WANN Model for Monthly Runoff Forecast

A wavelet artificial neural network to forecasting monthly runoff is proposed. The monthly runoff series is firstly decomposed to sub-series on different time scales, and each sub-series is modeled. The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration. Then the proposed network is trained with 49 years (1952-2000) actual data of one hydro power plant of Jiangxi province and is tested for target year (2001-2003). Finally, some actual results for mid and long term water inflow forecasting are obtained and which show the proposed method has a good precision for forecasting.

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Monthly Runoff Probabilistic Forecast Model Based on Similar Process Derivations

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.737.710 ◽

2015 ◽

Vol 737 ◽

pp. 710-714

Author(s):

Cai Lin Lee ◽

Dong Mei Wang

Keyword(s):

Forecast Accuracy ◽

Forecast Model ◽

Probabilistic Forecast ◽

Similar Process ◽

Simulation Experiments ◽

Model Combining ◽

Forecast Result ◽

Probabilistic Forecasts ◽

Runoff Forecast ◽

Monthly Runoff

In this paper, a runoff forecast model combining similar process derivation with probabilistic forecasts is proposed. Certain forecast result is computed by similar processes derivations, and on the basis of certain results, a confidence interval under given confidence coefficient is worked out by probabilistic forecast part. The model is simple in structure, easy in establishing and unnecessary to concern for predictor selections. Applying above model in simulation experiments, the results show the forecast model have excellent forecast accuracy and can be used in monthly runoff forecast effectively.

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Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽

10.3390/w13091226 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1226

Author(s):

Pakorn Ditthakit ◽

Sirimon Pinthong ◽

Nureehan Salaeh ◽

Fadilah Binnui ◽

Laksanara Khwanchum ◽

...

Keyword(s):

Exchange Rate ◽

Rainfall Runoff ◽

Management Planning ◽

Runoff Variation ◽

Groundwater Exchange ◽

Monthly Runoff ◽

Rainfall Runoff Model ◽

Two Parameters ◽

Runoff Model

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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Monthly Runoff Prediction Using Wavelet Transform and Fast Resource Optimization Network (Fron) Algorithm

Journal of Physics Conference Series ◽

10.1088/1742-6596/1302/4/042005 ◽

2019 ◽

Vol 1302 ◽

pp. 042005

Author(s):

Jiahai Lu ◽

Aiyun Li

Keyword(s):

Wavelet Transform ◽

Resource Optimization ◽

Runoff Prediction ◽

Monthly Runoff

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Assessment of the feasibility of floodwater utilization in North-East China

Water Policy ◽

10.2166/wp.2016.114 ◽

2016 ◽

Vol 18 (4) ◽

pp. 862-876

Author(s):

Jianwei Liu ◽

Limin Kou ◽

Qiang Zhou

Keyword(s):

Large Scale ◽

Local Conditions ◽

North East ◽

Management Measures ◽

Main River ◽

Monthly Runoff ◽

Floodwater Utilization ◽

Flood Resource Utilization ◽

Storage Characteristics ◽

Utilization Strategy

In order to alleviate the water supply–demand problem, a flood resource utilization strategy is proposed, called ‘Flood Utilization’. The strategy focuses on building large-scale water conservancy facilities and improving management measures. This paper presents the probability analysis of floodwater utilization in a confluence area, where a tributary joins a main river. Baicheng is used as the study area, where the Taoer River joins the Nenjiang River. After a large number of analyses, the main results and conclusions are as follows: First, the upper limit of available floodwater corresponds to the Taoer River's flood with a 5% probability of occurrence. Secondly, there are compensation characteristics between the two rivers which mean that the Nenjiang River can supply water to the Taoer River area. The analysis of monthly runoff, shows that there are compensation characteristics in 50.9% of the data period. The compensation rates (CRs) for the months from June to October are 0.2, 0.27, 0.25, 0.27, and 0.2, respectively. Thirdly, the differences in the runoff characteristics show that it is suitable for floodwater utilization. Finally, it is proposed that floodwater utilization measures are based on local conditions, such as the regional water storage characteristics and the runoff characteristics of the two rivers, and should be applied for different periods.

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Impact of climate change on water availability in Tropical Andean catchments: a case study in the Vilcanota catchment, Peru

10.5194/egusphere-egu21-10292 ◽

2021 ◽

Author(s):

Selina Meier ◽

Randy Munoz ◽

Christian Huggel

Keyword(s):

Climate Change ◽

Water Management ◽

Water Demand ◽

Climate Models ◽

Local Population ◽

Time Step ◽

Glacier Surface ◽

Hydrological Simulation ◽

Sustainable Water Management ◽

Monthly Runoff

Water scarcity is increasingly becoming a problem in many regions of the world. On the one hand, this can be attributed to changes in precipitation conditions due to climate change. On the other hand, this is also due to population growth and changes in consumer behaviour. In this study, an analysis is carried out for the highly glaciated Vilcanota River catchment (9808 km2 &#8211; 1.2% glacier area) in the Cusco region (Peru). Possible climatic and socioeconomic scenarios up to 2050 were developed including the interests from different water sectors, i.e. agriculture, domestic and energy.The analysis consists of the hydrological simulation at a monthly time step from September 2043 to August 2050 using a simple glacio-hydrological model. For historic conditions (1990 to 2006) a combination of gridded data (PISCO precipitation) and weather stations was used. Future scenario simulations were based on three different climate models for both RCP 2.6 and 8.5. Different glacier outlines were used to simulate changes in glacier surface through the time for both historic (from satellite data) and future (from existing literature) scenarios. Furthermore, future water demand simulations were based on the SSP1 and SSP3 scenarios.Results from all scenarios suggest an average monthly runoff of about 130 m3/s for the Vilcanota catchment between 2043 and 2050. This represents a change of about +5% compared to the historical monthly runoff of about 123 m3/s. The reason for the increase in runoff is related to the precipitation data from the selected climate models. However, an average monthly deficit of up to 50 m3/s was estimated between April and November with a peak in September. The seasonal deficit is related to the seasonal change in precipitation, while the water demand seems to have a less important influence.Due to the great uncertainty of the modelling and changes in the socioeconomic situation, the data should be continuously updated. In order to construct a locally sustainable water management system, the modelling needs to be further downscaled to the different subcatchments in the Vilcanota catchment. To address the projected water deficit, a new dam could partially compensate for the decreasing storage capacity of the melting glaciers. However, the construction of the dam could meet resistance from the local population if they cannot be promised and communicated multiple uses of the new dam. Sustainable water management requires the cooperation of all stakeholders and all stakeholders should be able to benefit from it so that they will support future projects.

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