Milad Jajarmizadeh

Impact of Direct Soil Moisture and Revised Soil Moisture Index Methods on Hydrologic Predictions in an Arid Climate

The soil and water assessment tool (SWAT) is a physically based model that is used extensively to simulate hydrologic processes in a wide range of climates around the world. SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. The objective of the present study was to compare the performance of two approaches used for the calculation of curve numbers in SWAT, that is, the Revised Soil Moisture Index (SMI), which is based on previous meteorological conditions, and the Soil Moisture Condition II (SMCII), which is based on soil features for the prediction of flow. The results showed that the sensitive parameters for the SMI method are land-use and land-cover features. However, for the SMCII method, the soil and the channel are the sensitive parameters. The performances of the SMI and SMCII methods were analyzed using various indices. We concluded that the fair performance of the SMI method in an arid region may be due to the inherent characteristics of the method since it relies mostly on previous meteorological conditions and does not account for the soil features of the catchment.

Modeling daily stream flow using plant evapotranspiration method

In hydrological models, soil conservation services (SCS) are one of the most widely used procedures to calculate the curve number (CN) in rainfall run-off simulation. Recently, another new CN accounting procedure has been mentioned, namely the plant evapotranspiration (ET) method or simply known as the plant ET method. This method is embedded in the Soil and Water Assessment Tool (SWAT) model which has been developed for watersheds covered by shallow soils or soils with low storage characteristics. It uses antecedent climate and plant evapotranspiration for calculation of daily curve number. In this study, the same method had been used to simulate the daily stream flow for Roodan watershed located in the southern part of Iran. The watershed covers 10570 km2 and its climate is arid to semi-arid. The modeling process required data from digital elevation model (DEM), land use map, and soil map. It also required daily meteorological data which were collected from weather stations from 1988 to 2008. Other than that, the Sequential Uncertainty Fitting-2 (SUFI-2) algorithm was utilized for calibration and uncertainty analysis of daily stream flow. Criteria of modeling performance were determined through the Nash-Sutcliffe and coefficient of determination for calibration and validation. For calibration, the values were reported at 0.66 and 0.68 respectively and for validation; the values were 0.51 and 0.55. Moreover, percentiles of absolute error between observed and simulated data in calibration and validation period were calculated to be less than 21.78 and 6.37 (m3/s) for 95% of the data. The results were found to be satisfactory under the climatic conditions of the study area.

Prediction of Surface Flow by Forcing of Climate Forecast System Reanalysis Data

Meteorological data are key variables for hydrologists to simulate the rainfall-runoff process using hydrological models. The collection of meteorological variables is sophisticated, especially in arid and semi-arid climates where observed time series are often scarce. Climate Forecast System Reanalysis (CFSR) Data have been used to validate and evaluate hydrological modeling throughout the world. This paper presents a comprehensive application of the Soil and Water Assessment Tool (SWAT) hydrologic simulator, incorporating CFSR daily rainfall-runoff data at the Roodan study site in southern Iran. The developed SWAT model including CFSR data (CFSR model) was calibrated using the Sequential Uncertainty Fitting 2 algorithm (SUFI-2). To validate the model, the calibrated SWAT model (CFSR model) was compared with the observed daily rainfall-runoff data. To have a better assessment, terrestrial meteorological gauge stations were incorporated with the SWAT model (Terrestrial model). Visualization of the simulated flows showed that both CFSR and terrestrial models have satisfactory correlations with the observed data. However, the CFSR model generated better estimates regarding the simulation of low flows (near zero). The results of the uncertainty analysis showed that the CFSR model predicted the validation period more efficiently. This might be related with better prediction of low flows and closer distribution to observed flows. The Nash-Sutcliffe (NS) coefficient provided good- and fair-quality modeling for calibration and validation periods for both models. Overall, it can be concluded that CFSR data might be promising for use in the development of hydrological simulations in arid climates, such as southern Iran, where there are shortages of data and a lack of accessibility to the data.

Flood Frequency Analysis Based on Gaussian Copula

Flood duration, volume, and peak flow are important considerations in flood risk analysis and management of hydraulic structures. The conventional flood frequency analysis assumed that the marginal distribution functions of flood parameters follow a certain pattern. However, such assumption is impractical because a flood event is multivariate and the flood parameter distributions can be different. These discrepancies were addressed using bivariate joint distributions and Copula function which allow flood parameters having different marginal distributions to be analyzed simultaneously. The analysis used hourly stream flow data for 45 years recorded at the Rantau Panjang gauging station on the Johor River in Malaysia. It was found that flood duration and volume are best fitted by the generalized extreme value distribution while peak flow by the Generalized Pareto. Inference function for margin (IFM) method was applied to model the joint distributions of correlated flood variables for each pair and the results showed that all the calculated θ values were in acceptable range of Gaussian Copula. By horizontally cutting the joint cumulative distribution function (CDF), a set of contour lines were obtained for Gaussian Copula which represented the occurrence probabilities for the joint variables. Also the joint return period for pair of flood variables was calculated.

Contribution of Climate Forecast System Meteorological Data for Flow Prediction

One of the difficulties in hydrological models is the collection of meteorological data especially in large catchments. This issue is more obvious in the case of semidistributed hydrological models, which need long periods of meteorological data such as precipitation and temperature to perform watershed practices such as hydrological cycle and sustainable development. Moreover, this issue is significant for arid regions that generally have sparse data and lack of weather stations, especially in developing countries. In this study, Climate Forecast System Reanalysis model had been applied for Soil and Water Assessment Tool hydrologic simulator generate daily flow prediction for a catchment including dry climate. Required data for development of hydrologic simulator have been prepared in Geographic Information System database. Then, model has been calibrated via semiautomatic method namely Sequential Uncertainty Fitting 2. Results of study show that application of renewed meteorological data is promising for flow prediction. Also, accuracy of model according to Nash and Sutcliffe obtained efficiency of 0.54 for calibration and 0.45 for validation, respectively. In summary, it can be concluded that results’ quality classified as good for calibration and fair for validation according to Nash and Sutcliffe efficiency.

An assessment of a proposed hybrid neural network for daily flow prediction in arid climate

Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN) has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers--input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristicmethod was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the networks optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict streamflow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfallrunoff relations.

An Overview: Flood Catastrophe of Kelantan Watershed in 2014

One of the challenging topics in Malaysia is flood occurrence, which have important impacts in human life and socioeconomic subjects. Malaysia, periodically, have faced with huge floods since previous years. Kelantan river basin, which located in the northeast of Peninsular Malaysia, is prone to flood events in Malaysia. Kelantan River has been badly affected with flood during recent monsoon season on December 2014 due to heavy monsoons rainfall and climate change issues. In this study, available rainfall and water-level data are analyzed and presented based on the flood event on December 2014. Generally, the flood area affected includes the districts of Kota Bharu, Kuala Krai, Machang, Pasir Mas, Pasir Puteh, Tanah Merah, Gua Musang, and Tumpat at Kelantan State. In the northeast monsoon season, the Kelantan State suffers from two phase of flood. The first phase began on December 14–17, 2014, and the second phase occurred on December 20–24, 2014. A comparison between accumulated rainfall on December and whole year of 2014 at Gagau station shows that contribution of rainfall on December is roughly 50 % of all of 2014. Overview of water-level results at Kelantan watershed shows that all areas are involved with highest record in 2014 in comparison with previous decades except Golok area. Results of water-level ranges show that most of the parts of Kelantan watershed are involved with over danger values for flood in 2014, which Lebir and Kelantan rivers have high increasing. In conclusion, it is suggested that there is a need to have study on flood mitigation and recognition of critical hydrological phenomena for sustainable strategies in Kelantan watershed. Consequently, this research provides primary information as baseline study for upcoming research for water resource management projects.

Comparison of a Hybrid Neural Network and Semi-distributed Simulator for Stream Flow Prediction

Hydrological models are widely used for the simulation of stream flow in order to aid water resources planning and management in catchment or river basin. Numerous hydrological models have been developed based on different theories. Performance of such models depends on hydro-climatic setting of a catchment. In the present study, performance of a widely used physically based distributed model known as Soil and Water Assessment (SWAT) and a data-driven model, namely hybrid artificial neural network (HANN), has been evaluated to simulate stream flow in an arid catchment located in the south of Iran. Data related to topography, hydrometeorology, land cover, and soil were collected and processed for this purpose. The models were calibrated and validated with same time period to evaluate the advantage and disadvantages of different models. The results showed SWAT outperformed HANN in terms of relative errors such as Nash-Sutcliffe efficiency and percent of bias during model validation. Other error indicates, namely root mean square error (RMSE), mean square error, and mean relative error (MRE), were found close to zero for SWAT during both model calibration and validation. The study suggests that both models have their own promising flow prediction due to their own features and capabilities for daily flow.

Identification of Vulnerable Regions for TNB’s Electric Substations during Flood in Peninsular Malaysia

Flood is usually an environmental hazard which has been increased in recent years by forcing the pushing factors such as climate change and urbanization. This study presents flood-prone area related to the electric substations for Tenaga Nasional Berhad (TNB) in Peninsular Malaysia. The objective of this research was to identify the related regions that the electric substations are in vulnerable condition by flood risk. For this research, two types of maps are generated, namely flood inundation map (FIM), which indicates the area and capacity of the flood, and flood hazard map (FHM), which provides the area, depth, velocity, and extension of the flood for the TNB’s location of substation. For this issue, different classes of substations are involved in analysis, namely transmission main intake (PMU), main distribution (PPU), main switching station (SSU), and distribution substation (PE). An integration of TNB’s substation maps performed with FIM and FHM due to identify substations which are in flood-prone regions. Generally, result shows that Kelantan is classified as the highest flood-prone region for TNB’s infrastructures especially for PMU which they are affected by flood. Kelantan, Terengganu, and Perlis are involved with the highest flooded, respectively, based on PPU and SSU infrastructure. Finally, for PE substations, Kelantan, Perlis, and Terengganu have the highest contribution for flooded substations for TNB’s structures.

Gross Pollutant Traps: Wet Load Assessment at Sungai Kerayong, Malaysia

The purpose of this project is to carry out assessment on the effectiveness and performance of Gross Pollutant Traps (GPTs) stormwater quality control in the urban areas. The study aims to provide a management and planning tool for effective management of the gross pollutants in the urban areas specifically in River of Life (ROL) project. ROL project is a Malaysian Government initiative under the Economic Transformation Program. One of the program in the greater Klang Valley is to transform Klang River into a vibrant and livable waterfront by the year 2020. The main river in ROL catchment is Sungai Klang (upper catchment), with main tributaries Sungai Gombak, Sungai Batu, Sungai Jinjang, Sungai Keroh, Sungai Bunus, Sungai Ampang and Sungai Kerayong. This paper objective is to study the gross pollutant wet load at Sungai Kerayong 1 and Sungai Kerayong 2 which is located at the downstream location of the ROL project. The result shows that Sungai Kerayong 2 produced higher gross pollutant wet load (8025.33 kg/ha/yr) than Sungai Kerayong 1 (4695.12 kg/ha/yr). This could be due to high contributions amounts of gross pollutant traps from residential area, the degree of develop area, and also the location of the river itself related to climate and rainfall.