Model For Streamflow Simulation Research Articles

Snow and glacier melt runoff model to estimate hydropower potential

Himalayan region has high concentrations of mountain glaciers. Large extent of this region is covered by seasonal snow during winter. Runoff generates from melting of these snow and glaciers is one of the important sources of water for the Himalayan Rivers. Glaciers and snowfields are distributed throughout the Himalayas and form a source of numerous streams. Due to steep slopes, all such streams have potential sites for hydropower generation. If this potential is fully utilized, it will help in generating power from environmentally friendly Run-of-River (RoR) hydropower stations. Considering these aspects, a stream flow simulation model was developed for small streams. This will help in estimation of average seasonal unrestricted hydropower potential of snow and glaciated streams for winter, summer, monsoon and autumn seasons. Information generated through remote sensing technique as glacier, permanent snow cover, seasonal snow cover, altitude of snow and glaciers were used in conjunction with daily maximum and minimum temperature, rainfall and discharge. The model was developed for Malana nala located in Parbati River basin near Kullu in Himachal Pradesh. It was validated at adjacent Tosh nala in the same basin. Seasonal runoff computed from the model is comparable with observed data for all seasons except Monsoon. Good results in autumn, winter and summer seasons demonstrates usefulness of runoff model to assess hydropower potential of snow and glaciated streams and therefore, the model was applied to ungauged Sorang Gad and Kirang Khad. In winter runoff was estimated as 1.8 and 1.69 cumecs for Kirang Khad and Sorang Gad, respectively. This is important, as viability of hydropower station depends upon winter stream runoff. These results suggest that the model is useful tool to assess initial estimate of hydropower potential for large number of snow and glaciated streams, for which no hydrological data is available.

A hybrid stochastic model for multiseason streamflow simulation

A hybrid model is presented for stochastic simulation of multiseason streamflows. This involves partial prewhitening of the streamflows using a parsimonious linear periodic parametric model, followed by resampling the resulting residuals using moving block bootstrap to obtain innovations and subsequently postblackening these innovations to generate synthetic replicates. This model is simple and is efficient in reproducing both linear and nonlinear dependence inherent in the observed streamflows. The first part of this paper demonstrates the hybrid character of the model through stochastic simulations performed using monthly streamflows of Weber River (Utah) that exhibit a complex dependence structure. In the latter part of the paper the hybrid model is shown to be efficient in simulating multiseason streamflows, through an example of the San Juan River (New Mexico). This model ensures annual‐to‐monthly consistency without the need for any adjustment procedures. Furthermore, the hybrid model is able to preserve both within‐year and cross‐year monthly serial correlations for multiple lags. Also, it is seen to be consistent in predicting the reservoir storage (validation) statistic at low as well as high demand levels.

Introduction of block-wise use of TOPMODEL and Muskingum-Cunge method for the hydroenvironmental simulation of a large ungauged basin

For the sustainable management of water quantity and quality, a hydrological model that can simulate the hydro-environmental dynamics of river basins at arbitrary locations is valuable. There are several streamflow simulation models suitable for such a purpose. Yet the lack of data commonly poses a serious problem for their application. This is a serious problem encountered for the sustainable management of water resources at a basin scale. This paper describes a combination of TOPMODEL with the Muskingum-Cunge flow routing method to overcome the problem at least partially. The resultant block-wise version of TOPMODEL is used to simulate daily flows in the data-rich 3570 km2 Fuji-kawa basin, central Honshu, Japan and in the data-poor 20 750 km2 northern part of Minjiang basin above Dujiangyan, Sichuan Province, China. The results demonstrate the potential use of the model for hydro-environmental simulation in a large data-poor basin.

Kernel bandwidth selection for a first order nonparametric streamflow simulation model

A new approach for streamflow simulation using nonparametric methods was described in a recent publication (Sharma et al. 1997). Use of nonparametric methods has the advantage that they avoid the issue of selecting a probability distribution and can represent nonlinear features, such as asymmetry and bimodality that hitherto were difficult to represent, in the probability structure of hydrologic variables such as streamflow and precipitation. The nonparametric method used was kernel density estimation, which requires the selection of bandwidth (smoothing) parameters. This study documents some of the tests that were conduced to evaluate the performance of bandwidth estimation methods for kernel density estimation. Issues related to selection of optimal smoothing parameters for kernel density estimation with small samples (200 or fewer data points) are examined. Both reference to a Gaussian density and data based specifications are applied to estimate bandwidths for samples from bivariate normal mixture densities. The three data based methods studied are Maximum Likelihood Cross Validation (MLCV), Least Square Cross Validation (LSCV) and Biased Cross Validation (BCV2). Modifications for estimating optimal local bandwidths using MLCV and LSCV are also examined. We found that the use of local bandwidths does not necessarily improve the density estimate with small samples. Of the global bandwidth estimators compared, we found that MLCV and LSCV are better because they show lower variability and higher accuracy while Biased Cross Validation suffers from multiple optimal bandwidths for samples from strongly bimodal densities. These results, of particular interest in stochastic hydrology where small samples are common, may have importance in other applications of nonparametric density estimation methods with similar sample sizes and distribution shapes.

Effects of Global Warming on Hurricane‐Induced Flooding

Global simulation models used to predict climate change typically have resolutions too coarse for resolving small drainage basins, creating a major obstacle to evaluating the hydrologic impacts of climate variability. The writers circumvent this difficulty by using a physical model for severe hurricanes to derive changes in hurricane rainfall under a predicted global warming. The modified rainfall is then transferred to a linked hydrologic‐hydrodynamic model to compute the impact of global warming on hurricane‐induced flooding in a susceptible watershed. As an example, using simulations of tropical, doubled‐CO2 climate change, the hurricane model predicts a 40% precipitation increase for severe hurricanes affecting southeastern Florida. The 40% increase is assumed for a 100‐year, three‐day event used for flood‐management planning in South Florida and applied to the Basin Runoff and Streamflow Simulation (BRASS) model of the Western C‐11 drainage system. The increased precipitation provokes substantially greater flooding that could negate most of the benefits of present water‐management practices in this basin.

Markov‐Weibull Model of Monthly Streamflow

A conceptually simple, monthtomonth lag1 Markov streamflow simulation model is developed and tested extensively. Streamflow in any month is represented probabilistically by a family of threeparamet...

Comparison of two daily streamflow simulation models of an alpine watershed

Abstract A comparison of daily streamflow simulation by two different computer models has been completed for an alpine basin in central Colorado, U.S.A. The catchment is part of a 33-km2 area providing most of the City of Boulder's raw water supply. It is located between 2964 and 4103 m a.s.l. in elevation along the Continental Divide 24 km west of Boulder, Colorado. The SSARR model simulation, which makes generous use of lumpedtype parameters, was compared with the PRMS model simulation, which makes greater use of individually defined parameters, by examining their respective ability to fit observed hydrograph volumes and shape. Both computer models were found to simulate streamflow equally well. The PRMS model was found to be more suitable overall because of its ability to handle small catchments and the generous use of individual physically-based parameters.

APPLICATION OF THE HEC‐4 MONTHLY STREAM FLOW SIMULATION MODEL1

ABSTRACT: The HEC‐4 monthly stream flow simulation model, developed by the Hydrologic Engineering Center, Davis, California, is used to extend the available historical stream flow records in the Central Ohio area. The principal objective of this paper is to examine the effectiveness of the HEC‐4 model in generating synthetic monthly flows. Important statistical parameters are evaluated in order to relate the statistical properties of the historical and generated flows. In doing so, it is observed that the mean, standard deviation, and skewness of the generated flows are consistently larger than the corresponding estimates based on historical flows. However, results show that these statistics, as well as the lag‐1 serial correlation, are generally well maintained by the generated sequences. The degree to which any statistical dissimilarities would be critical, from an engineering design point of view, is demonstrated by utilizing their low flow characteristics. Estimates of reservoir safe‐yields, based on a nonsequential mass‐curve analysis of the historical and generated low flows, indicate a nominal difference in this particular study.

Evaluation of TVA Streamflow Model on Small Kentucky Watersheds

ABSTRACT THE TVA Daily Streamflow Simulation Model was evaluated on six Kentucky watersheds. Results in-dicate that the model is reasonably easy to apply and produces simulation results that are in fair agreement with daily flows and good agreement with monthly and annual flows.

Optimal monthly operation of interconnected hydroelectric power storages

Many optimizing studies of water resource systems have given emphasis to the conceptual problem of formulating the design of a system as a set of mathematical functions to which optimizing techniques may be applied. By considering a real-world case of two interconnected reservoirs in the South Island of New Zealand where decisions must be made each month about the quantities of water to release for hydroelectric power generation, the practical problems of applying an optimizing technique are illustrated. The technique used is deterministic dynamic programming coupled with a multivariate stream-flow simulation model. The hydro power generating plants are operated in conjunction with thermal generating plants to provide a specified energy load for each month; the object in operation is to minimize the expected present value of the thermal generating costs; a real-time operating policy which specifies a planned release for each month as a function of the quantities of water stored at the beginning of each month is developed. The study illustrates important advantages of this particular dynamic programming approach over other optimizing techniques; in particular good account is taken of the stochastic nature of the inflows. These occur at four places in the river basin, show considerable seasonal variability, and substantial spatial and temporal intercorrelations. Excessive computational demands for more than two storages are a major restriction of the technique.