CASC2D user's manual: a two-dimensional watershed rainfall-runoff model

CER90-91PYJ-BS-12.Includes bibliographical references (page 50).March 1991.Funded by the U.S. Army Research Office Grant No. ARO/DAAL03-86-K-0175

Resistance to sheet flow

CER88-89BS-PYJ13.Includes bibliographical references (pages 58-62).January 1989.The results of a literature review on resistance to sheet flow are presented. The effects of surface roughness, rainfall, and vegetation are considered. At least in the case of laminar flow, it is found that the total flow resistance is the sum of the contributions of individual effects. The friction factor for the surface roughness effect in laminar flow is directly proportional to the relative roughness and varies inversely with the Reynolds number. A power function of rainfall intensity in laminar flow can represent the effect of rainfall on the product of friction factor and Reynolds number. For turbulent flow, however, the friction factor depends on the surface conditions which are partitioned into smooth, transition, and fully rough. The analysis of flow through vegetation is more complex and calls for further studies. For densely vegetated surfaces, the Darcy-Weisbach friction factor is shown to decrease significantly at Reynolds number well beyond the critical value of Re = 2000 for smooth surfaces. In some cases, the flow behaved as laminar flow at Re = 100,000.Funded by the U.S. Army Research Office Grant No. ARO/DAAL 03-86-K-0175

Modeling flood estimation using fuzzy logic & artificial neural network

Estimates of flood discharge with various risks of exceedance are needed for a wide range of engineering problems: examples are culvert and bridge design and construction floods in major projects. At a site with a long record of measured floods, these estimates may be derived by statistical analysis of the flow series. Alternatively the storm magnitude of an appropriate duration, aerial coverage and return period may be estimated and converted into the flood of a given return period using a rainfall/runoff model such as the unit hydrograph. However, in cases where adequate rainfall or river flow records are not available at or near the site of interest, it is difficult for hydrologists and engineers to derive reliable flood estimates directly and regional studies can be useful. This is particularly true in the case of semi-arid areas, where, in general, flow records are scarce. The problem of assigning a flood risk to a particular flow value is one which has received considerable attention in the literature. The estimation of flood risk through the evaluation of a flood frequency distribution is complicated, however, by the lack of a sufficient temporal characterization of the underlying distribution of flood events. The inadequacies in the data availability necessitate the estimation of the flood risk associated with events which have a return period beyond the length of the historical record. Regional flood frequency analysis can be effective in substituting an increased spatial characterization of the data for an insufficient temporal characterization, although problems exist with the implementation of regional flood frequency analysis techniques

Regionalization by fuzzy expert system based approach optimized by genetic algorithm.

In recent years soft computing methods are being increasingly used to model complex hydrologic processes. These methods can simulate the real life processes without prior knowledge of the exact relationship between their components. The principal aim of this paper is perform hydrological regionalization based on soft computing concepts in the southern strip of the Caspian Sea basin, north of Iran. The basin with an area of 42,400 sq. km has been affected by severe floods in recent years that caused damages to human life and properties. Although some 61 hydrometric stations and 31 weather stations with 44 years of observed data (1961–2005) are operated in the study area, previous flood studies in this region have been hampered by insufficient and/or reliable observed rainfall-runoff records. In order to investigate the homogeneity (h) of catchments and overcome incompatibility that may occur on boundaries of cluster groups, a fuzzy expert system (FES) approach is used which incorporates physical and climatic characteristics, as well as flood seasonality and geographic location. Genetic algorithm (GA) was employed to adjust parameters of FES and optimize the system. In order to achieve the objective, a MATLAB programming code was developed which considers the heterogeneity criteria of less than 1 (H < 1) as the satisfying criteria. The adopted approach was found superior to the conventional hydrologic regionalization methods in the region because it employs greater number of homogeneity parameters and produces lower values of heterogeneity criteria

Spatial patterns and temporal variability of drought in Western Iran

An analysis of drought in western Iran from 1966 to 2000 is presented using monthly precipitation data observed at 140 gauges uniformly distributed over the area. Drought conditions have been assessed by means of the Standardized Precipitation Index (SPI). To study the long-term drought variability the principal component analysis was applied to the SPI field computed on 12-month time scale. The analysis shows that applying an orthogonal rotation to the first two principal component patterns, two distinct sub-regions having different climatic variability may be identified. Results have been compared to those obtained for the largescale using re-analysis data suggesting a satisfactory agreement. Furthermore, the extension of the large-scale analysis to a longer period (1948–2007) shows that the spatial patterns and the associated time variability of drought are subjected to noticeable changes. Finally, the relationship between hydrological droughts in the two sub-regions and El Niño Southern Oscillation events has been investigated finding that there is not clear evidence for a link between the two phenomen

Effect of El Nino-Southern Oscillation on Annual Maximum Flood in Southwestern of Iran

Flood forecasting and the study of the factors involved in flood events form important issues of concern in water resources management. In recent years, the influence of large-scale global climate phenomena on flood peaks has attracted more attention. In this paper, the effect of El Nino-Southern Oscillation (ENSO) is investigated on annual maximum flood (AMF) in Iran’s southwestern basins. The basins are located upstream of Dez and Karun-I dams. ENSO effects on probability, magnitude, and intensity (probability times magnitude) of AMF in the Esfand-Farvardin (March-April) period are examined. The results indicate that following El Nino, the probability of an AMF occurrence larger than the AMF corresponding to neutral ENSO conditions is higher in the study period. The opposite is true for La Nina conditions. It is also seen that El Nino effects are more intense than those of La Nina period

Susceptibility of Hydropower Generation to Climate Change: Karun III Dam Case Study

Climate change can cause serious problems for future hydropower plant projects and make them less economically justified. Changing precipitation patterns, rising temperatures, and abrupt snow melting affect river stream patterns and hydropower generation. Thus, study of climate change impacts during the useful life of a hydropower dam is essential and its outcome should be considered in assessing long-term dam feasibility. The aim of this research is to evaluate the impacts of climate change on future hydropower generation in the Karun-III dam located in the southwest region of Iran in two future tri-decadal periods: near (2020&#8211;2049) and far (2070&#8211;2099). Had-CM3 general circulation model predictions under A2 and B2 SRES scenarios were applied, and downscaled by a statistical downscaling model (SDSM). An artificial neural network (ANN) and HEC-ResSim reservoir model respectively simulated the rainfall&#8211;runoff process and hydropower generation. The projections showed that the Karun-III dam catchment under the two scenarios will generally become warmer and wetter with a slightly larger increase in annual precipitation in the near than the far future. Runoff followed the precipitation trend by increasing in both periods. The runoff peak also switched from April to March in both scenarios, due to higher winter precipitation, and earlier snowmelt, which was caused by temperature rise. According to both scenarios, hydropower generation increased more in the near future than in the far future. Annual average power generation increased gradually by 26.7&#8211;40.5% under A2 and by 17.4&#8211;29.3% under B2 in 2020&#8211;2049. In the far period, average power generation increased by 1.8&#8211;8.7% in A2 and by 10.5&#8211;22% under B2. In the near future, A2 showed energy deduction in the months of June and July, while B2 revealed a decrease in the months of April and June. Additionally, projections in the 2070&#8211;2099 under A2 exhibited energy reduction in the months of March through July, while B2 revealed a decrease in April through July. The framework utilized in this study can be exploited to analyze the susceptibility of hydropower production in the long term

An Integrated Approach for Site Selection of Snow Measurement Stations

Snowmelt provides a reliable water resource for meeting domestic, agricultural, industrial and hydropower demands. Consequently, estimating the available snow water equivalent is essential for water resource management of snowy regions. Due to the spatiotemporal variability of the snowfall pattern in mountainous areas and difficult access to high altitudes areas, snow measurement is one of the most challenging hydro-meteorological data collection efforts. Development of an optimum snow measurement network is a complex task that requires integration of meteorological, hydrological, physiographical and economic studies. In this study, site selection of snow measurement stations is carried out through an integrated process using observed snow course data and analysis of historical snow cover images from National Oceanic Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) at both regional and local scales. Several important meteorological and hydrological factors, such as monthly and annual rainfall distribution, spatial distribution of average frequency of snow observation (FSO) for two periods of snow falling and melting season, as well as priority contribution of sub-basins to annual snowmelt runoff are considered for selecting optimum station network. The FSO maps representing accumulation of snowfall during falling months and snowpack persistence during melting months are prepared in the GIS based on NOAA-AVHRR historical snow cover images. Basins are partitioned into 250 m elevation intervals such that within each interval, establishment of new stations or relocation/removing of the existing stations were proposed. The decision is made on the basis of the combination of meteorological, hydrological and satellite information. Economic aspects and road access constraints are also considered in determining the station type. Eventually, for the study area encompassing a number of large basins in southwest of Iran, several new stations and relocation of some existing stations are proposed