Daily streamflow prediction in ungauged basins: an analysis of common regionalization methods over the African continent

Abstract. Objective criteria for catchment classification are identified by the scientific community among the key research topics for improving the interpretation and representation of the spatiotemporal variability of streamflow. A promising approach to catchment classification makes use of unsupervised neural networks (Self Organising Maps, SOM's), which organise input data through non-linear techniques depending on the intrinsic similarity of the data themselves. Our study considers ~300 Italian catchments scattered nationwide, for which several descriptors of the streamflow regime and geomorphoclimatic characteristics are available. We qualitatively and quantitatively compare in the context of PUB (Prediction in Ungauged Basins) a reference classification, RC, with four alternative classifications, AC's. RC was identified by using indices of the streamflow regime as input to SOM, whereas AC's were identified on the basis of catchment descriptors that can be derived for ungauged basins. One AC directly adopts the available catchment descriptors as input to SOM. The remaining AC's are identified by applying SOM to two sets of derived variables obtained by applying Principal Component Analysis (PCA, second AC) and Canonical Correlation Analysis (CCA, third and fourth ACs) to the available catchment descriptors. First, we measure the similarity between each AC and RC. Second, we use AC's and RC to regionalize several streamflow indices and we compare AC's with RC in terms of accuracy of streamflow prediction. In particular, we perform an extensive cross-validation to quantify nationwide the accuracy of predictions in ungauged basins of mean annual runoff, mean annual flood, and flood quantiles associated with given exceedance probabilities. Results of the study show that CCA can significantly improve the effectiveness of SOM classifications for the PUB problem.

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Streamflow prediction in ungauged basins by regressive regionalization: a case study in Huai River Basin, China

Hydrology Research ◽

10.2166/nh.2015.155 ◽

2015 ◽

Vol 47 (5) ◽

pp. 1053-1068 ◽

Cited By ~ 9

Author(s):

Jiyun Song ◽

Jun Xia ◽

Liping Zhang ◽

Zhi-Hua Wang ◽

Hui Wan ◽

...

Keyword(s):

Water Resources ◽

River Basin ◽

Environmental Changes ◽

Ungauged Basins ◽

Streamflow Prediction ◽

Predictive Tool ◽

Huai River Basin ◽

Huai River ◽

Regressive Method ◽

Water Resources Utilization

Streamflow information is of great significance for flood control, water resources utilization and management, ecological services, etc. Continuous streamflow prediction in ungauged basins remains a challenge, mainly due to data paucity and environmental changes. This study focuses on the modification of a nonlinear hydrological system approach known as the time variant gain model and the development of a regressive method based on the modified approach. This method directly correlates rainfall to runoff through physically based mathematical transformations without requiring additional information of evaporation or soil moisture. Also, it contains parsimonious parameters that can be derived from watershed properties. Both characteristics make this method suitable for practical uses in ungauged basins. The Huai River Basin of China was selected as the study area to test the regressive method. The results show that the proposed methodology provides an effective way to predict streamflow of ungauged basins with reasonable accuracy by incorporating regional watershed information (soil, land use, topography, etc.). This study provides a useful predictive tool for future water resources utilization and management for data-sparse areas or watersheds with environmental changes.

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

E3S Web of Conferences ◽

10.1051/e3sconf/202016301001 ◽

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.

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Multi-model ensemble hydrologic prediction and uncertainties analysis

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-364-249-2014 ◽

2014 ◽

Vol 364 ◽

pp. 249-254 ◽

Cited By ~ 1

Author(s):

S. Jiang ◽

L. Ren ◽

X. Yang ◽

M. Ma ◽

Y. Liu

Keyword(s):

Parameter Optimization ◽

Optimization Algorithms ◽

Model Parameters ◽

Model Ensemble ◽

Streamflow Prediction ◽

Parameter Uncertainties ◽

Input Uncertainty ◽

Daily Streamflow ◽

Shuffled Complex Evolution ◽

Modelling Uncertainties

Abstract. Modelling uncertainties (i.e. input errors, parameter uncertainties and model structural errors) inevitably exist in hydrological prediction. A lot of recent attention has focused on these, of which input error modelling, parameter optimization and multi-model ensemble strategies are the three most popular methods to demonstrate the impacts of modelling uncertainties. In this paper the Xinanjiang model, the Hybrid rainfall–runoff model and the HYMOD model were applied to the Mishui Basin, south China, for daily streamflow ensemble simulation and uncertainty analysis. The three models were first calibrated by two parameter optimization algorithms, namely, the Shuffled Complex Evolution method (SCE-UA) and the Shuffled Complex Evolution Metropolis method (SCEM-UA); next, the input uncertainty was accounted for by introducing a normally-distributed error multiplier; then, the simulation sets calculated from the three models were combined by Bayesian model averaging (BMA). The results show that both these parameter optimization algorithms generate good streamflow simulations; specifically the SCEM-UA can imply parameter uncertainty and give the posterior distribution of the parameters. Considering the precipitation input uncertainty, the streamflow simulation precision does not improve very much. While the BMA combination not only improves the streamflow prediction precision, it also gives quantitative uncertainty bounds for the simulation sets. The SCEM-UA calculated prediction interval is better than the SCE-UA calculated one. These results suggest that considering the model parameters' uncertainties and doing multi-model ensemble simulations are very practical for streamflow prediction and flood forecasting, from which more precision prediction and more reliable uncertainty bounds can be generated.

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An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Water ◽

10.3390/w11040709 ◽

2019 ◽

Vol 11 (4) ◽

pp. 709 ◽

Cited By ~ 8

Author(s):

Mohammad Rezaie-Balf ◽

Sajad Fani Nowbandegani ◽

S. Zahra Samadi ◽

Hossein Fallah ◽

Sina Alaghmand

Keyword(s):

Correlation Function ◽

Gene Expression Programming ◽

Ensemble Empirical Mode Decomposition ◽

Streamflow Prediction ◽

Daily Streamflow ◽

Auto Correlation ◽

Agricultural Planning ◽

Mode Decomposition ◽

Auto Correlation Function ◽

Input Variables

Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

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Multi-year variability in hydrological extremes in Africa: what are the main drivers?

10.5194/egusphere-egu21-7981 ◽

2021 ◽

Author(s):

Job Ekolu ◽

Bastien Dieppois ◽

Moussa Sidibe ◽

Jonathan Eden ◽

Yves Tramblay ◽

...

Keyword(s):

Machine Learning Algorithms ◽

Low Flow ◽

African Continent ◽

Threshold Method ◽

Daily Streamflow ◽

Hydrological Variability ◽

Hydrological Extremes ◽

Importance Analysis ◽

High Level ◽

The Impact

<p>Africa is affected by a high-level of temporal and spatial variability in climate, with large impacts on water resources, human lives and economies. Due to data scarcity, the impact of multi-year climate variations on hydrological variability and extremes, i.e. flood and drought, as well as how catchment properties could modulate those impacts, are generally poorly understood across the African continent. In this study, we first use machine learning algorithms to develop a new complete reconstructed daily streamflow dataset using more than 1500 stream gauges between 1950 and 2018, and covering most of Africa. We then examine historical trends and variability in hydrological extremes over the entire African continent, focusing on different hydrological characteristics, such as the timing, frequency and duration of high- and low-flow events, based on the peaks-over-threshold method. Following an assessment of the relative sensitivities of hydrological extreme indices to interannual (2-8-years) and decadal (>10-years) variability in the different regions of Africa, we analyze the respective contribution of different rainfall, temperature and soil moisture indices (e.g. frequency, duration and intensity of wet/dry or warmer/colder days) at both timescales, using relative importance analysis. We finally discuss how catchment properties (e.g. area, topography, land use/ land cover, drainage path lengths) modulate the relationship between hydrological extremes and climate.</p>

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