Journal of Hydrologic Engineering

Future streamflow patterns of three subbasins, i.e., East Holland, Beaver, and Pefferlaw River basins, located in the upstream of Lake Simcoe of Canada are assessed for 2021–2099. The individual set of parameters of a conceptual hydrological model, HBV (Hydrologiska Byråns Vattenbalansavdelning)-light, are first calibrated for these three subbasins. The calibrated model was validated and further used to estimate the future streamflow driven by statistically downscaled projected precipitation and air temperature from the Pacific Climate Impacts Consortium under Representative Concentration Pathways 8.5 and 4.5 scenarios. The uncertainty of annual streamflow, hydrograph, flow duration curve (FDC), and flood frequency were evaluated. The results reveal that the annual streamflows of the Beaver and Pefferlaw River Basins (PRB) are projected to slightly increase in 2020–2099 while the annual streamflows of the East Holland River Basin (EHRB) are expected to be similar in 2020–2099. The monthly streamflow in winter is projected to increase but to decrease in spring across three subbasins. Based on the projected FDCs, daily streamflow of EHRB and PRB will likely increase by 2070–2099.

Different parameters of dimension $m$ and tolerance threshold $r$ in approximate entropy (ApEn) and sample entropy (SampEn) can cause inconsistency of complexity comparison in hydrologic time series. A new approach to determine the optimal common parameters $m$ and $r$ is presented to solve this inconsistency. Time series of runoff and precipitation during 1956–2010 in the Jinghe watershed of the Chinese Loess Plateau were analyzed. The optimal common parameters were determined to be $m=2$ and $r=0.11$ standard deviation of time series. The runoff and precipitation show significant decreasing trends, which are opposite of the significant increasing trends of ApEn and SampEn. The runoff and precipitation have close relationships with their corresponding complexity. The decreasing trend of precipitation has important influences on the increase of runoff complexity. The ApEn is better than the SampEn for describing the general trend of the time series, while the SampEn has a stronger capacity to identify the turning points in the time series. This new approach has wide applicability.

Future precipitation projections—and subsequent variation in simulated runoff response—can have a large impact on the planning and design of hydraulic structures and water systems and are, therefore, an important input in hydrologic modeling. These projections are often derived from coarse-scaled climate models and may require downscaling and bias-correction techniques to be suitable for local-scaled applications. Here, one simple and commonly used downscaling approach, called change factor methodology (CFM), is modified to combine both additive and multiplicative change factors depending on the characteristics of the empirical cumulative distribution functions and limitations of precipitation data. The combined change factor methodology (CCFM) is applied as a secondary bias-correction technique to general circulation model (GCM) data for a comparison period of 1985–2014 and a future period of 2055–2084 in six locations throughout the United States that differ greatly in local climate and precipitation patterns to examine the method in a range of settings: Salt Lake City, Utah; Toledo, Ohio; Seattle, Washington; Houston, Texas; Miami, Florida; and Phoenix, Arizona. The CCFM successfully addresses several common issues inherent with traditional CFM, including negative precipitation, overestimation, and artificially inflated numbers of precipitation events. During the comparison period, the CCFM results in precipitation time series that more closely match observed precipitation patterns (average, extreme values, number of events) than traditional CFM, and are generally closer than the values produced by the uncorrected projections. This study also identifies remaining limitations to the CCFM, such as representation of potential nonstationarity in future precipitation events or differences in extreme precipitation values. The uncorrected and CCFM-scaled projections are also used as inputs for a hypothetical urban hydrologic model to demonstrate the consequences of using the CCFM in modeling applications. This modeling exercise shows that on a monthly scale, the projections with no secondary correction result in greater magnitudes of change (compared to historical conditions) in average runoff than the CCFM projections. This highlights that the use of the CCFM as a secondary bias-correction technique has potential to have a substantial impact on the resulting hydrologic analysis and subsequent planning, design, or management strategies.

Application of multivariate adaptive regression spline ensembles (En-MARS) in a coupled simulation-optimization methodology to derive multiple-objective optimal groundwater extraction strategies for a multilayered coastal aquifer system is demonstrated. Two conflicting objectives of groundwater extraction strategies are solved using a controlled elitist multiobjective genetic algorithm. A three-dimensional density-dependent coupled flow and salt-transport numerical simulation model is used to generate the training patterns of groundwater extraction strategies and resulting saltwater concentrations. Prediction capability of En-MARS is compared with that of the best multivariate adaptive regression spline (MARS) model in the ensemble. En-MARS is then linked externally within the optimization algorithm to develop the management model. The optimal solutions obtained from the En-MARS models are verified by running the numerical simulation model. The results indicate that MARS-based ensemble modeling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. The adaptive nature of MARS models and use of ensembles and parallel processing results in a computationally efficient, accurate, and reliable methodology for coastal aquifer management that also incorporates uncertainties in modeling.

This study quantified the impacts of climate change on water availability over the Flint River subwatershed (FRW) located in the Wheeler Lake Watershed (WLW) in northern Alabama. The observed climate data were obtained from the Alabama Mesonet Stations, and the modeled climate was obtained from multiple models, multiple scenarios, and multiple projections available through the World Climate Research Program’s database. The hydrologic model, the soil and water assessment tool (SWAT), was calibrated and validated for the period of 2004 to 2013, based on daily meteorological forcing and monthly streamflow data. Various climate scenarios developed with respect to anticipated future climate until 2100, based on representative concentration pathways (RCPs), were forced into the calibrated SWAT model to quantify future water availability. The simulated average change in monthly streamflow varied from $+23$ to $-46\%$, $+29$ to $-48\%$, $+40$ to $-48\%$, and $+38$ to $-48\%$ for scenarios RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, respectively; the simulated total annual streamflow varied from $+18$ to $-28\%$ for the same scenarios. However, average change in total annual streamflow based on all scenarios showed a decreasing streamflow pattern from $-0.3$ to $-1.2\%$ for future periods, except a slight increase of $+0.17\%$ at the end of this century. The simulated results that quantified water availability under changing future climate conditions can be used by water managers, stakeholders, and decision makers in planning and managing available water resources and their allocations based on users and water demands, and in considering alternatives for monitoring and mitigating long-term impacts.

Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot. Climate-based conditional streamflow simulation techniques recently have been shown to perform even better in capturing the spectral characteristics of streamflow coupled with block bootstrap simulation. This paper presents a comparison of three recently developed time series models in these frameworks: the climate wavelet autoregressive model (CWARM), the climate hidden Markov model (CHMM), and the climate wavelet-based k-nearest neighbor (K-NN) time series bootstrap (CWKNN) model. The purpose is to determine their applicability in water resources planning and management. These three methods incorporate two large-scale climate forcings, Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO)—recognized as the drivers of underlying nonstationarity—to condition the streamflow simulation. Comparisons are made of performance in both simulation and projection modes using the Lees Ferry (Arizona) flow in the Colorado River basin (CRB). The three methods are generally very good at capturing the distributional statistics and nonstationary features of the historical data in simulation mode. For short-term projections (1–8 years), important for midterm reservoir operations and planning, the CHMM appears to perform slightly better than the other two models. For longer-term projections ($\sim 20\mathrm{years}$), useful for decadal and multidecadal water resources planning, the CWKNN performs much better.

Two or more processes of soil erosion simultaneously exist in a hillslope landscape, and those processes can interact with each other. Yet, how one process impacts another remains unresolved. The objective of the study was to determine the relationship between tillage erosion and soil hydrological properties in the hillslope landscape. Five slopes with lengths of 20 to 22 m were selected from hilly areas of the Sichuan Basin, China. Tillage erosion appeared to be most intensive at upper slope positions ranging from 80 to $125\mathrm{t}{\mathrm{ha}}^{-1}{\mathrm{year}}^{-1}$. Drastic differences in total soil depth were observed among different landscape positions, averaging 17, 28, and 39 cm deep for upper, middle, and lower slope positions, respectively. Soil water storage capacity (SWSC) at saturation exhibited remarkable differences among different landscape positions. The lowest steady-state infiltration rates were found in tillage-eroded areas (upper slope) with a mean of $1.21\mathrm{mm}{\min }^{-1}$, followed by deposited areas (lower slope) with a mean of $2.32\mathrm{mm}{\min }^{-1}$, while the largest steady-state infiltration rates were observed in the balanced areas between soil loss and gain (middle slope) with a mean of $3.34\mathrm{mm}{\min }^{-1}$. The thickness of soil profiles and local slope gradients were both associated closely with tillage erosion rates and were found to be two important determinants of soil infiltrability. It is suggested that soil degradation by tillage erosion alters soil hydrological properties, thereby resulting in poor soil infiltrability in the hilly landscape.

This paper investigates the impact of climate change on low flows in Xiangjiang River Basin in central China. Projections from four global climate models (GCMs) under representative concentration pathway (RCP) 4.5 and RCP8.5 are used to drive the hydrological models. An ensemble prediction in the future period (2021–2050) from three competing hydrological models is generated using the Bayesian model averaging (BMA) method. Though hydrological models do well in simulating daily discharges, all underestimate the observed low flows to some extent. Such underestimation of low flows could be compensated by application of the BMA method. Uncertainty from GCMs is a predominating source for monthly mean discharges. An increase in intensity of 7Q10, 7Q20, and 7Q30 is found under both RCP4.5 and RCP8.5 for most cases. The increase of 7Q10, 7Q20, and 7Q30 could be more related to minimum monthly precipitation rather than the amount of monthly mean precipitation. The magnitude of the increase is smaller under RCP8.5 than RCP4.5, which could be explained by the higher temperature increase under RCP8.5.

In urban areas, it is important to have spatially and temporally dense rainfall measurements for flood modeling, monitoring, and prediction. The objective of this paper is to quantify the effect of rain gauge proximity on area-averaged rainfall estimation for small ($<1{\mathrm{km}}^2$) problematic urban watersheds in Virginia Beach, Virginia, United States. To achieve this, rainfall was estimated by including and then excluding nearby rain gauges from a Kriging analysis. This was done for seven focus watersheds draining to flood-prone areas in Virginia Beach for the 20 days with the highest total rainfall depth during the period 07-02-2013 to 01-01-2016. Results show that including local rain gauges in the Kriging analysis resulted in an average percent difference in area-averaged rainfall of close to 60% at a 15-min time step (24.5 mm max) and 20% at a daily time step (55.6 mm max) across the focus watersheds. The results suggest that a rain gauge within 0.5 km of the target watershed would be needed for flash-flood warning applications in Virginia Beach. Radar-derived rainfall estimations were used for comparison and showed significant differences from the rain gauge rainfall estimates greater than 20 mm over an hour [$r=0.72$, $\text{root mean}\text{square error}(\mathrm{RMSE})=9.72\mathrm{mm}$].

Green roofs are a popular sustainable drainage systems technology. They provide multiple benefits, amongst which the retention of rainfall and detention of runoff are of particular interest to stormwater engineers. The hydrological performance of green roofs has been represented in various models, including the Storm Water Management Model (SWMM). The latest version of SWMM includes a new low-impact development green roof module, which makes it possible to model the hydrological performance of a green roof by directly defining the physical parameters of a green roof’s three layers. However, to date, no study has validated the capability of this module for representing the hydrological performance of an extensive green roof in response to actual rainfall events. In this study, data from a previously monitored extensive green roof test bed have been used to validate the SWMM green roof module for both long-term (173 events over a year) and short-term (per-event) simulations. With only 0.357% difference between measured and modeled annual retention, the uncalibrated model provided good estimates of total annual retention, but the modeled runoff depths deviated significantly from the measured data at certain times (particularly during summer) in the year. Retention results improved [with the difference between modeled and measured annual retention decreasing to 0.169% and the Nash–Sutcliffe model efficiency (NSME) coefficient for per-event rainfall depth reaching 0.948] when reductions in actual evapotranspiration (ET) due to reduced substrate moisture availability during prolonged dry conditions were used to provide revised estimates of monthly ET. However, this aspect of the model’s performance is ultimately limited by the failure to account for the influence of substrate moisture on actual ET rates. With significant differences existing between measured and simulated runoff and NSME coefficients below 0.5, the uncalibrated model failed to provide reasonable predictions of the green roof’s detention performance, although this was significantly improved through calibration. To precisely model the hydrological behavior of an extensive green roof with a plastic board drainage layer, some of the modeling structures in SWMM green roof module require further refinement.

The estimation of an appropriate variogram is a crucial step toward the description of spatial dependence, the geostatistical interpolation of environmental variables, and the subsequent hydrological engineering. The classical variogram in the literature ideally necessitates a normal distribution of the variable and is not robust against outliers within the data. These presumptions are hardly given under empirical conditions and, therefore, a new estimation method is proposed for the variogram. The new method is based on the description of spatial dependence by the robust rank coefficient $\tau$ and generalizes the method from the Gaussian to the general case of empirical distributions. The conversion of the robust estimate using a Monte-Carlo simulation and subsequent quantile-quantile transformation with the empirical marginal distribution performs the generalization. Monthly precipitation data from South Africa serve as the variable and were artificially contaminated with outliers. The effects on the variogram and subsequent geostatistical interpolation were investigated for the proposed, classical, and four existing robust variogram models in this comparative study. The investigation revealed that the proposed variogram describes a distinct spatial dependence structure under empirical conditions, which is robust against outliers. The cross validation of the linear estimator demonstrates that the proposed variogram tends to improve the bias and spread of the resulting error distribution, and hence the quality of the geostatistical interpolation.

Uncertainty in hydrological and nutrient modeling is a challenging task over large snow-fed and complex Himalayan watersheds. Digital elevation models (DEMs) are an important input for hydrological models. At times, DEM grid size needs to be altered for hydrological simulation in large basins. Therefore, appropriate grid sizes need to be investigated for modeling of hydrological variables. Many studies have investigated the effect of DEM grid size on flow and nutrient modeling. However, to the best of the authors’ knowledge, no one has attempted to understand the uncertainties in SWAT model outputs due to DEM grid size and resampling methods over large, snow-affected, mountainous river basins. In this study, the Gandak River Basin, a large snow-covered river basin in the Himalayas, was examined at monthly and annual time steps using 40–1,000-m grid sizes resampled with nearest neighborhood, bilinear, and cubic convolution methods. Results showed that sediment and flow are greatly affected by DEM resolutions and by the choice of resampling method. Flow and sediment are overestimated for DEM grid sizes $>300$ and $>150\mathrm{m}$, respectively. However, total nitrogen (TN) and total phosphorous (TP) change at DEM grid sizes of $\ge 150\mathrm{m}$ via slope and volume of flow. T-test statistics indicate the significance of changes in SWAT outputs due to DEM resolution and resampling. They are significant for SWAT outputs at $>500\text{-}\mathrm{m}$ grid sizes at a yearly time step. Results also showed significant increases in relative difference (RD) with changes in DEM grid size during snowmelt periods, which indicates changes in the SWAT snowmelt parameter due to the resampled DEM. The proposed results can be applied to flow and nutrient modeling over any snow-affected, large, mountainous river basin.

Comprehensive observations of total terrestrial water storage changes (TWSC) which include all the hydrological components (such as snow/glacier, surface water, soil moisture, and groundwater) are rarely available, so the predisposition of a region to flood is not fully clear. This paper combines the gravity recovery and climate experiment (GRACE) gravity observations, a hydrological model, and in-situ hydrological station data to establish the relationship between Yangtze River discharge and TWSC by a time-lagged autoregressive model and presents the TWSC data that apply for Yangtze River basin (YRB) flood forecasting. The TWSC in the YRB is inferred by the Lagrange multiplier method from GRACE gravity observations between April 2002 and December 2013. The root-mean-square error (RMSE) is optimal (2.1 cm) and the trend of TWSC in the YRB increased by $0.63\pm 0.11\mathrm{cm}/\mathrm{year}$. A case study of the flood catastrophe during summer 2010 is used to establish a relationship between river discharge from the Datong hydrological surveying station and basin water storage changes from GRACE by adopting a time-lagged autoregressive model, which shows that the total water storage changes from GRACE gravity observations can be used to estimate the tendency of a river basin to flood at 3–6 month lead times. This study concludes the basin scale of total water storage changes determined from satellite observations of time-variable gravity provides a new and effective tool for characterizing regional flood potential and may eventually lead to longer early flood warning times.

This paper assesses the possible impact of climate change on the hydrology of the subhumid and perhumid river regimes originating from the western mountain range (Western Ghats) of India. The modified Mann-Kendall test evaluates the trend of observed data (1975–2004) and RCP 4.5 data (2006–2070) of climatic variables. The results indicate a decreasing trend for annual rainfall over the Malaprabha River catchment (26 mm per year at the 5% significance level), whereas no trend is observed over the Netravathi River catchment at the 10% level. Indian southwestern monsoon rainfall shows a decreasing trend from 84 to 80% of total rainfall in the Malaprabha River catchment and from 80 to 77% in the Netravathi River catchment. Summer rains are found to be increasing in the Malaprabha River catchment (3–4.5% of total rainfall), whereas there is no significant trend for the Netravathi River catchment. Furthermore, the postmonsoon rainfall also shows a significant increase in the Malaprabha catchment (40 mm per decade at the 5% significance level) and the Netravathi catchment (30 mm per decade at the 10% significance level). The Netravathi River shows a decreasing trend for annual flow ($0.22{\mathrm{Mm}}^3$ per year at the 10% significance level). However, for both catchments the temperature is found to be increasing by 0.2–0.8°C per decade. The soil and water assessment tool (SWAT) model is used to simulate the river catchments and exhibits a Nash–Sutcliffe efficiency of 0.831 and 0.857 for the Malaprabha and Netravathi River catchments, respectively. In addition, a decreasing trend in the high flow is estimated for Netravathi, whereas the trend is increasing for Malaprabha. Thus the impacts of climate change over the Western Ghats are very evident, but the flow of each river responds differently.

Extreme flooding in the metropolitan area of Monterrey, México has led to an interest from local stakeholders in potential mitigation strategies in the Santa Catarina watershed. The authors analyze a set of hydraulic infrastructure options using two hydrologic models of varying complexity in the context of a major flood caused by the landfall of Hurricane Alex in 2010. A consistent approach was used to provide terrain, soil, vegetation, and meteorological data to each model—hydrological modeling system (HEC-HMS), and triangulated irregular network (TIN)–based real-time integrated basin simulator (tRIBS)—and to test the models with streamflow and water level observations. Simulation analyses focus on the differential ability of the two models in capturing precipitation and watershed properties and its effects on the hydrologic response in the presence of hydraulic infrastructure options. A scenario with a single, large dam reduced the flood peak more favorably than three smaller structures. The model comparison is effective in addressing stakeholder-driven mitigation strategies and revealing the added value of spatially-distributed approaches.