A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling

Without a doubt the first step in any water resources management is the rainfall–runoff modeling over the watershed. However considering high stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall–runoff modeling as well as other fields of hydrology. In the current research, the wavelet analysis was linked to the ANN concept for modeling Ligvanchai watershed rainfall–runoff process at Tabriz, Iran. For this purpose the main time series of two variables, rainfall and runoff, were decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the runoff discharge 1 day ahead. The obtained results show the proposed model can predict both short and long term runoff discharges because of using multi-scale time series of rainfall and runoff data as the ANN input layer.     

Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin

This work presents the development of a rainfall-runoff model for the Iguaçu River basin in southern Brazil. The model was developed to support the operation planning of hydroelectric power plants and is intended to predict the natural flow based on meteorological rain forecasts. A recurrent fuzzy system model was employed with parameters estimated by a genetic algorithm using observed rainfall as input. The model performs well using observed rainfall as input; however, its performance using predicted rainfall as input decays with the forecasting horizon, illustrating the effect of meteorological prediction errors. The prototype implementing the model has been used for dispatch planning by the Brazilian Electric System Operator.     

Environmental Integration of Small Hydropower Development

On the basis of cooperative research with European professionals, problems relating to environmental and ecological protection were discussed while small hydropower development in China currently was introduced in this paper. Guidelines of small hydropower development dealing with environmental protection were sug- gested hereby. Various specific technical environmental solutions focusing on small hydropower development applied in European and other developed countries were also introduced in this paper. Main environmental solutions are discussed as follows： integrated design should be advocated during plan/design stage,＂ ecohydraulic engineering and its utilization in SHP design and construction; effective technical approaches to relieve and eliminate the impact of river interception and fix the reasonable minimum biological flow; environment-friendly mitigation and compensation measures for high/low-head hydropower schemes applied in diversion works, intake, nature-like fish-passes, penstock and powerhouse design; study and application of the new technology, material and equipments; launching renovation of environmental protection in existing stations; emphasiz- ing public participation and their acceptability for the SHP environment; coordinated inter-disciplinary study at national level, etc. Two case studies in Sweden and Australia are given here.     

Estimation of Clark’s Instantaneous Unit Hydrograph Parameters and Development of Direct Surface Runoff Hydrograph

We present a method to estimate Time of Concentration (T _c) and Storage Coefficient (R) to develop Clark’s Instantaneous Unit Hydrograph (CIUH). T _c is estimated from Time Area Diagram of the catchment and R is determined using optimization approach based on Downhill Simplex technique (code written in FORTRAN). Four different objective functions are used in optimization to determine R. The sum of least squares objective function is used in a novel way by relating it to slope of a linear regression best fit line drawn between observed and simulated peak discharge values to find R. Physical parameters (delineation, land slope, stream lengths and associated drainage areas) of the catchment are derived from SPOT satellite imageries of the basin using ERDAS: Arc GIS is used for geographic data processing. Ten randomly selected rainfall–runoff events are used for calibration and five for validation. Using CIUH, a Direct surface runoff hydrograph (DSRH) is developed. Kaha catchment (5,598 km²), part of Indus river system, located in semi-arid region of Pakistan and dominated by hill torrent flows is used to demonstrate the applicability of proposed approach. Model results during validation are very good with model efficiency of more than 95% and root mean square error of less than 6%. Impact of variation in model parameters T _c and R on DSRH is investigated. It is identified that DSRH is more sensitive to R compared to T _c. Relatively equal values of R and T _c reveal that shape of DSRH for a large catchment depends on both runoff diffusion and translation flow effects. The runoff diffusion effect is found to be dominant.     

Development and Analytical Verification of an Integrated 2-D Surface Water—Groundwater Model

DIVAST is a two-dimensional hydrodynamic and water quality numerical model developed for estuarine and coastal modelling. The original model enables the simulation of problems such as pollution and flooding in surface waters. In this paper the existing model is extended to allow the modelling of 2-D groundwater as well as surface water in the same model, using an integrated approach rather than two disparate models. The changes to the original model are summarised and the method of implementation is outlined. The new extended model (DIVAST-SG) is then tested against an analytical solution to verify that the model solves the equations correctly. The model is shown to predict the analytical solution for two different scenarios to within approximately 1 % of the height of flood wave.     

Performance Enhancement of Rainfall Pattern – Water Level Prediction Model Utilizing Self-Organizing-Map Clustering Method

During recent two decades, Artificial Neural Network (ANN) has become one of the most widely used methods in hydrology. One solution for better capturing the existing non-linear and complex nature of data is to develop new hybrid approaches. These hybrid models can be developed in a way that two or more techniques are combined in order to benefit from the advantages of these available approaches and eliminate their limitations. The main scope of this paper is to improve the performance of rainfall-water level modeling by combining ANN with Self Organizing Map (SOM) as an unsupervised clustering method. The proposed method in this study consists of two phases. In the first phase, with the aim of reducing the complexity and dimensionality of input data, a two-step clustering using SOM technique is carried out. Then, in the second phase, separate ANN models are used to model each cluster of data, and final results are obtained by combining the outputs of all models. The proposed new hybrid approach is evaluated using real hydrological data of Johor River. The results of the study indicate that the new proposed SOM-ANN hybrid model has a better performance in daily rainfall-water level forecasting compared to ANN model alone.     

Climate Change and Hydrological Response in the Trans-State Oologah Lake Watershed–Evaluating Dynamically Downscaled NARCCAP and Statistically Downscaled CMIP3 Simulations with VIC Model

Statistically and dynamically downscaled climate projections are the two important data sources for evaluation of climate change and its impact on water availability, water quality and ecosystems. Though bias correction helps to adjust the climate model output to behave more similarly to observations, the hydrologic response still can be biased. This study uses Variable Infiltration Capacity (VIC) model to evaluate the hydrologic response of the trans-state Oologah Lake watershed to climate change by using both statistically and dynamically downscaled multiple climate projections. Simulated historical and projected climate data from the North American Regional Climate Change Assessment Program (NARCCAP) and Bias-Corrected and Spatially Downscaled–Coupled Model Intercomparison Phase 3 (BCSD-CMIP3) forced the hydrologic model. In addition, different river network upscaling methods are also compared for a higher VIC model performance. Evaluation and comparison shows the following the results. (1) From the hydrologic point of view, the dynamically downscaled NARCCAP projection performed better, most likely in capturing a larger portion of mesoscale-driven convective rainfall than the statistically downscaled CMIP3 projections; hence, the VIC model generated higher seasonal streamflow amplitudes that are closer to observations. Additionally, the statistically downscaled GCMs are less likely to capture the hydrological simulation probably due to missing integration of climate variables of wind, solar radiation and others, even though their precipitation and temperature are bias corrected to be more favorably than the NARCCAP simulations. (2) Future water availability (precipitation, runoff, and baseflow) in the watershed would increase annually by 3–4 %, suggested by both NARCCAP and BCSD-CMIP3. Temperature increases (2.5–3 °C) are much more consistent between the two types of climate projections both seasonally and annually. However, NARCCAP suggested 2–3 times higher seasonal variability of precipitation and other water fluxes than the BCSD-CMIP3 models. (3) The hydrologic performance could be used as a potential metric to comparatively differentiate climate models, since the land surface and atmosphere processes are considered integrally.     

A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction

Water Resources Management - Precise and reliable monthly runoff prediction plays a vital role in the optimal management of water resources, but the nonstationarity and skewness of monthly runoff...     

Field Applicability of the SCS-CN-Based Mishra–Singh General Model and its Variants

The general soil conservation service curve number (SCS-CN)-based Mishra and Singh (Mishra and Singh, 1999, J. Hydrologic. Eng. ASCE, 4(3), 257–264) model and its eight variants were investigated for their field applicability using a large set of rainfall-runoff events, derived from a number of U.S. watersheds varying in size from 0.3 to 30351.5 ha, grouped into five classes based on the rainfall magnitude. The analysis based on the goodness of fit criteria of root mean square error (RMSE) and error in computed and observed mean runoff revealed that the performance of the existing version of the SCS-CN method was significantly poorer than that of all the model variants on all the five data sets with rainfall 38.1 mm. The existing version showed a consistently improved performance on the data with increasing rainfall amount, but greater than 38.1 mm. The one-parameter modified SCS-CN method (a = 0.5 and = a median value) performed significantly better than the existing one on all the data sets, but far better on rainfall data less than 2 inches. Finally, the former with = 0 was recommended for routine field applications to any data set.     

Impacts and Adaptation to Climate Change Using a Reservoir Management Tool to a Northern Watershed: Application to Lièvre River Watershed,Quebec, Canada

The purpose of this study is to evaluate the impact of climate change (CC) on the management of the three reservoirs in the Lièvre River watershed and to investigate adaptation strategies to CC. To accomplish this objective, a reservoir management tool was developed. The tool integrates: hydrological ensemble streamflow predictions; a stochastic optimization model; a neural network model; and a water balance model. Five climate projections from a regional climate model, under current (1961–2000) and future (2041–2070) climate scenarios, were used. Adjustments to the reservoirs operating rules were used as an adaptation strategy to limit flooding in the watershed and also in the Montreal Archipelago located downstream of the watershed. A number of constraints in the reservoirs of the Lièvre watershed are related to summer recreational activities, which would start earlier in future climate. Modifications of these constraints were simulated to take into account socio-economic impacts of climate change on reservoirs operation. Results show that greater quantities of water would have to be stored in the Lièvre River watershed in the future, to decrease the risk of flooding in the Montreal Archipelago. The reservoir located at the downstream end of the watershed would be more vulnerable and its reliability may decrease in the future. Adaptation measures reduced the inter-annual variability of the reservoir level under future climate conditions. The reservoir management tool is an example of a no-regrets strategy, as it will contribute to improve the tools currently available to manage the reservoirs of the Lièvre River watershed.     

Development of the Great Lakes Ice-circulation Model (GLIM): Application to Lake Erie in 2003–2004

To simulate ice and water circulation in Lake Erie over a yearly cycle, a Great Lakes Ice-circulation Model (GLIM) was developed by applying a Coupled Ice-Ocean Model (CIOM) with a 2-km resolution grid. The hourly surface wind stress and thermodynamic forcings for input into the GLIM are derived from meteorological measurements interpolated onto the 2-km model grids. The seasonal cycles for ice concentration, thickness, velocity, and other variables are well reproduced in the 2003/04 ice season. Satellite measurements of ice cover were used to validate GLIM with a mean bias deviation (MBD) of 7.4%. The seasonal cycle for lake surface temperature is well reproduced in comparison to the satellite measurements with a MBD of 1.5%. Additional sensitivity experiments further confirm the important impacts of ice cover on lake water temperature and water level variations. Furthermore, a period including an extreme cooling (due to a cold air outbreak) and an extreme warming event in February 2004 was examined to test GLIM's response to rapidly-changing synoptic forcing.     

NeStRes – Model for Operation of Non-Strategic Reservoirs for Irrigation in Drylands: Model Description and Application to a Semiarid Basin

Water availability in dry inhabited environments has usually been promoted by large strategic reservoirs, but small non-strategic ones, built by farmers and communities, are unable to cope with long term droughts and inappropriate for human supply. Nevertheless, small reservoirs promote water spatial distribution and play a major role for livelihood in rural areas. To fill the gap of operation methods for non-strategic reservoirs used for irrigation where water is a limiting factor, the NeStRes model was developed. The model is composed of three modules: i) hydrological: to define the reliability of water withdrawals from the reservoir; ii) agricultural: to simulate crop production based on water availability; iii) economic: to compute the possible income from irrigated agricultural crops. NeStRes was applied to 91 reservoirs of the semiarid Banabuiú River Basin – BRB, Brazil. The simulations indicated that the maximum income from the cultivation of maize is obtained when the reservoirs are intensely used, drying completely in one to two thirds of the time. Adoption of a fixed reliability level of daily water supply (54%, in the BRB) generates at least 85% of the maximum possible income for all simulated reservoirs. This model application suggests a paradigm change in the operation of small non-strategic reservoirs in drylands: to use water for crop production and save the revenue, instead of saving water, which is susceptible to evaporation. Although high reliability level is desired for human supply by strategic reservoirs, non-strategic ones can be more intensely explored to generate income from irrigated agriculture in drylands.

     

What Is the Potential of Integrating Phase Space Reconstruction with SVM-FFA Data-Intelligence Model? Application of Rainfall Forecasting over Regional Scale

Rainfall modeling is one of the major component process in the meteorological engineering. Hence, exploring an advance and reliable intelligent model for its forecasting is essential for water resources engineering. In this current research, novel integrative intelligence model coupled with phase space reconstruction is proposed to forecast monthly rainfall in Chhattisgarh State, India. The proposed model is a hybridization of support vector machine (SVM) model with firefly optimization algorithm (FFA). The modeling is undertaken based on three stages starting with configuring the delay time and embedding dimension using mutual information and false nearest neighbors to determine the input matrix of the forecasting model. In the second stage, the firefly optimizer is employed to tune the SVM model. Finally, the hybrid model is conducted to forecast the monthly time scale rainfall time series. Monthly time scale rainfall data for sixteen raingauge stations over a century (1901–2002) are utilized and tested. A validation of the capacity of the suggested model is carried out by comparing the accuracy results with classical SVM and hybrid SVM-FFA “without mutual information analysis” models. The three predictive models are trained using 75% of available data set and tested the remaining 25% dataset. The model’s results were statistically verified using mean absolute error and best-good-fitness measurements in addition to Taylor diagram visualization. In conclusion, the proposed model was significantly improved the forecasting accuracy of the modeling. Also, it was exhibited a very robust intelligent model that can be applied for the Indian regional zone for monthly rainfall forecasting.     

Optimal Design and Operation of a Hydropower Reservoir Plant Using a WEAP-Based Simulation–Optimization Approach

Water Resources Management - Optimal design and operation of a hydropower reservoir is a complex optimization problem in terms of formulation and solution. In this study, a...     

Water–Health Nexus: Modeling the Pathways and Barriers to Water-Related Diseases

Under pressure from climate change and other drivers, the risks of water-related infectious and chronic diseases seem poised to increase. Addressing these risks requires shared work in the fields of public health and water resources management. To help in risk assessment, the paper presents a comprehensive conceptual model of water-related health effects and the potential barriers that can be established by water management actions. The model does not indicate unique solution strategies because multiple water management factors cause multiple types of water-related diseases. It uses the water-health nexus to conceptualize the pathways to diseases and help clarify health-related responsibilities for water management and public health officials. Experiences of the U.S. are presented as an exemplar of the links between water management and disease incidence. However, parts of the water management system are not regulated effectively and many countries lack organized utilities and effective water governance.

     

Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation

This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.     

A Hydrologic Model of Kemptville Basin—Calibration and Extended Validation

An integrated hydrologic/hydraulic model of the Kemptville Creek basin has been built using the Mike11 modeling system of the Danish Hydraulic Institute and available GIS-based watershed data. This watershed system is complex, comprising of channels, local drainage areas, lateral inflows, wetlands, and a regulated dam. The model was calibrated using measured streamflow data for five years and then validated for another five years. A wide range of methods??both qualitative and quantitative??were used to evaluate the model performance. It was found that the model can simulate high flows with a high degree of accuracy, and the low flows less satisfactorily. Additional (split-sample) validation tests were conducted for another two five-year periods, which revealed that the model is capable of performing equally well for time periods beyond those used for calibration and validation. This model is now being used for various watershed management purposes, including synthetic hydrograph generation, flood forecasting, design flood estimation, wetland function analysis, etc.