Journal of Hydrologic Engineering

Compound extremes can be expressed as the joint distribution or dynamic interaction of multiple variables and the interdependence of several extremes that have major effects on the agricultural sector. Analysis of these compound extremes in space-time varying domains is a challenging task for climate experts. It is even difficult to forecast such compound extremes in future climate when several widely accepted global climate models (GCMs) and regional climate models (RCMs) are available. Thus, an attempt is made to develop a compound extreme index considering precipitation, temperature, and runoff, with a specific focus on promoting sustainable water resource management, particularly for the benefit of the agricultural sector. This work presents a new outlook on the modeling framework for compound drought and hot extremes (CDHE) by developing the Standardized Compound Extreme Event Index (SCEEI), and highlights their time-varying evaluation (trend and prediction) along with severity assessments. The SCEEI is derived using three individual indices: Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Temperature Index (STI), which are obtained from three physical variables (precipitation, runoff, and temperature) followed by multivariate Gaussian distribution. The applicability of the SCEEI is explained with a case study on the Brahmani and Baitarani rivers of eastern India, aiming at the modeling of dry and hot extremes at a regional scale. Further, the time-varying evaluation of CDHE events—the projection of the extremes—is studied in machine learning (ML)-based ensemble future. Random forest (RF) and support vector regression (SVR) MLs are employed to derive ensemble future from seven RCM models of the Coordinated Regional Downscaling Experiment (CORDEX). The linear scaling approach (LSA)-based bias-corrected data were used as input for the ensembling process. The RF-predicted ensembles performed statistically better for the study area. The extreme events and their severities are further assessed in RF-predicted future ensembles. The two-tail-based Mann–Kendall test, followed by Sen’s slope estimator, is used to investigate the trend-based time-varying evaluation of CDHE events. The notable conclusion drawn from this study is that the severity and frequency of CDHE events are increasing in the ensemble future climate, particularly in representative concentration pathway (RCP) 8.5. The developed multivariate framework might allow new prospects for suitable statistical analysis techniques for dependent compound extremes in climate-related studies and hydrology, which is further necessary for applying an adaptation strategy to boost resilience and adapt society for future climatic extremes.

Accurate water level forecasting is essential for agricultural water resources management, hydropower generation, flood control, drought relief, and watershed planning. A combined model (ICEEMDAN-VMD-WOA-ELM) is proposed based on improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN), variational mode decomposition (VMD), extreme learning machine (ELM) and whale optimization algorithm (WOA). First, the historical data with high similarity to the forecast date are extracted using a hierarchical clustering method, and the extracted data are analyzed by box plot. Then the analyzed river level data sets from different Heihe River stations are preprocessed with ICEEMDAN, and the obtained high-frequency subsequence intrinsic mode functions 1 (IMF1) is decomposed secondarily by VMD. Afterwards, the ELM parameters are optimized during the prediction of each subsequence using the global optimization capability of WOA, and the predicted values are accumulated to generate the ultimate water level prediction results. Experiments using five data sets and seven comparative models show that (1) the prediction accuracy of the single model is much lower than that of the combined models; (2) the secondary decomposition of ICEEMDAN-VMD has smaller prediction errors than the single decomposition of ICEEMDAN; and (3) the RMSE values of the ICEEMDAN-VMD-WOA-ELM model in the five data sets are 0.04365, 0.19644, 0.16856, 0.06412, and 0.37150, respectively, with much higher prediction accuracy than the ELM single model, which verifies the important research value of the proposed model in water level prediction.

A reliable estimate of the areal reduction factor (ARF), useful for implementing the upscaling procedure of point rainfall information, is of crucial relevance in many hydrological applications aimed at hydraulic structures design. Despite the availability of different methodologies in the literature, the choice of the best formulation to be applied for ARF assessment in a specific region remains an open problem. In fact, the transposition of ARF formulations to areas different from those where they were developed, even with similar geographical features, could lead to incorrect ARF estimates and relevant errors in design rainfalls, especially for short durations. In this paper, a new deterministic fixed-area approach is proposed; it relies upon an averaging procedure of the ratios between areal and local annual maximum rainfalls. The proposed approach was applied to the study area of Umbria region in central Italy, where a parametric relation expressing ARF in a wide range of rainfall duration (5–2,880 min) and areas (up to about 6,100${\mathrm{km}}^2$) was derived. A comparison of the proposed approach with the most widely used empirical methodologies was also performed. The methodology can be adopted in any other region where a network characterized by a minimum length of rainfall time series of 7 years and a proper spatial density of stations is available.

This study proposes a hierarchical approach to urban subcatchment division that incorporates topography and road network factors, as well as a subcatchment-pipe network connection strategy. By taking into account all relevant hydrological components and the efficiency of drainage networks, the precision of urban flooding simulation is boosted. The suggested approach makes use of each stormwater well in the subcatchment as a confluence outlet. The intensity variations of flood simulations conducted using the traditional Thiessen polygon method and the proposed method are compared. The current study focused on the area of Panyu District in Guangzhou City, China. Using the InfoWorks ICM model, four comparison scenarios were made to simulate flooding conditions during four return periods. The results suggest the following: (1) While maintaining the production and confluence parameters at a constant level, the simulation results of flooding present discrepancies depending on the methodology used for subcatchment division and confluence connection with the pipe network system. (2) The utilization of the Thiessen polygon method for subcatchment division results in a certain degree of overestimation of the flooding condition as compared to the more comprehensive subcatchment classification. (3) When considering all the collection nodes inside the subcatchment, the estimation of the inundation situation is notably lower compared to when only a single stormwater well is chosen as the subcatchment outlet. In the event of 100-year rainfall, the overall overflow can be decreased by 8.44%. A more accurate model is a solid foundation for mimicking reality. This study revises the urban stormwater model, and the proposed enhancement is applicable to simulations of urban waterlogging in any global region and can guide the development of a more scientific approach to urban water management.

The response to flood disasters is of great importance to protect people’s lives. Proper recognition of the factors affecting floods will lead to the prevention of negative consequences. In this study, three types of Archimedean copulas, including Clayton, Gumbel, and Frank, have been applied to the depth and duration variables of maximum annual precipitation in four rain gauges’ data sets (eight variables) throughout the primarily urban eastern catchment of Tehran, Iran. The results indicated that the Gumbel copula is the most suitable function of the Archimedean copulas. The average depth of rainfalls produced by the selected copula increased up to 24% compared with different varieties of single-station scenarios. Also, the average duration of produced rainfalls differed up to 14% difference compared with the single-station scenarios. Finally, the average volume of surface flooding varied between $+54\%$ and $-154\%$ with respect to the single-station scenarios. As a result, taking into account spatial distribution in rainfall will have a significant impact on the generation of runoff. The overall distribution pattern of runoff is significantly influenced by several factors. Firstly, the simultaneous impact of four rain gauge stations on synthetic runoffs plays a crucial role. The second factor is the spatial distribution of precipitation within the catchment, which is influenced by the distribution of rain gauge stations and the interpolation technique used. Additionally, the routing of synthetic surface runoff through the channel system also contributes to the overall distribution pattern of runoff.

Abstract

The progressive reservoir construction and operation have led to nonstationary sediment load response to water discharge and a new equilibrium between sediment supply and transport of a river system. However, an increase in the reservoir impoundments on the nonstationary behaviors could demonstrate significant temporal variability over the river system. In this study, the statistical relationships of annual sediment load with both streamflow and reservoir storage capacity during 1956–2018 are analyzed in the Upper Yangtze River Basin (UYRB), China. The trend and abrupt detection analyses show that the significant departure of annual sediment load to annual water discharge occurs only when the cumulative storage capacity over the course of progressive impoundments reaches a critical value for the cascade reservoirs in UYRB. The critical storage capacity gradually appeared in the 1980s, 1990s, and 2000s, accounting for the sharp increases of small- and medium-sized reservoirs in the tributaries and large-sized reservoirs in the mainstem. Once the critical storage is exceeded, the capacity to trap sediment increases exponentially with the cumulative storage. The significant increase of trapped sediment (e.g., 26%–88% of the free-flow capacity in the tributary and mainstem stations) has led to the shift of sediment-controlling factors from water discharge to the impounded runoff index. The significant reduction of sediment transportation and the altered dominant factors of sediment load could result in the steady erosion of channels and coastlines and adversely impact ecosystems in the downstream Yangtze River.

Practical Applications

Human activities, especially dam construction, have severely altered the global river system. Dams disrupt the continuity of the river, inevitably altering the natural transport of sediment and reducing sediment loads. Since the 19th century, a large number of reservoirs have been built in the upper reaches of the Yangtze River where water resources are abundant. When the cumulative storage capacity over the course of progressive impoundments reaches a critical value for the cascade reservoirs in the Upper Yangtze River, the capacity for trapping sediment increases exponentially with the cumulative storage. The significant increase in trapped sediment has led to a shift of sediment-controlling factors. The significant reduction of sediment transportation and the altered dominant factors of sediment load could lead to several environmental problems, such as the limited availability of nutrients in the middle and lower reaches, river bed erosion, and the destruction of benthic habitat. The change in the relationship between sediment, water discharge, and reservoir capacity can provide a reference for the environmental management and protection of the Yangtze River.

Recent developments in computational techniques and data-driven machine-learning models (MLMs) have shown great potential in capturing the rainfall-runoff relationship. However, whether MLMs outperform the classical physical-based models (PBMs) in streamflow simulation is still controversial. In this study, we chose three representative catchments across the continental United States for a comparative analysis of these two model categories, including two PBMs, i.e., a conceptual hydrological model (EXP-HYDRO) and a classical semidistributed hydrological model (SWAT), and three MLMs, i.e., support vector regression (SVR), backpropagation artificial neural networks (BP-ANN), and a deep learning model, termed long short-term memory (LSTM). Results indicate that the bias of SVR and BP-ANN models is greater than PBMs under the baseline input scenario, while LSTM outperforms other models for the rainfall-runoff relationship. For delayed input scenarios, MLMs have great potential and perform satisfactorily. In addition, MLMs show better performance in the high-flow regime, while PBMs perform better in the low-flow regime, implying that both PBMs and MLMs have their own merits and should be jointly employed holistically to analyze the streamflow. Our comparison analysis demonstrates that the performance of MLMs and PBMs is variable under different seasonal, climatic, and topographic conditions, and conclude that MLMs can better capture the rainfall-runoff relationship than PBMs, when the coefficient of variation (COV) is large.

The solution of the governing equation representing the drawdown in a horizontal confined aquifer, where groundwater flow is unsteady, is provided in terms of the exponential integral, which is famously known as the well function. For the computation of this function in practical applications, it is important to develop not only accurate but also a simple approximation that requires evaluation of the fewest possible terms. To that end, introducing Ramanujan’s series expression, this work proposes an approximation to the exponential integral using Ramanujan’s series for the small argument ($u\le 1$) and an approximation based on the bound of the integral for the other range ($u\in (\mathrm{1,100}])$. The evaluation of the proposed approximation results in the most accurate formula compared with existing studies, which possess the maximum percentage error of 0.05%. Further, the proposed formula is much simpler to apply because it contains just the product of exponential and logarithm functions. To further check the efficiency of the proposed approximation, we considered a practical example for evaluating the discrete pumping kernel, which shows the superiority of this approximation over the others. Finally, the authors hope that the proposed efficient approximation can be useful for groundwater and hydrogeological applications.