scholarly journals Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach

2011 ◽  
Vol 14 (2) ◽  
pp. 424-442 ◽  
Author(s):  
Maheswaran Rathinasamy ◽  
Rakesh Khosa
Keyword(s):  
Nonlinear Models ◽  
Model Performance ◽  
Low Flow ◽  
Data Series ◽  
Streamflow Forecasting ◽  
Volterra Models ◽  
Proposed Model ◽  
Monthly Streamflow ◽  
Streamflow Data ◽  
Flow Medium

The dynamics of the streamflow in rivers involve nonlinear and multiscale phenomena. An attempt is made to develop nonlinear models combining wavelet decomposition with Volterra models. This paper describes a methodology to develop one-month-ahead forecasts of streamflow using multiscale nonlinear models. The method uses the concept of multiresolution decomposition using wavelets in order to represent the underlying integrated streamflow dynamics and this information, across scales, is then linked together using the first- and second-order Volterra kernels. The model is applied to 30 river data series from the western USA. The mean monthly data series of 30 rivers are grouped under the categories low, medium and high. The study indicated the presence of multiscale phenomena and discernable nonlinear characteristics in the streamflow data. Detailed analyses and results are presented only for three stations, selected to represent the low-flow, medium-flow and high-flow categories, respectively. The proposed model performance is good for all the flow regimes when compared with both the ARMA-type models as well as nonlinear models based on chaos theory.

scholarly journals A Bayesian approach to probabilistic streamflow forecasts

2012 ◽  
Vol 15 (2) ◽  
pp. 381-391 ◽  
Author(s):  
Hui Wang ◽  
Brian Reich ◽  
Yeo Howe Lim
Keyword(s):  
Climate Models ◽  
Principal Component ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Streamflow Forecasting ◽  
Promising Alternative ◽  
Probabilistic Information ◽  
Monthly Streamflow ◽  
Streamflow Forecasts ◽  
Streamflow Data

One-month-ahead streamflow forecasting is important for water utilities to manage water resources such as irrigation water usage and hydropower generation. While deterministic streamflow forecasts have been utilized extensively in research and practice, ensemble streamflow forecasts and probabilistic information are gaining more attention. This study aims to examine a multivariate linear Bayesian regression approach to provide probabilistic streamflow forecasts by incorporating gridded precipitation forecasts from climate models and lagged monthly streamflow data. Principal component analysis is applied to reduce the size of the regression model. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the posterior distribution of model parameters. The proposed approach is tested on gauge data acquired during 1961–2000 in North Carolina. Results reveal that the proposed method is a promising alternative forecasting technique and that it performs well for probabilistic streamflow forecasts.

scholarly journals Multi-variable SWAT model calibration with remotely sensed evapotranspiration and observed flow

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Ana Clara Lazzari Franco ◽  
Nadia Bernardi Bonumá
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Swat Model ◽  
Model Performance ◽  
Low Flow ◽  
Single Variable ◽  
Daily Step ◽  
Streamflow Prediction ◽  
Negro River ◽  
Monthly Streamflow

ABSTRACT Although intrinsic, uncertainty for hydrological model estimation is not always reported. The aim of this study is to evaluate the use of satellite-based evapotranspiration on SWAT model calibration, regarding uncertainty and model performance in streamflow simulation. The SWAT model was calibrated in a monthly step and validated in monthly (streamflow and evapotranspiration) and daily steps (streamflow only). The validation and calibration period covers the years from 2006 to 2009 and the study area is the upper Negro river basin, situated in Santa Catarina and Paraná. SWAT-CUP was used to calibrate and validate the model, using SUFI-2 with KGE (Kling-Gupta Efficiency) as objective function. Different calibration strategies were evaluated, considering single-variable and multi-variable calibration, using streamflow and evapotranspiration. Compared to conventional single-variable calibration (streamflow only), multi-variable calibration (streamflow and evapotranspiration, simultaneously) produce better streamflow performance, especially for low flow periods and daily step validation. Despite that, no evidence of reduction of streamflow prediction uncertainty was observed. SWAT model calibration using solely evapotranspiration still requires further studies.

scholarly journals A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Muhammad Sibtain ◽  
Xianshan Li ◽  
Snoober Saleem
Keyword(s):  
Prediction Models ◽  
Ensemble Prediction ◽  
Data Series ◽  
Streamflow Prediction ◽  
Two Stage ◽  
Decomposition Approach ◽  
Decomposition Stage ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Monthly Streamflow

The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction.

Wavelet and neuro-fuzzy conjunction model for streamflow forecasting

2011 ◽  
Vol 42 (6) ◽  
pp. 447-456 ◽  
Author(s):  
Özgür Kişi ◽  
Turgay Partal
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Model ◽  
Discrete Wavelet ◽  
Streamflow Forecasting ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Comparison Results ◽  
Monthly Streamflow ◽  
Models Comparison ◽  
Streamflow Data

In this study the wavelet-neuro-fuzzy model, which combines the wavelet transform and the neuro-fuzzy technique, has been employed to forecast monthly streamflows. The observed monthly streamflow data are decomposed into some sub-series (components) by discrete wavelet transform and then appropriate sub-series are used as inputs to the neuro-fuzzy models for forecasting monthly streamflows. The data from two stations, Durucasu and Tanir, in Turkey are used as case studies. The wavelet-neuro-fuzzy forecasts are compared with those of the single neuro-fuzzy models. Comparison results indicate that the wavelet-neuro-fuzzy model is superior to the classical neuro-fuzzy method especially for the peak values. For the Durucasu and Tanir stations, it was found that the wavelet-neuro-fuzzy models are superior in forecasting monthly streamflows than the optimal neuro-fuzzy models.

scholarly journals GRACE Satellites Enable Long-Lead Forecasts of Mountain Contributions to Streamflow in the Low-Flow Season

2021 ◽  
Vol 13 (10) ◽  
pp. 1993
Author(s):  
Xingcai Liu ◽  
Qiuhong Tang ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Xiaogang Shi ◽  
Min-Hui Lo ◽  
...  
Keyword(s):  
Linear Models ◽  
Yellow River ◽  
Low Flow ◽  
Lead Times ◽  
High Mountain ◽  
Streamflow Forecasting ◽  
Mountain Areas ◽  
Model Based ◽  
Monthly Streamflow ◽  
Terrestrial Water

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas.

scholarly journals Hydrological Model Calibration in Data-Limited Catchments Using Non-Continuous Data Series with Different Lengths

10.29007/5hv1 ◽  
2018 ◽  
Author(s):  
Chuanzhe Li ◽  
Jia Liu ◽  
Fuliang Yu ◽  
Jiyang Tian ◽  
Yang Wang ◽  
...  
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Data Series ◽  
Observation Data ◽  
Calibration Data ◽  
Series Length ◽  
Streamflow Data ◽  
The Impact

This paper evaluates the effects of calibration data series length on the performance of a hydrological model in data-limited catchments where data are non-continuous and fragmental. Non-continuous calibration periods were used for more independent streamflow data for SIMHYD model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

scholarly journals Application of Entropy Spectral Method for Streamflow Forecasting in Northwest China

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 132 ◽  
Author(s):  
Gengxi Zhang ◽  
Zhenghong Zhou ◽  
Xiaoling Su ◽  
Olusola Ayantobo
Keyword(s):  
Flood Control ◽  
Performance Metrics ◽  
Configurational Entropy ◽  
Northwest China ◽  
Low Flow ◽  
Future Research ◽  
Streamflow Forecasting ◽  
Periodic Patterns ◽  
Forecasting Accuracy ◽  
Monthly Streamflow

Streamflow forecasting is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation and navigation. Although monthly streamflow time series are statistic, they also exhibit seasonal and periodic patterns. Using maximum Burg entropy, maximum configurational entropy and minimum relative entropy, the forecasting models for monthly streamflow series were constructed for five hydrological stations in northwest China. The evaluation criteria of average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and determination coefficient (DC) were selected as performance metrics. Results indicated that the RESA model had the highest forecasting accuracy, followed by the CESA model. However, the BESA model had the highest forecasting accuracy in a low-flow period, and the prediction accuracies of RESA and CESA models in the flood season were relatively higher. In future research, these entropy spectral analysis methods can further be applied to other rivers to verify the applicability in the forecasting of monthly streamflow in China.

scholarly journals Monthly Flow Duration Curve Model for Ungauged River Basins

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 338 ◽  
Author(s):  
Halil Ibrahim Burgan ◽  
Hafzullah Aksoy
Keyword(s):  
River Basin ◽  
Performance Metrics ◽  
Low Flow ◽  
Flow Duration Curve ◽  
Flow Duration ◽  
Normal Probability ◽  
Monthly Streamflow ◽  
Flow Part ◽  
Streamflow Data ◽  
Normal Probability Distribution

Flow duration curve (FDC) is widely used in hydrology to assess streamflow in a river basin. In this study, a simple FDC model is developed for monthly streamflow data. The model consists of several steps including the nondimensionalization and then normalization in case the monthly streamflow data do not fit the normal probability distribution function. The normalized quantiles are calculated after which a back transformation is applied to the normalized quantiles to return back to the original dimensional streamflow data. In order to calculate annual streamflow of the river basin, an empirical regression equation is proposed using the drainage area and the annual total precipitation only as the input. As the final step of the model, dimensional quantiles of FDC are calculated. Ceyhan River basin in southern Turkey is chosen for the case study. Forty-two streamflow gauging stations are considered; two thirds of the gauging stations are used for the model calibration, and one third for validation. The modeled FDCs are compared to the observation and assessed with a number of performance metrics. They are found similar to the observed ones with a relatively good performance; they are good in the mid and high flow parts particularly while the low flow part of FDCs might require further detailed analysis.

Fiction and reality in the modelling world-balance between simplicity and complexity, calibration and identifiability, verification and falsification

1999 ◽  
Vol 39 (9) ◽  
pp. 1-8 ◽  
Author(s):  
P. Harremoës ◽  
H. Madsen
Keyword(s):  
Model Prediction ◽  
Model Performance ◽  
Data Series ◽  
Urban Drainage ◽  
Model Structure ◽  
And Control

Where is the balance between simplicity and complexity in model prediction of urban drainage structures? The calibration/verification approach to testing of model performance gives an exaggerated sense of certainty. Frequently, the model structure and the parameters are not identifiable by calibration/verification on the basis of the data series available, which generates elements of sheer guessing - unless the universality of the model is be based on induction, i.e. experience from the sum of all previous investigations. There is a need to deal more explicitly with uncertainty and to incorporate that in the design, operation and control of urban drainage structures.

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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