A METHOD FOR SHORT-RANGE FORECASTING OF WATER DISCHARGE FOR THE KAMA RIVER BASIN BASED ON THE HBV MODEL

2021 ◽  
pp. 55-65
Author(s):  
Yu. A. Simonov ◽  
◽  
N. K. Semenova ◽  
A. V. Khristoforov ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Lead Time ◽  
Short Range ◽  
High Efficiency ◽  
Water Discharge ◽  
Model Parameters ◽  
Runoff Formation ◽  
Operational Forecasts ◽  
Forecasting Method ◽  
Hbv Model

The experience of constructing a method for short-range forecasting of water discharge in the Kama River basin is described. The forecast method is based on the HBV-96 conceptual model of runoff formation in a watershed with optimized parameters, as well as on the algorithm for the correction of operational forecasts. It is shown that if the runoff formation model parameters are optimized and the forecast correction algorithm is applied, the model simulates variations in water discharge at gaging stations with high efficiency and can be used for operational short-range hydrological forecasting and for the evaluation of the hazard of expected hydrological conditions on the rivers. The implementation of the forecasting method allows obtaining water discharge forecasts for gaging stations in the Kama River basin with a lead time up to 3 days using meteorological forecasts with a corresponding lead time.

Using HEM surveys to evaluate disposal of by-product water from CBNG development in the Powder River Basin, Wyoming

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. B77-B84 ◽  
Author(s):  
Brian A. Lipinski ◽  
James I. Sams ◽  
Bruce D. Smith ◽  
William Harbert
Keyword(s):  
River Basin ◽  
Produced Water ◽  
Water Discharge ◽  
Geophysical Methods ◽  
Powder River Basin ◽  
Coal Bed ◽  
Coal Beds ◽  
Occam’S Inversion ◽  
Product Water ◽  
Location Strategies

Production of methane from thick, extensive coal beds in the Powder River Basin of Wyoming has created water management issues. Since development began in 1997, more than 650 billion liters of water have been produced from approximately 22,000 wells. Infiltration impoundments are used widely to dispose of by-product water from coal bed natural gas (CBNG) production, but their hydrogeologic effects are poorly understood. Helicopter electromagnetic surveys (HEM) were completed in July 2003 and July 2004 to characterize the hydrogeology of an alluvial aquifer along the Powder River. The aquifer is receiving CBNG produced water discharge from infiltration impoundments. HEM data were subjected to Occam’s inversion algorithms to determine the aquifer bulk conductivity, which was then correlated towater salinity using site-specific sampling results. The HEM data provided high-resolution images of salinity levels in the aquifer, a result not attainable using traditional sampling methods. Interpretation of these images reveals clearly the produced water influence on aquifer water quality. Potential shortfalls to this method occur where there is no significant contrast in aquifer salinity and infiltrating produced water salinity and where there might be significant changes in aquifer lithology. Despite these limitations, airborne geophysical methods can provide a broadscale (watershed-scale) tool to evaluate CBNG water disposal, especially in areas where field-based investigations are logistically prohibitive. This research has implications for design and location strategies of future CBNG water surface disposal facilities within the Powder River Basin.

scholarly journals Improving TIGGE Precipitation Forecasts Using an SVR Ensemble Approach in the Huaihe River Basin

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Chenkai Cai ◽  
Jianqun Wang ◽  
Zhijia Li
Keyword(s):  
River Basin ◽  
Lead Time ◽  
Flood Control ◽  
Ensemble Methods ◽  
New Method ◽  
Support Vector ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Reservoir Regulation ◽  
The Huaihe River

Recently, the use of the numerical rainfall forecast has become a common approach to improve the lead time of streamflow forecasts for flood control and reservoir regulation. The control forecasts of five operational global prediction systems from different centers were evaluated against the observed data by a series of area-weighted verification and classification metrics during May to September 2015–2017 in six subcatchments of the Xixian Catchment in the Huaihe River Basin. According to the demand of flood control safety, four different ensemble methods were adopted to reduce the forecast errors of the datasets, especially the errors of missing alarm (MA), which may be detrimental to reservoir regulation and flood control. The results indicate that the raw forecast datasets have large missing alarm errors (MEs) and cannot be directly applied to the extension of flood forecasting lead time. Although the ensemble methods can improve the performance of rainfall forecasts, the missing alarm error is still large, leading to a huge hazard in flood control. To improve the lead time of the flood forecast, as well as avert the risk from rainfall prediction, a new ensemble method was proposed on the basis of support vector regression (SVR). Compared to the other methods, the new method has a better ability in reducing the ME of the forecasts. More specifically, with the use of the new method, the lead time of flood forecasts can be prolonged to at least 3 d without great risk in flood control, which corresponds to the aim of flood prevention and disaster reduction.

scholarly journals RNA Modification Level Estimation with pulseR

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 619
Author(s):  
Etienne Boileau ◽  
Christoph Dieterich
Keyword(s):  
Experimental Approach ◽  
High Efficiency ◽  
Rna Modification ◽  
Model Parameters ◽  
Metabolic Labeling ◽  
Rna Seq ◽  
Rna Modifications ◽  
Log Odds ◽  
Pros And Cons ◽  
Wide Scale

RNA modifications regulate the complex life of transcripts. An experimental approach called LAIC-seq was developed to characterize modification levels on a transcriptome-wide scale. In this method, the modified and unmodified molecules are separated using antibodies specific for a given RNA modification (e.g., m6A). In essence, the procedure of biochemical separation yields three fractions: Input, eluate, and supernatent, which are subjected to RNA-seq. In this work, we present a bioinformatics workflow, which starts from RNA-seq data to infer gene-specific modification levels by a statistical model on a transcriptome-wide scale. Our workflow centers around the pulseR package, which was originally developed for the analysis of metabolic labeling experiments. We demonstrate how to analyze data without external normalization (i.e., in the absence of spike-ins), given high efficiency of separation, and how, alternatively, scaling factors can be derived from unmodified spike-ins. Importantly, our workflow provides an estimate of uncertainty of modification levels in terms of confidence intervals for model parameters, such as gene expression and RNA modification levels. We also compare alternative model parametrizations, log-odds, or the proportion of the modified molecules and discuss the pros and cons of each representation. In summary, our workflow is a versatile approach to RNA modification level estimation, which is open to any read-count-based experimental approach.

Study on High Efficiency Technology of Industrial Circulating Water in Yellow River Estuary Area

2015 ◽  
Vol 737 ◽  
pp. 719-722
Author(s):  
Xiao Feng Yang ◽  
Bao Xiang Zhang ◽  
Yu Zhi Shi ◽  
Ming Yuan Fan ◽  
Hai Jiao Liu
Keyword(s):  
Water Resources ◽  
Brackish Water ◽  
High Efficiency ◽  
Yellow River ◽  
Supply And Demand ◽  
Water Discharge ◽  
River Estuary ◽  
Yellow River Estuary ◽  
Recycled Water ◽  
Circulating Water

Most of the water in yellow river estuary mixed with irrigation and leaching water, saline and brackish water, recycled water discharge into the sea without reuse except part for agriculture irrigation and aquaculture, a small part for recycling. In order to improve the efficiency and benefits of water resources utilization, this paper proposes a new way to study industrial water recycling method of the mixed water including irrigation and leaching water, saline and brackish water, recycled water. The research could have an important realistic significance to ease the contradiction between supply and demand of water resources, reduce reliance on the Yellow River and improve regional eco-environment.

scholarly journals On the Issue of Water/economic Balances and Water Resources Abstraction Limits Calculation

2011 ◽  
pp. 16-32
Author(s):  
Keyword(s):  
Waste Water ◽  
Water Resources ◽  
River Basin ◽  
Water Discharge

Analysis of methodic recommendation on calculation of the relationships and characteristics has been given on the basis of experience in calculation of water/economic balances and water resources abstraction limits, as well as waste water discharge limits for water/economic parts of the Kama River basin within the frameworks of the Scheme of Water Resources Integrated Use and Protection (SWRIUP) development. Some clarification of the above methods has been proposed.

scholarly journals Skill of Rain–Snow Level Forecasts for Landfalling Atmospheric Rivers: A Multimodel Assessment Using California’s Network of Vertically Profiling Radars

2020 ◽  
Vol 21 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Brian Henn ◽  
Rachel Weihs ◽  
Andrew C. Martin ◽  
F. Martin Ralph ◽  
Tashiana Osborne
Keyword(s):  
Sierra Nevada ◽  
Lead Time ◽  
Flood Hazard ◽  
Critical Factor ◽  
Forecast Model ◽  
Lead Times ◽  
Grid Spacing ◽  
Atmospheric Rivers ◽  
Operational Forecasts ◽  
Resources Planning

AbstractThe partitioning of rain and snow during atmospheric river (AR) storms is a critical factor in flood forecasting, water resources planning, and reservoir operations. Forecasts of atmospheric rain–snow levels from December 2016 to March 2017, a period of active AR landfalls, are evaluated using 19 profiling radars in California. Three forecast model products are assessed: a global forecast model downscaled to 3-km grid spacing, 4-km river forecast center operational forecasts, and 50-km global ensemble reforecasts. Model forecasts of the rain–snow level are compared with observations of rain–snow melting-level brightband heights. Models produce mean bias magnitudes of less than 200 m across a range of forecast lead times. Error magnitudes increase with lead time and are similar between models, averaging 342 m for lead times of 24 h or less and growing to 700–800 m for lead times of greater than 144 h. Observed extremes in the rain–snow level are underestimated, particularly for warmer events, and the magnitude of errors increases with rain–snow level. Storms with high rain–snow levels are correlated with larger observed precipitation rates in Sierra Nevada watersheds. Flood risk increases with rain–snow levels, not only because a greater fraction of the watershed receives rain, but also because warmer storms carry greater water vapor and thus can produce heavier precipitation. The uncertainty of flood forecasts grows nonlinearly with the rain–snow level for these reasons as well. High rain–snow level ARs are a major flood hazard in California and are projected to be more prevalent with climate warming.

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

scholarly journals SPECIFIC FEATURES OF WATER CHEMISTRY AND QUALITY IN THE LENA RIVER BASIN

2021 ◽  
Vol 26 (3) ◽  
pp. 33-43
Author(s):  
R. G. Dzhamalov ◽  
◽  
O. S. Reshetnyak ◽  
K. G. Vlasov ◽  
K. G. Galagur ◽  
...  
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Water Discharge ◽  
Surface Water Quality ◽  
Temporal Variations ◽  
Soil Freezing ◽  
Commercial Fishing ◽  
Lena River ◽  
Time Dynamics ◽  
Annual Water Discharge

Introduction. The Lena River is one of the largest rivers in Russia and the main transport artery of Yakutia. Methods. In the course of the study, we considered the water regime of the Lena River in 1981–2019 in relation to the monthly average water discharge. The hydrochemical runoff was quantitatively assessed based on the widely used landscape-hydrological method. The analysis made it possible to estimate the relationship between the natural water quality and the environmental state of catchments. Results. An increase in the winter temperature reduced the depth of soil freezing and increased the drainage properties of soil as well as the number and duration of winter thaws. The most pronounced annual water discharge was observed in the Aldan River basin in the eastern part of the Lena River basin. The current state of the surface water quality was assessed by the main hydrochemical characteristics: water salinity, principal ions (sulfates (SO4 2–)), nutrients (nitrite nitrogen (NO2–)), organic matter (BOD5 and COD), oil products, phenols, and iron (Fe) and copper (Cu) compounds. The water has quality class 3 and is characterized as “polluted” or “very polluted” in different zones of the river basin, with the situation being most acute in the Olekma River. Conclusions. We present the results of an analysis of the spatial and temporal variations in the content of the most informative hydrochemical components for two periods (2001–2009 and 2010–2019) in the Lena River basin in accordance with the most stringent commercial fishing standards in force. We also plotted and mapped the temporal variations in the main pollutants. Graphs and maps of the time dynamics of the main pollutants are constructed.

scholarly journals Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

2015 ◽  
Vol 19 (4) ◽  
pp. 2079-2100 ◽  
Author(s):  
N. Tangdamrongsub ◽  
S. C. Steele-Dunne ◽  
B. C. Gunter ◽  
P. G. Ditmar ◽  
A. H. Weerts
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rhine River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

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