Simplified two-parameter gamma distribution for derivation of synthetic unit hydrograph

Several methods for synthetic unit hydrographs are available in the literature. Most of them involve manual, subjective fitting of a hydrograph through few data points. Because it is difficult, the generated unit hydrograph is often left unadjusted for unit runoff volume. To circumvent this problem, a simplified version of the existing two-parameter gamma distribution is introduced to derive a synthetic hydrograph more conveniently and accurately than the popular Gray, Soil Conservation Service, and Synder methods. The revised version incorporates the approximate, but accurate, empirical relations developed for the estimation of beta and lambda (factors governing the shape of the dimensionless unit hydrograph) from the Nash parameter n (= number of reservoirs). The Marquardt algorithm was used to develop the nonlinear relationships. The applicability of the simplified version is tested on both text and field data

Parameter estimation of beta distribution for unit hydrograph derivation

Traditionally used methods for developing a synthetic unit hydrograph (SUH) are well known for their limitations. In last few decades, use of probability distribution functions in developing SUHs has received much attention. In this study, the potentials of a three-parameter beta distribution are explored. Using a analogy between the three-parameter beta-distribution shape and the SUH shape, approaches are developed to evaluate the unknown parameters. Based on nondimensional analysis and optimization, a simple accurate relation is introduced to estimate the three parameters of the beta distribution that is useful for unit hydrograph derivation. The relation yields results closer to those obtained by an available trial and error procedure. The unit hydrographs from the proposed method fit observed hydrographs better than those from the widely used two-parameter gamma distribution. The potential of the approach is demonstrated using data from different catchments lying within and outside India. The methodology is found to work consistently better in most cases

ANN-based sediment yield models for Vamsadhara river basin (India)

Most universally accepted feed-forward error back-propagation artificial neural network models, supported by batch- and pattern-learning, daily, weekly, ten-daily and monthly sediment yield were developed for the Vamsadhara River basin of India. The fast gradient descent optimisation technique improved with variable learning rate (α) and momentum term (β) was used for optimisation. In the process of optimisation and updating of weights, criteria adopted to terminate the process of learning was selected as a per-decided high number of iteration and the other is the generalisation of model through crossvalidation. In all cases of model formulation, the data were normalised with the maximum value of the variable of the series individually. The pattern-learned models were found superior to batch-learned models. High numbers of iterations adopted for model development were found to reduce the value of the objective function, but with model's over-learning and that is reflected? Unclear what is meant by an increase and decrease of the performance in calibration and cross-validation, respectively. The generalised pattern- learned models for different time scales were compared with linear transfer function models and it was found that the pattern-learned models developed with generalisation through cross-validation were superior in general, except weekly for the study area. Key words: back propagation artificial neural network, sediment yield modelling, generalised modelling. Water SA Vol.31(1) 2005: 95-10

Hybrid model for derivation of synthetic unit hydrograph

Splitting the Nash single linear reservoir into two serially connected reservoirs of unequal storage coefficients (one hybrid unit) for a physically realistic response, a hybrid model is introduced for derivation of a synthetic unit hydrograph. Empirical relations are given for estimation of the two storage coefficients from known peak flow (q(p)) and time to peak (t(p)). The hybrid model with two serially connected units is found to work significantly better than the most widely used methods such as those of Snyder, the Soil Conservation Service (SCS), and the two-parameter gamma distribution when tested on synthetically generated data and the data from four catchments from India and one from Turkey. The workability of the proposed approach was also tested for partial and no data availability situations