM4, respectively, by the end of the century. The water use predictions suggested increases above 20% in the utilization of water by 2100, inferring the noticeable dynamics of inter-annual as well as inter-month variability in water resources in the river basins of Chennai soon. The study is novel through its implementation of a coupled modeling approach to improve the practicality of the SWAT-FEM model and to deliver useful projections of land and climate change impacts on hydrological responses. The results provide useful insights into how the variability in climate conditions alters the spatiotemporal water responses in catchment scales.

Droughts are one of the multidimensional extreme events that negatively influence agriculture, water availability and food security in the World. The Upper Indus Basin (UIB) is the major river system in the world that contributes 80% of the surface water availability in Pakistan. The objective of the current study is to analyze the spatial patterns of drought severity and magnitude using the standardized precipitation index and streamflow drought index. These drought indices were used to estimate the characteristics and a variation of the droughts annual, pre-monsoon and post-monsoon seasons for the period of 1991–2017. Results revealed that the northeastern side of the Upper Indus Basin was greatly influenced by the droughts as compared to the southwestern side. The duration from 1997 to 1998 was strongly correlated with El Nino and La Nina (ENSO) in a strong way. This type of ENSO indicates the severe drought for 5 years from 1998 to 2002 in the UIB. The episode of the warm water oceans mechanism was also strengthened in 1996, 1997, 1998, 2000, 2001 and 2002 drought years. La Nina effect has been observed in the wet and cold phase during the year of 1999. The study indicated the severe SDI drought in the years of 1991 and 2001. The study also indicated that the streamflow drought index in the post-monsoon season was marked by extreme level drought magnitude at G. Habibullah (2001), Muzaffarabad (2001), Yogo (1993) and Dhok Pathan (1998). The results of the current investigation would help policymakers to introduce new types of drought risk reduction measures for drought preparedness at the regional and the national level in Pakistan. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mann-Kendall trend test were used over the spatio-temporal scale. • Impact of land use was determined by MODIS (MCD12Q1) and digital elevation model. • Land use changes are major factors to change the hydrological process of CRB. Adverse impacts of climate change on the ecosystem have been a significant concern in the last decades. However, the studies related to the impacts of climate change on water resources, especially in northern Pakistan are of great importance as this region is the main supplier of freshwater to the downstream areas. So, the present study was carried out in Chitral River Basin (CRB) to investigate the long term climatic and topographic changes. Spatiotemporal datasets from MODIS Land Cover Type product (MCD12Q1) from 2001 to 2018, ground-based observational climatic and hydrological data were used. Moreover, the Mann-Kendall trend test, linear regression analysis, correlation, and Sen's slope values for the mean annual and seasonal flows were assessed. The acquired results show that land use changes are the key non-natural factors in transforming the ecological and hydrological processes of CRB. The mixed and evergreen forest, shrubland, savannas, and barren land respectively decreased from 0.07 to 0.03%, 0.07 to 0.05%, 3.64 to 3.25%, and 70.10 to 67.17%, from 2001 to 2018. In addition, a considerable increment in snow cover from 8.79% to 10.71%, and slight increment in grasslands, wetlands, and croplands were also found between the period of observation. In addition, total annual precipitation and mean annual stream flow showed slight upward trends. Annual increment in total rainfall and snow covered area could be the possible reasons for the observed increased river flow. [ABSTRACT FROM AUTHOR]