scholarly journals Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1614 ◽  
Author(s):  
Somchit Amnatsan ◽  
Sayaka Yoshikawa ◽  
Shinjiro Kanae
Keyword(s):  
River Basin ◽  
Flood Control ◽  
High Flow ◽  
Low Flow ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Inflow Forecasting ◽  
Analogue Method ◽  
Reservoir Inflow ◽  
Chao Phraya River

Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies have been developed and applied for inflow forecasting, with good results. Nevertheless, most were reported to have weaknesses in capturing the peak flow, especially rare extreme flows. In this study, an analogue-based forecasting method, designated the variation analogue method (VAM), was developed to overcome this weakness. This method, the wavelet artificial neural network (WANN) model, and the weighted mean analogue method (WMAM) were used to forecast the monthly reservoir inflow of the Sirikit Dam, located in the Nan River Basin, one of the eight sub-basins of the Chao Phraya River Basin in Thailand. It is one of four major dams in the Chao Phraya Basin, with a maximum storage of 10.64 km3, which supplies water to 22 provinces in this basin, covering an irrigation area of 1,513,465 hectares. Due to the huge extreme monthly inflow in August, with inflow of more than 3 km3 in 1985 and 2011, monthly or longer lead time inflow forecasting is needed for proper water and flood control management of this dam. The results of forecasting indicate that the WANN model provided good forecasting for whole-year forecasting including both low-flow and high-flow patterns, while the WMAM model provided only satisfactory results. The VAM showed the best forecasting performance and captured the extreme inflow of the Sirikit Dam well. For the high-flow period (July–September), the WANN model provided only satisfactory results, while those of the WMAM were markedly poorer than for the whole year. The VAM showed the best capture of flow in this period, especially for extreme flow conditions that the WANN and WMAM models could not capture.

Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin

2018 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
River Basin ◽  
Suspended Solids ◽  
Water Quality Monitoring ◽  
High Flow ◽  
Quality Monitoring ◽  
Low Flow ◽  
Inverse Relation

This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.

scholarly journals Using Dual Isotopes of Nitrate to Identify the Source of Nitrogen Pollution in the Songhua River Basin, Northeast China

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2721
Author(s):  
Kuangmin Ye ◽  
Fansheng Meng ◽  
Lingsong Zhang ◽  
Yeyao Wang ◽  
Hao Xue ◽  
...  
Keyword(s):  
River Basin ◽  
High Flow ◽  
Nitrogen Pollution ◽  
Low Flow ◽  
Organic N ◽  
Severe Problem ◽  
Songhua River ◽  
Songhua River Basin ◽  
The Songhua River Basin ◽  
The Songhua River

Nitrogen pollution is a severe problem in the Songhua River Basin (SHR) in China. Samples were collected from 36 sections of the SHR during the high, low, and flat seasons of the river, and the main sources of nitrogen in the water were qualitatively analyzed with isotope data for nitrogen and oxygen of nitrate. The contribution rates of each major pollution source were quantitatively analyzed using the Iso Source mass balance model. The results from these experiments indicate that the values for δ15N-NO3 and δ18O-NO3 in the flat flow season range from 1.52‰ to 14.55‰ and −14.26‰ to 2.03‰, respectively. The values for δ15N-NO3 and δ18O-NO3 in the low flow season range from 6.66‰ to 15.46‰ and −5.82‰ to 65.70‰, respectively. In the low flow season, nitrogen comes from the input of domestic and manure sewage (53%) and soil organic N (45%). The values of δ15N-NO3 and δ18O-NO3 in the high flow season range from 2.07‰ to 14.24‰ and −3.99‰ to 8.03‰, respectively. In the high flow season, nitrogen comes from soil organic nitrogen (41%), domestic and manure sewage (32%), and nitrogen fertilizer (27%), which are the main sources of nitrogen pollution in the SHR. The conclusions from the qualitative and quantitative analysis of nitrogen sources in the SHR can provide a scientific basis for the source control and treatment of nitrogen pollution.

scholarly journals Impacts of climate change on the water regime of the Inn River basin – extracting adaptation-relevant information from climate model ensembles and impact modelling

2012 ◽  
Vol 32 ◽  
pp. 99-107 ◽  
Author(s):  
J. Korck ◽  
J. Danneberg ◽  
W. Willems
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Regional Climate ◽  
Relevant Information ◽  
High Flow ◽  
Low Flow ◽  
Climate Projections ◽  
Study Region ◽  
Regional Climate Projections ◽  
Impacts Of Climate Change

Abstract. The Inn River basin is a highly relevant study region in terms of potential hydrological impacts of climate change and cross boundary water management tasks in the Alpine Space. Regional analyses in this catchment were performed within the EU co-funded project AdaptAlp. Objective of the study was to gain scientifically based knowledge about impacts of climate change on the water balance and runoff regime for the Inn River basin, this being fundamental for the derivation of adaptation measures. An ensemble of regional climate projections is formed by combinations of global and regional climate models on the basis of both statistical and bias-corrected dynamical downscaling procedures. Several available reference climate datasets for the study region are taken into account. As impact model, the process-oriented hydrological model WaSiM-ETH is set up. As expected, regional climate projections indicate temperature increases for the future in the study area. Projections of precipitation change are less homogenous, especially regarding winter months, though most indicate a decrease in the summer. Hydrological simulation results point towards climate induced changes in the water regime of the study region. The analysis of hydrological projections at both ends of the ensemble bandwidth is a source of adaptation relevant information regarding low-flow and high-flow conditions. According to a "drought-prone scenario", mean monthly low flow could decrease up to −40% in the time frame of 2071–2100. A "high-flow-increase-scenario" points towards an increase in mean monthly high flow in the order of +50% in the winter, whilst showing a decrease in autumn.

scholarly journals Hydrological Evaluation of Satellite and Reanalysis-based Rainfall Estimates Over the Upper Tekeze Basin, Ethiopia

2021 ◽  
Author(s):  
Kidane Reda ◽  
Xingcai Liu ◽  
Gebremedhin Haile ◽  
Siao Sun ◽  
Qiuhong Tang
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Swat Model ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
High Flow ◽  
Low Flow ◽  
Northern Ethiopia ◽  
Monthly Streamflow ◽  
Rainfall Estimates

Spatial rainfall data is an essential input to physically based, parametrically distributed hydrological models, and a main contributor to hydrological model uncertainty. Two important issues should be addressed before use of satellite and reanalysis rainfall product at basin level: 1) how useful are these rainfall estimates as forcing data for regional hydrological modeling? 2) which should be preferred for hydrological modelling at high flow and low flow seasons? To this end, rainfall estimates from a satellite-based product, CHIRPSv8, and reanalysis data, EWEMBI, were used as input to SWAT model, and mode performances were evaluated against streamflow measured at three gauge stations in the Upper Tekeze River basin, northern Ethiopia for the period of 2006-2015. Results showed that (I) the daily rainfall from both CHIRPSv8 and EWEMBI are close to the rain gauge data, with relative errors 2.12% and 3.85%, respectively; (II) the monthly streamflow simulated by the SWAT model driven by the CHIRPSv8 and EWEMBI had a Kling-Gupta Efficiency value of 0.6-0.79 and 0.58-0.64, respectively; (III) the SWAT model calibrated with the CHIRPSv8 and EWEMBI rainfall estimates has shown an improvement in hydrological performance compared with that calibrated with interpolated ground observations; (IV) the hydrological performance during high flow seasons is superior to low flow seasons for both CHIRPSv8 and EWEMBI, thus promoting the use of the products for applications focusing on the high flow conditions. In particular, CHIRPSv8 showed relatively better hydrologic performance than EWEMBI. This study provides insight on the usefulness of the gridded rainfall products for hydrological modeling and under which conditions they can be used to generate a plausible level of adequacy and reliability over the Upper Tekeze River basin.

Application of Xin'anjiang Model and Wetspa Model in the Inflow Forecasting of Shiquan Reservoir

2021 ◽  
Author(s):  
Siyu Cai ◽  
Ruifang Yuan ◽  
Weihong Liao ◽  
Liang Wu
Keyword(s):  
Model Calibration ◽  
Grid Size ◽  
High Flow ◽  
Low Flow ◽  
Wetspa Model ◽  
Inflow Forecasting ◽  
Han River Basin ◽  
Calibration And Verification ◽  
Simulation Results ◽  
Xin’Anjiang Model

<p>In order to improve the accuracy of the inflow forecasting of Shiquan Reservoir in the Han River Basin, this paper compared the application effects of Xin'anjing model and Wetspa model. The study collected the rainfall and runoff data from 2009 to 2015, as well as the DEM, land use and soil data with 1000´1000m grid size. The model calibration and verification periods were from 2009 to 2012 and from 2013 to 2015, respectively. In addition to using the runoff depth and the determination coefficient to evaluate the accuracy of the two models, the flow relative error CR1, model confidence coefficient CR2, Nash-Sutcliffe efficiency CR3, logarithmic version of Nash-Sutcliffe efficiency CR4 for low flow, improved Nash-Sutcliffe efficiency CR5 for high flow were adopted to analyze the simulation results of the two models. The results showed that the simulation results of the Wetspa model could be used as a supplement to the simulation results of the Xin'anjiang model, providing high-precision flood forecasting results for the scheduling decisions of Shiquan Reservoir in terms of time and space.</p>

scholarly journals An Assessment of Dam Operation Considering Flood and Low-Flow Control in the Han River Basin

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 733
Author(s):  
Jaewon Kwak
Keyword(s):  
Flow Control ◽  
River Basin ◽  
Flood Control ◽  
Reduction Rate ◽  
Cumulative Distribution ◽  
Low Flow ◽  
Simple Method ◽  
Han River ◽  
Dam Operation ◽  
Han River Basin

An assessment of dam operation is essential in dam management; however, there is a lack of a simple method that could be used in actual practice. This study aims for an actual dam operation evaluation method for flood and low-flow control of the three multi-purpose dams of Soyanggang, Chungju, and Hoengseong in the Han River basin, South Korea. Frequency matching method was applied to make a pair of cumulative distribution function (CDF) using daily dam inflow and outflow records. Runoff increasing and flood reduction rates are derived using CDFs of total and annual records. As a result, the average flood mitigation rates of the Chungju dam is approximately 35% annually and is relatively disadvantaged than the Soyanggang dam, which is 67.7% annually, due to small flood control capacity. The Hoengseong dam appeared to have a small flood reduction rate, but its runoff increasing rate is 94.7% annually because of the 209 km2 upper basin area. The suggested method in this study could be used as a simple and intuitive field method to evaluate dam operations. Also, according to the annual evaluation, the Soyanggang and Chunju dam need more aggressive and anticipative operations for flood control such as pre-discharge before flooding or modify the Restricted Water Level (RWL) for flood seasons. On the other hand, Hoengseong dam need further data and studies.

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