Journal of Watershed Management Research

   The use of retarding reservoirs is one of the methods of urban flood management structures to reduce the volume or intensity of floods as well as repulsed of flooding flows. In this research, the effect of intercity retarding reservoirs on flood peak discharge and the dimensions of conduits was analyzed in one of the subcatchment of Sari (Gozarkhan) for this purpose, after determine the designing rainfall with 5 years return period based on SCSII's method and the concentration time of subcatchments was calculated by using the experimental methods of Kerby and Kirpich, the simulation of the network without reservoirs was performed by using SWMM software and the flood peak discharge and the dimensions of conduits were determined then, with flood retarding reservoirs, re-simulation and the effect of reservoirs on peak of output discharge and the dimensions of conduits were determined. In order to determine the volume of flood retarding reservoirs, first by considering several quantity for volume and drawing output discharge and  variations graphs in relation to the volume of the reservoir, for each reservoir, the volume with the lowest rate of variation of the discharge was considered as the optimal amount. The results show that the retarding reservoirs reduce the flood rate in output node 3266, 86% and in output node 4004, 89%, as well as the dimensions of the conduits (average cross-section of conduits) reduce 57% in study area.
 
 

In this paper, the precipitation and runoff time-series data of the Shaharchay River basin from 2000 to 2017 were simulated by using a novel hybrid artificial intelligence (AI) technique. In order to develop this AI model, the extreme learning machine (ELM), differential evolution (DE) and wavelet transform (WT) are combined and then the SAELM and WASAELM hybrid models are provided. Initially, the most effective lags of the time-series data are distinguished using the autocorrelation function. After that, using these lags, seven artificial intelligence models are defined for each of the SAELM and the WSAELM models. Additionally, 70% of the observational data are employed for training the artificial intelligence models and the rest (30%) for testing them. For WSAELM7 as the best model, the values of R², the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) for simulating precipitation are yielded 0.967, 0.208 and 0.965, respectively. Furthermore, a sensitivity analysis exhibits that the lags (t-1), (t-2) and (t-12) are regarded as the most effective input lags. Ultimately, an uncertainty analysis is carried out for the superior models.
 

Extended Abstract
Introduction and Objective: The present study was conducted to conceptualize and evaluate the health of the Asiabrood Watershed in Chalus Township, Mazandaran Province, Iran.
Material and Methods: The existing regional data, including climatic, hydrologic, erosion, sedimentation, and economic and social data, were collected and analyzed. In addition, several field visits were also made to make a general assessment of the ecological, economic, and social situation of the region and to complete the information on the study watershed. Then, the watershed health was assessed using the conceptual model of pressure, state, and response at the sub-watershed scale. Accordingly, the pressure index was first investigated by analyzing the driving forces of human activities and climate change over the study watershed. Then, the current state of the natural environment and the watershed performance to the pressures was analyzed as the status index. Further, the response index of the Asiabrood Watershed was calculated as a criterion for expressing the degree of community response or different outcomes of the watershed to the changes and concerns imposed on the watershed system.
Results: The results showed that the total health index of the Asiabrood Watershed with respective and relative contributions of pressure, state, and response indices of 18, 39, and 43% and an overall health index of 0.50 has a moderate health condition.
Conclusion: Although the health status of the Asiabrood Watershed is in moderate condition, the health status of the study watershed may decline, and, of course, adverse responses are possible if appropriate management policies are not adopted.