Journal of Hydrologic Engineering

The rainfall distribution in space and time, its consistency, probability of occurrences, and drought conditions in three selected wadis in the northern area of the United Arab Emirates (UAE) have been investigated. Like other arid and semi-arid regions, rainfall in the selected wadis is limited, scattered, and random. The standard deviation and coefficient of asymmetry of the rainfall patterns are relatively high. The three wadis have similar rainfall patterns based on the monthly, annual, and $24\text{-}\mathrm{h}$ maximum rainfalls. Empirical equations were developed to estimate the annual and $24\text{-}\mathrm{h}$ maximum rainfalls. A probability analysis is carried out for the annual and $24\text{-}\mathrm{h}$ maximum rainfalls and probability graphs are developed with a confidence level of 95%. Intensity–duration–frequency curves are developed for the three wadis. A conceptual model is used in drought characterization. The available rainfall data of the three wadis are analyzed for drought intensity, duration, and frequency. Based on the drought characterization, the study region is classified as “arid.” The present study constitutes the first comprehensive analysis for rainfall in UAE.

In this study, a hydrological analysis of rainwater harvesting facilities was conducted using a model based on the IHACRES model. Using this model, the rainfall, rainfall loss, inflow to the storage tank, tank storage volume, overflow from the tank, and rainwater consumption data were simulated to evaluate the hydrological characteristics of the rainwater harvesting facilities. This study evaluated three rainwater harvesting facilities in Korea. The results of the study are summarized as follows. (1) The proposed model was found to well simulate the rainfall-runoff processes of rainwater harvesting facilities. (2) As the target amount of rainwater consumption increases, the amount of rainwater consumption also increases, but the number of days with available rainwater decreases. Especially in Korea, the difference in rainwater use when applying different target amounts of rainwater consumption is larger during the rainy compared to the dry season. (3) The effect of increasing the tank capacity was found to be limited to the months of October and November, just after the end of the rainy season. This result is also closely related with the climatic characteristics of Korea. (4) The flood reduction effect was estimated to be approximately 1% when using 10% of the entire city area as the rainwater collecting surface. This result is simply due to that the storage tank capacity is limited to a certain level. (5) The estimated benefit-to-cost ratio was less than 20% when a discount rate of 5.75% was applied, which was basically because the water supply is very inexpensive in Korea.

A new approach to calibration of the Australian water balance model (AWBM) daily rainfall–runoff model was tested by transposing calibrated parameter values among 18 catchments in coastal Queensland, Australia. The calibration procedure adjusts input rainfall and evaporation by linear scaling with constraints on the calibrated value of average surface storage capacity to maximize the coefficients of efficiency based on monthly totals of runoff. The calibrated parameter values on each catchment were able to estimate runoff on each of the other 17 catchments with an average error of only 5.5%. Some 94% of the 306 $(18\times 17)$ estimates of runoff were within 15% of actual runoff. Some 71% of the coefficients of efficiency from the tests of transposing parameter values were $⩾0.8$ . A second stage of the study was to average the 18 sets of calibrated parameter values to a single set that estimated runoff from the 18 catchments with an average error of only 5.3% and a maximum error of 15.8%. The new method of calibrating the AWBM offers potential for estimating runoff from ungauged catchments with small errors.

The original and modified versions of the advection-aridity (AA) model of regional evapotranspiration are tested with data from the Solar and Meteorological Surface Observation Network (SAMSON). The resulting long-term mean annual evapotranspiration estimates are validated against water balances of 25 watersheds that are minimally affected by human activity and contain at least one SAMSON station, as well as with similar closures of SAMSON-station/gridded precipitation and runoff. In general, model performance is very similar among the two versions, explaining at least 80% of the spatial variance in the long-term means, simultaneously remaining well within 10% of the water balance-based values in their station-averaged long-term mean annual evapotranspiration estimates. The modified AA model, however, can be used in humid as well as in arid regions with the same set of calibrated parameters, whereas the original AA model may require a recalibration.

In some parts of the world, watersheds are experiencing changes in land cover that affect their hydrological regime. This paper presents a methodology to assess the potential hydrological response of the Chaudière River watershed (Québec) to historical land cover changes. The objectives of this study are: (1) to determine acceptable and plausible land cover scenarios for the past $30\text{years}$ based on spatial scenario intercomparison and land cover evolution analyses; and (2) to assess the influence of land cover changes on the hydrological regime of the watershed. Historical land cover changes were assessed from seven optical satellite images using an object-oriented approach. Each scenario was entered into GIBSI, an integrated modeling system. Hydrological simulations showed strong correlations between land cover changes and water discharge at the outlet of the watershed. Results demonstrated a homogeneous effect of land cover on annual runoff. Simulated annual and seasonal low flows were also strongly correlated to agricultural and forested land cover changes. Globally, results corroborate that land cover constitutes a governing factor of watershed hydrology in the Chaudière River watershed.

In recent years, several drought monitoring indexes have found application to describe and compare droughts among different time periods and regions as well as to forecast the evolution of ongoing droughts, in order to select appropriate mitigation measures and policies for water resources management under shortage risk conditions. However, limited efforts have been made to investigate the possibilities of using information conveyed by large-scale climatic indexes to improve the forecasting ability of drought indexes, provided they exert some influence on the climatic variability in a region. The aim of the paper is to develop models for forecasting Palmer Hydrological Drought Index series in Sicily (Italy) based on artificial neural networks, and to extend such models in order to include information from large-scale climatic indexes. First, the influence of North Atlantic Oscillation (NAO) and European Blocking (EB) indexes on Palmer index series, computed on areal monthly precipitation from 1955 until 1999 in Sicily, has been investigated by means of a correlation analysis. Results indicate that NAO and EB series are significantly correlated with Palmer index series for winter and autumn months, with special reference to the last decades. Then, forecasting models based on neural networks have been developed, using different approaches. The comparison between the prediction for winter and autumn months obtained by either including or not including the NAO and EB indexes within the forecasting model indicates some improvements in terms of $R^2$ when NAO and especially EB are considered. A different model behavior has been observed for the spring and summer predictions for which no significant improvements in terms of model predictive capability, due to the introduction of the climatic indexes as input variables, have been observed.

In large combined- and storm-sewer systems it is impractical to model every pipe, manhole, and component of the system. Time and budget constraints, combined with a lack of relevant input data, compel modelers to make assumptions in a quest to create a simplified model that adequately represents the hydrologic and hydraulic behavior of the system. Two of the most commonly applied simplification techniques are conduit skeletonization and subcatchment aggregation. This paper aims to highlight the potential dangers of making such simplifications, allowing modelers to make qualified simplifying assumptions. A base model and four simplified models were tested for a small $\left(5.2\mathrm{ha}\right)$ catchment in Chicago using the simulation packages ILLUDAS, HEC-HMS, and InfoSWMM. In addition, a base model and five simplified models were compared for a $341\mathrm{ha}$ catchment using InfoSWMM. The effects of conduit skeletonization and subcatchment aggregation were found to be dependent on the simulation package being used and sensitive to the degree of simplification of the system. In using simplified models there is a danger that the user may not correctly predict the magnitude, timing, and shape of the outfall hydrograph.

Trend analysis of annual flood peaks on 12 small urbanizing watersheds in northeastern Illinois indicated that annual peaks, and thus frequency and impact of flooding, increased over the past several decades. An increase in flood peaks could be attributed to intensive urbanization and increasing incidences of heavy rainfall. Average urbanization of the 12 watersheds increased significantly from 10.6% in 1954 to 61.8% in 1996. In addition, numerous studies have reported increasing frequency and intensity of heavy rainfall in the region. This outcome is consistent with lower design rainfall estimates produced by older studies, such as U.S. Weather Bureau Technical Paper No. 40 (TP-40), compared to more recent sources, such as National Oceanic and Atmospheric Administration Atlas-14. This study used a design storm approach and the Hydrologic Engineering Center for Hydrologic Modeling System (HEC-HMS) model to calculate design flood peaks. Hydrologic model parameters were calibrated using hourly rainfall-runoff data of two large regional floods, observed in 1954 and 1996 at 12 small urbanizing watersheds in the metropolitan Chicago area. A sensitivity analysis was performed to evaluate the effects of urbanization and climate variability on increasing flood peaks. Results indicated that, on average, urbanization caused a 34% greater increase in peak flows than climate variability. In addition, this study indicated that present discharges are, on average, at least 19% larger than regulatory discharges. Ongoing urbanization may cause flood peaks to become even higher. The proposed framework can be used to provide input for flood study prioritization by comparing published regulatory discharges and flood discharges computed for current conditions, and investigating potential impacts of future land use changes and precipitation on flood peaks.

Drought is a natural phenomenon that occurs in many places on the planet and may cause considerable damage. Selection of an integrated index for quantifying drought severity is a challenge for decision makers in developing water resources and operation management policies. In this study, the standardized precipitation index, water surface supply index, and Palmer drought severity index have been combined to develop an integrated index, called the hybrid drought index (HDI), using associated damage of drought events. Application of the HDI in drought severity prediction has been examined using two different types of artificial neural networks, namely, a probabilistic neural network and a multilayer perceptron network. These models have been selected due to their special characteristics that are suitable for prediction schemes. The proposed algorithm for developing HDI and drought prediction has been applied to the “Gavkhooni/Zayandeh-rud” basin in the central part of Iran. The results show the merits of each model in prediction of drought severity and model adaptation. The results also show the significant value of the proposed algorithm in formulation of a combined index for drought prediction.

An ensemble seasonal streamflow forecasting model is developed for two watersheds in the South Saskatchewan River Basin of southern Alberta, Canada. The ensembles are generated from a mean forecast by using a modified $K$ -nearest neighbor algorithm. The mean forecasts are produced by a robust M-regression model that uses snow water equivalent and large-scale climate information as predictors where the best combination of predictors is automatically selected by the generalized cross-validation criterion. It is shown that skillful forecasts of the April–September flow can be obtained as early as the beginning of December preceding the runoff year, thus extending the current forecast lead time by up to two months. An assessment of the potential economic value of the forecasts shows that with the same set of predictors, ensemble forecasts offer superior economic value for a wide range of end-users as compared to conditional median forecasts.

This note is based on the generally accepted fact that soil boundaries are usually gradual rather than abrupt, and this is followed by an application of fuzzy set theory to what are already known to be diffuse boundaries. Sediment yield measurements are used to describe the control condition against which the fuzzy and conventional boundaries are assessed. In terms of the $K$ factor, the fuzzy boundary representation showed less difference (up to 16.8%). The Nash–Sutcliff coefficient of efficiency and the root mean square error for the fuzzy boundary are 0.96 and $1.3\times {10}^{-3}$ , respectively, whereas those for the conventional boundary are 0.76 and $3.4\times {10}^{-3}$ , respectively. The estimation by the soil erosion model shows that the fuzzy representation of geographic boundary is more accurate than the conventional method. But the difference between erosion predictions using conventional boundaries and fuzzy boundaries is relatively small.

Flood forecasting at Jamtara gauging site of the Ajay River Basin in Jharkhand, India is carried out using an artificial neural network (ANN) model, an adaptive neuro-fuzzy interference system (ANFIS) model, and an adaptive neuro-GA integrated system (ANGIS) model. Relative performances of these models are also compared. Initially the ANN model is developed and is then integrated with fuzzy logic to develop an ANFIS model. Further, the ANN weights are optimized by genetic algorithm (GA) to develop an ANGIS model. For development of these models, 20 rainfall–runoff events are selected, of which 15 are used for model training and five are used for validation. Various performance measures are used to evaluate and compare the performances of different models. For the same input data set ANGIS model predicts flood events with maximum accuracy. ANFIS and ANN model perform similarly in some cases, but ANFIS model predicts better than the ANN model in most of the cases.