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Showing 2 results for Maximum Discharge
Impact of Climate Change on Maximum Discharges: Case Study of Aidoghmoush Basin, East Azerbaijan
P. Ashofteh, A. R. Massah Bouani,
Volume 14, Issue 53 (10-2010)
Abstract
Climate change has different impacts on extreme events such as flood and drought. However, in Iran there are few researches about the impacts. This research was aimed to investigate maximum annual discharge (magnitude and frequency) that may occur due to climate change in Aidoghmoush Basin during 2040-2069 (2050s). At first, monthly temperature and precipitation data of HadCM3 model under the SRES emission scenario, namely A2 , was provided for the basin. Then, these data were downscaled spatially and temporally to Aidoghmoush basin by proportional and change factor downscaling methods. Results showed that the temperature increases (between 1.5 to 4) and the precipitation varies (30 to 40 percent) in 2040-2069 compared with baseline period (1971-2000). A semi-conceptual model (IHACRES) for simulation of daily runoff was calibrated. Downscaled temperature and Precipitation for 2050s were introduced to IHACRES and daily runoff was simulated for the future. Probability distribution was fitted to maximum annual discharge series and the maximum discharge regime of the future was compared with the baseline. Results indicated that climate change affects Maximum discharge in the regime of the basin. Also, the analysis showed that the intensity of maximum discharges for the time period less than 50 does not show any significant difference but by increasing the return period, the intensity increases in future periods. Moreover, it was shown that the probability of maximum discharges with constant intensity will decrease in the future compared to the baseline.
Comparison of Artificial Neural Network, Decision Tree and Bayesian Network Models in Regional Flood Frequency Analysis using L-moments and Maximum Likelihood Methods in Karkheh and Karun Watersheds
K. Ghaderi, B. Motamedvaziri, M. Vafakhah, A.a. Dehghani,
Volume 25, Issue 4 (3-2022)
Abstract
Proper flood discharge forecasting is significant for the design of hydraulic structures, reducing the risk of failure, and minimizing downstream environmental damage. The objective of this study was to investigate the application of machine learning methods in Regional Flood Frequency Analysis (RFFA). To achieve this goal, 18 physiographic, climatic, lithological, and land use parameters were considered for the upstream basins of the hydrometric stations located in Karkheh and Karun watersheds (46 stations with a statistical length of 21 years). The best Probability Distribution Function (pdf) was then determined using the Kolmogorov-Smirnov test at each station to estimate the flood discharge with a return period of 50-year using maximum likelihood methods and L-moments. Finally, RFFA was performed using a decision tree, Bayesian network, and artificial neural network. The results showed that the log Pearson type 3 distribution in the maximum likelihood method and the generalized normal distribution in the L moment method are the best possible regional pdfs. Based on the gamma test, the parameters of the perimeter, basin length, shape factor, and mainstream length were selected as the best input structure. The results of regional flood frequency analysis showed that the Bayesian model with the L moment method (R2 = 0.7) has the best estimate compared to other methods. Decision tree and artificial neural network were in the following ranks.

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