JOURNAL OF WATERSHED MANAGEMENT RESEARCH
Year:2015 | Volume:5 | Issue:10|Papers Count:13

The occurrence of a great number of floods in rivers on the one hand, and industrial, commercial and social activities’ accumulation, just near the rivers, on the other hand, reveal the obligation of applying appropriate flood management strategies (before, during and after the flood). The role and importance of employing Flood management strategies on flood damage reduction could be observed clearly, at the Nekaroud River over flow which had occurred in 1999. In this year two floods occurred in June and August in Nekarood’s basin, respectively. The first one destroyed the city center’s bridge and obstructed the river which finally caused back water at the second flood which was the reason of extremely major damage in the city of Neka. In this article, the effects of city main bridge destruction on exacerbation of flood damage, in August 1999, have been modeled using HEC-RAS software; therefore, the geometric characteristic of 25 river cross sections at the upstream of the bridge, Manning's roughness coefficient, flow type and the boundary conditions, were inputted. Besides, the model was calibrated using discharge-stage at the existing hygrometry station on the river. Then, the water surface profile were calculated in two different conditions first normal river condition and 70 percent blockage at the bridge section, then, compared with measured water stage of the flood occurred in August. The results show that the amount of the second flood discharge was not the original factor which led to such extremely flood damage, but it was the bridge blockage, as a consequence of the second flood. Eventually, a flood with 970 CMS caused severe damage equal to a flood with 2450 CMS and if the bridge spans were cleaned after the first flood, no back water and such damage would not be happened at the second flood.

Storage dams are constructed for several aims such as; water supply, flood control and energy generation. Dams are being threat by breakage and cause severe financial and human losses in downstream. If dam breakage phenomenon and its resulted effects are furcated before dam construction, the losses will be reduced in the future. In this study, the Polrood dam breakage due to overtopping has been investigated by BREACH GUI model. Results showed that the flood hydrograph due to dam breakage possessed a discharge peak of 47253 m3/s. This peak occurs 27 hrs after flood beginning an hour after overtopping. Then, flood routing was carried out in order to determine the effects of dam breakage by HEC-RAS model. The results of flood routing showed that the discharge peak arrived at the last cross section of 15 km distances in the dam downstream after 54 minute which possessed a decrease of 62%. The overlay of floodplain maps with Google Earth model showed that 18 villages and some regions of Kelachay and Rahim-Abad cities are affected by flooding in dam downstream. The flood resulted of this dam breakage, also causes river erosion near to the dam.

Instantaneous unit hydrograph (IUH) is a hydrograph that has used for calculation of direct runoff hydrograph in watersheds and recent addition to formal methods, Shannon entropy theory consider for calculation of direct runoff hydrograph. If the system disorderly is measured in terms of entropy then the probability distribution of the events, provides in terms of entropy. In this theory, defined travel time as a random variable, the maximizing the entropy on based this parameter and also specifies constraints, the least-biased probability density function, determining parameters of the IUH and finally obtaining applicable IUH equation. In this study, six events of Lighvan watershed has been studied that located in Northwest Iran. After calculating travel time and determining parameters on based entropy, derivation applicable IUH equation and calculated direct runoff hydrograph. For estimated and more evaluated IUH and direct runoff hydrograph has used Gamma distribution that result showed good accuracy of the entropy theory. The results indicate acceptable performance of the entropy model shows the average correlation coefficient (0.828), mean error of peak (17.35%), entropy index (2.47 knot) and nash-sutcliff value (NS=0.742) in verification events.

Juneghan-Farsan, a north-Karun sub-basin, is located in Chaharmahal and Bakhtiyari province-Iran, in which various storage dams are either built, under construction, or are subject of ongoing studies. In this research, the influence of storage dam construction on downstream flood reduction is investigated using flow routing modeling. The results indicated that complete construction of all considered dams in Juneghan-Farsan basin will reduce maximum flood discharge in downstream up to 34%. That was higher for floods with lower return periods (Two years). The results also showed that the off-stream storage dams would be less effective in reducing peak flood discharge. The study showed that 20% increase in the spillway length had no significant effect on the maximum output rate and cause on average only a 6% increase in it.

The most important issues of watershed management, is predicting hydrological processes. Using new models in this field can help to management and proper planning. In addition, predicting of river flow, especially in flood conditions, will allow the authorities to reduce flood damage with the preparation. One of the ways which have recently been used to predict and estimate the flow rate of rivers is neuro-fuzzy model. Neuro-fuzzy with review and determine the relationships between inputs and output, estimate the desired output deals. In this study, the three years values of the daily rainfall and discharge of different stations in Taleghan watershed were used as input to the neuro-fuzzy model and according to the statistical coefficients (RMSE, R2 and E), the best structure and inputs composition of neuro-fuzzy to predict the river flow was determined. Results demonstrated that the best estimates were of the Gaussian fuzzfier. Although different input modes, provided acceptable results, Best estimates with coefficients RMSE and R2 (training data 0.02 and 0.98-checking data 0.06 and 0.87), was discharge of Mehran and Joestan and previous day discharge of Garab and Dehdar. The results indicated that neuro-fuzzy can predict the daily flow with high accuracy and can be used in watershed management and flood control.

Rainfall-runoff modeling is an essential process and very complicated phenomena that is necessary for proper reservoir system operation and successful water resources planning and management. There are different methods like conceptual and numerical methods for modeling of this process. Theoretically, a system modeling required explicit mathematical relationships between inputs and outputs variables. Developing such explicit model is very difficult because of unknown relationship between variables and substantial uncertainty of variables. In this case, Data driven methods which are based on imprecise conditions and evolutionary algorithms have shown capabilities in many nonlinear systems identification and control issues. One of these intelligent methods is Group Method of Data Handling (GMDH). This method produce complex model during the performance evaluation of input and output information sets increasingly. So in this research we have developed a model based on GMDH for rainfall-runoff modeling in Polrood basin located in North of Iran in Guilan province. Results have evaluated using statistical criteria and compared with an artificial neural network (ANN) model. Results have shown the high performance of two methods for rainfall runoff modeling in dairy scales. Based on statistical criteria, experiment results indicates that the GMDH model was powerful tool to model rainfall runoff time series and can be applied successfully in complex hydrological modeling.

In purpose to performance programs soil protection and reduce sediment, also calculation and design of dam volume in introduction store dams, have necessity that evaluation and calculated the rate of sediment production in a watershed. Generally erosion and sediment transport is of most complex issues the hydrodynamic that not possible simply, determination equations governing because effects of various parameters. About attention in potential artificial intelligence in identify the relationship between variables input and output of a problem without taking the physics of the problem and because swoon of physical models and mathematical in modeling of sedimentary processes too, can be used in modeling sediment transport problem. The purpose of this study was to obtain suitable algorithms with using of artificial neural networks feed-forward back propagation, fitting and Cascade Forward back prop to intent estimate the sediment rate. In this intent for estimate the amount suspended sediment, used of discharge - precipitation and sediment data monthly. Intransitive of recitation that suspended sediment data in the output (Cham anjir station) is more appropriate of the distribution. Among the three networks used in this study was more appropriate to estimate the amount of sediment fitting network. Thirteen the algorithm used in this study was selected TRAINLM as the best algorithm, with a correlation coefficient R=0.99, RMSE=0.01.

Nowadays, the accurate estimation of rivers suspended sediment load (SSL), from various aspects, such as water resources engineering, environmental issues, water quality and so on is important. In this regard, because of various roles of fixed and dynamic variables of watersheds, the watershed hydrological models have not showen a proper efficiency in statimation of SSL. Also, the most SSL studies are based on only flow discharge variable whereas the results of the present study have proved that the efficiency of these modeles is very poor. On the other hands, the parameters such as rainfall type, year seasons and flow hydrograph shape have important role in watershed sediment yield that were ignored in the most SSL simulations. In the present study, multi layers perceptron neural network and hydro-meteorological data (daily flow discharge, suspended sediment concentration, daily rainfall and temperature) of Karaj dam watershed in a 30-year period (1981 to 2011) were used to estimate daily suspended sediment concentration of Sierra station. Due to the role of seasonal changes  and flow conditions in sediment yield and sediment transport of the watershed, based on rainfall regime, hydrograph condition and runoff type, the data used in this study were first seperated into 5 groups and then for each group, a separate model was designed. In order to increase the generalization ability of the neural network models, self-organizing map (SOM) and Silhouette coefficient were used for data clustering and determination of the optimal number of clusters respectively. The research results showed that the use of daily precipitation and temperature variables along with flow discharge and data separating based on watershed time and hydro climatic conditions has had an important role in increasing the accurate estimation of the river sediment. In this regard, among the models, the maximum calculated error is when only a single model is used for all year seasons. The research results and its used structure can be used as a pattern to estimate and to forecast many environmental variables of watersheds.

Statistical downscaling methods are widely used for prediction of climatic variables e.g. temperature because of importance of these factors in environmental planning and management. In this study, the performance of Statistical Downscaling Model (SDSM) was investigated to predict temperature. The input data of the study include minimum, maximum and mean temperature of Kerman and Bam Synoptic stations, NCEP (National Centers for Environmental Prediction) data and the A2 and B2 emission scenarios HadCM3 for the reference period, 1971-2001. The first 15 years data (1971-1985) was applied for the calibration and the second 15 years data (1986-2001) for model validation. Temperature for three periods including 2010-2039, 2040-2069 and 2070-2099 was predicted and then compared with the temperature data of reference period i.e.1971-2001 using HadCM3A2, B2 data. Statistical measures of model performance such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Nash-Sutcliffe efficiency (NS) and the analysis of output results from SDSM model shown that this model is able to predict temperature indexes more accurately in arid climate than in hyper-arid climate. The results indicate temperatures rising in all months for both stations.

Due to spatial correlations between climatic variables and natural features like altitude, the gradients finding is the first option in zoning of them. Various gradients in large area and lack of knowledge of them is resulted in applying of geostatistical and deterministic interpolation techniques by researchers. Geographically Weighted Regression (GWR) is a method which can identify local regressions as well and in spite of gradient increases interpolation accuracy. Accordingly, this research was conducted in order to evaluation of GWR’s accuracy than another interpolation methods based on 30 years average of air temperature and relative humidity data of 240 synoptic and climatologically station in Iran. The results of this research on air temperature which was a function of altitude showed that GWR method had significant difference than other methods, while there was no significant difference on relative humidity and These results may be due to no specific correlation between altitude and relative humidity.

Area of land under irrigation has increased in the last decade due to the increasing population, need plenty of food and lack of surface water resources, this policy has changed soil and water uses. Rainwater enters soil through tracks and pores in soil surface and as hit to impervious layer make groundwater table. In previous years drought caused severe decrease in surface water resources and increasing use of groundwater which resulted in water table drawdown and decline of groundwater quality. Considering these points, and groundwater importance and role, groundwater quantity variation during 1998, 2002 and 2007 and qualitative factors changes include EC, Cl, Hco3, K, Mg, Na, So4, TDS, TH, pH and at the beginning and end of the period were studied in a part of Babol-Amol plain located in Caspian Sea Basin with GIS techniques and changes in each of the qualitative factors were determined with using Mann-Kendall exam. For groundwater quality data of 10 parameters including. The results show that water table drawdown is not considerable and there is relative improvement in water quality.

Landslide is a type of hillside movement which leads to the movement of surface materials in gradient hillsides. In Northern hillsides of Alborz mountain range, by resean of occurring local condition and humid climate, can be observed a high land slide hazard potential as compared with the other parts. Among of these regions is Shalmanrood watershed that, it was located in Langrood city and over 42 land slide were recorded in this area. In this study, For Land slide hazard zonation two regression and Analytical hierarchy Process (AHP) methods were applied. At first, Using ARC GIS Software the information layers containing of land use, slope, lithology, distance from road, distance from river, distance from fault were provided and then, the land slide hazard maps with applying two mentioned methods, were provided on five classes (very low, low, medium, high and very high hazard zone). Order to assessment of two applied methods in this study, the land slide hazard zonation map from each method was compared with land slide scattering map (Land slide index). Results showed that, two varieties regression method is more suitable than AHP method. Also, Result showed that land use (garden of tea). Lithology and slope (45-65%) were effective factors on the land slide hazard occurring.

Lands unsuitable use effects on vegetation and spatial variability of soil characteristics. In this regard, geostatistics method according to the modeling of factors cause to better evaluation of the parameters. Therefore, this research was investigated the role of the utilization on soil parameters changes as suitable tools for management in Baladeh basin in central Elborz mountains. The different utilization intensities as critical, key and reference sites were selected based on proximity to water sources and traffic routes. The sampling was done by systematic method by five transects 100cm in each site after determination of plant types. The points of transects intersection was obtained as sampling points (25 points) and soil samples was taken from the surface layer (0-10cm). Then, soil factors such as OC, N, P, K, pH and EC was measured in lab. Soil spatial variability was done by geostatistics method and GS+ 5 (Gamma Design Software) software. The results showed that OC and N were decreased due to utilization intensity in critical sites but pH was increased. Also, geostatistics analysis showed that spatial variation of soil parameters were more in critical sites to other sites due to destroyer effect of utilization and the spatial dependence in this site was less too.