scholarly journals Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

2014 ◽  
Vol 18 (10) ◽  
pp. 3923-3936 ◽  
Author(s):  
X. Xie ◽  
S. Meng ◽  
S. Liang ◽  
Y. Yao
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Similarity Measures ◽  
State Parameter ◽  
Model Parameters ◽  
Prediction Errors ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Short Period

Abstract. The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations.

scholarly journals Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

2013 ◽  
Vol 10 (11) ◽  
pp. 13441-13473
Author(s):  
X. Xie ◽  
S. Meng ◽  
S. Liang ◽  
Y. Yao
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Similarity Measures ◽  
State Parameter ◽  
Model Parameters ◽  
Prediction Errors ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Short Period

Abstract. The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a portioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source datasets, e.g. ground-based and remote-sensing observations.

scholarly journals Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

2019 ◽  
Author(s):  
Aynom T. Teweldebrhan ◽  
John F. Burkhart ◽  
Thomas V. Schuler ◽  
Morten Hjorth-Jensen
Keyword(s):  
Machine Learning ◽  
Parameter Identification ◽  
Hydrological Model ◽  
Response Surfaces ◽  
Model Parameters ◽  
Large Set ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Main Challenge ◽  
Mc Simulation

Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to get an adequate sample size which may take from days to months especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual based response surfaces. Here, we apply emulators of MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time relaxed limits of acceptability concept (pLoA). Three machine learning models (MLMs) were built using model parameter sets and response surfaces with limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time relaxed limits of acceptability approach based on the predicted pLoA values; and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations; and the models identified using the coupled ML emulators and the limits of acceptability approach have performed very well in reproducing the median streamflow prediction both during the calibration and validation periods.

scholarly journals Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

2020 ◽  
Vol 24 (9) ◽  
pp. 4641-4658 ◽  
Author(s):  
Aynom T. Teweldebrhan ◽  
Thomas V. Schuler ◽  
John F. Burkhart ◽  
Morten Hjorth-Jensen
Keyword(s):  
Machine Learning ◽  
Parameter Identification ◽  
Hydrological Model ◽  
Response Surfaces ◽  
Model Parameters ◽  
Large Set ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Main Challenge ◽  
Mc Simulation

Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to acquire an adequate sample size, which may take from days to months – especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual-based response surfaces. Here, we apply emulators of an MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time-relaxed limits of acceptability concept (pLoA). Three machine-learning models (MLMs) were built using model parameter sets and response surfaces with a limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time-relaxed limits of acceptability approach, based on the predicted pLoA values, and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations with an R2 value of 0.7 to 0.92. Similarly, the models identified using the coupled machine-learning (ML) emulators and limits of acceptability approach have performed very well in reproducing the median streamflow prediction during the calibration and validation periods, with an average Nash–Sutcliffe efficiency value of 0.89 and 0.83, respectively.

scholarly journals Diagnostic calibration of a hydrological model in an alpine area

2014 ◽  
Vol 11 (1) ◽  
pp. 1253-1300 ◽  
Author(s):  
Z. He ◽  
F. Tian ◽  
H. C. Hu ◽  
H. V. Gupta ◽  
H. P. Hu
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Generation Mechanism ◽  
Spatiotemporal Variability ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Alpine Area ◽  
Long Time ◽  
Tailan River Basin

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

scholarly journals Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

2019 ◽  
Vol 23 (9) ◽  
pp. 3823-3841 ◽  
Author(s):  
Maria Laura Poletti ◽  
Francesco Silvestro ◽  
Silvio Davolio ◽  
Flavio Pignone ◽  
Nicola Rebora
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Distributed Approach ◽  
Nwp Model ◽  
Small Catchments

Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

scholarly journals Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning

2020 ◽  
Vol 163 ◽  
pp. 01001
Author(s):  
Georgy Ayzel ◽  
Liubov Kurochkina ◽  
Eduard Kazakov ◽  
Sergei Zhuravlev
Keyword(s):  
Deep Learning ◽  
Hydrological Model ◽  
State Of The Art ◽  
Flash Flood ◽  
Early Warning Systems ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Streamflow Prediction ◽  
Basin Scale ◽  
The Impact

Streamflow prediction is a vital public service that helps to establish flash-flood early warning systems or assess the impact of projected climate change on water management. However, the availability of streamflow observations limits the utilization of the state-of-the-art streamflow prediction techniques to the basins where hydrometric gauging stations exist. Since the most river basins in the world are ungauged, the development of the specialized techniques for the reliable streamflow prediction in ungauged basins (PUB) is of crucial importance. In recent years, the emerging field of deep learning provides a myriad of new models that can breathe new life into the stagnating PUB methods. In the presented study, we benchmark the streamflow prediction efficiency of Long Short-Term Memory (LSTM) networks against the standard technique of GR4J hydrological model parameters regionalization (HMREG) at 200 basins in Northwest Russia. Results show that the LSTM-based regional hydrological model significantly outperforms the HMREG scheme in terms of median Nash-Sutcliffe efficiency (NSE), which is 0.73 and 0.61 for LSTM and HMREG, respectively. Moreover, LSTM demonstrates the comparable median NSE with that for basin-scale calibration of GR4J (0.75). Therefore, this study underlines the high utilization potential of deep learning for the PUB by demonstrating the new state-of-the-art performance in this field.

Regionalisation of land surface hydrological model parameters in subarctic and arctic environments

2008 ◽  
Vol 33 (17-18) ◽  
pp. 1081-1089 ◽  
Author(s):  
Pablo F. Dornes ◽  
Bryan A. Tolson ◽  
Bruce Davison ◽  
Alain Pietroniro ◽  
John W. Pomeroy ◽  
...  
Keyword(s):  
Land Surface ◽  
Hydrological Model ◽  
Model Parameters

scholarly journals Extending flood forecasting lead time in large watershed by coupling WRF QPF with distributed hydrological model

2016 ◽  
Author(s):  
Ji Li ◽  
Yangbo Chen ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Jie Li
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Southern China ◽  
Weather Forecast ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Flood Warning

Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. Latest numerical weather forecast model could provide 1–15 days quantitative precipitation forecasting products at grid format, by coupling this product with distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe Model with the WRF QPF for a large watershed flood forecasting in southern China. The QPF of WRF products has three lead time, including 24 hour, 48 hour and 72 hour, the grid resolution is 20 km × 20 km. The Liuxihe Model is set up with freely downloaded terrain property, the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also, it suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of WRF QPF decreases, so does the flood forecasting capability. Flood forecasting products produced by coupling Liuxihe Model with WRF QPF provides good reference for large watershed flood warning due to its long lead time and rational results.

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