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Evaporation is one of the vital components of hydrological cycle. Precise estimation of pan evaporation (Epan) is essential for the sustainable water resources management. The current study proposed a novel approach to estimate daily Epan across the humid region of Iran using support vector regression (SVR) technique coupled with Krill Herd Algorithm (SVR-KHA). Meteorological data were collected from three stations (Bandar Abbas, Rudsar, and Osku) over a period from 2008 to 2018 and used for application. Of the data, 70% were used for training and remaining 30% were used for testing. The study considered seven different combinations of input variables for predicting daily Epan at each station. The influence of KHA hybridization is examined by comparing results of SVR-KHA algorithm with simple SVR through a multitude of statistical performance evaluation criteria such as coefficient of determination (R2), Wilmot’s index (WI), root-mean-square error (RMSE), Mean Absolute Error (MAE), Relative Root Mean Square Error (RRMSE), Mean Absolute Relative Error (MARE), and several graphical tools. Single input SVR1 model hybrid with KHA (SVR-KHA1) showed improved performance (R2 of 0.717 and RMSE of 1.032 mm/day) as compared with multi-input SVR models, e.g., SVR5 (with RMSE and MAE of 1.037 mm/day and 0.773 mm/day), while SVR7 model hybridized with KHA (SVR-KHA7), which considers seven meteorological variables as input, performed best as compared with other models considered in this study. Epan estimates at Bandar Abbas and Rudsar by SVR and SVR-KHA are similar (with R2 statistics values of 0.82 and 0.84 at Bandar Abbas station, and 0.88 and 0.9 at Rudsar station, respectively). However, better improvements in Epan estimates are observed at Osku station (with R2 of 0.91 and 0.86, respectively), which is situated at interior geographical location with a higher altitude than the other two coastal stations. Overall, the results showed consistent performance of SVR-KHA model with stable residuals of lower magnitude as compared with standalone SVR models.

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...

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.

Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.

In the original soil and water assessment Tool (SWAT) model (SWAT-O), the contributions of shallow aquifers and deep aquifers to streamflow are simulated using the linear reservoir method. The movement of groundwater was limited in the hydrological response unit which is a minimum calculation unit in the SWAT. However, this computational method may not be suitable for the areas where a groundwater system is complicated, and the river is predominately recharged by groundwater. In this paper, we proposed an enhanced groundwater module which divides shallow aquifers into upper and lower aquifers, integrates all the deep aquifers of a sub-basin into a regional aquifer, and simulates interactive water amount between lower aquifer and deep aquifer using water depth difference. The modified groundwater module was introduced to the original SWAT model, hereby referred to as SWAT-MG. The SWAT-MG and SWAT-O models were applied to the Hailiutu River catchment, which is a semi-arid wind sandy grass shoal catchment. Results showed that both models underestimated streamflow in peak flow, while the simulated streamflow of SWAT-MG was closer the observed values than that of SWAT-O. Three evaluation criteria (NSE, RSR, PBIAS) were applied to evaluate the performance of the models and the results showed that SWAT-MG had a better performance than SWAT-O. The baseflow index of Hailiutu River which was calculated by the results of SWAT-MG was 96.78%, which means the streamflow is predominately recharged by groundwater, and this conforms to the actual situation of Hailiutu River catchment. This indicates that a SWAT model with a modified groundwater module could better represent the groundwater flow behavior in the study area.

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 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.

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.