A Conceptual Rainfall-Runoff Model Using the Auto Calibrated NAM Models in the Sarisoo River

This paper describes the application of a conceptual rainfall runoff model to investigate the peak and monthly flows at the Sarisoo River Basin on the North West of Iran. The model was calibrated using measured stream flow data and then validated for three years. Calculations of level and time of peak flows are vital for designing structures downstream in the catchment areas. The simulated peak flows were occurring in the months of February in 2003, 2006 and 2007 with approximate values of 6.32, 9.35 and 6.13 m3s-1 respectively. After calibrating 9 NAM parameters using record data of daily rainfall, monthly evaporation and daily discharge in the period of 1th October 2003 to 31th March 2006 and validating the model daily discharges were calculated for 12 years. The outputs of the calibrated model are able to be used in the assessment of water resources management models like Mike Basin, WEAP… because they normally work based on monthly flows with a large time horizon. The results show that monthly averages of mean, maximum and minimum flows are about 10%, 2% and 33% less than daily computed Nash–Sutcliffe coefficients, all calculated over a period of 12 years. The optimum values of the 9 NAM parameters obtained during the calibration procedure are presented. The reliability of MIKE11 NAM was evaluated based on the Nash–Sutcliffe coefficient (R2), Root Mean Square Error (RMSE), peak flow (RMSE) and low flow (RMSE). The R2 obtained during this study is 0.74

Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow

Different statistical, non-statistical and black-box methods have been used in forecasting processes. Among statistical methods, K-nearest neighbour non-parametric regression method (K-NN) due to its natural simplicity and mathematical base is one of the recommended methods for forecasting processes. In this study, K-NN method is explained completely. Besides, development and improvement approaches such as best neighbour estimation, data transformation functions, distance functions and proposed extrapolation method are described. K-NN method in company with its development approaches is used in streamflow forecasting of Zayandeh-Rud Dam upper basin. Comparing between final results of classic K-NN method and modified K-NN (number of neighbour 5, transformation function of Range Scaling, distance function of Mahanalobis and proposed extrapolation method) shows that modified K-NN in criteria of goodness of fit, root mean square error, percentage of volume of error and correlation has had performance improvement 45% , 59% and 17% respectively. These results approve necessity of applying mentioned approaches to derive more accurate forecasts

Using the Prey-Predator Equation for the Water Allocation Problem and Its Comparison with Conventional Water Allocation Methods, A Case Study of The Atrak River Basin

Allocating the water resources in a basin to several stakeholders is a common issue at both national and international levels. Despite the many extensive studies carried out on the water allocation problem, a method still needs to be developed for the equitable and sustainable allocation of water to all the stakeholders in a shared basin. Over the last few decades, a number of mathematical methods such as the Nash bargaining, area monotonic, equal loss, and Kalai-Smorodinsky solutions have been applied to the problem of conflict resolution that are collectively known as optimization methods, each one yielding a single solution. In this study, a novel mathematical model based on the prey-predator equation is employed for water allocation to resolve conflicts among stakeholders in the agricultural sector. The advantage of the proposed model lies in its capability to calculate balanced allocation of irrigation water to stakeholders aimed at the sustainable development of the region. The model calculates the stakeholders’ profits and payoffs and determines their interactions in a time series. Finally, the model is employed for resolving conflicts in the Atrak River basin in the northeast of Iran which is now facing a serious water tension. Comparison of the results obtained from the proposed model and those from four conventional conflict resolution methods applied to the same basin implies the superiority of the proposed model in yielding dynamic solutions rather single ones

Reservoir daily inflow simulation using data fusion method

Information about the parameters defining water resource availability is a key factor in its management which improves the operation policies for water resource systems. One of the most important parameters in this area is river streamflow. In this research, two different strategies of data fusion were tested for daily inflow simulation of the Taleghan Reservoir. Four artificial neural network models as well as two Hammerstein–Wiener models were used as individual simulation models. The results showed that the data fusion method has the capacity to improve substantially the results of individual simulation models. The individual models were also tested in combination with a weather generator model which was used to generate 100 yr of daily temperature and precipitation data. The results demonstrated that although some models performed well in calibration and validation phases, in combination with a weather generator they could result in eccentric outcomes. This research also showed that the data fusion method can combine the results of single simulation models to improve the final estimate and decrease the bandwidth of errors

Developing an Adaptive Neuro-fuzzy Model to Predict the Maximum Daily Discharge Using 5-day Cumulative Rainfall

Rainfall is one of the factors involved in increasing soil moisture. Soil moisture, in turn, is a key parameter in the rise and fall of water in the soil which plays an important role in the rainfall-runoff process. It, therefore, requires to be carefully investigated in order to determine its effect on peak flood discharge. One method commonly used for this purpose is the CN-NRCS (curve-number method). Based on this approach, the sum of rainfalls during the 5 days preceding the flood event is taken to represent the soil moisture conditions prior to the event. Given the fact that natural phenomena are always associated with different degrees of uncertainty due to the involvement a multitude of factors, an efficient method for investigating their behavior is the Adaptive Neuro-Fuzzy Intelligent System (ANFIS). Here, we used ANFIS for determining the effect of rainfalls over the five days prior to the flood event in order to predict the maximum daily flood discharge. The model employed the two training algorithms of Back Propagation and Hybrid, which were then tested using different statistical tests and the results were analyzed for each model. The results indicate that the hybrid method outperformed the back propagation method. The best correlation coefficient of the 5-day model was 0.985 and the RMSE (Root Mean Squared Error) was 0.162 in the hybrid method

Comparison of multi linear regression, nonparametric regression and times series models for estimation and prediction of evaporation values

In order to simulate time series, various methods are presented such as times series models (AR, ARMA and ARMAX), multi-linear regression (MLR), and nonparametric regression (K-NN). In this research, performance of these models for estimation of missing values and prediction of future values of evaporation series (from open water) were assessed. ARMAX model with standardized input time series of Tmin, Tmax, Tav, Wind, RH, and sunshine hours, outperformed the other models and the K-NN and MLR were in the next ranks, respectively. Also after the principal component analysis, ARMAX model showed noticeable deviation for estimating missing values and MLR and K-NN in calibration and MLR in validation stage performed the best. For short-term predictions, ARMAX model has the best performance, but MLR performed better in long-term predictions, Time series models were not robust for long term predictions

Using GRACE satellite observations for separating meteorological variability from anthropogenic impacts on water availability.

Gravity Recovery and Climate Experiment (GRACE) observations provide information on Total Water Storage Anomaly (TWSA) which is a key variable for drought monitoring and assessment. The so-called Total Water Storage Deficit Index (TWSDI) based on GRACE data has been widely used for characterizing drought events. Here we show that the commonly used TWSDI approach often exhibits significant inconsistencies with meteorological conditions, primarily upon presence of a trend in observations due to anthropogenic water use. In this study, we propose a modified version of TWSDI (termed, MTWSDI) that decomposes the anthropogenic and climatic-driven components of GRACE observations. We applied our approach for drought monitoring over the Ganges-Brahmaputra in India and Markazi basins in Iran. Results show that the newly developed MTWSDI exhibits consistency with meteorological drought indices in both basins. We also propose a deficit-based method for drought monitoring and recovery assessment using GRACE observations, providing useful information about volume of deficit, and minimum and average time for drought recovery. According to the deficit thresholds, water deficits caused by anthropogenic impacts every year in the Ganges-Brahmaputra basin and Markazi basins is almost equal to an abnormally dry condition and a moderate drought condition, receptively. It indicates that unsustainable human water use have led to a form of perpetual and accelerated anthropogenic drought in these basins. Continuation of this trend would deplete the basin and cause significant socio-economic challenges