Journal of Hydrologic Engineering

Estimates of probable maximum precipitation (PMP) are necessary for designing the maximum reservoir storage necessary to prevent dam overtopping. Recent investigations in Ohio, Virginia, and Pennsylvania implied that reducing PMP rainfall estimates in West Virginia may be appropriate; however, additional studies in this data-deficient region with changing climate are necessary before any reductions are contemplated. The objective of this study was to re-evaluate PMP for the Lake Tuckahoe watershed in West Virginia. The 6-h, $26\text{-}{\mathrm{km}}^2$ ($10\mathrm{sq}\mathrm{mi}$) PMP was evaluated for current (2019) and projected (2100) climates and compared with the design PMP. Then, the impact of the PMP alternatives was determined for Howard Creek Dam, which was selected to be a case study of a region delineated as a data-deficient region. The estimated current (2019) PMP is 4% greater than the design PMP, and the projected (2100) PMP is 14% greater than the design PMP. Analysis of the dynamic nonstationary climate anticipated for the Howard Creek Dam, designed for a stationary climate, showed inadequate storage that will increase the likelihood of dam overtopping. Therefore, this study cannot support a reduction in PMP for the Lake Tuckahoe watershed and similar surrounding watersheds.

Streamgage record extension methods such as the maintenance of variance Type 3 (MOVE3) method improve flood frequency estimates at a target streamgage by incorporating information from a nearby, hydrologically similar index streamgage. Bulletin 17C recommends using a variation of the MOVE3 method to estimate values at the target streamgage for only a subset of the available data at the index streamgage to account for uncertainty in values estimated using MOVE3. Bulletin 17C recommends the most recent index streamgage data be used for the subset unless these data misrepresent the skewness of the extended record. However, no method is provided to select the subset if the most recent data are inappropriate. The objective of this study is to develop such a method to select the subset of peaks by extending Bulletin 17C’s MOVE3 methodology to control the skewness of the extended streamgage record. The new method allows the extended record skewness to be informed by all available peak-flow data at the index streamgage while accurately computing the variance and resulting confidence intervals for flood frequency estimates. An example is presented comparing three different variations of MOVE3 record extension, which produce extended streamgage records with the same mean and variance, but different values of skewness. In the example, the difference in skewness between the three methods causes the results of flood frequency analysis for the 1% annual exceedance probability flood to differ by about 15%, illustrating the importance of considering skewness when using MOVE3 record extension.

The incorporation of information on the current system states and forecasts makes reservoir operations more efficient; however, forecast uncertainties often occur. Therefore, excellent reservoir operations should integrate forecast uncertainties and current system states to achieve trade-offs among the objectives. This study proposed a medium-term multiobjective operation (MTMOO) mode, which combines the coupled rainfall forecast (CRF) and the current system state as multisource information to intuitively guide the multiobjective scheduling decision-making of cascade reservoirs. The CRF in different forecast periods was determined to mitigate forecast uncertainties. The MTMOO mode was formulated by establishing a multiobjective optimization model and then using the decision tree algorithm. For verification, the CRF, 5-day rainfall forecast, and observed rainfall were simulated using the MTMOO mode, and the results were compared to those of conventional scheduling. The case study shows that the electricity generation and water supply benefits under the MTMOO mode using CRF are much greater than those in conventional scheduling and slightly larger than in the MTMOO mode that directly uses rainfall forecast. This confirms the applicability and superiority of implementing the MTMOO mode with CRF as an input in multipurpose reservoirs.

Urban flooding is a common disaster occurring every year, leading to the loss of lives and properties throughout the world. Its frequency and severity have increased over the years and are expected to increase further due to climate change impacts. Therefore, a robust urban flood management framework is needed to mitigate and/or lessen the adverse impacts of urban floods, which requires flood models capable of quickly producing accurate flood forecasts at key locations in an urban area. Previous attempts at urban flood modeling have focused on the use of physics-based rainfall-runoff models or data-driven models in isolation, with reasonable accuracy. In this study, a novel physics-guided neural network modeling approach is proposed that is capable of exploiting the advantages of both the physics-based and data-driven techniques. We employed the MIKE FLOOD model as the physics-based rainfall-runoff model and artificial neural networks (ANNs) as the data-driven technique in this study. The climatic, hydrologic, and physiographic data derived from the Indian Institute of Technology (IIT) Kanpur, a small urban area in Northern India, were employed to develop and test the proposed models and methodologies. The results obtained in this study demonstrated that both the MIKE FLOOD and ANN models employed in this study were able to represent the rainfall-runoff behavior of IIT Kanpur catchment very well. The performance of the ANN models was found to be better than that of the MIKE FLOOD models based on the performance evaluation measures considered in this study. A close examination of the results around the peak of the flood hydrographs revealed that all the models presented here performed much better around the flood peak than in the rest of the flood hydrograph, which is an encouraging result. It was found that the ANNs are powerful tools capable of producing accurate flood forecasts quickly under extreme weather conditions, and therefore they can be employed as surrogate models to achieve greater efficiency in urban flood management activities.

This paper evaluates several efficiency indexes of hydropower energy generation in Iran’s Karkheh Basin water reservoir system (i.e., Seymareh and Karkheh Rivers) under climate change based on the system dynamics approach. The climate change effects are investigated with simulations of surface temperature and rainfall driven by downscaled climate projections from atmosphere–ocean circulation models in the current literature, and the models with the highest correlation and lowest error for rainfall and temperature in the 1976–2005 baseline period are identified. Rainfall and temperature are projected over two future periods, 2040–2069 and 2070–2099, and are downscaled to basin scale. Results project that the basin temperature over future periods will be higher than in the baseline period. Downscaled results under climate change indicate that rainfall will not follow a specific pattern. An artificial neural network applied to predict runoff indicates that the runoff and flood peak will decrease. Software is implemented to simulate the operation of hydropower reservoirs in baseline and future periods. Five reservoir operation states are considered, with simulation results of energy production showing that runoff and energy production have a complex pattern. Seven indexes of hydropower reservoir efficiency are also calculated for the five operation states corresponding to targets of energy production.

This study investigates the capability of both quantile mapping (QM) bias correction and kriging merging techniques to improve precipitation accuracy of Tropical Rainfall Measuring Mission (TRMM) and Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) satellite estimations over the Langat River Basin, an important river basin in Malaysia as it is the main source of potable water supply to Kuala Lumpur, in the 5-year period (2014–2018). This analysis also integrates both techniques to investigate whether the estimations can be further improved. Findings show that the estimations that undergo QM first followed by kriging merging (QK-TRMM and QK-IMERG) give significant improvement at almost all aspects of rainfall and streamflow comparison. At point-to-pixel rainfall comparison, around 50% improvement can be seen in both time series– and frequency-based statistics as well as an able to perform with a coefficient of correlation ($CC$) over 0.80 in terms of areal rainfall. The study performs streamflow simulation by employing the hydrological modeling system (HEC-HMS) to validate the performance of raw and enhanced satellite estimations for the 2014–2015 extreme flood events. Both QK-TRMM and QK-IMERG show a great improvement in the overall streamflow simulation with a Nash–Sutcliffe efficiency ($\mathit{NSE}$) of more than 0.70. The results reveal that the newly proposed bias correction method (merging of the QM and kriging methods) has significantly contributed to the improvement of precipitation estimation, which is crucial in water resources planning and flood forecasting.

Flood-induced damages have increased due to increased frequency of extreme rainfall events owing to climate change and continued development in flood-prone areas. Among the flood mitigation measures, flood early warning systems (FEWS) have been popular due to their low cost and easy installation, and have been found to be highly effective. A FEWS provides warning and alarm signals according to the water level of a river. Water-level measurements are often made at bridges because of easy access to the middle of a river and concrete ground. However, a measurement site is often not related to the earliest flooded site. A thorough investigation and ground surveying are therefore required to select the site. Therefore, the current study tried to resolve this problem with a newly developed unmanned aerial vehicles (UAV) photogrammetry technique and proposed a novel UAV-based approach to establish the water-level standards for the FEWS. The digital surface model (DSM) obtained from UAV photogrammetry provides useful three-dimensional (3D) information and allows one to determine the earliest embankment site. The approach was applied to the Migok-cheon Stream, South Korea. Results showed that the proposed approach can be a useful alternative for establishing a water-level standard for the FEWS. The proposed alternative can be further developed with advancing UAV and 3D photogrammetry technologies.

Singular spectrum analysis (SSA) is a nonparametric model-free time-series analysis and filtering technique with a wide variety of applications in numerous data-intensive fields. The grouping stage is the most crucial step in SSA, where the analyst selects significant components from the time series for further processing. However, there is no universal rule in this stage of grouping and the components need to be grouped based on the data characteristics. In this study, a few methods that can be adopted for grouping are discussed and their efficiencies in reconstructing the time series are compared. The results of the study will be helpful in understanding the procedure and will act as a guide in the selection of a method for grouping based on the data characteristics. Real-world daily rainfall time-series data were used as a case study.