Identification of an appropriate low flow forecast model\ud for the Meuse River

This study investigates the selection of an appropriate low flow forecast model for the Meuse\ud River based on the comparison of output uncertainties of different models. For this purpose, three data\ud driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression\ud model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to\ud be represented by the difference between observed and simulated discharge. The results show that the ANN\ud low flow forecast model with one or two input variables(s) performed slightly better than the other statistical\ud models when forecasting low flows for a lead time of seven days. The approach for the selection of an\ud appropriate low flow forecast model adopted in this study can be used for other lead times and river basins\ud as well

Forecasting future irrigation water sustainability in upper Bernam river basin Malaysia

The Bernam river basin where Proton city is located is rapidly developing, changing from agriculture based to an industrial area. Land development can be associated with increased impervious areas causing increase in surface runoff and decrease in ground water recharge. The study area is the main source of irrigation water supply for paddy fields in the downstream of the watershed. The required water for paddy irrigation should be made available continuously for double cropping via maintaining high base flows so that enough water is available for irrigation during the dry season. Soil and Water Assessment Tool (SWAT) model was used to study the effects of land-use changes on water resources sustainability. The study results confirmed that change of land-use pattern altered the runoff volume. In the year 2020 runoff is predicted to increase in the rainy season due to large increase of land use changes especially urban and forest, which then accelerate runoff and decrease base flow due to an increase in the impervious area. Providing such information in AgriGRID will help planners and decision makers to take the effect of land-use changes into account when formulating future plans for land development and include some structural best management practices (BMPs) within their future plan to control and manage water resources in the watershed

Assessing the damming effects on runoff using a multiple linear regression model: A case study of the Manwan Dam on the Lancang River

AbstractThe Lancang River in Yunnan Province, with a length of 1170km and a 1780-m drop from northwest to southeast, is the most controversial river in southwest China because 14 cascade hydropower stations have been planned on the main waterway. The Manwan Dam, the first of the 14 dams, began operating in 1993, and the associated downstream runoff may have been affected by its construction. To assess this impact, we first investigated the relationships between monthly runoff observed from the Gajiu station and meteorological data obtained from four meteorological gauging stations with a time-lag of 0-3 months over the pre-dam period (1957-2000). Second, we established and validated a multiple linear regression equation employing monthly meteorological and hydrological data during the pre-dam period. Finally, we simulated the monthly runoff after dam construction (1993-2000) using the established equations and assessed the impact of dam construction on runoff by comparing the observed actual monthly runoff with the simulated monthly runoff. Our results suggested a very high hydro-meteorological correlation for the pre-dam period, which opened up the possibility of runoff forecasting. Further, the multiple linear regression equation displayed good simulation performance as coefficient of determination (R2) and the Nash-Suttcliffe coefficient (NS) reached 0.84 and 0.82 respectively. By comparing the observed and the predicted monthly runoff, we found that construction of the Manwan Dam caused a visible disturbance on monthly runoff that, with the disturbance value, displayed a multi-peak fluctuation of up-down variation in the annual hydrologic regime circl

A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014.  Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies.  The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar

Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought

Rice Intensification in a Changing Environment: Impact on Water Availability in Inland Valley Landscapes in Benin

This study assesses the impact of climate change on hydrological processes under rice intensification in three headwater inland valley watersheds characterized by different land conditions. The Soil and Water Assessment Tool was used to simulate the combined impacts of two land use scenarios defined as converting 25% and 75% of lowland savannah into rice cultivation, and two climate scenarios (A1B and B1) of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. The simulations were performed based on the traditional and the rainfed-bunded rice cultivation systems and analyzed up to the year 2049 with a special focus on the period of 2030–2049. Compared to land use, climate change impact on hydrological processes was overwhelming at all watersheds. The watersheds with a high portion of cultivated areas are more sensitive to changes in climate resulting in a decrease of water yield of up to 50% (145 mm). Bunded fields cause a rise in surface runoff projected to be up to 28% (18 mm) in their lowlands, while processes were insignificantly affected at the vegetation dominated-watershed. Analyzing three watersheds instead of one as is usually done provides further insight into the natural variability and therefore gives more evidence of possible future processes and management strategie

Prediction of River Discharge by Using Gaussian Basis Function

For design of water resources engineering related project such as hydraulic structures like dam, barrage and weirs river discharge data is vital. However, prediction of river discharge is complicated by variations in geometry and boundary roughness. The conventional method of estimation of river discharge tends to be inaccurate because river discharge is nonlinear but the method is linear. Therefore, an alternative method to overcome problem to predict river discharge is required. Soft computing technique such as artificial neural network (ANN) was able to predict nonlinear parameter such as river discharge. In this study, prediction of river discharge in Pari River is predicted using soft computing technique, specifically gaussian basis function. Water level raw data from year 2011 to 2012 is used as input. The data divided into two section, training dataset and testing dataset. From 314 data, 200 are allocated as training data and the remaining 100 are used as testing data. After that, the data will be run by using Matlab software. Three input variables used in this study were current water level, 1-antecendent water level, and 2-antecendent water level. 19 numbers of hidden neurons with spread value of 0.69106 was the best choice which creates the best result for model architecture after numbers of trial. The output variable was river discharge. Performance evaluation measures such as root mean square error, mean absolute error, correlation of efficiency (CE) and coefficient of determination (R2) was used to indicate the overall performance of the selected network. R2 for training dataset was 0.983 which showed predicted discharge is highly correlated with observed discharge value. However, testing stage performance is decline from training stage as R2 obtained was 0.775 consequently presence of outliers have affect scattering of whole data of testing and resulted in less accuracy as the R2 obtained much lower compared to training dataset. This happened because less number of input loaded into testing than training. RMSE and MSE recorded for training much lower than testing indicated that the better the performance of the model since the error is lesser. The comparison of with other types of neural network showed that Gaussian basis function is recommended to be used for river discharge prediction in Pari river

Modification Methods For Soil And Water Assessment Tool (SWAT) Performance In Simulating Runoff And Sediment Of Watersheds In Cold Regions

Streamflow predication is an important task in water management studies. It is needed in the operation and optimization of water resources and flood control projects. The accuracy of these predictions has a great influence on the water resources management and decision making processes. Various models and tool packages have been developed for simulation and prediction of streamflow. Among them, the Soil and Water Assessment Tool (SWAT) is one of the most widely used models, which was originally developed to predict the impacts of land management on water, sediment and agricultural chemical yield in large watershed simulations. Results of the SWAT streamflow simulations have indicated that this tool has deficiencies in simulating the peaks in streamflow generated by snow melting processes in the cold regions. Since global temperature is projected to be increased and the phenomena will change the snow melting characteristics in the snow dominant areas, such as the time of first melt and rate of melting. This trend along with more precipitation will cause more flooding problems in these regions. To improve daily streamflow prediction in these regions, two methods were developed. Firstly, a method was performed by separation of winter and summer seasons simulated streamflow with subsequent validation conducted in two different seasons using Calibration Uncertainty Procedure (SWAT_CUP). It should be noted that sensitivity analysis was performed on each of the seasons separately. The second method was conducted based on coupling Artificial Neural Networks (ANNs ) with calibrated and validated results of SWAT_CUP without any separation of the seasons. The calibrated streamflow, precipitation, maximum temperature, minimum temperature, snow depth, wind speed, and relative humidity were used as inputs to the ANNs model. The results of both methods have indicated significant improvements in the simulated series. In comparison between these two methods, the operation of the second method is considered better than the first method. Although, the first method has shown improvement in the simulated results but there is still a difference between the peak streamflow and the measured streamflow by USGS (United State Geological Survey) stations. However, this difference was found diminished in the simulations using the second method. ANNs method have increased peak streamflow predication in about 70%. With this improvement, the weakness of the SWAT model in simulating sediment accumulation due to improper peak run off simulation was eliminated

Implications of Climate-Driven Variability and Trends for the Hydrologic Assessment of the Reynolds Creek Experimental Watershed, Idaho

The Soil and Water Assessment Tool (SWAT) model was used to assess the implications of long-term climate trends for the hydroclimatology of the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains, Idaho of the Intermountain West over a 40- year period (1967-2006). Calibration and validation of the macroscale hydrology model in this highly monitored watershed is key to address the watershed processes that are vulnerable to both natural climate variability and climate change and . The model was calibrated using the streamflow data collected between 1997 and 2006 from the three nested weirs, the Reynolds Mountain East (RME) , Tollgate and Outlet. For assessing the performance of the calibrated model, this study used 30 years of streamflow data for the period between 1966 and 1996. This investigation suggested that the model predicted streamflow was best at RME, and inadequate at Outlet. Simulated soil moisture was also verified using the data available from five soil moisture measurement sites. The model was able to capture the seasonal patterns of changes in soil water storage considering the differences in the spatial extent of the observed and predicted soil water storage (point measurements against the spatially averaged values for the HRU) and uncertainty associated with the soil moisture measurements due to instrument effects. Water budget partitioning during a wet (1984) water year and a dry (1987) water year were also analyzed to characterize the differences in hydrologic cycles during the extreme hydrologic conditions. Our analysis showed that in the dry water year , vegetation at the higher elevation were under water stress by the end of the water year. Contrastingly, in the wet water year only the vegetation at low and mid elevations were under water stress whereas vegetation at the at the higher elevations derived substantial soil moisture for ET processes even towards the end of the growing season. To understand the effect of climate change on the hydrologic cycle, the observed and simulated streamflow were analyzed for trends in Center of Timing (CT). Earlier CT timings for the simulated and observed streamflow at RME weir was obvious thus manifesting global warming signals at the watershed scale level in the Intermountain west region. Observed streamflow at the Tollgate and Outlet weirs, where streamflow is partially affected by the agricultural diversions, showed later CT timings and these results appeared to suggest that climate impact assessment studies need to carefully distinguish the system behavior that is altered by both natural and human-induced changes