Journal of Hydrologic Engineering

Accurate hydrologic forecasting plays a significant role in water resource planning and management. To improve the prediction accuracy, this study develops a hybrid hydrological forecasting method based on signal decomposition reconstruction and swarm intelligence. Firstly, the ensemble empirical mode decomposition is utilized to divide the nonlinear runoff data series into several simple subsignals. Secondly, the least-squares support vector machine using the gravitational search algorithm is used to recognize the relationship between previous inputs and the target output in each subsignal. Next, the forecasting result is obtained by summarizing the total outputs of all the models. Four famous indexes are used to evaluate the performances of various forecasting models in monthly runoff of two hydrological stations in China. The applications in different scenarios show that the hybrid method obtains better results than several control models. For the runoff at Cuntan Station, the hybrid method makes 58.9% and 52.4% improvements in the root-mean squared error value compared with the artificial neural network and support vector machine at the training phase. Thus, a practical data-driven tool is developed to predict hydrological time series.

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.

Thinning or localized absence (a breach) of an aquitard warrant concern because this limits the protection it affords water-supply aquifers beneath. The objective of this study was to assess potential spatial configurations of breaches within the aquitard overlying a water-supply aquifer in an urban well field. A three-dimensional groundwater flow model was utilized to investigate leakage pathways through the aquitard and simulate five potential breach configurations with three different hydraulic conductivity values. Through particle tracking analysis, estimates for the modern water percentage and apparent age of the modern water extracted by the production wells at the well field were obtained and compared to published age-dating data. Breach configurations resembling a broad paleochannel, which could originate through erosion of clay and silt within the aquitard, match the extent and proportion of modern water in the water-supply aquifer at the well field. This methodology has utility in evaluating the vulnerability of water-supply aquifers that are partially confined and susceptible to contamination, while assessing the likelihood of potential zones of increased vulnerability and offering targets for further investigations.

Continuous streamflow prediction is crucial in many applications of water resources planning and management. However, streamflow prediction is challenging, particularly in data-scarce regions. This paper demonstrates an approach to regionalize the flow duration curve for predicting daily streamflow in the data-scare region of the central Himalayas. We developed a regression-based model to estimate streamflow at various segments of a flow duration curve by incorporating basin characteristics and climate variables. This study analyzes the sensitivities of proximity and characteristics between the donor (gauged) and receptor (ungauged) basins for time-series streamflow prediction. Our results show that regionalization techniques perform better in low to medium flows over high flows. Our findings are significant in the central Himalayan regional context to inform operational and management decisions in water sector projects like hydropower plants, which generally rely on low-to-medium streamflow information. Although the quantitative results are region-specific, the approach and insights are generalizable to the Himalayan region.

The ability to retrospectively evaluate the vulnerability of a watershed can identify potential risks influencing sustainable water development over time. More importantly, the effect of watershed management policies can be evaluated to help government assess and improve current policies. In order to identify vulnerability factors and evaluate the vulnerability state of a watershed, this paper proposes a time-series-based vulnerability analysis framework that integrates several approaches and methods, including: (1) a combined driving force-pressure-state-impact-response (DPSIR) and hydrology-environment-life (HEL) framework for the selection and classification of evaluation parameters; (2) the coefficient of variation method to determine the weights of the evaluation parameters; and (3) the space geometry model to determine the vulnerability level. In addition, the paper describes a robustness analysis that was performed to evaluate the impact of changing the evaluation parameters on the results. A case study of the Xiang Jiang watershed in China is presented; empirical analysis showed that hydrologic vulnerability experienced large interannual variations. The environmental vulnerability of the Xiang Jiang watershed is subject to a high level of risk, whereas the living vulnerability is subject to a low level of risk. Despite many challenges, Xiang Jiang watershed water development is sustainable; this was determined based on the decreasing trend of total vulnerability. The findings demonstrated the effectiveness of the local government’s policies for watershed management. Moreover, the results of the robustness analysis showed that the proposed vulnerability analysis framework was adaptable to using slightly different evaluation parameters in cases in which data for the proposed parameters are not readily available. The proposed framework can be used by local governments and decision makers to effectively manage the sustainable development of watersheds.

Determining the contributions of climate change and human activities to runoff variation is important for water resource management. An improved double mass curve method (IDMC), which can distinguish the individual impact of each climatic factor (precipitation and potential evapotranspiration), is proposed in this study to attribute runoff variation to climate change and human activities. The runoff variation characteristics of four subregions of the Jinsha River Basin (JRB) were detected using statistical methods, and the contributions of climate change and human activities to runoff variation were comprehensively assessed using IDMC, Budyko-based elasticity method, and slope change ratio of cumulative quantity analysis. Results show that annual runoff had an increasing trend in the upstream, midstream, and downstream of JRB and a decreasing trend in Yalong River Basin (YRB) of JRB during 1966–2016. Changed points in 2004, 1986, 1996, and 2005 were detected for the upstream, midstream, and downstream of JRB and YRB, respectively; the study period was, therefore, partitioned into the base period and impacted period for each subregion. Suggested attribution methods gave consistent conclusions that climate change dominated the runoff variation in the upstream of JRB with an average contribution rate of 86.5%, whereas human activities were the major force for the runoff variation in the midstream and downstream of JRB and YRB with average contribution rates of 73.0%, 113.6% and 75.8%, respectively. Furthermore, precipitation caused the increase of runoff in the upstream and midstream of JRB and the reduction of runoff in the downstream of JRB and YRB. Potential evapotranspiration caused the reduction of runoff in the subregions of JRB except for the midstream of JRB. The findings of this study are meaningful for water resources management in the JRB.