Journal of Hydrologic Engineering

Anticipating future trends in land development and climate change is a constant challenge for engineers and planners who wish to effectively compensate for the resulting changes in storm water runoff that will inevitably follow. This study is a regional attempt at predicting how changes in land use will affect runoff characteristics in 17 watersheds throughout the Delaware-Maryland-Virginia (DelMarVa) Peninsula when compared to the current conditions. The authors coupled demographic and urban growth models to create maps showing the location of predicted urban land use for the year 2030, and then used nutrient loading and rainfall-runoff models to forecast the consequences of such growth on the magnitude of changes that can occur in runoff quality and quantity. The authors found that likely change in urban land use would lead to decreases in sediment and nitrogen loads by up to 8 and 37%, respectively; that phosphorus loads would increase or decrease depending on the type of existing land use that was replaced by urban land use; and that the 2-year peak flow would change by 2–9% across all scenarios, whereas relative changes to flood peaks for the 100-year peak flows were considerably smaller. Sensitivity analysis also was performed. The authors’ modeling provides a planning-oriented look into the effects of increased urban development on the predominantly agrarian study area, the majority of which drains to the Chesapeake Bay. The modeling also illustrates a useful approach for evaluating consequences of future planning and management decisions within a desired region.

A collection of archived hydrographs for 28 ephemeral streams provided a unique opportunity to quantify hydrograph characteristics for channels of the arid/semiarid Southwestern United States. Recurrence of flow and volume calculated using annual maxima converge with those of the complete series by 7 years, suggesting that records of annual maxima are sufficient for estimates of extreme event recurrence. The relationship between peak discharge and volume for a flow event cannot be tightly constrained without consideration of event duration. As a result, volumes $V(T)$, for recurrence intervals $T$, cannot be estimated from corresponding peaks $Q_p(T)$. Instead, volume recurrence should be estimated based on the joint distribution of peak discharge and duration. Although the density governing event duration in these semiarid study regions decays as a power law with time, the authors could not identify a probability density that provides a good fit to volume magnitude. Deterministic relationships exist between peak discharge and volume for an event of a given magnitude for the Las Vegas, Nevada; Phoenix, Arizona; and Albuquerque, New Mexico regions.

Sediment discharge is computed by using different combinations of entropy-based and empirical methods of channel cross-section velocity and suspended sediment concentration distribution, and the results of these different methods are compared. The comparison shows that the entropy-based methods are more accurate than the empirical method and that the Tsallis entropy–based method is more accurate than the one based on Shannon entropy. The accuracy of the computation for all methods can generally be improved by introducing a correction factor; however, the fully entropy-based methods still remain the most accurate.

Missing values in in situ monitoring data is a problem often encountered in hydrologic research and applications. Values in a data set may be missing because of sensor error or failure of data recording devices. Whereas various imputation techniques have focused on hydrometeorological data, very few studies have investigated gap-filling methods for soil moisture data. This paper aims to fill that gap by investigating well-established statistical and data-driven methods for infilling missing values in a high resolution, soil moisture time series. Since 2006, the authors collected hourly soil moisture data in the Hamilton-Halton Watershed, Southern Ontario, Canada at four research sites. Each site contained nine stations with time domain reflectometry (TDR) soil sensors at six soil depths. From these distributed data sets, the authors removed values randomly ($\sim 5\%$) and systematically ($\sim 20\%$) from the data to evaluate the effectiveness of the monthly average replacement (MAR), soil layer relative difference (SLRD), linear and cubic interpolation, artificial neural networks (ANN), and evolutionary polynomial regression (EPR) infilling methods. When values were randomly removed, interpolation, ANN, and EPR were able to infill the missing values with similar efficiency, whereas MAR and SLRD were the least effective methods. Similarly, when large systematic gaps were present in the data, interpolation and ANN were the most effective methods of infilling, respectively. However, the effectiveness of both infilling methods is limited as serial gaps become larger than 72–100 h.

Distributed hydrological models have been commonly used in research involving water management because of their consideration of spatial variability. However, practical applications still encounter technical challenges such as complicated modeling, low computational efficiency, and parameter equifinality. A user-friendly model, EasyDHM, was developed and was shown effective over the years. In this paper, the essential parts of this model, namely, discretization of the spatial units, preparation and initiation of data and parameters, and the main physical processes are briefly introduced. In particular, the roles of the parameter sensitivity analysis and optimization for this model, which have considerably improved the prediction accuracy, are highlighted in this study. From the application to the upstream basin of Han River in China, the simulation and parameter estimation by EasyDHM turned out to be effective and easy to operate. EasyDHM can, therefore, be widely used for practical water management applications.

A limited number of publications in the literature address the measurement of actual evapotranspiration (AET) from peat soils. AET is an important parameter in the description of water pathways of an ecosystem. In peatlands, where the water table is near the surface, the measurement of AET represents a challenge. This paper discusses the development of an automated lysimeter installed between July 12 and 27, 2010, at an 11-ha bog site, Pont-Rouge (42 km west of Quebec City, Canada). This system was made of an isolated block of peat, maintained at the same water level as the surrounding water table by a system of submersible pressure transmitters and pumps. The change in water level in millimeters in the isolated block of peat was used to calculate the water lost through evapotranspiration while for accounting the precipitation. The rates of AET were calculated for each day of the study period. This relatively inexpensive and simple to install system may eventually be used to calculate AET on peaty soils in the years to come.

Flow-routing at a tributary (Koros River) of the Tisza River in Hungary was achieved by relating the storage coefficient ($k$) of the state-space formulated discrete linear cascade model (DLCM) to the concurrent discharge rate of the Tisza. As a result, the root mean square error of the 1-day forecasts decreased from $25{\mathrm{m}}^3·{\mathrm{s}}^{-1}$ ($k=1.7{\mathrm{days}}^{-1}$ and the number of storage elements is 2) with the corresponding Nash-Sutcliffe-type performance value of $0.95\mathrm{to}11{\mathrm{m}}^3·{\mathrm{s}}^{-1}$ in the calibration period and to $15{\mathrm{m}}^3·{\mathrm{s}}^{-1}$ in the validation period (the corresponding Nash-Sutcliff-type performance values are 0.99 and 0.98, respectively). During floods of the Tisza, the $k$ value decreased to as little as $0.35{\mathrm{days}}^{-1}$, indicating a significant slowdown of the tributary flood-wave because of the resulting backwater effect. Subsequent stage-forecasts were aided by a coupled autoregressive moving-average (1,1) model of the DLCM error sequence and the application of the Jones formula in addition to a conveyance curve, the latter yielding the most accurate 1-day forecasts with a root mean square error of 28 cm and Nash-Sutcliff-type performance value of 0.99 for the combined (validation and calibration) time periods. The method requires no significant change in the mathematical structure of the original DLCM and thus is well-posed for inclusion of existing operational streamflow-forecasting schemes.

Using laboratory experiments and a numerical model, this study examined the influence of dune dimensions on the separation of flow. Different conditions, depending on the dune height, material, and angle and flow depth and velocity, were considered. Experiments were conducted in a horizontal flume with one side wall being transparent for application of optical measuring devices. A train of two-dimensional (2D) fixed dunes were installed on the bottom of the flume starting just downstream of the entrance on the flume. Dunes were prepared with well-sorted gravel particles. Experiments were carried out using four types of dunes with different lee slopes. The numerical model used was sediment simulation in intakes with multiblock option (SSIIM), which is an open-source computational fluid dynamics model. Both experimental observations and numerical results were in agreement about the influence of dune characteristics on the flow separation, with particular regard to dune height, particle size, angle on the leeside, and flow velocity.

Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin.

Small-scale variations of hydrologic processes both in space and time have a significant impact on the simulation of hydrologic processes at different scales in the watershed. The objectives of this study were to investigate: (1) how the spatial and temporal grid scales affect the results of hydrologic process simulations; and (2) how the variability of input driving parameters (e.g., elevation and precipitation intensity) at different grid scales is related to the simulated discharge response. A hydrologic model system (HMS) was used to simulate hydrologic processes at different spatial and temporal grid scales in a small watershed. The spatial distributions of various hydrologic properties, such as soil and land-use/land-cover data, were included in the simulations along with a digital elevation model (DEM) and precipitation data. 5-min precipitation records collected from four gauge stations within the watershed were used to drive fifty model simulations, which were designed to examine the effects of grid size change and different parameterization schemes on hydrologic responses. Simulation results showed that small-scale variations in elevation and precipitation both in space and time have significant impacts on the streamflow hydrograph and the discharge volume. Results illustrated that the grid scale effect on the hydrologic response is highly correlated to the variability of elevation and precipitation at the corresponding scales; and therefore, the variations in elevation and precipitation are strong indications of the hydrologic response in the watershed.

A database containing more than 16,300 discharge values and ancillary hydraulic attributes was assembled from summaries of discharge measurement records for 391 USGS streamflow-gauging stations (streamgauges) in Texas. Each discharge is between the 40th- and 60th-percentile daily mean streamflow as determined by period-of-record, streamgauge-specific, flow-duration curves. Each discharge therefore is assumed to represent a discharge measurement made for near-median streamflow conditions, and such conditions are conceptualized as representative of midrange to baseflow conditions in much of the state. The hydraulic attributes of each discharge measurement included concomitant cross-section flow area, water-surface top width, and reported mean velocity. Two regression equations are presented: (1) an expression for discharge and (2) an expression for mean velocity, both as functions of selected hydraulic attributes and watershed characteristics. Specifically, the discharge equation uses cross-sectional area, water-surface top width, contributing drainage area of the watershed, and mean annual precipitation of the location; the equation has an adjusted R-squared of approximately 0.95 and residual standard error of approximately 0.23 base-10 logarithm (cubic meters per second). The mean velocity equation uses discharge, water-surface top width, contributing drainage area, and mean annual precipitation; the equation has an adjusted R-squared of approximately 0.50 and residual standard error of approximately 0.087 third root (meters per second). Residual plots from both equations indicate that reliable estimates of discharge and mean velocity at ungauged stream sites are possible. Further, the relation between contributing drainage area and main-channel slope (a measure of whole-watershed slope) is depicted to aid analyst judgment of equation applicability for ungauged sites. Example applications and computations are provided and discussed within a real-world, discharge-measurement scenario, and an illustration of the development of a preliminary stage-discharge relation using the discharge equation is given.

Flood risk assessment in many locations, especially in low-income countries, is severely hindered by a lack of hydrologic data. This study illustrates the application of regional rainfall frequency analysis, combined with ungauged basin analysis, to estimate flood discharges in the Gonaives basin in Haiti. Using limited daily rainfall data from seven gauges within and near the watershed, a regional extreme value distribution is selected and design storms are developed. These design storms are then input to a rainfall-runoff model, the parameters of which are estimated from spatial analysis of physical watershed characteristics. As a case study, the flood discharge and associated return period are estimated for the 2004 event that claimed 1,650 lives, and model outputs for other synthetic design storms are compared with large observed floods in other Caribbean basins similar in size to the Gonaives basin. Results of the comparative analysis indicate that the simulated peak flood flows of the Gonaives watershed are within the range of the maximum floods observed in other Caribbean basins with similar drainage areas, topography, and vegetation cover.

Groundwater table response to arbitrarily varied stream stage is a practical engineering issue related to stream-aquifer interaction processes. A one-dimensional (1D) linear Boussinesq equation is invariably used in the interpretation. Previous studies have emphasized the employment of convolution integration and pulse or function fitting methods to solve this problem. Consequently, complicated mathematical derivations and numerical calculations are involved during an analysis. In this paper, we transformed the previously published solutions into an easily solved formation to investigate this problem. Then the solution is solved based on the trapezoidal method and applied in cases. The solution fits favorably with the previous classic solutions in the simulations of groundwater table responses to sinusoidal, linear, and unit step rise varied stream stages. This approach was also applied to interpret groundwater table fluctuations induced by typical flood waves and field measured stream stage fluctuations. The developed approach performs well compared with those solved numerically by function fitting methods and modular finite-difference flow model (MODFLOW). This analytical solution potentially provides a rapid and simple way to estimate aquifer responses to any type of stream stage variation.

The Horqin Sandy Land is a typical desertification region in China, which is beset with ecological and environmental problems that affect economic and social development. Hence, the hydrological impact factors (underground water level and surface runoff) in this region need to be investigated. The current study reconstructed the runoff sequences from the southwest edge of the Liaohe River to the XiaWa station of the Jiaolai River during the months of October to November from 1826 to 2005. A comprehensive timeline for the regional tree wheel width of the Horqin Sandy Land was employed. The timeline has been used for 183 years. For the past 180 years, the runoff has experienced six and four consecutive wet and dry seasons, respectively. From 1982 to 2005, the runoff reached the longest stream segment of a continuous low-flow runoff, with a mean average runoff of only 63.58% for the entire period. The runoff had 3, 11, 15, 24, and 30-year quasi-periodic variations, consistent with changes in similar areas worldwide. The change was gentler from 1826 to 1917. In 1956, the runoff increased, then significantly decreased for nearly 50 years. The drop rate was $1.7766\mathrm{million}{\mathrm{m}}^3$ every 10 years, which shows a consistent downward trend with the precipitation ($14.74\mathrm{mm}/10\mathrm{year}$). The overall reduction in precipitation accounted for 29.86% of the initial value, which is significantly less than 75.58% for the runoff. If the runoff and precipitation drop continue, more extensive and lengthy ecological and environmental problems are foreseen to occur.

Large changes in runoff in the north-central United States have occurred during the past century, with larger floods and increases in runoff tending to occur from the 1970s to the present. The attribution of these changes is a subject of much interest. Long-term precipitation, temperature, and streamflow records were used to compare changes in precipitation and potential evapotranspiration (PET) to changes in runoff within 25 stream basins. The basins studied were organized into four groups, each one representing basins similar in topography, climate, and historic patterns of runoff. Precipitation, PET, and runoff data were adjusted for near-decadal scale variability to examine longer-term changes. A nonlinear water-balance analysis shows that changes in precipitation and PET explain the majority of multidecadal spatial/temporal variability of runoff and flood magnitudes, with precipitation being the dominant driver. Historical changes in climate and runoff in the region appear to be more consistent with complex transient shifts in seasonal climatic conditions than with gradual climate change. A portion of the unexplained variability likely stems from land-use change.

In this study, hydraulic bulking was evaluated based on a physiographic and hydrologic perspective of eight small semiarid watersheds within New Mexico. Attributes related to drainage and sediment load were characterized using an ArcGIS (Esri, Redlands California) approach. An in-depth terrain analysis coupled with land cover, precipitation, soil, and hydrography raster data sets provided characterization of key watershed attributes. Associated raster maps of derived attributes were generated, in addition to watershed peak flow and runoff volume based on a 100-year, 24-h precipitation. With this composite data, a hydraulic bulking factor, or ratio of total flow with sediments to clean water flow, was estimated. Bulking factors ranged from 1.01 to 1.65. Significant trends and correlations were evident and supported by literature, thus offering the potential for development of a statewide bulking factor map and ultimately an engineering design factor for drainage structures. A procedure for estimating this bulking factor is presented; however, field verification of the proposed methodology is needed.

A review is given of some currently available solutions for the calculation of stream depletion from pumped aquifers. A brief history is given of the development of analytical and semianalytical models. A solution published in 2003, which is also capable of duplicating many previously published solutions, is described in detail. In this solution, the stream partially penetrates a Boulton delayed-yield aquifer and is shown herein to apply when different segments of a stream may be either gaining or losing water to the pumped aquifer. Excel computational tools freely available to practitioners are referenced and used in an illustration for the calculation of the 2003 solution, and the general behavior of the solution is discussed. Generalizations and additional solutions are discussed for streams and aquifers with finite width, for systems containing several aquifers and for abstraction wells that are either horizontal or slanted. The review concludes with a very brief discussion of the advantages and disadvantages of using numerical solutions for modeling stream-depletion behavior.

The mathematics of filling and draining a permeable geotextile bag were developed in a previous investigation of how large geotextile containers can be used to dewater dredged material and mine tailings. The resulting outflow from the draining bag looks remarkably like an overland flow hydrograph. An analog model for overland flow is developed from the draining bag and compared with experimental runoff data obtained in the laboratory for a number of rolled erosion control blankets. The analog model is defined by a dimensionless parameter that includes a permittivity term that characterizes the rate of runoff. A comparison of the analog model with measured runoff is excellent across the entire range of natural and geosynthetic erosion control products investigated.

This paper presents a set of generalized charts that can be used as an aid for quick hydrologic evaluation of vegetative filter strips to determine the runoff residence time and reductions in storm runoff volume and peak discharge. These charts are developed by using the predetermined numerical solutions to the governing equations for the overland flow and infiltration processes. Kinematic wave equations are used for overland flow and the Green and Ampt equations are employed for infiltration calculations. An explicit finite-difference scheme is employed to couple and solve these equations. The basic mathematical model is verified by using a set of published experimental data. The model is extended to incorporate a design inflow and the governing equations are written and solved numerically in terms of a set of dimensionless variables. The solutions thus obtained are generalized by using the principle of hydrologic similarity and presented in chart form.

In this investigation, an effort was made to model the hydrology and to identify critical erosion-prone areas of the Barakar Basin ($\mathrm{6,293}{\mathrm{km}}^2$) in Jharkhand state, India using the soil and water assessment tool (SWAT). This effort is helpful to select and adopt suitable soil conservation measures to reduce soil erosion. Input data include daily rainfall, and air temperature; and monthly runoff, sediment yield and reservoir inflow data of 5 years. Additional data were the topographical map, soil map, land resource data, and satellite imageries of the study area. The model was calibrated and validated for monthly inflow to the Maithon and Tilaiya Reservoirs for the periods of 1997–1999 and 2000–2001, respectively. In addition, it was also calibrated and validated for monthly runoff and sediment yield from two subwatersheds (Banha and Karso), located within Barakar Basin for the monsoon season of the same period. The calibration and validation results established good agreement (less than 10% deviation) between the observed- and model-predicted values in all cases. The identified critical areas using model-predicted sediment yield were compared with results of a report obtained from the Soil Conservation Department of Damodar Valley Corporation (DVC), Hazaribagh. The erosion class map developed using simulation results matched spatially well with the DVC-prepared map based on sediment yield index. A management plan was developed to treat the subwatersheds with conservation practices. Subwatersheds were divided into seven priority classes for treating them sequentially to reduce reservoir sedimentation rate. In the model, conservation practices were accounted for through curve number, universal soil loss equation (USLE) cover factor, USLE support factor, and farm pond. Adaptation of the management plan resulted in decreased sedimentation rate. The sedimentation rate decreased from the current rate of 0.76 and $4.59{\mathrm{mm}}^3/\mathrm{year}$ to 0.56 and $1.41{\mathrm{mm}}^3/\mathrm{year}$ for Tilaiya and Maithon Reservoirs, respectively. Furthermore, the life of Tilaiya and Maithon Reservoirs increased by 29 and 166 years, respectively. These results suggest that SWAT can be used to identify critical subwatersheds and to develop management plans to control erosion from critical areas by reducing the reservoir sedimentation rate.

Rainfall intensity-duration-frequency (IDF) curves are one of the most commonly used tools in water resources engineering. Two multifractal rainfall models with multifractal behavior are used to estimate IDF curves in this paper. The models are composed of exterior and interior processes with constant duration $D$ and “bare” intensity $I_D^{\prime }$. Using high resolution data of rainfall, previous research concluded that the results of the IDF curves tend to be insensitive to the length of data record. In this paper, six years of rainfall data at a 5-min resolution at two stations (Nan Xiong and Ai Zi), Guangdong province, China, are analyzed with the multifractal models to estimate IDF curves. Compared with the traditional parametric models for determining IDF curves, the multifractal approach reduces the amount of parameters required to be calculated, and six years of high resolution rainfall data are sufficient to estimate the IDF curves. Forty-six years of annual maxima rainfall data for different durations at the Nan Xiong stations and 35 years at the Ai Zi station are fitted to the Pearson-3 distribution to obtain the IDF curves. The results of the two multifractal models are similar to those from the traditional parametric model for low return periods $T\le 50\mathrm{years}$. This study has significant practical importance in China for regions lacking long records of rainfall data.

Recent climate changes have observable impacts on hydrologic processes and will further affect hydrologic systems in the future. The temperature and precipitation modeled with eight global circulation models (GCMs) (selected from 22 GCMs published in the Fourth Assessment of the Intergovernmental Panel on Climate Change) under three typical emission scenarios entitled A1B, A2, and B1 were evaluated in this study for future projections in the Yangtze River Basin, China. The artificial neural network model was used to assess the evolutional trend of hydrologic processes (e.g., streamflow) and the possibility of extreme floods in the Yangtze River Basin by using data generated by selected GCMs under future climate changes. The results indicate that the future annual streamflow tends to decrease in the Yangtze River Basin. The future average annual flow is reduced by $500{\mathrm{m}}^3/\mathrm{s}$ compared with that of the historic record (1951–2005) observed at Yichang Hydrologic Station of the Middle Yangtze River, and the percentage of dry years increases by 46.8%. However, at Datong Hydrologic Station of the Lower Yangtze River, the average annual flow is reduced by $210{\mathrm{m}}^3/\mathrm{s}$ compared to the historic record and the percentage of normal year increases up to 75.5%. Extreme floods are divided into three categories of catastrophic, great, and large floods. The results show that large floods are most likely to occur in the future, whereas the likelihood of extreme floods is the minimum under the B1 scenario. In the three decades of the 2020s, 2050s, and 2080s, the likelihood of extreme floods at Yichang and Datong Hydrologic Stations tends to increase; the likelihood of extreme floods at Yichang Hydrologic Station is much greater than that of the Datong Hydrologic Station. Variability in the quantity of water in these areas will pose a greater challenge to the integrated deployment and management of water resources in the Yangtze River Basin.

This study examined how the level of catchment discretization influenced the model parameterization and output uncertainty of the Storm Water Management Model (SWMM) 5.0. Two catchment delineations for a highly urbanized sewershed in Syracuse, New York were developed: (1) the macroscale model containing a minimum required number of subcatchments to retain the original sewer network properties; and (2) the microscale model in which each subcatchment was defined for a unique soil and land-use combination. For both scales, the model parameters were calibrated and the uncertainty of model outputs was quantified using the generalized likelihood uncertainty estimation (GLUE) methodology. Then, calibrated posterior parameter sets were applied at micro- and macroscales individually to a second sewershed, which was also delineated at both micro- and macroscales, to test observed versus simulated flows. The results indicated that the catchment disaggregation level had a great impact on both parameterization and simulation results, and the majority of the parameters were sensitive to the modeling scales. Overall, the posterior parameters calibrated based on the microdelineation resulted in a higher degree of reduction in output uncertainties for both calibrated and validated sewersheds. Hence, it can be argued that the calibrated parameters obtained, based upon the macrodelineation, would result in reduced confidence in simulated runoff for another site unique in its characteristics, whereas the posterior parameters derived from the microdelineation could provide a higher confidence level in terms of parameter transferability for modeling other, particularly ungauged sites.

A method for indirect estimation of design flood in poorly gauged urbanized river basins by using distributed hydrological modelling is presented. It is based on the critical flood design criterion that maximizes peak flow for a given return period by transforming precipitation of depth duration frequency curve into runoff. The indirect method is compared against the direct method based on regional approach and index flood estimation. This paper shows that the direct method significantly underestimates design flood in case discharge measurements are strongly affected by artificial alteration of water courses with possible catastrophic consequences in terms of loss of life and damage to property when the cause of alteration would be removed. Moreover, the use of a spatially distributed model allows taking into account the heterogeneity that generally characterizes river basins with an high degree of urbanization. The presented indirect method provides a design hydrograph that is useful for those cases in which design discharge only is not sufficient for designing or planning purposes.

Estimates of actual evapotranspiration (${\mathrm{ET}}_c$) in the wheat and maize fields are essential in effective planning of irrigation water use in the North China Plain. A widely used method for ${\mathrm{ET}}_c$ estimation in agriculture is crucially dependent on the determination of crop coefficient curves. Estimating the coefficient coefficients from vegetation index (VI) is useful for regional ${\mathrm{ET}}_c$ simulation because the VI can represent the actual crop conditions and capture the spatial variability. In this study, the basal crop coefficient and soil evaporation coefficient were combined with the commonly used VI obtained from satellite sensor based on the observed data from a flux tower. The basal crop coefficient had a fairly good linear relationship with the VI, and the soil evaporation coefficient was well related to the vegetation fraction, which was calculated from the VI. Using the VI-derived crop coefficient curves, ${\mathrm{ET}}_c$ can be well simulated by a widely used ${\mathrm{ET}}_c$ estimation method. Moreover, simulation of ${\mathrm{ET}}_c$ was improved by the VI-derived basal crop coefficient curves. The new relationships between the crop coefficients and VI employed in the dual crop coefficient approach have great potential in the estimation of regional ${\mathrm{ET}}_c$ in the region.

Synthetic weather generation models that depend on statistics of past weather observations are often limited in their applications to issues that depend on historical weather characteristics. Enhancing these models to take advantage of increasingly available and skillful seasonal climate outlook products would broaden applications to include proactive soil and water resources management, better prediction of achieving production targets, and weather-related risk assessment. In this paper, an analytical method was developed that enables generation of daily precipitation time series for seasonal forecasts up to 12 months ahead. The method uses historical weather observations to establish reference precipitation statistics (monthly precipitation amount, number of rainy days per month, and wet–wet and dry–wet day transition probabilities) and subsequently adjusts these statistics to reflect the forecast departures from long-term average monthly precipitation. This reference and forecast departure approach ensures that generated precipitation is consistent and compatible with the forecast and the local climate characteristics as well. The method was tested with precipitation data from the USDA Agricultural Research Service (ARS) weather station at Temple, Texas, and the National Weather Service Cooperative Observer Program (NWS-COOP) data at Tallahassee, Florida, for a hypothetical seasonal precipitation forecast. Several 100-year time series of generated daily precipitation reproduced average monthly precipitation within $\pm 6\%$ of expected forecast values and mean absolute error (MAE) of less than 3%, wet–dry day transition probabilities within 5% and MAE of less than 2%, and average number of rainy days per calendar month within $\pm 2\%$ and MAE of less than 1%. The successful testing of the method validated the approach, analytical solution, and implementation of the method in an experimental climate generator. This forward-looking capability of synthetic weather generation will benefit water resource managers, farm loan officers, agricultural consultants, risk management agencies, and anyone relying on seasonal climate forecast information for decision making.

The Rational method is widely used in the design of hydraulic structures, such as storm sewers and culverts. Many engineers consider the Rational equation a rule-of-thumb method and dismiss it in favor of regional flood-frequency equations. This paper presents a regional flood-frequency analysis of 72 gaged watersheds in Kansas, ranging in size from $0.44–76.7{\mathrm{km}}^2$. The analysis results in regression equations for the 2-year to 100-year peak discharges that are statistically indistinguishable from the Rational equation. The resulting Rational equations explain more of the variance in flood quantiles than the standard form of the USGS regional flood-frequency equations that are used in Kansas.