Journal of Hydrologic Engineering

With the goal of identifying deficiencies in water allocation and to demonstrate how the existing water supply might be integrated into the overall water supply system, a regional optimization model is developed for the Gaza Strip that includes explicit simulation of groundwater flow and the economics of domestic and agricultural water uses. This model considered management options of building desalination and wastewater treatment plants, tertiary treatment of wastewater, as well as allocating and transferring water between agricultural and urban sectors. Model results show several crucial outcomes: (1) groundwater use for agriculture cannot happen at the current level after 2020 without introducing a combination of new supplies from treated wastewater (TWW) and desalination; (2) an annual reduction of 2% agriculture would significantly reduce groundwater depletion; (3) the current reliance on agriculture could continue if the reuse of TWW, desalination, and tertiary treatment of wastewater are introduced; (4) introducing TWW for agriculture would drastically reduce the shadow value of water for all Gaza districts; (5) introducing desalination can increase vegetable cultivation, which is the most profitable crop per cubic meter of water; (6) desalination of seawater after considering TWW would reduce the shadow value of water below the desalination cost of $\mathrm{US}\$0.60/{\mathrm{m}}^3$; and (7) introducing tertiary treatment of TWW would further reduce the shadow value of water and increase availability of water to agricultural and municipal uses. Moreover, a sensitivity analysis shows that improved information considering parameter uncertainty would provide accurate prediction of infrastructure sizing and efficient allocating of water among competing users.

Artificial neural networks (ANNs) have proved to be an efficient alternative to traditional methods for hydrological modeling. One of the most important steps in the ANN development is the determination of significant input variables. This study proposes a new method based on the copula-entropy (CE) theory to identify the inputs of an ANN model. The CE theory permits to calculate mutual information (MI) and partial mutual information (PMI), which characterizes the dependence between potential model input and output variables directly instead of calculating the marginal and joint probability distributions. Two tests were carried out for verifying the accuracy and performance of the CE method. The CE theory-based input determination methodology was applied to identify suitable inputs for a flood forecasting model for a real-world case study involving the three gorges reservoir (TGR) in China. Test results of application of the flood forecasting model to the upper Yangtze River indicates that the proposed method appropriately identifies inputs for the ANN with the smallest root-mean-square error (RMSE) for training, testing, and validation data.

Hydrologic design of water management infrastructures is on the basis of specific design storms derived from historical rainfall events available in the form of intensity-duration-frequency (IDF) curves. However, it is expected that the frequency and magnitude of future extreme rainfalls will change due to the increase in greenhouse gas concentrations in Earth’s atmosphere. This study evaluated potential changes in current IDF curves for Alabama under projected future climate scenarios. Three-hour precipitation data simulated by five combinations of global and regional climate models were temporally downscaled using artificial neural networks (ANNs). A feed-forward, back-propagation model was developed to estimate maximum 15-, 30-, 45-, 60-, and 120-min precipitation. The results were compared with disaggregated rainfall derived using a stochastic method. Comparison of these two methods indicates that the ANN model provides superior performance in estimating maximum rainfall depths, whereas the stochastic method tends to under-predict maximum rainfall depths. Developed IDF curves indicate that future rainfall intensities for the events with duration $<2\mathrm{h}$ are expected to decrease by 33–74% compared with those of current events when the ANN model is used, whereas large uncertainty exists in the projected rainfall intensities of longer-duration events. This result was independent of the temporal downscaling method used.

Extraction of watershed characteristics (stream position, stream length, watershed area, slope, and others) using a digital elevation model (DEM) is an important prerequisite of hydrologic analysis. Detecting these surface hydrologic features in flat regions using a DEM is a known problem. Using a known stream network to improve DEM performance has been done in a variety of methods; many methods can improve calculation of watershed area, stream position, and length, but there are strong impacts (e.g., slope) on subsequent terrain analysis from altering the DEM. To solve this problem, a new method, water depth gradient burning (WDGB), is proposed in this paper to extract watershed characteristics and subsequent terrain analysis in a flat region. In WDGB, the stream network map is used as an input dataset; a burning value at headwater, stream connection, and outlet nodes of the stream is defined, which is consistent with three parts: water depth correction, peak depth correction, and additional depth for gradient, ensuring that an upstream node elevation is higher than a downstream one. Other stream elevations are calculated by linear interpolation using the aforementioned node elevations. Because the adjusted DEM (the stream) has a positive gradient with no pits or inverse slope along the flow direction, it can be used to extract watershed characteristics and obtain the hydrologically correct result. At the same time, the elevation of the stream is not altered too much in WDGB. The effect of the adjusted DEM on subsequent terrain analysis is addressed with regard to the D8 algorithm, AGREE, and WDGB methods. The result indicates that WDGB can not only achieve watershed characteristics correctly in a flat region but also have the least influence on the original DEM and the subsequent terrain analysis.

Accurate estimation of point rainfall at ungauged locations from the measurements at surrounding sites is critical in obtaining a continuous surface of rainfall information. This can be accomplished through numerous interpolation methods, which have different strengths and weaknesses. The accuracy of the resulting continuous surface of rainfall information depends on the density of the point data and observational errors, which in turn affect the integrity of hydrological studies that utilize the data as input. In this study, four interpolation methods—Thiessen polygon, inverse distance weighting (IDW), thin plate, and Kriging—were evaluated at an experimental catchment in South West England at three gauge densities through the leave-one-out cross-validation (LOOCV) method. The numbers of rain gauges used for the three densities were 49, 28, and 10, which were translated to 2.75, 4.82, and $13.5{\mathrm{km}}^2$ per gauge since the area of the catchment was $135.2{\mathrm{km}}^2$. The gauge density was found to have an effect on the accuracy of the interpolated results as there was a gradual improvement in the error statistic with a corresponding increase in the gauge density. The results also showed that IDW and Kriging were better than the Thiessen polygon and thin plate methods at all the three gauge densities. The performances of IDW and Kriging were similar, suggesting that Kriging, though complex in nature, does not show greater predictive ability than IDW. It is important to note that there is a significant difference in $R^2$ between the cross-sectional approach and longitudinal approach.

In this study, a multiobjective model for optimal monitoring network design is presented, which aims to improve the accuracy of groundwater pollution source identification using concentration measurement from a designed optimal monitoring network. The proposed methodology combines the capability of genetic programming (GP) and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. A selected subset of trained GP models is used to compute the impact factor of pollutant source fluxes at candidate monitoring locations. This impact factor is used as design criteria to find the best monitoring locations. While constraining the maximum number of permissible monitoring locations, the designed monitoring network improves the results of source identification by choosing monitoring locations that reduce the possibility of missing a pollution source. At the same time, the designed monitoring network decreases the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the improved efficiency in source identification when concentration measurements from the designed monitoring network are used.

Climate change is anticipated to result in changes to the statistical properties of both precipitation depths and precipitation intensity. As a general representative for storm water infrastructure, this work examines changes in detention basin performance under several different climate change model scenarios at the study location north of Washington, DC. Frequency analysis of simulated climate model precipitation data indicates that both precipitation depths and intensities are predicted to change under future climate. The magnitude and direction of these changes vary from one climate model to the next. 24-h design storms consistent with the future climate precipitation data are used to drive a rainfall-runoff model simulating a watershed/detention basin system. In most cases, the performance of a detention basin design based on present climate is inadequate under future climate conditions. This work explores detention basin performance based on future precipitation depths only, storm intensity only, and both depth and intensity acting jointly. Results are interpreted in terms of the adequacy of current infrastructure to carry future flood loads, and in terms of potential strategies for design of infrastructure, in the face of a nonstationary climate.

This paper addresses the determination of the areal reduction factor (ARF) as a function of area and duration. The analysis is carried out using the data recorded in a 10-year period at the rainfall gauge network of the city of Milan (Italy). Four types of probability distributions [exponential, the extreme value type 1 (EV1), the generalized extreme value (GEV), and generalized Pareto], two parameter estimation methods (probability-weighted moments and partial probability-weighted moments), and four different regression models of ARF on area and duration are considered. A sensitivity analysis is carried out to outline the effect exerted on ARF by the choice of probability distribution and the parameter estimation method and by that of the model. The effect of model choice is more important than the choice of the distribution and estimation method. The models that fit the data best are the newly developed ones. The ARF model that has the best fit presents a root-mean-square error (RMSE) equal to 0.0204. This ARF error is very acceptable for practical purposes. The computed areal rainfall quantiles show inconsistencies, which are likely due more to insufficient coverage of the rain gauge network, particularly in the outskirts of the area being considered, than to the limited amount of data. For a proper investigation of the ARF dependence on area and duration, both a longer series and a better gauge distribution are necessary.

The effect of obstruction in the form of creation of additional turbulence caused by roughness elements on evaporation rates and hence on the convective mass transfer coefficient has been investigated under varying conditions of air velocities and water temperatures. The respective ranges of flow Reynolds number and water temperature for this study were 20,000–120,000 and 30–50°C, respectively. The range of relative roughness height was 0.011–0.075. It has been found that there is a significant effect of the presence of roughness elements, and this effect is a strong function of flow Reynolds number and the relative roughness height of its obstructions. An empirical correlation has been developed to relate Sherwood number as a function of Reynolds number and relative roughness height to facilitate prediction of mass transfer and evaporation rate under varying Reynolds number and relative roughness height.

Pervious concrete pavement can effectively reduce both the volume of water and the concentration of many sediment-associated contaminants in urban runoff. However, chloride from road salt is recognized as a threat to surface and groundwater resources because it is a conservative ion and does not readily bind to soil particles. To better understand and manage water resources in urban environments experiencing annual freeze-thaw cycles, this study examines the impact of road salt (sodium chloride) and sand applications on water and chloride movement in pervious concrete structures in a laboratory setting. Water movement and salt retention were characterized within pervious concrete slabs under frozen and thawed conditions. Laboratory experiments were repeated using both brine (23% salt solution) and fresh water as well as varying additions of sand (typical of winter sand application rates in Canada) to provide a range of temperatures experienced in cold-climate urban environments. Performance testing (via infiltration capacity) was conducted to assess the suitability of pervious concrete in climates where road sand and salt application is necessary. For all experimental conditions studied, chloride was rapidly transported through the pervious concrete. The complete freezing of pore water throughout the concrete slab reduced water and salt movement within the concrete matrix, while sand application reduced water movement through pores and delayed peak flow. The infiltration capacity of the pervious concrete structures, as tested, exceeds the probable maximum water loading rate that will be encountered in Southern Ontario, Canada, with or without sand, frozen or unfrozen. From a groundwater management and source water protection perspective, the data indicate that pervious concrete pavement structures may contribute to chloride contamination of groundwater if used in salt vulnerable areas and groundwater recharge zones.