Journal of Hydrologic Engineering

Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances.

This paper examines model uncertainties associated with streamflow by characterizing it into extreme high (peak) flows and low flows in two different catchments, viz. a snowmelt-induced hilly catchment (Satluj) and an inland catchment (Tungabhadra). The streamflow was initially simulated and calibrated using the soil and water assessment tool (SWAT) model and SWAT CUP (calibration and uncertainty program) based sequential uncertainty fitting approach (SUFI2) by analyzing 14 different hydrological parameters. The multiple criteria evaluation was based on the multiple linear regression equations and noncommensurable measures of information derived from river flow series by means of a number of sequential time-series processing tasks, including separation of the river flow series into extreme peak flows and low-flow hydrograph periods. A reliable set of rules for model calibration was applied to all linear-regression-based objective functions. The authors found that extreme peak flow conditions were more critical than low-flow conditions and that the streamflow corresponding to the snowmelt process was more uncertain. Factors such as temperature (p-value∼0.027) and snowmelt (p-value∼0.000) were identified as sensitive parameters which showed large influence on the calibration parameters for mountainous hilly terrain conditions. Overall, calibration of the SWAT modeling was found more reliable for the Tungabhadra catchment in both extreme peak flow and low flow simulations whereas the model performance was slightly complex for the snowmelt-induced Satluj catchment, especially during extreme peak flow events.

An empirical statistical system for quantitative forecasting of monthly precipitation in Korea has been developed using the cyclostationary empirical orthogonal function (CSEOF) and the canonical correlation analysis (CCA) with sea surface temperature (SST) data as the predictor. Monthly Korean precipitation and SST data are comprehensively analyzed using the empirical orthogonal function (EOF) technique and the CSEOF technique, respectively, and the CSEOF technique can exhibit the spatial distribution and temporal evolution characteristics of variability along with recurrent seasons of precipitation in Korea. Through a multivariate regression method, the CCA technique is used to forecast precipitation with different lead times, and the forecasting results indicate that the CSEOF-CCA forecasting model agrees well with the observation data and is particularly useful in forecasting seasonal precipitation variations in Korea.

Aquifers at the coasts are influenced by both oceanic and estuarine water stage variations, resulting in complex behavior of groundwater level fluctuation in coastal aquifers. The two-dimensional linear Boussinesq equation is invariably employed to understand the tidal dynamics of coastal aquifers. Previous studies emphasized L-shaped aquifer responses to sinusoidal tidal waves in an estuary and ocean, ignoring the groundwater-level fluctuation induced by arbitrary water stages variations in non-L-shaped aquifers. To resolve this problem, a universal analytical solution is derived according to the superposition principle. Groundwater level fluctuations induced by oceanic and estuarine water stage variations are translated into the sum of noninteracting cross shore and alongshore tidal wave propagations in the aquifers. Then the developed solution was applied to predict groundwater levels in an L-shaped aquifer in Kumamoto, Japan. The simulated results fitted favorably with the observed data. The developed solution was also applicable to simulate groundwater responses to anomalous oceanic and estuarine water stage variations. The dynamic change of groundwater level in both L-shaped and non-L-shaped aquifers during exponential oceanic storms and step rising estuarine water stage variations was evaluated using this approach. The proposed solution performs well in both L-shaped and non-L-shaped cases. The developed solution provides an effective method to describe the non-L-shaped aquifers responses to arbitrary oceanic and estuarine water stage variations.

Green infrastructure, including stormwater control measures (SCMs), meant to reduce runoff volumes are typically designed for smaller, frequently occurring events (e.g., 2.5 cm or the 2-year, 24 h event), and their performance are not always considered for larger, more infrequent extreme event management. However, even during larger rainfall events, a SCM is still capturing at least the design rainfall and reducing runoff volume. Further, if a SCM relies on infiltration as its primary volume reduction strategy, infiltration continues throughout the rainfall event indicating that SCMs can be considered in flood mitigation strategies. This phenomenon of greater than designed runoff volume removal has been noted in several infiltrating SCMs, and the present work uses the Villanova green infrastructure site comprising an infiltration trench with pretreatment (i.e., linear vegetated SCMs) designed for the 2.5 cm rainfall event as a basis for discussion. The analysis focuses on the infiltration trench volume capture and includes the effect of storm volume, duration, intensity, and antecedent dry time. Seasonally, infiltration rates within the infiltration trench were shown to vary with temperature in the lower ponding depths. While there was some decrease in infiltration rates in the lowest depth increment (0–0.3 m) in the systems’ third winter and spring seasons, there was a general increase in infiltration rates over the course of the study period demonstrating the infiltrating SCMs’ ability to be a reliable stormwater management solution. With regards to large storm event performance, the results from 2 years of monitoring the infiltration SCM system have been quite impressive. The system has consistently met the volume reduction design goals and usually exceeded them by significantly contributing to storm runoff mitigation during large extreme events. The infiltration SCM system captured and removed at least 59% of the volume of every storm event analyzed from July 2012 through June 2014, with an average of 93% capture for events greater than the design volume of 2.5 cm. Furthermore, there were 159 storm events totaling 259 cm of rainfall during the study period. Given the drainage area loading, approximately $\mathrm{2,365}{\mathrm{m}}^3$ of rainfall entered the linear vegetated component, with approximately $420{\mathrm{m}}^3$ (18%) reaching the infiltration trench and only $103{\mathrm{m}}^3$ (4%) of rainfall determined to overflow from the system. These performance results demonstrate that infiltration SCMs can reduce significant volumes of runoff during larger events and that new design strategies are needed to account for their performance.

The daily monsoon rainfall data recorded at Subang Airport, Malaysia, from 1960 to 2011 is examined in terms of probability structure for the estimation of extreme daily rainfall precipitation during the Northeast (NE) and Southwest (SW) Malaysian monsoons. The discrete autoregressive and moving average [DARMA(1,1)] model is preferable to the first-order Markov chain [DAR(1)] model. The conditional probabilities of $t$ consecutive rainy days are time dependent. Nevertheless, a simple two-parameter gamma distribution appropriately fits the frequency distribution of multiday rainfall amounts. An algorithm is developed by combining the DARMA(1,1) and gamma models to estimate the return period of multiday rainfall. Extensive comparisons showed that the DARMA(1,1)-gamma model gives a reliable estimate of the return period of rainfall for both NE and SW monsoons at Subang Airport. Furthermore, values generated from the models enable the analysis of the frequency distribution of extreme rainfall events.

Design rainfall time series are a critical input for urban flood modeling and require depth-duration-frequency curves along with a design temporal pattern. The association between rainfall depth and duration, which depicts the inherent structure of rainfall patterns, plays an important role in flood-causing potential; this association can be revealed through a bivariate rainfall frequency analysis to obtain the depth-duration-frequency curves. Unlike earlier approaches that use either parametric or nonparametric models, the present study proposes a new semiparametric model approach, which can evaluate all possible combinations of the parametric-nonparametric marginals without restrictions. A comparison between the copula-based bivariate frequency analysis and the generalized least-squares regression shows the former to be better in performance. The first three best-fit models—Gaussian kernel, triangle kernel, and Burr type XII combined with the generalized Pareto distribution—yield consistently similar results, indicating the robustness of the approach. Realistic design rainfall temporal pattern could also be derived from the observed set of temporal patterns using their skew, kurtosis, and bimodality measure to quantify the maximum flood-causing potential and also to generate the design rainfall time series. The proposed framework has been demonstrated for a severely flood-prone coastal megacity, Mumbai, in India.

Infiltration basins are commonly utilized to reduce or eliminate stormwater runoff and are commonly located on coarse soils due to relatively high infiltration rates. Forty infiltration basins in Florida were included in this field study to evaluate whether basin infiltration rates varied significantly from their designed infiltration rates. Basins were located among Leon, Alachua, and Marion counties in Florida, while watershed land uses were equally divided between Florida Department of Transportation and residential developments. Generally, six test sites within each basin were selected for infiltration rate measurement by double-ring infiltrometer (DRI). Also, a soil boring was collected from each test site for analyses, including soil texture, bulk density, and organic matter content. Infiltration rates were log-transformed and statistical analysis was used to determine if DRI measurements were significantly different from design rates. Basin soil textures were well distributed between sand, loamy sand, sandy loam, and sandy clay loam. Most (91%) bulk densities ranged between 1.30 and $1.80\mathrm{g}/{\mathrm{cm}}^3$ and organic matter percentages ranged from 0.1 to 48.5%, with 49% of soils having less than 3.0% organic matter. Nearly all bulk densities were not limiting to vegetation growth for the respective soil texture. Based on DRI rates, 16 (40%) basins had rates significantly less than their designed rates, 10 (25%) had rates equal to their designed rates, and 14 (35%) basins had rates significantly greater than their designed rates. Basins with coarser soils were also more likely to have DRI rates greater than designs. In addition, a higher proportion of basins located within Florida Department of Transportation watersheds had DRI rates greater than or equal to design rates, which may be related to vegetation size and diversity.

Accurate streamflow prediction is required in sustainable water resources management. Direct use of observed data in developing prediction models has resulted in inaccuracies. Discrete wavelet transform (DWT) is widely used to decompose observed data (raw data) into spectral bands and eliminate trends and periodicity to improve the accuracy of the models. However, DWT is known to have serious drawbacks, and predictions of daily streamflow have been with short lead times. In this study, a simple method called the SEASON algorithm was used to decompose the observed data into components with the objective of overcoming the drawbacks of DWT so that daily streamflow can be predicted with better accuracy and longer lead times. Data decomposed by SEASON and DWT were used as input into multilayer perceptron (MP) approaches to develop new approaches for predicting daily streamflow for lead times up to 7 days, and termed as seasonally adjusted series-multilayer perceptron (SAS-MP) and wavelet-multilayer perceptron (W-MP), respectively. Twelve years of approved daily streamflow data were obtained from Station 02231000 (located in the St. Marys River watershed) and Station 07288280 (located in the Big Sunflower River watershed), USA. Seven years of data were used for calibration (training) and the remaining 5 years of data were used for prediction (testing). The new approaches were compared with the stand-alone MP model by taking root mean squared error, coefficient of efficiency, and skill score into consideration. The results showed that the SAS-MP and W-MP models performed better than the stand-alone MP model, and the prediction accuracy increased with the use of decomposed signals. However, for all lead times, the SAS-MP model outperformed the W-MP model, which performed less after a lead time of 4 days. This indicates that the SEASON algorithm has the capability to capture periodicity better than DWT and can be used to extend lead time with better prediction reliability.

Index-flood related regional frequency analysis (RFA) procedures are in use by hydrologists to estimate design quantiles of hydrological extreme events at data sparse/ungauged locations in river basins. There is a dearth of attempts to establish which among those procedures is better for RFA in the $L$-moment framework. This paper evaluates the performance of the conventional index flood (CIF), the logarithmic index flood (LIF), and two variants of the population index flood (PIF) procedures in estimating flood quantiles for ungauged locations by Monte Carlo simulation experiments and a case study on watersheds in Indiana in the U.S. To evaluate the PIF procedure, $L$-moment formulations are developed for implementing the procedure in situations where the regional frequency distribution (RFD) is the generalized logistic (GLO), generalized Pareto (GPA), generalized normal (GNO) or Pearson type III (PE3), as those formulations are unavailable. Results indicate that one of the variants of the PIF procedure, which utilizes the regional information on the first two $L$-moments is more effective than the CIF and LIF procedures. The improvement in quantile estimation using the variant of PIF procedure as compared with the CIF procedure is significant when the RFD is a generalized extreme value, GLO, GNO, or PE3, and marginal when it is GPA.

The propagation of flood waves in rivers is governed by the Saint Venant equations. Under certain simplifying assumptions, these nonlinear equations have been solved numerically via computationally intensive, specialized software. There is a cogent need for simple analytical solutions for preliminary analyses. In this paper, new approximate analytical solutions to the nonlinear kinematic wave equation and the nonlinear dynamic wave equations in rivers are presented. The solutions have been derived by combining Adomian’s decomposition method (ADM), the method of characteristics, the concept of double decomposition, and successive approximation. The new solutions compare favorably with independent simulations using the modified finite-element method and field data at the Schuylkill River near Philadelphia. The time to peak calculated by the analytical and numerical methods is in excellent agreement. There appears to be some minor differences in the peak magnitude and recession limb, possibly because of numerical dissipation. Including the momentum equation in the analysis causes a decrease in the magnitude of the flow rate at all times. Except for the flood peak, the nonlinear analytical solution exhibits lower flow rates than the numerical solution. The numerical solution also shows higher dispersion. The new analytical solutions are easy to apply, permit an efficient preliminary forecast under scarce data, and an analytic description of flow rates continuously over the spatial and temporal domains. They may also serve as a potential source of reference data for testing new numerical methods and algorithms proposed for the open channel flow equations. The ADM nonlinear kinematic and nonlinear dynamic wave solutions exhibit the usual features of nonlinear hydrographs, namely, their asymmetry with respect to the center of mass, with sharp rising limbs and flatter recession limbs. Linear approximations of the governing equations usually miss these important features of nonlinear waves. The greatest portion of the magnitude of discharge is given by the initial nonlinear kinematic wave component, which implies that in the lower Schuylkill River the translational components dominate the propagation of flood waves, in agreement with previous research. The nonlinear dynamic wave better predicts the flow rate during peak times and especially during recession and low-flow periods. Thus, while both the nonlinear kinematic and the nonlinear dynamic wave models are based on simple approximate analytical solutions that are easy to implement, the nonlinear kinematic wave equation model requires less data and less computational effort.

Hydrologists are concerned with high-intensity rainfall and peak runoff rates for stormwater infrastructure designs, post-event assessments, and mitigation of environmental impacts. In the southwestern United States the need for accurate information about these rates is increasingly important as population growth and associated development are projected to exceed national averages. Design storm totals for various durations and return period frequencies are routinely derived from the National Oceanic and Atmospheric Administration (NOAA) Atlas 14 and are commonly used as input to hydrologic models to estimate peak runoff rates and runoff volumes. For the southwestern United States during the North American Monsoon, NOAA relies on sparse rain gauge networks to measure rainfall from limited area convective storms primarily at daily time steps and estimates of subdaily event intensities are derived by temporal downscaling from a few point locations. The USDA, Agricultural Research Service, Southwest Watershed Research Center (SWRC) operates the Walnut Gulch Experimental Watershed (WGEW) in the vicinity of Tombstone, Arizona. SWRC maintains a database of 60 years of subdaily, high temporal-precision rainfall intensities and runoff rates for WGEW. Updated, temporally extended, rainfall intensity-duration-frequency relations for WGEW are presented. The current analysis includes intensity-duration-frequency relations for July, August, and September for 53 years, 1961–2013, for durations of 2, 5, 10, 15, 30, and 60 min and return periods of 2, 5, 10, 25, 50, 100, and 1,000 years. The $149{\mathrm{km}}^2$ WGEW is large enough to select groups of four rain gauges whose event totals are independent. This allows combining of the four independent gauges’ 53-year time series into a longer time series of 212 years. A comparison of WGEW-generated intensity-duration-frequency curves to those of NOAA Atlas 14 indicated good agreement. However, across the range of durations, many observed events on WGEW from gauges not used in the frequency analysis are much greater than the estimated 100-year event. The dense gauge network appears to capture a substantially greater number of low-frequency, extreme rainfall events not typically observed in sparse networks. To assess the hydrologic consequences of these extreme events they were used as input to a well-tested watershed model for a small gauged watershed that did not experience events of similar magnitude. Simulated runoff volumes and peak discharge rates were up to four times as large as the largest observed runoff event of record. These analyses offer insights into the benefit of long-term watershed research with spatially dense and high temporal resolution observations.

A satellite-based potential evapotranspiration (PET) product for streamflow simulations is tested for 15 forecast basins in the Upper Mississippi and Red River watersheds under the forecasting responsibility of the National Weather Service (NWS) North Central River Forecast Center (NCRFC). PET demand curves, which are long-term average estimates of daily PET, are derived using the National Aeronautics and Space Administration’s moderate resolution imaging spectroradiometer sensor (MODIS) on board the Terra and Aqua earth observation satellites. The PET demand curves (referred to as M-PET) are then used as input to the NWS Sacramento soil moisture accounting model (SACSMA) and simulated discharge and evapotranspiration (ET) are evaluated. Simulations using M-PET input are compared to simulations produced using the demand curves of the NCRFC (referred to as NC-PET). The M-PET data correlate better with PET estimated using tower data from three sites located within the study region compared to the NC-PET. The M-PET overall has low positive bias, averaging approximately $0.25\mathrm{mm}{\mathrm{day}}^{-1}$ while the NC-PET has larger, negative bias, averaging almost $2\mathrm{mm}{\mathrm{day}}^{-1}$. The M-PET discharge simulations have acceptable performance (i.e., $\text{Nash}\text{Sutcliffe}>0.30$) for eight of the 15 basins. The simulated ET produced by the M-PET matches the range of observed ET better than the NC-PET when compared to data from the three flux sites. Overall results indicate there is potential for using the M-PET as input into the SACSMA though further work is needed to assess potential data bias.

Snow line altitude (SLA) defines the boundary of snow-free and snow-covered area. The efficiency of snow/ice-melt-runoff model and snowy/Himalayan watershed model depends on the estimation accuracy of the mean SLA for varying saturated snow cover (melting area) within seasonal and permanent SLA. Remotely sensed data are still in the phase of development to present quick and dependable input of SLA. This study is aimed to predict seasonal SLA, annual equilibrium line altitude (ELA), maximum annual SLA, melting zone, and their intraseasonal or/and interannual variations based on observed temperature in a watershed of the western Himalayas. The study area extends from 2,100 to 6,000 m altitudes (latitude: 32° 10′N to 32° 25′N; longitude: 77°E to 77° 40′E). Varying catchment participation (VCP) zonal altitude of melting and simulated relation naming applied basic oscillation (ABO) have been developed for SLA movement on weekly and monthly mean temperature data and simulation of monthly observed/estimated SLA, respectively. The VCP results suggested an upward shift of spring SLA by 700 m in March, whereas ABO results showed a 300-m upward shift of ELA during September in two decades from the 1980s. The VCP calibration parameters—temperature lapse rate of $0.554°\mathrm{C}/100\mathrm{m}$ and saturated SLA mean air temperature of 5.75°C—have been designed to predict SLA using temperature and ablating snowpack at Solang (2,485 m altitude). The periodical shift in SLA has been verified with the snow cover disappearance data and combined impact of temperature and solar radiation at two stations. The VCP-computed SLA and ELA varied by $\pm 5\%$ with observed values, whereas the error in ABO-predicted monthly SLA including ELA was $\pm 4\%$. Simplified VCP pattern variability, ABO trend of SLA, and SLA nomogram have been developed for the snowmelt hydrological models and other applications.

For the past several decades, the United States Geological Survey (USGS) and others have been tasked with development of regional predictor equations to determine flood peaks in unregulated and ungaged rural locations. This study tests several new watershed parameters not commonly used in past studies that have been extracted from available geomorphologic and climatic data using both manual and programmatic methods with the goal of improving the predictive power of the regional equations. Parameters include a derived basin slope corresponding to surface flow direction, discrete travel time for each location in a watershed to the outlet, and development of a watershed subarea correlated with the location of the maximum length isochrone line within each watershed. A case study is presented that tests these parameters in the Valley and Ridge physiographic region of Virginia. Results indicate that the proposed parameters tend to reduce standard model error in predictor equations ranging between 0.97 and 18.08% when compared with equations developed using drainage area as the lone predictor variable.

With the development of an agricultural economy, agricultural water demand has increased. The increased demand has resulted in extremely unbalanced development and utilization of surface and groundwater and has seriously affected the sustainable development of a regional economic ecology. To understand the law covering the complex phenomena of groundwater systems, it is important to select the appropriate method to measure the complexity of groundwater resource systems. Previous analyses of regional groundwater resource systems had the shortcomings of ignoring the system complexity as well as difficulty to realize the importance in deed of scientific management of groundwater resources. The Jiansanjiang Administration research platform was used to detect the monthly depth series of groundwater complexity with an aim to determine the complexity of the series. The average complexity of groundwater table depth series of each subarea on a monthly basis by was determined adopting symbolic dynamics. The diagnostic analysis results showed that the complexity of monthly groundwater table depth series of each subarea of the Jiansanjiang Administration featured highest complexity in the northern area, lowest complexity in the middle area, and medium complexity in the southern area. Local agricultural production activities are the key driving factors of dynamic change of groundwater table depth. Compared with the traditional rescaled range (R/S) analysis fractal theory and wavelet entropy theory, they showed that the results of diagnostic analysis by symbolic dynamics have a higher degree of reasonableness, providing an effective method for the research on the complexity of regional hydrological processes. The research results have uncovered the evolution characteristic of complexity of the local groundwater depth, providing a scientific basis for partition management and sustainable utilization of regional groundwater resources. The goal of the research is the rational exploitation of Jiansanjiang groundwater. The study results reveal the complex spatial distribution pattern of local groundwater depth and provide scientific evidence for management and sustainable utilization of regional groundwater resources.

Human water use can place considerable stress on hydrologic systems. Humans directly affect the water cycle through water withdrawals and return flows. Aside from climate change, humans indirectly impact hydrologic systems through dam building, land use change, and urbanization. Given the increasing incidence of water overdraft and water scarcity globally, there is a growing need to understand and quantify these effects so that appropriate water management and policy decisions can be made both in the United States and abroad. Massachusetts, in the northeastern United States, is located in a “water-rich” region, and yet issues of water overdraft and exceptional low flows have been documented throughout the state. This study demonstrates the application of water use regimes for characterizing the water balance in two suburbanized basins in eastern Massachusetts: The Charles River watershed (CRW) and the Ipswich River watershed (IRW). Water use regimes categorize river basins based on the magnitude of human water withdrawals and return flows relative to streamflow. Water use regimes for the upper/middle portions of the CRW and the IRW were examined at annual and subannual timescales for the period 2000–2004, which was the only time during which detailed water use data were available. Although the water use regimes of these watersheds displayed only minor alterations on an average annual scale, increased regime indicator values were noticeable at the annual scale of a low-flow (drought) year. At the subannual scale, water use intensity was low in high-flow months (e.g., April), but increased by more than an order of magnitude during low-flow months (e.g., August). A shift in water regime, from natural flow–dominated toward withdrawal-dominated, was observed at the average monthly timescale and during the low-flow months of a single low-flow year. Despite the fact that the CRW and IRW are similar in their suburban characteristics and experience a similar climate, the two displayed different water use characteristics and variability in regime change. Therefore, water use characteristics and subannual variability in water regime shifts should be emphasized when examining the relationship between water use and water resource availability.

The use of partial duration series (PDS) with generalized Pareto (GPA) distribution in hydrological frequency analysis has been popular during the last few decades because PDS is capable of including more extreme events than annual maximum series (AMS). In Malaysia, the most updated rainfall intensity-duration-frequency (IDF) relationships that serve as the official hydrological design guideline are based on the fitting of AMS to the generalized extreme value (GEV) distribution. The purpose of this paper is to identify the statistical model that best represent Peninsular Malaysia for deriving rainfall IDF relationships. Besides GEV and GPA distributions, other distributions such as generalized logistic (GLO), Gumbel (EV1), lognormal (LN3), and log-Pearson Type III (LP3) are also included in this study. Rainfall data with record length varying between 10 and 41 years from 60 rainfall stations in Peninsular Malaysia are computed into AMS and PDS, and fitted to the selected distributions. The $L$-moment ratio diagram is used to determine the appropriateness of these frequency distributions, then Anderson-Darling tests are performed to compare the goodness of fit of the selected models, and the estimated rainfall intensities are observed. The results show that the PDS-GPA model performs better in goodness-of-fit tests, which encourage the use of the PDS-GPA model for generating rainfall IDF relationships in Peninsular Malaysia.

In 2004 the State of New Jersey updated its stormwater management regulations with stricter design standards for attenuating peak flow, improving water quality, and recharging groundwater. This case study modeled four properties in Middlesex County, New Jersey to examine the potential of basins built before 2004 to comply with the new design standards. None of the models meets all aspects of the 2004 regulations. The model of a 10-year-old residential site produces results within 10% of two of the peak flow reduction targets and provides 18% of the target groundwater recharge volume. The model of a 32-year-old basin does not meet any of the peak flow reduction targets, has a detention time of less than 1 h and infiltrates 1% of its target recharge volume. A hypothetical hydraulic conductivity of $5\mathrm{cm}/\mathrm{h}$ was added to the models to explore the potential effects of a soil restoration technique such as deep tillage. With this rate, the groundwater recharge for the 10-, 21-, and 32-year-old models increases to 101, 21, and 22% of their target volumes.

Climate change’s impact on the Sabah and Sarawak water resources in the Northern sector of the Borneo Island, Malaysia, was assessed based on the dynamically-downscaled general circulation model projections (GCMPs) by means of a regional hydroclimate model (RegHCM). Four future projections under the special report on emissions scenarios (SRES) A1B emission scenario from two general circulation models (GCMs) were selected for this study. The RegHCM, which is a coupled nonhydrostatic atmospheric and upscaled land surface process model, is capable of downscaling the outputs of these GCMPs (GCM projections) to the watershed scale at a 9-km grid resolution at hourly time intervals for hundreds of years—a simulation for 420 years was performed in this study. This dynamic downscaling by the RegHCM can incorporate the detailed soil and land-cover data. It is shown in this article that utilizing a methodology that incorporates a GCM, a RegHCM, and a hydrological routing model allows assessing climate change on the hydrologic conditions at the watershed scale. It is revealed that the effect of climate change in the states of Sabah and Sarawak can be quite heterogeneous. Furthermore, it is shown that the effect of a projected land-cover change over a geographical region, such as Sabah and Sarawak, can be evaluated in the future using the RegHCM described in this study. Results indicate that the increase in oil palm plantations in Sabah and Sarawak may not significantly affect the local water resources. In order to improve the assessment accuracy of land-use change, further investigation on the model parameters associated with future land-cover information is desirable.

This study investigated the possible problems of using the concept of critical rainfall duration (CRD) for estimating the design flood. The investigation focused on the evaluation of the difference between the annual maximum independent rainfall event series (IRES) and the annual maximum short-duration rainfall series in Seoul, Korea. Annual maximum IRES was determined by applying Freund’s bivariate exponential distribution, which were also used for frequency analysis by applying the Gumbel’s bivariate logistic model. Both the annual maximum IRES and the frequency analysis results were then compared with those of the conventional annual maximum fixed-duration rainfall series (FDRS). The results are summarized as follows. First, the mean rainfall intensity of the annual maximum IRES was found to be little changed regardless of their durations. Thus, most of the annual maximum short-duration rainfall series would be selected in the annual maximum IRES. Second, results of the bivariate frequency analysis of the annual maximum IRES were found to be similar to those of the annual maximum long-duration rainfall series. It means that the annual maximum long-duration rainfall series could represent the annual maximum IRES. In conclusion, the short-duration rainfall series is rather artificial and the possibility of their occurrence in reality is rare. Considering the typical shape of rainfall temporal distribution, the peak of a short-duration design rainfall could be much smaller than that of a long-duration one. The use of the concept of CRD finally could result in smaller design floods especially in small basins.

This article demonstrates a procedure for simulation of baseflow in the EPA Storm Water Management Model (SWMM) using the SWMM groundwater flow editor. The procedure is based on simulation of baseflow by usage of the one-dimensional (1D) Dupuit analytical solution to groundwater flow. The analytical basis for the procedure is presented, and a solution within SWMM is validated.