Journal of Hydrologic Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Due to enhanced impacts of compound events, the importance of assessing climate change impacts on extremes from a multivariate perspective has recently been receiving considerable attention. This study provides a state-of-the-art review of compound events from the perspective of the dependence of multiple contributing variables, based on both synthetic data sets and observations. The cause of dependence, the relationship between dependence and likelihoods of compound events, and changes in risks associated with dependence changes are reviewed, with the illustration of two typical examples of compound dry–hot events and compound flooding events. Also discussed are related topics, including impacts of sample sizes, multivariate bias correction of dependence, and separating driving factors of compound event changes. Insights provided by this study will be useful for building resilience to cope with compound extremes under changing climate.

An optimal sensor placement of a precipitation station network should fulfill different regulations and requirements, such as coverage maximization, easy access, and uniform distribution. However, few studies have focused on an integrated way to optimize the precipitation network design from the perspective of monitoring efficiency in space. In this paper, given the complex requirements and diversified goals for precipitation monitoring, a new multiobjective location model is established for optimizing the network’s monitoring efficiency with a comprehensive weighting scheme. Based on the precipitation station siting regulations, the spatial coverage, accessibility, and dispersion of stations are considered in the model. The Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) is used to obtain a set of Pareto-efficient solutions. The Jinsha River Basin is selected as the study region to test the proposed method. The results show that the proposed method satisfies the complex precipitation monitoring requirements and achieves higher coverage than the real-world deployment. The decision making for siting schemes, comparison of other dispersion models, and the extensibility of the proposed method are also discussed.

To construct a valid model, hydrologists face challenges in determining sensitivity to the forcing data and uncertainty in model parameters. These require basin data and forcing data from different sources, which may be incommensurate. The study reported here calibrated the Variable Infiltration-Capacity (VIC) platform to quantify model result sensitivity to model parameters and uncertainty in those parameters. The modeled basin was the Diyala River in Iraq, above the Derbendikhan Dam. The study produced the first complete set of daily forcing data for the basin using different sources. Besides ground observations from the Iraqi Ministry of Water Resources, two additional data sources were tested: Tropical Rainfall Measurement Mission (TRMM) and Global Implemented Data (GIDAL). Several methods were implemented to adjust the data, and model sensitivity and parameter uncertainty were examined by Generalized Likelihood Uncertainty Estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM). Neither of these techniques had been applied before in Iraq. The VIC model was then calibrated manually using Kling–Gupta efficiency (KGE). The analyses indicate that neither TRMM nor GIDAL data are adequate for gridded precipitation analysis in the study basin. TRMM tends to underestimate and GIDAL tends to overestimate actual data. Multiplicative random cascade and Schaake Shuffle were used to determine daily precipitation data. A set of correction equations was developed to adjust GIDAL temperature and wind speed. Results for the GLUE and DREAM analyses imply that the depth of the second soil layer is the parameter that causes the most sensitivity in the model. The VIC model outputs were calibrated on a daily timescale with a KGE average of 0.743.

Hydrological modeling has become a common approach for the design of stormwater management strategies, which increasingly rely on permeable and decentralized systems in order to mitigate the impacts of urbanization. In most applications, little attention is paid to the temporal variability of infiltration fluxes resulting from soil moisture redistribution between rain events. In this paper, a conceptual infiltration-redistribution model is introduced to investigate the importance of the description of infiltration fluxes for the modeling of sustainable urban drainage systems (SUDS). The model was first verified against the numerical solution of the Richards equation. Performance indicators simulated with the model for a large range of infiltration scenarios were then compared to those obtained using simpler approaches commonly used in urban hydrology. The model was shown to replicate, at a low computational cost, numerical solutions of the Richards equation. Regarding SUDS modeling, the results indicated that a correct description of the temporal variability of infiltration fluxes (1) may be needed for some configurations in order to assess long-term volume-reduction efficiencies, and (2) is more generally required when examining frequency-based performance indicators.

This research compared natural neighbor interpolation with other interpolation methods commonly implemented in ArcGIS. It evaluated the relative performance of interpolation methods for various spatial data distributions, including line transects. It characterized locations which are associated with large prediction errors. To assess the relative performance of interpolation methods, a validation procedure was used consisting of 75% training data and 25% test data. Statistical error measures were used to measure the predictive performance of the interpolation methods, and the spatial distribution of errors was used to characterize areas where interpolation methods performed poorly. Results showed that Topo to Raster, natural neighbor, ordinary kriging, and empirical Bayesian kriging methods consistently outperformed other interpolation methods for a variety of spatial distributions of the data. However, natural neighbor interpolation was unsuitable for linear transects. In general, the accuracy of most of the interpolation methods increased with narrow spatial data distributions. Spatial distribution of large prediction errors was predominantly similar, regardless of the interpolation method used, and was related to changes in physical characteristics.

Remote sensing has revolutionized the assessment of evapotranspiration by continuous monitoring of the variable at a global scale. However, it is difficult to accurately estimate actual evapotranspiration (AET) in areas with high spatial heterogeneity along with very little ancillary data available. The presence of spatial heterogeneity is observed over India and being a country, whose economy largely depends on agriculture, estimation of AET is crucial for efficient water management. This study proposed a new framework to evaluate AET at a fine spatial scale suitable for such heterogeneous and data-sparse environments, with spatial and temporal validation using global datasets. Landsat 8 data were used to estimate AET for eight cloud-free days of 2014 over the Malaprabha River Basin in India using the modified surface energy balance algorithm for land (M-SEBAL) and two-source energy balance (TSEB) models. These two AET estimates were compared with Moderate resolution Imaging Spectroradiometer (MODIS) and Global Land Evaporation Amsterdam Model (GLEAM) AET for spatial and temporal validation respectively. M-SEBAL outperformed TSEB in capturing the magnitude and spatial variability of AET (e.g., spatial correlation between M-SEBAL and MODIS AET was 0.56 and 0.45 for day of year (DOYs) 040 and 136, respectively, whereas for the same DOYs the correlation between TSEB and MODIS AET was 0.16 and 0.36). The results demonstrate the challenge in AET estimation at a fine spatial resolution and highlight the importance of choosing a suitable algorithm.

To analyze the process and mechanism of slope confluence of rainstorm and flood in small watersheds, a two-dimensional hydrodynamic model was built based on high-precision lidar data of point cloud and the field flood process of the Baogaisi watershed in Hunan Province was simulated in 2017. Then the simulation results were compared with the hydrological model simulation results and observations. In the process of model simulation, a graphics processing unit (GPU) parallel algorithm was applied in approaching the problems of a large number of grids and slowing the operation speed. The results show that high-resolution topographic data collected by laser lidar are helpful in accurately simulating the rainfall-runoff process on the slope of a watershed and the effect of microtopography on the flood process, and then the flood confluence process on the whole watershed can be obtained. The GPU parallel algorithm increased the computational efficiency of the model nearly 50 times, so that the computational time was less than the runoff confluence time of the watershed. The peak-to-present time error of hydrodynamic model calculations is 1 h, and the peak error is 19.8%, which indicates that the calculations of the model have good accuracy. The model is available for forecasting, warning, and three-dimensional deduction of torrential rains and mountain torrents.

Onsite and Earth observation (EO) data are used for the calibration of the Natural Resources Conservation Service curve number (NRCS-CN) value in a hydrological simulation model. The model was developed for La Muga catchment (eastern Pyrenees) highly vulnerable to flood and drought episodes. It is an integral part of a regional reservoir management tool, which aims at minimizing the flood risk while maximizing the preservation of water storage. The CN values were optimized for five recorded events for the model to match the observed hydrographs at the reservoir when supported with the measured rainfall intensities. This study also investigates the possibilities of using antecedent moisture conditions (AMC) retrieved from satellite data to inform the selection of the NRCS-CN losses parameter. A good correlation was found between the calibrated CN values and the AMC obtained from satellite data. This correlation highlights the interest in using EO data to update NRCS-CN estimates. This advances in hydrologic-hydraulic coupled modeling combined with new remote sensing datasets present valuable opportunities and potential benefits for flood risk management and water resources preservation.

This study addresses how uncertainty propagates within climate models, statistical postprocessing tools, and the hydrological model, while taking into account the observed forcing and response of the hydrological system. Propagation of uncertainty is assessed using three regional climate models (RCMs), four bias correction methods (BCMs), two averaging methods, three representative concentration pathway (RCPs) scenarios, and a single hydrological model. The hydrological simulations are performed for the baseline period 1971–2000, and for the future time period 2006–2055. The Lim River basin in Serbia (southeast Europe) is selected as the study region. The dominant sources of uncertainty are the bias correction methods (9.7%–16.1%), whereas uncertainty from the choice of regional climate models is generally smaller (7.4%–13.9%). The results from the hydrological simulations for the future time period suggest relatively moderate annual changes in streamflows, from $-6.3\%$ to $+12.5\%$ under the RCP2.6, RCP4.5, and RCP8.5. The most significant changes are expected at the seasonal level, especially for the winter flows ranging from $+27.1\%$ to $+35.7\%$.

Applicability of satellite rainfall products must be explored since rain gauge networks have limitations to provide adequate spatial coverage. In this study, Climate Hazards InfraRed Precipitation (CHIRP) satellite-only product was evaluated for rainfall-runoff modeling whereas the simulated runoff served as input to simulate the water levels of Lake Ziway from 1986 to 2014. CHIRP dataset was bias-corrected using power transformation and used as input to Hydrologiska Byråns Vattenbalansavdelning (HBV) model to simulate streamflow of Meki and Katar catchments. Results showed that gauged catchments of Meki and Katar contributed 524 and 855 mm to the annual lake inflow, respectively. The estimated runoff from ungauged catchments is 182 mm that amounts to approximately 8.5% of the total lake inflow over the period 1986–2000. The results of lake level simulation show good agreement from 1986 to 2000, but deteriorating agreement after 2000, which is mainly attributed to errors in water balance terms and human-induced impacts. For the period 1986–2000, the water balance closure error for the lake was 67.5 mm per year, which accounts for 2.9% of the total lake inflow from rainfall and river inflow. This study shows bias correction increases the applicability of CHIRP satellite product for lake water balance studies.

Quantifying the impacts of climate change and human activities on runoff variation has drawn considerable concern. In this study, a modified method of slope change ratio of accumulative quantity (modified SCRAQ) has been proposed to estimate the contributions of climate change and human activities on runoff variation. The variation characteristics of hydrometeorological series were detected in the upstream of Minjiang River (UMR), China, during 1960–2012 using the Mann-Kendall test. The contributions of climate change and human activities on runoff variation in the UMR were estimated using the double mass curve method and modified SCRAQ method. The results showed that annual precipitation of the UMR had an insignificant decreasing trend, annual mean temperature presented a significant increasing trend, and annual runoff exhibited a significant decreasing trend. The years of 1969 and 1995 are taken as representative abrupt change points; the total study period was divided into the natural period (1960–1969) and change period (1970–2012), which includes 1970–1995 and 1996–2012. Precipitation caused the decrease of runoff except for the change period of 1970–1995; mean temperature led to the increase of runoff in each change period. Furthermore, runoff in the UMR was more sensitive to variation of mean temperature than that of precipitation. Under their combined influence, climate change resulted in the increase of runoff; human activities (e.g., agriculture irrigation, deforestation and afforestation, and urban water supply, as well as the construction of large reservoirs and dams) caused the runoff reduction. Overall, the impacts of climate change and human activities on runoff varied differently in the UMR, and human activities were the main driving factor leading to runoff variation. The results in this study can provide necessary information for water resources management in the UMR.

The present study focuses on the dependence modeling of hydroclimatic variables such as the El Niño–Southern Oscillation (ENSO) index, precipitation, tidal height, and groundwater level (GWL) in humid tropical coastal region of India. The rank-based correlation coefficient was used to determine the dependence between the pairs of cumulative monsoon precipitation of June–July–August–September (P_JJAS) and the postmonsoon groundwater level (PMGWL), ENSO–P_JJAS, ENSO–PMGWL, and GWL–tidal height. The results indicated that P_JJAS–PMGWL, ENSO–PMGWL, and GWL–tidal height had significant dependence, whereas P_JJAS–ENSO had no significant dependence. The best fit distributions for P_JJAS, PMGWL, and tidal height were found to be lognormal, extreme value, and generalized extreme value distributions, respectively, whereas for the ENSO index, it was the normal kernel-density function. The Archimedean families of copulas were used for dependence modeling, and it was observed that the ENSO–PMGWL was best modeled by the Frank copula, the P_JJAS–PMGWL by the Gumbel-Hougaard copula, and the GWL–tidal height by the Frank copula. The copula-based conditional probability for the Gumbel-Hougaard and Frank copulas for GWL were obtained to understand the risk associated with other hydroclimatic variables. Thus, copula-based dependence modeling could be useful for understanding the risk among hydroclimatic variables including groundwater.

Systematic analysis of the riverbed evolution regularity in the bifurcated reaches in the middle reaches of the Yangtze River (MRYR) found that under different flow levels, the main stream line usually locates at three main plane positions, which has obvious influence on riverbed evolution. The flow levels corresponding to specific riverbed locations, called the characteristic flow levels, should be divided into three grades: initial migration discharge, floodplain discharge, and translocation discharge. The river evolution has a close relationship with the duration of the characteristic flow level (DCF) acting on the corresponding riverbed position. When the DCF is longer than the critical value, the corresponding riverbed position will suddenly deform. Wavelet analysis shows that, taking the duration series of characteristic flow level as an example, the sudden change year when the river evolution and adjustment occurred suddenly is consistent with the peak years of the wavelet transform coefficients’ real parts of this sample; because the sample is periodic, the corresponding riverbed deformation also is periodic. Before the operation of the Three Gorges reservoir (TGR), the main cycle of the DCF from initial migration discharge to floodplain discharge (FD) was 4–16 years, the cycle from FD to translocation discharge (TD) was 5–16 years, and the cycle above TD was 4–14 years. Influenced by the operation of TGR, the main cycle of the three series had variation tendencies of extension, reduction, and extension, respectively. This study fills the gap of the dynamic mechanism between the runoff process and the riverbed evolution process, and has great significance for predicting river evolution trends and scientifically governing river channels.

Synthetic aperture radar (SAR) observations of real world flood extents are often the only source of information in data scarce catchments and the only reliable resource for channels with subkilometer widths. Accordingly, this study aimed at evaluating the reliability of SAR-based flood maps for flood model performance assessment for an extremely data poor region in India. SAR images were converted to probabilistic flood maps by combining inundation extents obtained using visual interpretation, histogram thresholding, and texture-based classification. Flood extents simulated by a hydrodynamic model were compared with SAR-derived inundation extents using spatial objective functions to calibrate the lumped channel and floodplain friction parameters. The agreement between the modeled and observed flood extents showed an $R^2$ value of 0.938 and a root mean squared error of 0.278 pixels for the validation. The results indicate that the proposed method has the potential to support flood model calibration and evaluation in ungauged basins.

Streamflow plays a critical role in sediment transport. Traditional frequency analysis of annual maximal (AM) suspended sediment concentrations (SSCs) is usually based on univariate distribution of SSCs and may provide a limited assessment of SSC state, leading to an underestimate or overestimate of the corresponding probability. To address this issue, a multivariate frequency analysis framework is proposed for extreme sediment events using copula functions that account for the joint behavior of SSC and flood flow (i.e., peak discharge and volume). The uncertainties associated with copula modeling are also discussed. The Jinsha River in China is used as a case study. The results show that the copula-based multivariate method provides a more comprehensive assessment of extreme SSCs and their recurrence intervals than univariate frequency analysis. The uncertainty of copula modeling decreases with longer observation time and increases with larger return periods. The uncertainty ranges of the most likely design quantiles vary with different copulas, and the uncertainty range for the best-fit copula is not the smallest. This study is useful for the risk assessment of extreme SSC events under flood conditions.

Block-wise use of TOPMODEL with the Muskingum–Cunge method (BTOPMC) is a physically based distributed hydrological model with five parameters that quantitatively reflect basin physical features, including soil type, vegetation, and land use of each grid-cell. In order to determine the sensitive model parameters and related variables more reasonably and efficiently, and to improve the model’s practical applicability and simulation accuracy, BTOPMC was integrated with the Uncertainty Quantification Python Laboratory (UQ-PyL) and used in the Fuji River Basin of Japan, by which qualitative and quantitative sensitivity analysis (SA) of variables related to the BTOPMC parameters was performed, and the sensitive ones were optimized by shuffled complex evolution (SCE-UA). The results showed that optimizing only the sensitive variables related to the three sensitive parameters of BTOPMC can ensure simulation accuracy with higher optimization efficiency, which indicates that the BTOPMC model could be applied more simply while guaranteeing the reliability of modeling.

Urbanization significantly affects hydrological processes, but the influence level of urban landscape area change (ULAC) on flood magnitude has not been quantified, the response laws of floods with different characteristics to ULAC still needs further discussion, and the effects of urban landscape structure and shape on flooding have not been illustrated. This paper used a Markov chain and cellular automata (CA-Markov) model, the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model, and the regression analysis method to assess the effects of ULAC on flooding. The Qinhuai River Basin (QRB) was selected to conduct the study. The results showed that (1) for minor, medium, and major flood groups, the statistical ranges of the linear curve [the relationship between flood volume ($Q_v$) relative change and urbanization] slopes were 0.46–3.44, 0.74–1.82, and 0.24–0.76, respectively, whereas for peak flow ($Q_p$) they were 0.8–3.27, 0.34–1.61, and 0.09–0.12, respectively; (2) the influence degree of ULAC on flood processes is gradually weakened with flood evolution; (3) for double-peak floods, the increase of the urban landscape area can significantly increase the $Q_p$ in the early stage and lead to earlier appearance of $Q_p$, but for single-peak floods, the influence on peak time is not significant; and (4) urban landscape structure is more important than shape in terms of flood protection.

The role of surface elevation and spatial resolution in statistical correction approaches for temperature and precipitation forcing is investigated using four global climate model (GCM) and regional climate model (RCM) combinations. A Mediterranean basin characterized by steep orography and prone to extreme flooding is chosen as a test case. For this aim, precipitation is statistically downscaled using a parametric scheme for bias correction and high-resolution downscaling and a widely used nonparametric approach, with nominal resolution equal to that of the GCM/RCM. Temperature fields are reprojected from climate model to terrain elevation at high resolution. The response of the basin in terms of discharge, actual evapotranspiration, and leakage is simulated using the TOPographic Kinematic APproximation and Integration (TOPKAPI-X) model from 1951 to 2099 and at multiple spatial scales. To investigate the role of orography, simulations are run applying the downscaling schemes on a flat terrain. The results show that, independently of the size of the basin, the elevation factor minimally affects the simulated hydrological response, whereas the effect of the spatial resolution of downscaled precipitation fields on the hydrological budget components is significant, and depends on the catchment size.

An implicit solution determining the seepage-face dimension formed in steady-state flow through an aquifer connecting two water bodies is achieved based on the Dupuit assumption and conservation-of-flow-rate considerations. The aquifer features sloping upstream and downstream boundaries. The solution approach presented here yields the seepage-face dimension, the flow rate through the aquifer, the geometry of the phreatic surface, and travel times along streamlines. The Dupuit-based groundwater flow estimates compare well with numerical solutions calculated for the exact groundwater flow problem.