Journal of Hydrologic Engineering

Streamflow regionalization is a technique used in areas where hydrological data are scarce or nonexistent. Although numerous studies have been conducted with the aim of improving this technique, unsatisfactory results are still evident. This study, therefore, aims to propose and evaluate the performance of a new explanatory variable for regionalization, which represents the streamflow formation process and considers the actual evapotranspiration (ETR) obtained from remote sensing products. For this purpose, the regional regression method was used to estimate long-term mean streamflow and minimum flow with 90% of permanence in time. The explanatory variables were the precipitated volume ($Peq$), precipitated volume minus the empirical value of 750 mm ($Pe{q}_{750}$), water balance for each segment of the water course ($W{B}_{eq}$), and water balance for each hydrologically homogenous region ($W{B}_{eqM}$). These variables were obtained using a combination of drainage area, precipitation, and ETR. The ETR was estimated using two remote sensing products: MOD16 and Global Land Evaporation Amsterdam Model. The models of streamflow regionalization were evaluated by statistical, physical, and risk analyses. The study was applied for Grande River basin. All the variables, with the exception of $Peq$, presented good statistical performance, good representativeness of regionalized streamflows, and safe estimates for the planning and management of water resources. The $WBe{q}_G$ was the most recommended variable for streamflow regionalization in Grande River basin, because it considers variations in edaphoclimatic and vegetative conditions along the basin. This work contributes to improving the predictive capacity of the streamflow through a method that is potentially applicable to other areas of study, allows easy physical interpretation, utilizes easily obtained variables, and represents the streamflow formation process.

A step-drawdown test is a common hydrogeological investigation tool employed for identifying the hydraulic characteristics of an aquifer as well as assessing the efficiency of the pumping conditions. Several graphical and optimization-based solution techniques have been devised for analyzing data sets obtained from step-drawdown tests to retrieve aquifer and well loss parameters. This study aimed to introduce the use of a differential evolution (DE) algorithm as an alternative and practical option for interpretation of step-drawdown tests conducted in confined aquifers. The proposed estimation procedure was tested for a large number of synthetically generated noise-free and noisy data sets for evaluating its estimation performance. The DE search method exhibited superior accuracy with considerably higher convergence speed when compared with other competitive and widely used population-based algorithms. Sensitivity analysis was performed to explore the capability of the method in estimating each investigated variable. The DE algorithm was implemented for analyzing a real field data set as well, and it was able to produce parameter estimation results consistent with those reported in previous studies. As demonstrated in this study, the DE search method can be an eligible algorithm for solving inverse problems in the field of hydrogeology, regarding its accuracy, high convergence speed, robustness, and simplicity in coding.

Changes in precipitation and flow regimes may cause more catastrophic floods in the future. This study examines the trends in a subbasin of Bartin Stream in the Western Black Sea Basin, Turkey, and questions the influence of non-stationarity by performing flood frequency analyses on one stationary and nine non-stationary models by incorporating the covariates of time and annual precipitation as a linear function of their location and scale parameters. In this context, variabilities in annual precipitation, (AP), mean annual flow (MAF), and maximum daily flow (MDF), of five precipitation gauging stations and four streamflow gauging stations were examined using the Mann-Kendall and modified Mann-Kendall tests, and Sen’s slope estimator. The breakpoints were identified in the series by employing homogeneity tests, and the relative changes before and after the shifts were analyzed. The modified Mann-Kendall showed a significantly increasing trend at Bartin precipitation station, with an increase of $0.69\mathrm{mm}/\mathrm{year}$, and significant decreasing trends at Cide and Ulus precipitation stations, with a rate of change $7.10\mathrm{mm}/\mathrm{year}$ and $3.26\mathrm{mm}/\mathrm{year}$, respectively, at the 5% significance level. Moreover, significant downward trends were detected at Kocairmak station (MAF varied $0.33{\mathrm{m}}^3/\mathrm{s}/\mathrm{year}$ and MDF varied ${4.55\mathrm{m}}^3/\mathrm{s}/\mathrm{year}$); at Kocanaz station (MAF varied ${0.04\mathrm{m}}^3/\mathrm{s}/\mathrm{year}$); and at Bayiryuzu station (MDF varied $1.49{\mathrm{m}}^3/\mathrm{s}/\mathrm{year}$). The greatest relative changes in MAF and MDF were found at Kocairmak station, which can be considered as an indicator of non-stationarity. Thus, a non-stationary frequency analysis of the MDF series at Kocairmak station was performed, and the best-fitting model was found to use the AP series as a covariate, the same as other streamflow stations in the basin. In all stations, the non-stationary design flood quantiles were estimated to be greater than the stationary ones by up to 30%, which should be taken into account, particularly for designing flood mitigation facilities.

The present study investigates the spatiotemporal variabilities of long-term (1950–2016) rainfall and regional groundwater levels for annual and seasonal periods over the west coast of India. The study area is a narrow strip of land between Western Ghats (mountainous terrain) and the Arabian Sea, extending over 1,500 km from south to north. The Mann Kendall (MK) and Sen’s slope estimator established the long-term trend and magnitude of rainfall and groundwater. The nature of trends in the time series of hydroclimatic variables was identified through singular spectrum analysis (SSA). The SSA extracted nonlinear trends along with the shape for both increasing and decreasing trends. Annual and southwest monsoon rainfall exhibited prominent decreasing trends. The percentage departure analysis of rainfall revealed that earlier decades (1950–1980) were the wettest, followed by the drier decades (1980–2016) for Periyar, Varrar, and Netravati and vice versa for Vasishti and Bhatsol. The wavelet spectra for rainfall indicated short- and long-term modulations. The long-term groundwater level trends of 725 wells on the entire west coast showed a significant decline in 13% of wells, and 6% of wells indicated increasing trends. The Monte Carlo–based numerical investigations on the modified MK (mMK) test power indicated the influence of parent distributions on trend detection. The field significance of trends at a 5% significance level was examined using the bootstrap test. The precipitation data were then compared with groundwater level variation at each site, and correlations were established. The declining southwest monsoon rains and their uneven spatial distribution could be attributed to a subsequent decline in the region’s postmonsoon groundwater levels.