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.

Effect of climate change on hydrologic flow regimes, particularly extreme events, necessitates modeling of future flows in order to best inform water resources management. This study simulated future flows in the Cache Creek watershed in California over the 21st century using a hydro-climate model (Watershed Environmental Hydrology Hydro-Climate Model; WEHY-HCM) forced by future climate projections. The future climate projections, based on four emission scenarios simulated by two global climate models (GCMs), the fifth-generation atmospheric global climate model and third-generation community climate model (ECHAM5 and CCSM3), under several initial conditions, were dynamically-downscaled using the fifth-generation mesoscale atmospheric model (MM5), a regional climate model. The downscaled future precipitation data were bias-corrected before being input into the WEHY model to simulate the detailed flow at hourly intervals along the main Cache Creek branch and its tributaries during 2010–2099. The results suggest an increasing trend in flood magnitudes and their intensities at the outlet of the study region throughout the 21st century. Similarly, estimates of the 100-year and 200-year floods increased throughout the study period. The observed differences in the estimated future flood frequencies between the first half and the second half of 21st century may be an evidence of the nonstationarity in the 21st century hydrological regime over the study region.

The western slope of the Sierra Nevada contains 13 major river basins with sufficient long-term seasonal forecast and runoff data to assess forecast skill. These seasonal forecasts are issued by the California Department of Water Resources on February 1, March 1, April 1, and May 1 of each year for the major watersheds in California. Annual average precipitation in these river basins goes from 1,500 mm in the Yuba in the north, to 600 mm in the Kern to the south. Average runoff fraction (April to July) for the various watersheds ranged from 0.1 to 0.5 with the lower-elevation watersheds, and the Kern in the south having lowest values. The difference between precipitation and runoff, an index of evapotranspiration, was highest in the lower-elevation Cosumnes and Mokelumne Basins. Approximately half of the April 1 annual forecasts had a percent bias of $\pm 15\%$. Skill scores for the 13 watersheds showed low scores (0.3) for forecasts in February, increasing through the forecast season to 0.8 for forecasts issued May 1, with 1.0 being a perfect forecast. Correlation skill measures, such as the Nash Sutcliffe scores, also exhibited increases in skill through the season from 0.45 in February to 0.95 in May. A linear regression between Nash Sutcliff scores and watershed elevation yielded a strong relationship with a coefficient of determination of 0.77. This relationship between higher elevation basins and greater forecast skill reflects the stronger statistical relationship between snow accumulations at index sites and seasonal runoff, versus more rainfall dominance in lower elevation watersheds. April through July runoff for each year was classified as the lower 30%, the mid 40%, and upper 30%; categorical skill measures were computed on the three runoff categories. Increases in forecast skill during the forecast season were visible in the low-flow and high-flow years versus the midflow years. Over forecasting of flow in the middle category was especially apparent early in the season, illustrated by high early season false-alarm rate and over forecasting bias. Difficulty in making accurate forecasts for midflow runoff along with the under forecast of high-runoff years and the over forecast of low-runoff years are shown to be common difficulties in runoff forecasting, especially early in the forecast season. Forecast skill is shown to be elevation dependent and can be expected to decrease with increasing temperatures.

The main purpose of this paper is to examine and recommend procedures that can be used to statistically test the exponentiality of large amounts of sample data for rainfall event volume, duration, and interevent time. Based on literature review and initial analysis, the Poisson and chi-square goodness-of-fit tests are selected first. Some misconceptions about parameter estimators and degrees of freedom associated with the use of the chi-square goodness-of-fit tests are then clarified. Using rainfall data from seven stations in the north-central region of the United States, the choice of the event volume threshold and the minimum interevent time for separating continuous rainfall data into individual events are examined in detail. Findings from this study suggest that the Poisson test can be used for testing the exponentiality of interevent times and for examining the statistical independence of consecutive rainfall events. The use of the minimum chi-square estimator combined with the chi-square goodness-of-fit test is recommended for rainfall event volume and duration. An equation that can be used to determine the appropriate number of bins for grouping sample data when conducting the chi-square goodness-of-fit tests is also proposed.

Lakes and reservoirs are widely used for water supply and flood control, especially during large storm events. In hydrologic modeling applications, accounting for the regulated behavior of reservoirs distributed throughout a river system poses a significant challenge, especially during flood events, when detailed reservoir operation rules and strategies are implemented. Building on this problem, this study addresses this question: Can we model reservoir water storage changes and outlet discharges based on satellite measurements of river water surface elevation and inundated areas especially during the flood event? A method is presented and evaluated using synthetic observations as a proxy for measurements from the forthcoming surface water and ocean topography (SWOT) satellite mission. The May 2010 flood event in the Cumberland River Basin is used as a case study. Based on synthetic SWOT observation, time series of water storage changes are generated and evaluated for eight reservoirs. As expected, although SWOT will provide relatively high temporal resolution measurements (i.e., three or four times per repeat cycle) compared with current point-based satellite altimeters, it provides only a 5% chance of direct observation of the 2-day flash flood event. To overcome this limitation, a new algorithm using the continuity equation and ensemble Kalman filter is presented to augment SWOT-estimated water storage changes for times between SWOT overpasses at eight reservoirs located throughout the watershed. The algorithm provides accurate storage changes with a 9% normalized RMS error (NRMSE). SWOT-estimated storage changes and reservoir routing are integrated into the Hillslope River Routing model to estimate inflows and outflows for the reservoirs. The average NRMSE for reservoir outflow is 28%, which is a 64% improvement compared with not including reservoir routing. At the watershed outlet, which integrates all eight reservoirs, the NRMSE and peak discharge error ($E{Q}_P$) for discharge estimates are 9 and 28%, respectively. The approach, which is entirely based on remotely sensed data and is potentially applicable in global scale models, reduced NRMSE by 73% and $E{Q}_P$ by 85% compared with the simulated discharges, without accounting for reservoir routing.

Typhoons and heavy storms frequently cause severe inundation in Taiwan. For early flood warning purposes, a real-time flood forecasting system was acknowledged as a pivotal tool and developed in this study. The system integrates four parts: (1) a gray-based rainfall prediction model, (2) the antecedent hydrological condition estimation method, (3) a deterministic runoff model, and (4) a runoff updating algorithm. The developed system was applied to the Hsia-Yun watershed in Taiwan. The time-invariant parameters, which characterize terrain features, were calibrated in advance. Additionally the time-variant parameters were modeled to change with temporal hydrological condition of the watershed. The gray theory was performed to predict the rainfall intensity in the next 1–3 h, which thus enables the runoff model to forecast incoming runoff in the next 1–3 h. By consulting the real-time feedback flow observation, a runoff updating algorithm was adopted and was helpful in improving the performance of the simulated hydrographs. In real-time application, the system can be initiated or terminated by detecting if the cumulative rainfall depth is larger or less than a predefined threshold value. The idea of the developed system has recently been constructed with a visual interface for real-time flood forecasting by authorities. The overall results confirm that the developed system is competent to forecast flash floods.

Construction-related activities typically disturb established vegetation, resulting in the potential for accelerated erosion. Because of the associated water quality impacts of construction activities, the USEPA has established regulatory programs to manage stormwater runoff emanating from construction sites. Currently, designers typically rely on “rules of thumb” for the design and selection of temporary erosion and sediment control practices. To provide a hydrologically sound approach, this research developed a quick and direct sizing method based on Technical Release–55 methodology. The analysis focused on Type-II and Type-III rainfall distributions, with 171 hydrological simulations performed. The results yielded applicable equations for the determination of flow rates representative of regional conditions for a typical 0.405-ha (1.0-acre) roadway median drainage basin. Geographical information system (GIS) analyses were used to develop and analyze regional hydrological characteristics. The GIS modeling results showed the average 2-year, 24-h rainfall depth and soil curve number for newly graded developing urban areas in the state of Alabama to be 11.3 cm (4.43 in.) and 88.5, respectively. Multiple linear regressions were performed on simulated data to develop equations to calculate expected total storm volume, peak flow rate, and average flows for 30-, 60-, and 90-min peak volumes based on regional input rainfall parameters and a weighted curve number. The developed relationships reported low standard errors when compared with a complete hydrologic analysis. Designers can implement these regression relationships as design aids in selecting and sizing appropriate runoff control practices for projects under consideration when developing stormwater pollution prevention plans.

In this study, a fuzzy vertex simulation method (FVSM) is developed for assessing parameter uncertainty on modeling outputs of Xiangxi River Basin in China. The FVSM couples a fuzzy vertex analysis approach with a semidistributed hydrological model to reflect effects of uncertain parameters on hydrological possesses. In FVSM, the topography-based hydrological model (TOPMODEL) is used for predicting streamflow of saturated area in the catchment by tackling the distribution of the topographic index; the fuzzy vertex analysis method specializes in dealing with ambiguous coefficients based on fuzzy sets theory and $\alpha$-cut analysis technique. The modeling outputs (i.e., Nash-Sutcliffe efficiency coefficient and the sum of the squared errors) indicate a good performance of TOPMODEL in describing the daily streamflow at four hydrological stations in the study basin. The uncertainties of two parameters ($M$ and $T_0$) are examined under different $\alpha$-cut levels. The results indicate that flow interval is wide under a low degree of plausibility (i.e., a low $\alpha$-cut level); conversely, a high degree of plausibility would lead to a narrow interval. Sensitivity analysis and factorial analysis are conducted, the results of which imply that the effect of $M$ is more significant than the effect of $T_0$ on hydrological modeling simulation. Results also reveal that the interactive effect of $M$ and $T_0$ is small, which can be neglected in the subsequent simulation. The findings can help quantify the multiple uncertain parameters and their interactions on modeling simulation as well as enhance the hydrological model’s capabilities for water resources management.

In the conventional zone-based hedging rule, as a common approach, the rationing factors are changed from one zone to another at once, which is not proper for actual reservoir operation. Hence a fuzzy-rule-based approach is employed to create a transition zone to assign rationing factors. This remedy causes gradual variation in the rationing factors and mitigates severe water shortages during drought periods. In this paper, a monthly simulation model linked to an evolutionary algorithm is developed. The proposed model was applied to the Zohre multireservoir system in southern Iran. There are two objective functions to supply minimum flow and agriculture demands over a long-term simulation period. Therefore, a multiobjective particle swarm optimization (MOPSO) algorithm was applied. The results showed that annual and long-term modified shortage index (MSI) values have been improved compared to the current conventional hedging rule. Implementation of the proposed hedging rule for the case study results in improving at least 34 and 21% in the maximum MSI, respectively, for the annual minimum flow and agriculture shortages.

Determination of imperviousness, which is defined by total impervious area (TIA) and effective impervious area (EIA), is mandatory for hydrological modeling of water quantity and quality in urban areas. The determination of TIA is relatively easy and preferred to determination of EIA, even though the latter is considered to be more appropriate for hydrological studies. Accurate and representative determination of EIA requires knowledge of drainage network connectivity with the impervious surface, which cannot be ascertained from remote sensing data alone. A more realistic semiautomated direct method is suggested in this study to determine EIA by integrating the remote sensing data, the digital format of the drainage network, and a digital elevation model (DEM) of the study area. Based on the results obtained from the proposed method, a power relationship $[\mathrm{EIA}=0.0035\times {(\mathrm{TIA})}^{2.17}]$ relating easily measurable TIA and hydraulically relevant EIA was determined for an ungauged urban catchment in northeast India. There are no such relationships available in the literature for an urban Indian catchment. EIA was also determined using two indirect methods reported in the literature, and the results were compared. The present study indicates that there is a distinct difference between the EIA values obtained by the direct method and those obtained by the indirect methods. Among indirect methods, Sutherland’s equation was observed to predict EIA values close to the directly determined EIA. The maximum overestimation of EIA by the Sutherland equation was 4 times, that by the Alley and Veenhuis equation was 20 times and, TIA was observed to be 56 times more than the directly estimated EIA. If EIA determined by indirect methods or easily measureable TIA were used as input for hydrological studies like flood modeling, it would result in considerable overestimation of peak discharge. The design of hydraulic structures or flood management strategies based on the overestimated peak discharge would have far-reaching undesirable impacts on efficient planning and management, causing overexpenditure.

The aims of this research are to investigate the hydrodynamics in the downstream part of a tidal river and to rapidly estimate the discharge in the case of the lower Chao Phraya River, Thailand. During flood tide, the depth of the maximum velocity shifts toward the river bottom, whereas during ebb tide, the maximum velocity is located near the water surface. The discharge can be estimated from the area and the mean velocity, which is obtained from the entropy parameter and measurement of maximum velocity. When the entropic linear relationship is assumed to be constant for a station, the estimated discharge is lower than the measured value at flood tide, and in contrast, the estimated discharge is somewhat higher than the measured value at ebb tide. To get a highly reliable value of discharge estimation, the variation in location of maximum velocity with flood and ebb tides must be considered, especially where the location of measurement has a strong influence from tides.

Sources of high-resolution topographic data, such as LiDAR, characterize urban terrains in great detail and can provide a resource for distributed hydrologic modeling. Incorporating these data in a distributed hydrologic modeling framework is complicated by the numerous real and artificial pits, barriers, and surface depressions contained in the urban landscape. These features create difficulties with simulating surface drainage that must be resolved to ensure model stability and convergence. This study presents a methodology to calculate distributed slope fields from LiDAR-based digital elevation models (DEM) that addresses this problem and preserves topographic details. This global slope enforcement approach ensures complete domain drainage, and the effects are demonstrated by performing a rainfall-recession test on an impervious domain using a distributed hydrologic model (ParFlow) of an urban watershed in Baltimore. This study shows that the fraction of rainfall input retained as surface storage in the model domain declined from 78 to 3.6%. Application to a high-resolution urban landscape demonstrates the capability of the method; the method can be applied to a variety of simulated topographies at coarser scales.