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Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used. HIGHLIGHTS Examining the applicability of TRMM and CFSR daily precipitation products in simulating runoff from a large watershed.; Calibrating a hydrological model in the absence of data associated with extensive anthropogenic activities for flood prevention and diversion.; Performing streamflow simulations using a SWAT model and parameter calibration and sensitivity analysis using a SUFI-2 algorithm.

Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm optimization (PSO) and coupled particle swarm optimization-multi-verse optimizer (PSOMVO) and a time series model, namely the bi-linear (BL). So, the boosted MLP-PSO, MLP-PSOMVO, and MLP-BL models are developed. The accuracy of all the boosted models is compared with the classical MLP and BL by the statistical metrics used. It is concluded that all the boosted models developed at the studied stations lead to superior modeling results of the daily streamflows to the classical MLP; however, the boosted MLP-BL models generally outperformed the MLP-PSO and MLP-PSOMVO ones.

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and acc...

Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three types of data preprocessing methods, namely Principal component analysis, Random forest, and Relief algorithm were used for finding the best input variables for prediction LWL by the SVR and SVR-GWO models. Before the LWL simulation on monthly time step using the hybrid model, an evolutionary approach based on different monthly lags was conducted for determining the best mask of the input variables. Results showed that based on the random forest method, the best scenario of the inputs was Xt&minus, 1,&nbsp, Xt&minus, 2,&nbsp, 3,&nbsp, 4 for the SVR-GWO model. Also, the performance of the SVR-GWO model indicated that it could simulate the LWL with acceptable accuracy (with RMSE = 0.08 m, MAE = 0.06 m, and R2 = 0.96).

Located in Batam City, Duriangkang Dam plays an important role in the area's clean water provision regarding the fact that it is the first and largest estuary dam in Indonesia. However, with its total effective volume of 107,000,000 m 3 , Duriangkang Dam is now heavily polluted due to the enormous wastewater, both domestic and industrial, that are directly discharged into the dam. Moreover, the practice of fish farming has also contributed in making it worse. From the water sampling taken from 2016 to 2018, the water condition in Duriangkang Dam is no longer classified as the 2 nd class of raw water standard in Indonesia. This study employs the WASP model to evaluate and simulate the possible solution to control the water pollution rate in Duriangkang Dam. The water quality indicators, namely dissolved Oxygen (DO), total phosphorus (TP), and biochemical oxygen demand (BOD) will be modeled in this study. The calibration and verification results indicate the model shows excellent fitting for DO and TP but slightly less for BOD. Under several scenarios, it is identified that the best way to control the pollution rate within the dam is by implementing scenario 8 that totally eliminates the fish farm and domestic wastewater load.

Growing population, increasing urbanization, and rural to urban migration, coupled with the ongoing climate change, threaten the sustainability of cities, particularly in developing countries. Previous studies indicate numerous deficiencies in the water supply and sewage systems of Islamabad, however, a comprehensive insight into the water security assessment has not been carried out. Therefore, this study is aimed at assessing the urban water security of Islamabad by taking both human and environmental aspects into consideration. In principle, we achieve this objective by implementing the Water Security Assessment Framework, using five distinct parameters to calculate an urban water security index. The water supply dimension incorporates availability, accessibility, affordability, and the quality of drinking water in the city, whereas, sanitation and health dimension measures access to improved drainage systems as well as the state of overall hygiene of the city inhabitants. Furthermore, the water economy dimension includes water productivity and investment aspects in the study area, while the environment and ecosystem dimension looks into the current state of natural water bodies. Similarly, overall management and public support for freshwater resources are measured in the society and governance dimension. In general, we attempt to better comprehend water-security nexus in the federal capital considering it as a prerequisite to ensure a sustainable future for the city dwellers.

This paper presents the application of sluice gates to control the excess runoff in the wide and very mild channel in an industrial area which is located in the Cikande, Banten, Indonesia. St. Venant equation is used and solved by 1-D model HEC-RAS. The result shows that the main channel can be utilized as a long storage or long reservoir if it is combined with sluice gates. Various scenarios of gates opening and closing operation have been conducted in order to determine the most effective gates operation. Two options of gate operations have been analyzed. The first option is the automatic option in which the gates are able to open and close automatically at a certain water level in the main channel. The second is manual option where the gates constantly open at a certain height. The results show that the main channel successfully reduced the 10 years of flood discharge from 37.45 to 17.37 m3/s for the automatic gates and from 37.45 to 17.38 m3/s for the constant opening. Although both options give the same result for controlling the flood, there are several differences between the options, especially the capability of both the systems to provide raw water for the industrial purposes. In conclusion, the automatic operation is the most effective operation, despite the fact that it is more expensive and harder to implement than the manual operation.

The Hailiutu River basin is a typical area of semi-arid sandy land with relatively flat topography, the surface of which is covered by undulating dunes and the development of the river system is not obvious. The dominant hydrological cycle is precipitation infiltration through dunes to aquifers followed by discharge to rivers. Therefore, the baseflow is an important component of the streamflow in this basin, but few studies for the baseflow characteristics have been conducted. The isotope tracer technique was applied to investigate the contributions of groundwater, soil water, and surface water to streamflow during the flood period. The results showed that the contributions of these components to streamflow were approximately 70%, 27%, and 3% respectively. Several automatic baseflow separation methods including filtering and recursive digital filtering (RDF) techniques were adopted to separate the baseflow from the streamflow and the adaptabilities of these methods were evaluated. All the filtering methods including Hydrograph Separation Program (HYSEP) and UK Institute of Hydrology’s method (UKIH) clearly underestimated the baseflow when compared with the standard baseflow results which were provided by the previous study using the tracer-based method in this basin, while the recursive digital filtering with Eckhardt filter technique (RDF-E) produced better performance. In the nonprecipitation period, the RDF-E method misidentified quick flow values which caused deviations between the separated baseflow and the above standard value. Hence, we proposed a modified automatic baseflow separation method called RDF-M by introducing the precipitation information into RDF-E. In comparison with the above standard, the RDF-M method provided similar baseflow results which were consistent with the actual situation of the Hailiutu River basin.

For sustainable water resource management, it is important to determine the relationship between streamflow and other variables that influence availability of water resources. However, many catchments do not have sufficient data to allow for a more detailed study of these relationships. We faced a similar challenge in the Chengcun Basin (limited historical data: from 1986–1999); and therefore we used a new approach to overcome this. We found that, using nonparametric trend methods in conjunction with the climate elasticity analyses and the 2D visualization of hydrologic data, it is possible to assess the relationships between streamflow and other hydro-climatic variables. In the past, streamflow trends in the basin were more correlated with precipitation than with potential evapotranspiration (PET). In addition, there is a gradual shift in the hydrological regime of the catchment, which may affect the occurrence of available water resources and activities that depend on them. In addition, based on our climate sensitivity analyses, the streamflow is dependent and more sensitive to variations in precipitation than to PET (δQ=0.79δP+0.42δE; precipitation elasticity, ɛP =1.32; PET elasticity, ɛE =−2.10). Therefore pending more detailed studies, the use of our approach will provide a rapid means to assess the variation of water resources (streamflow) in a watershed. In the future, we hope to carry out related research in other watersheds and also perform a more detailed studies to improve upon the results of this study.

Soil erosion, more than any other factor, has more significant and direct impacts on a hydrological watershed in terms of environmental problems. After deriving the erosion hotspots, this s...

The Xinanjiang rainfall–runoff model has been successfully applied in many humid and sub-humid areas in China since 1973. The wide application is due to the simple model structure, the clear physical meaning of the parameters and the well-defined model calibration procedure. However, due to a data scarcity problem and short runoff concentration time, its applications to small drainage basins are difficult. Therefore, we investigate the model application in Lianghui, a small drainage basin of Zhejiang province in China. By using generalized likelihood uncertainty estimation (GLUE) methodology, the sensitivity of parameters of Xinanjiang model was investigated. The data clearly showed that equifinality phenomenon was evident in both water balance parameter calibration and runoff routing parameter calibration procedures. The results showed that K (evapotranspiration conversion coefficient), C s (recession constant in channel system) and S m (areal free water storage capacity of surface soil) are the most sensitive parameters for the water balance parameter calibration while C s , S m and W m (mean area tension water capacity) are the most sensitive parameters for runoff routing parameter calibration. The conclusion is favourable for understanding parameters of Xinanjiang model in order to provide valuable scientific information for simulating hydrological processes in small drainage basins.

The Hailiutu River basin is a typical semi-arid wind sandy grass shoal watershed in northwest China. Climate and land use have changed significantly during the period 1970&ndash, 2014. These changes are expected to impact hydrological processes in the basin. The Mann&ndash, Kendall (MK) test and sequential t-test analysis of the regime shift method were used to detect the trend and shifts of the hydrometeorological time series. Based on the analyzed results, seven scenarios were developed by combining different land use and/or climate situations. The Soil Water Assessment Tool (SWAT) model was applied to analyze the impacts of climate variability and land use change on the values of the hydrological components. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) was applied to enhance the spatial expressiveness of precipitation data in the study area during the period 2008&ndash, 2014. Rather than solely using observed precipitation or CMADS precipitation, the precipitation values of CMADS and the observed precipitation values were combined to drive the SWAT model for better simulation results. From the trend analysis, the annual streamflow and wind speed showed a significant downward trend. No significant trend was found for the annual precipitation series, however, the temperature series showed upward trends. With the change point analysis, the whole study period was divided into three sub-periods (1970&ndash, 1985, 1986&ndash, 2000, and 2001&ndash, 2014). The annual precipitation, mean wind speed, and average temperature values were 316 mm, 2.62 m/s, and 7.9 &deg, C, respectively, for the sub-period 1970&ndash, 1985, 272 mm, 2.58 m/s, and 8.4 &deg, C, respectively, for the sub-period 1986&ndash, 2000, and 391 mm, 2.2 m/s, and 9.35 &deg, C, respectively, for the sub-period 2001&ndash, 2014. The simulated mean annual streamflow was 35.09 mm/year during the period 1970&ndash, 1985. Considering the impact of the climate variability, the simulated mean annual streamflow values were 32.94 mm/year (1986&ndash, 2000) and 36.78 mm/year (2001&ndash, 2014). Compared to the period 1970&ndash, 1985, the simulated mean annual streamflow reduced by 2.15 mm/year for the period 1986&ndash, 2000 and increased by 1.69 mm/year for the period 2001&ndash, 2014. The main variations of land use from 1970 to 2014 were the increased area of shrub and grass land and decreased area of sandy land. In the simulation it was shown that these changes caused the mean annual streamflow to decrease by 0.23 mm/year and 0.68 mm/year during the periods 1986&ndash, 2000 and 2001&ndash, 2014, respectively. Thus, the impact of climate variability on the streamflow was more profound than that of land use change. Under the impact of coupled climate variability and land use change, the mean annual streamflow decreased by 2.45 mm/year during the period 1986&ndash, 2000, and the contribution of this variation to the decrease in observed streamflow was 27.8%. For the period 2001&ndash, 2014, the combined climate variability and land use change resulted in an increase of 0.84 mm/year in annual streamflow. The results obtained in this study could provide guidance for water resource management and planning in the Erdos plateau.

The impact of the El Nino-Southern Oscillation (ENSO) phenomenon within the Huangfuchuan watershed, one of the major first- order sub-basins in th e middle region of the Yellow River, has not clearly been established. Consequently, the co-varying relationships between rainfall and El Nino/La Nina spanning the period 1954 to 2010 are investigated. Trends and step changes in annual rainfall are investigated with the Mann-Kendall and the distribution free cumulative sum (CUSUM) tests. Wavelet transfor ms are employed to perform spectral analysis of temporal variations in rainfall rates within the watershed. Cross wavelet and wavelet coherence transforms are used to study localized co-varying relationships between rainfall and ENSO index. Results from statistical tests indicate that rainfall in the Huangfuchuan watershed is declining, although not significantly. In addition, wavelet coherency and cross wavelet analysis, and comparison of the extracted dominant annual rainfall and 2–7 year ENSO signals demonstrate that ENSO events impact Huangfuchuan rainfall with El Nino corresponding to rainfall de cline and La Nina to rainfall increment with a semiannual to annual lag.

This study aims to investigate trends in streamflow and precipitation in the period 1954–2010 in a semiarid region of the Yellow River watershed, Huangfuchuan basin, China. The combination of the wavelet transform and different Mann-Kendall (MK) tests were employed to figure out the basic trends structure in streamflow and precipitation and what time scales are affecting the observed trends. The comparative analysis with five MK test methods showed that the modified MK tests with full serial correlation structure performed better when significant autocorrelations exhibited for more than one lag. Three criteria were used to determine the optimal smooth mother wavelet, the decomposition level and the extension mode used in the discrete wavelet transform (DWT) procedure. The first criteria referred to the relative error of the wavelet approximated component and the original series. The second one was the relative error of MK Z-values of approximation component and the original series. Additionally, a new criterion (Er), based on the relative error of energy between the approximate component and the original series, was proposed in this study, with better performance than the previous two criteria. Further, a new powerful index, the energy of the hydrological time series, was proposed to verify the dominant periodic components for the observed trends. The analysis indicated that all monthly, seasonal and annual streamflow showed significant decreasing trends, while no significant trends were found in precipitation. Results from the DWT and MK tests revealed that the main factors influencing the trends in the monthly and seasonal series in Huangfuchuan watershed are intra-annual cycles, while the leading factors affecting the trends in the annual series are decadal events. Different driving factors (e.g., seasonal cycles, solar activities, etc.) related to the periodicities identified in these data types resulted in this discrepancy.