Journal of Hydrologic Engineering

Classification of watersheds into hydrologically similar groups prior to regionalization is essential for predicting streamflow in ungauged basins. The objective of this study was to improve the efficiency of classification for finding hydrologically similar watersheds by applying dimensionality reduction techniques. A new nonlinear dimensionality reduction technique called Isomap is applied for the first time along with nonlinear principal component analysis (NLPCA) and principal component analysis (PCA) to improve the efficiency of watershed classification. The dimensionality reduction techniques were applied to the selected attributes of 68 watersheds of the Arkansas-White-Red River basins and the Lower Mississippi River in the United States. The resulting reduced dimensions were used to classify the 68 watersheds into five homogenous groups, using a threshold of 90% accounted variance of the original data by $K$-means cluster analysis (KCA). In order to validate the results of classification, reference groups of watersheds identified using the runoff signatures of the 68 watersheds were compared with the classification results obtained using Isomap, NLPCA, and PCA employing a modified method of calculating the similarity index. Statistics of the sample distribution of the similarity index were used to analyze the efficiency of each technique. It was observed that the Isomap technique performed better than NLPCA or PCA for classification of watersheds, finding with the most hydrologically homogenous watersheds in each classification group. This study shows that the Isomap technique can be effectively used to extract and preserve the underlying essential structure of the watershed data that are relevant for hydrological processes to obtain more accurate watershed classification.

The release of high-resolution climatic projections through the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative provides unique opportunities to assess the risk of hydrological extremes in various world regions. The Brahmaputra is the second largest river in South Asia and is the main freshwater source of Bangladesh. Climate change in the Brahmaputra River Basin is a serious concern for Bangladesh, because of its possible influence on high flows (floods) and low flows (hydrological droughts). This research assesses the impact of climate change on the frequency, magnitude, and timing of extreme discharges and on the mean monthly discharges of the Brahmaputra River. Bias-corrected data from an ensemble of 11 climate projections with the representative concentration pathway (RCP) of RCP8.5 from the CORDEX-South Asia database were used to force the hydrological model Soil and Water Assessment Tool (SWAT). Results show that floods are likely to become more frequent in the future, and their magnitude will also become more severe. Hydrological droughts are projected to become less frequent in the future and their magnitude to become less severe. The average timing of both floods and hydrological droughts is projected to shift earlier compared to the present hydrological regime. Mean monthly discharges are projected to increase in the premonsoon months and decrease in the postmonsoon months.

The present study analyzes the various uncertainties and nonstationarity in the streamflow projections over the Wainganga River Basin, India, under representative concentration pathways (RCPs) 4.5 and 8.5 using the 3-layer variable infiltration capacity (VIC-3L) model. The uncertainties associated with the multiple climate models, parameters, and return levels were modeled using reliability ensemble averaging (REA), Bayesian analysis, and the delta method, respectively. With the recent development of extreme value theory (EVT), the annual maximum flows for the past and future were modeled with nonstationary assumption and validated using the Akaike information criterion (AIC) value and likelihood ratio test. The results obtained from the study indicate that the stationary assumption is a good fit for the observed and stabilized radioactive forcing scenarios (RCP4.5); whereas, for the highest greenhouse gas emission scenarios (RCP8.5), nonstationary modeling is more suitable. The obtained future flood quantiles under RCP4.5 and 8.5 are not likely to be critical in the coming century for both stationary and nonstationary assumptions. However, the nonstationary estimate of the return levels under lower return periods will be more useful to design low-capacity hydraulic structures. Further analysis of nonstationary return levels revealed that the change detection in the return levels under a lower return period was much earlier than the higher return period. The uncertainty analysis of the return levels showed larger uncertainty bound in the case of RCP8.5 rather than the RCP4.5. Furthermore, the quantification of the uncertainty between the stationary and nonstationary assumptions using Bayesian analysis with Markov chain Monte Carlo (MCMC) simulation provided a high uncertainty range in the case of nonstationary assumption compared with stationary assumption.

Modeling of the nitrate concentration in groundwater can help ensure proper groundwater management and utilization, especially as a drinking water supply. Here, a groundwater nitrate tank model (GNTM) is proposed to simulate nitrate concentrations in groundwater. The variables used to calibrate and validate the model were daily rainfall and weekly nitrate concentration data recorded from June 2012 to February 2016 at two representative observation wells (W1 and W2) in Kumamoto, Japan. The observed nitrate concentrations range from 4.46 to $6.02\mathrm{mg}\text{-}\mathrm{N}/\mathrm{L}$, and 8.60 to $24.56\mathrm{mg}\text{-}\mathrm{N}/\mathrm{L}$ in Wells W1 and W2, respectively. The Shuffled Complex Evolution–University of Arizona algorithm was used to determine the best-fit parameter values according to the root-mean-square error (RMSE). Calibration and validation results were evaluated by calculating the Nash-Sutcliffe efficiency coefficient (NSE) and the RMSE. The GNTM accurately reproduced nitrate concentration fluctuations: during the validation period the values of RMSE were 0.135 and 1.432 and NSE were 0.821 and 0.661 in Wells W1 and W2, respectively. These results indicate that this model provides a simple way to accurately simulate nitrate concentrations in groundwater.

Nappe flow applies to small discharges, and investigation of nappe flow surges helps to understand the mechanism of water flow in stepped open channels. The objective of this paper is to investigate the nappe flow surge propagation down stepped open channels. First, an analytical solution of dam-break shock waves or flood waves is derived. Then, the theoretical solution is used to calculate the wavefront and celerity and is verified using experiments on surging waters down a stepped slope in a 20-m-long and 0.5-m-wide open channel under three conditions: one for smooth bed surface and the others for rough bed surface glued with uniform sediment (of diameter $d_s=1.715\mathrm{mm}$ and $d_s=3.5\mathrm{mm}$). For calculating the wavefront propagation in a stepped open channel, a formula for resistance, composed of grain resistance and step geometry resistance, is developed. Unlike skimming flow, the roughness of step surface in the nappe flow regime influences the friction factor $f_s$. Results show that the friction factor increases with the relative roughness height ($d_s/d_0$). The theoretical solution for the wavefront location and celerity is then revised by combining the friction formula, and the revised solution is tested for a wide range of experimental data. Test results show that the revised solution fits the measured data quite well. Comparing with the solution previously derived, the revised solution derived in this study more accurately predicts the wavefront location and celerity for the nappe flow condition.

This paper modifies the response surface method (RSM) by combining the polynomial and exponential basic mathematical functions to improve the accuracies for modeling monthly pan evaporations. The hybrid response surface function (HRSF) is developed based on exponential approximation and second-order polynomial estimation using the normalized input data set that includes temperature, relative humidity, wind speed, and solar radiation to predict pan evaporations of the Siirt and Diyarbakir stations in Turkey. A novel sensitivity analysis is proposed to obtain the effects of input databases on pan evaporation. According to the sensitivity analysis, the temperature and solar radiation are found to be the most effective parameters on pan evaporation. The performance of HRSF is compared with second-order response surface function, adaptive neuro-fuzzy inference system (ANFIS), and M5 model tree (M5Tree) models in three different applications. The optimal predictions are evaluated based on different training and test data sets for each approach. In the first application, monthly pan evaporations of Siirt and Diyarbakir are separately estimated. At Siirt Station, HRSF models are found to be better than the RSM, ANFIS, and M5Tree models in almost all the selected data sets. In Diyarbakir, the HRSF and ANFIS models provide almost the same accuracy and they are found to be slightly better than the RSM for all the calibration cases. In the second application, the most sensitive two climatic input variables (temperature and solar radiation) of Siirt and Diyarbakir stations are separately used for approximate pan evaporation. The HRSF and RSM give better estimates than the ANFIS and M5Tree models in cases of limited climatic inputs. In the third application, Diyarbakir’s monthly pan evaporations are estimated using climatic data from Siirt or both Siirt and Diyarbakir stations. In all applications, the HRSF generally performs superior to the RSF, ANFIS, and M5Tree models, while the worst results are generally obtained from the M5Tree model. The accuracy of the models generally increases by increasing number of training data. The ANFIS and M5Tree are found to be more sensitive to training data length than the RSF and HRSF.

A variety of potential approaches and data sets can be used to develop future rainfall intensity-duration-frequency (IDF) statistics at the local scale. The aim of this study was to characterize the variability in an ensemble of future IDF curves generated using a combination of five different climate models, three climate change scenarios, and two downscaling methods. These data sets were prepared for six rainfall stations across two local study sites in southern Ontario: the Toronto and Windsor areas. Several distribution functions used in the derivation of IDF curves were also tested and the best-fit, generalized extreme value (GEV), was used during downscaling. For the 2050s, there was statistically significant variability in the direction of change and magnitude among IDF projections at the Toronto Area stations, with some member cases showing increases and decreases in intensity values within the ensemble. At the Windsor stations, there was a statistically significant trend of increasing storm intensity for the future, but variability in the magnitude of change within the ensemble was apparent. In general, variability among IDF projections for both study areas increased with storm intensity. The variability due to the selection of climate model data sets was greater than that arising from spatial variability in extreme rainfall, with downscaling methods and radiative forcing/emission scenarios contributing far less to the variability.

Recently, climate change and global warming issues with rapid global population growth and socioeconomic development have increased water demand and caused more pressure on water resources. Reservoirs in semiarid regions have always played a role in alleviating water scarcity. This study presents an improved approach to investigating the resilience of vulnerable reservoirs using a probabilistic technique for water resource management under climate change by simultaneously considering integrative elements, namely rainfall-runoff processing, agricultural water management, reservoir operation, and uncertainty analysis. To this end, the study investigates the climate change impacts on optimal operation of Jarreh reservoir in southwest of Iran, for the period from 2025 to 2054. A risk assessment based on multimodel ensemble scenarios is used to deal with uncertainties in climate change projection. The results indicated an increase in the mean annual temperature in the range of 1.1–1.7°C, and irregular changes (both decreases and sometimes increases) in the long-term mean monthly precipitation in the study area. In addition, an annual increase of the agricultural water volume demand is also expected by a factor of 1.18–1.47. The results also showed that the average long-term reservoir inflow volume would decrease by 13–41% under the future scenarios. The reservoir operation model has been developed based on minimizing the sum of square water allocation using the differential evolution algorithm (DEA). The modeling results depicted reduced reliability and increased vulnerability of the reservoir in the future relative to the base period. Reduced reliabilities of 41–13% (39–7%) are expected under A2 (B1) scenario for the critical and ideal conditions, respectively. The results of this study can be used in adaptation scenarios to reduce vulnerability and improve the water resources performance.

The present study was conducted to simulate the moisture dynamics for a multilayer soil under protected conditions for a pea crop (Pisum sativum). A field experiment was conducted in a temperate region of the Kashmir valley, India, during the period November 2014 to June 2015. A root water uptake (RWU) model was developed for a multilayer crop root zone. The moisture uptake prediction efficiency of the model in a multilayer crop root zone was evaluated on the basis of secondary and experimental data. The nonlinear mathematical model incorporated the spatial and temporal variations of the soil and plant parameters. The quantitative values of the reference evapotranspiration for open and protected conditions were recorded at 358.8 mm and 156.5 mm, respectively, for the whole crop period. The average seasonal crop evapotranspiration inside the polyhouse was recorded at $1.91\mathrm{mm}/\mathrm{day}$. The crop evapotranspiration inside the polyhouse for the whole crop period was found to be 372.45 mm. The crop evapotranspiration was partitioned into transpiration and evaporation and those values were used as input parameters in the model to determine the moisture uptake by the plants. The maximum value of transpiration for the pea crop inside the polyhouse was recorded at $3.51\mathrm{mm}/\mathrm{day}$ on May 21 (141 Julian day). Using a developed numerical model, the percentages of soil moisture extraction and soil moisture status at different depths were simulated inside the polyhouse. In the upper part of the root zone where the root density is high, moisture depletes very rapidly. The maximum moisture depletion was seen in the month of May inside the polyhouse. The results demonstrated the utility of the root water uptake model across different agro-climates and established its efficacy for nonuniform soils that vary in soil moisture properties along their depths, in predicting soil moisture in the crop root zone.