Journal of Hydrologic Engineering

Climate-change–based global sea-level rise is of concern because it contributes to significant loss of coastal wetlands and mangroves and to increasing damage from coastal flooding in many regions of the world. Physical mechanisms that describe the dynamic global climate systems and the effect of this system behavior on sea-level rise are inherently complex. In this study, conducted using systematic analysis of historic data on temperature change and sea-level rise, a linear dynamic system model is proposed to predict global sea-level rise and mean surface temperatures. Unlike the semiempirical approaches proposed in the recent literature, this model incorporates the inherent interaction between temperature and sea-level rise into the model. The resulting model, recognized from the historic data, shows that the rate of sea-level rise is proportional to temperature, and this rise is also a function of the temporal state of the sea level. Similarly, the rate of temperature change is a function of the temporal state of the temperature and is also affected by the sea-level rise. The proposed model is also used to predict the sea-level rise during the 21st century.

TOPMODEL predictions of surface runoff and subsurface flow are fundamentally developed on the basis of the topographic index distribution (TID). The scale dependency of the TID [i.e., dependency on the resolution of the digital elevation model (DEM) data used to compute the topographic indexes] determines that downscaling of the TID computed from a coarser resolution DEM to a finer resolution is needed before the TOPMODEL concepts can be applied to simulate hydrological processes at some larger scales than the scale of hillslopes. It was found that adjusting only the mean values cannot achieve an accurate downscaling of the TID because the difference between 2-m TIDs and the downscaled TIDs from a coarser resolution to 2 m through adjusting only mean values resulted in overestimation of the fraction of the saturation area and surface runoff under wet conditions and underestimation under dry conditions. It was found that downscaling by correcting for scale-dependencies in the first three moments of TIDs produced better predictions. A series of empirical relationships among mean, standard deviation, and coefficient of skewness of TIDs of nine catchments in eastern Tennessee at resolutions of 2, 10, and 100 m and 205 watersheds across the contiguous United States at resolutions of 10 m and 1 km were developed for downscaling TIDs from 1 km to 2 m through approximating TIDs by a 3-parameter gamma distribution function. The errors in the downscaled TIDs from 1 km to 10 m over 205 watersheds across the contiguous United States decreased with increasing watershed size and approached a minimum (approximately 6%) as the watershed drainage area was larger than approximately $500{\mathrm{km}}^2$. With the constructed empirical relationships, topographic indexes computed from 1-km DEM can be scaled down to 2 m for reducing errors and uncertainties in the TOPMODEL-based general circulation model (GCM) hydrological simulations.

A drainage basin is a flood-prone area in many parts of the world, especially in areas in which major urban development is underway, creating numerous social, economical, and psychological problems in both developed and developing countries. As a result, the use of flood forecasting and early warning systems is mandatory to reduce the economic losses and the risk incurred on people. The MIKE11 Flood Forecasting (FF) updating procedure can identify two different types of errors between measured and simulated data, namely amplitude and phase error, ignoring the nonuniform amplitude error around the flood peak region tentatively called shape error. In this paper, after making a clear distinction of such terms as simulation with and without an updating kernel (UK) and coupled versus decoupled calibration, the conventional UK incorporated into the MIKE11 FF hydrodynamic model is synthesized in some detail regarding the nature of amplitude and phase errors. In light of the nonuniform nature of amplitude error around the peak region, a three-parameter UK either coupled or decoupled is proposed for possible incorporation into MIKE11 FF. A highly controlled numerical experiment is devised to assess and evaluate the effectiveness of the proposed methodology compared with the conventional two-parameter UK implemented in MIKE11. Altogether, four different UKs are constructed and the forecasting results are compared and contrasted with plain simulation without any updating. Computed performance error indices clearly indicate the superiority of three-parameter UK to the two-parameter one. In summary, the proposed three-parameter UK could be regarded as a suitable alternative for adaptive forecasting of flood in drainage basins using MIKE11 or any other hydraulic and/or hydrologic simulation model.

The objective of this study is to validate a flow prediction model for a hydrometric station using a multistation criterion in addition to standard single-station performance criteria. In this contribution we used cluster analysis to identify the regional flow height, i.e., water-level patterns and validate the output of an artificial neural network (ANN) model of the Alportel River in Portugal. Measurements of precipitation, temperature, and flow height were used as input variables to the ANN model with a lead time of 12 h. The lead time of 12 h is assumed to be appropriate for a short-term hydrological prediction since it is meaningful for physical processes. The ANN model with three inputs, four hidden neurons, and ten epochs was tested using the new model-validation criterion. The high performance of the model (i.e., Nash-Sutcliffe coefficient is equal to 0.922) was confirmed by the cluster-analysis criterion. It can be concluded that a multistation-based approach can be used as an additional validation criterion and might result in a rejection of a model which initially passed a single-station validation criterion.

The writers investigated the relative performance of three different numerical techniques available in the SEAWAT/MT3DMS code by simulating two new variable-density-flow and transport experimental data sets. The experiments were designed to represent two distinctly different variable-density configurations that involve transport of a sinking groundwater plume and a rising groundwater plume. The numerical techniques used for simulating these experiments included the method of characteristics approach (MOC), total-variation-diminishing scheme (TVD), and finite-difference scheme (FD). Both homogeneous and heterogeneous hydraulic conductivity fields were employed in the numerical simulations to explore the effects of heterogeneities. The results indicate that all three numerical methods have limitations and were not able to satisfactorily reproduce the instabilities observed in the experimental data sets. The results show the need for improving the accuracy of numerical techniques that are currently being used for solving variable-density groundwater flow problems.

A comprehensive hydrological model, like the storm water management model (SWMM), has been widely used for rainfall-runoff simulation. In recent years, simple and effective modern modeling techniques have also brought great attention to the prediction of runoff with rainfall input. A comparative case study between SWMM and a presently developed fuzzy logic model for the predictions of total runoff within the watershed of Cascina Scala, Pavia in Italy is presented. A data set of 23 events from 2000 to 2003 including with the total rainfall and total runoff are adopted to train fuzzy logic parameters. Other data (1990–1995) with detailed time variations of rainfall and runoff are available for the setup and calibration of SWMM for runoff modeling. Among the 1990–1995 data, 35 independent rainfall events are selected to test the prediction performance of the SWMM and fuzzy logic models by comparing the predicted total runoffs with measured data. Comparisons and performance analyses in terms of the root-mean-squared error and coefficient of efficiency are made between the SWMM and the fuzzy logic model. The predicted total runoffs from either the SWMM or the fuzzy logic model are found to agree reasonably well with the measured data. For large rainfall events, the fuzzy logic model generally outperforms the SWMM unless the modification of the impervious ratio is applied to improve the SWMM results. However, the SWMM can produce the time varying hydrograph whereas fuzzy logic is subject to limitation of the methodology and is unable to generate such an output.

The objective of this paper is to develop a model to estimate soil erosion based on data from natural rainstorms on the hillslopes of burnt mountains. For this research, 52 plots were established on mountain slopes in the eastern coastal region of South Korea and operated for 6 years after the fire. Runoff and soil erosion were influenced greatly by rainfall and vegetation factors, respectively. The vegetation index that reflects the structure of vegetation recovered after the forest fire and the soil index that reflects soil characteristics in burnt forest lands showed higher correlations than factors of the revised universal soil loss equation (RUSLE). Sediment yield decreased generally with increasing slope steepness and length because of the effects of depression storage and flow interception by stems and litter on forest lands. The Soil Erosion Model for Mountain Areas in Korea (SEMMA), used to compute sediment yield from hillslopes, is a statistical model composed of four variables: rainfall, vegetation cover, soil, and topography. According to sensitivity analysis for each parameter, rainfall and vegetation showed high sensitivities. The SEMMA model was improved by developing several equations that were classified by rainfall depth and vegetation coverage. This model may be applied usefully to assess soil erosion risks in burnt mountains.

Water quality monitoring network design is an iterative procedure, where an existing network should be reassessed from time to time on the basis of changing environmental demands and objectives in water quality management. In recent years, the economic and financial constraints faced by monitoring agencies have led to the initiation of network reduction procedures in the space domain (i.e., the reduction of the number of stations within a network). In this regard, the dynamic programming approach (DPA) can be effectively used to determine which monitoring sites are to be preserved when the network is consolidated for a fixed number of stations. However, previous studies on the use of the DPA methodology in network redesign have not yet resolved two basic questions: (1) what is the required (optimum) number of stations to be retained in the network to best represent the basin? and (2) how many subbasins should be selected when segregating the basin into smaller units as foreseen by DPA? Accordingly, the primary objective of this paper is to modify the DPA approach to determine the optimum number of stations to be retained when consolidating a monitoring network. In previous studies, the selection of subbasins in DPA was realized basically by subjective approaches based on general experiences of the designers so that the secondary objective of the study is to expand the capabilities of DPA by utilizing a more systematic approach to subbasin identification. The previously mentioned modifications to the DPA methodology are demonstrated in the case of a water quality monitoring network in Gediz River basin along the Aegean coast of Turkey. The results obtained indicate that DPA can also be an effective tool in optimizing the number of stations to be retained in a monitoring network.

In this study, the authors investigated an approach to calculate the standardized streamflow index (SSI), which allows accurate spatial and temporal comparison of the hydrological conditions of a stream or set of streams. For this purpose, the capability of six three-parameter distributions (lognormal, Pearson Type III, log-logistic, general extreme value, generalized Pareto, and Weibull) and two different approaches to select the most suitable distribution the best monthly fit (BMF) and the minimum orthogonal distance (MD), were tested by using a monthly streamflow data set for the Ebro Basin (Spain). This large Mediterranean basin is characterized by high variability in the magnitude of streamflows and in seasonal regimes. The results show that the most commonly used probability distributions for flow frequency analysis provided good fits to the streamflow series. Thus, the visual inspection of the $L$-moment diagrams and the results of the Kolmogorov-Smirnov test did not enable the selection of a single optimum probability distribution. However, no single probability distribution for all the series was suitable for obtaining a robust standardized streamflow series because each of the distributions had one or more limitations. The BMF and MD approaches improved the results in the expected average, standard deviation, and the frequencies of extreme events of the SSI series in relation to the selection of a unique distribution for each station. The BMF and MD approaches involved using different probability distributions for each gauging station and month of the year to calculate the SSI. Both approaches are easy to apply and they provide very similar results in the quality of the obtained hydrological drought indexes. The proposed procedures are very flexible for analyses involving contrasting hydrological regimes and flow characteristics.

A conceptual model for rapid lateral subsurface flow under extreme storm events in wet vegetated hillslopes with a high preferential flow network is presented. The kinematic form of Darcy’s equation and a continuity equation with a sink term to account for lateral preferential flow are used to formulate a subsurface flow equation. The resulting equation is numerically solved using a finite difference approximation. The physical parameters of the model are derived from field experiments conducted in a hillslope in the Brahmaputra River basin of India. Apart from capturing the rapid buildup and recession of a saturated profile in the hillslope, the model gives an indication of hydrologically active lateral macroporosity and its dependency on the rate of recharge. The computed flow hydrographs showed that for the hillslope under investigation, the rapid subsurface storm response is primarily controlled by lateral preferential flow.

Forecasting streamflow is extremely significant in hydrological studies to optimize the operation of water resources systems. Upper Euphrates Basin, which is located in eastern Turkey, is a snow-dominated basin and its runoff is largely affected by snowmelt. Snowmelt is an important water resource to many aspects of hydrology, including water supply, flood control, and erosion. Selection of a suitable tool was necessary to assess potential impacts of climate change on the hydrological cycle of this region. Hydrologic models are useful tools for the prediction of runoffs and interactions among hydrological variables within the hydrologic cycle. Because of limited data availability, lumped conceptual hydrological models are often used instead of distributed hydrological models. To assess the accuracy of lumped conceptual hydrological models, this paper presents simulation results of snowmelt runoffs of Upper Euphrates Basin by using two models that use the temperature index approach to calculate snowmelt process. The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) and Large Basin Runoff Model (LBRM) were used for this purpose.

The accurate estimation of Muskingum model parameter is essential to give flood routing for flood control in water resources management. The Muskingum model continues to be a popular method for flood routing, and its parameter estimation is a global optimization problem with the main objective to find a set of optimal model parameter values that attains a best fit between observed and computed flow. Although some techniques have been employed to estimate the parameters for Muskingum model, an efficient method for parameter estimation in Muskingum model is still required to improve the computational precision. Therefore, in this paper, the differential evolution (DE) algorithm is studied for estimation of Muskingum model parameter. A case study with actual data from previous literature, the experimental results showed an excellent performance in its optimization result and performance analysis and demonstrates that DE is an alternative technique to estimate the parameters of the Muskingum model.