Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

Hagen, Jenny Sjåstad; Leblois, Étienne; Lawrence, Deborah; Solomatine, Dimitri; Sorteberg, Asgeir

doi:10.1016/j.jhydrol.2021.126086

Cited by 29 publications

(9 citation statements)

References 35 publications

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“…Multilayer perceptron (MLP) is considered a feed-forward neural network; it consists of inputs and outputs layers; in addition to hidden layers that can be one or more layers, each hidden layer contains one or more neurons, and each neuron in the hidden layer must have an activation function (Hagen et al, 2021).…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Machine Learning and Committee Models for Improving ECMWF Subseasonal to Seasonal (S2S) Precipitation Forecast

Elbasheer

Corzo

Solomatine

et al. 2023

Preprint

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Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) provides subseasonal to seasonal (S2S) precipitation forecasts; S2S forecasts extend from two weeks to two months ahead; however, the accuracy of S2S precipitation forecasting is still underdeveloped, and a lot of research and competitions have been proposed to study how machine learning (ML) can be used to improve forecast performance. This research explores the use of machine learning techniques to improve the ECMWF S2S precipitation forecast, here following the AI competition guidelines proposed by the S2S project and the World Meteorological Organisation (WMO). A baseline analysis of the ECMWF S2S precipitation hindcasts (2000–2019) targeting three categories (above normal, near normal and below normal) was performed using the ranked probability skill score (RPSS) and the receiver operating characteristic curve (ROC). A regional analysis of a time series was done to group similar (correlated) hydrometeorological time series variables. Three regions were finally selected based on their spatial and temporal correlations. The methodology first replicated the performance of the ECMWF forecast data available and used it as a reference for the experiments (baseline analysis). Two approaches were followed to build categorical classification correction models: (1) using ML and (2) using a committee model. The aim of both was to correct the categorical classifications (above normal, near normal and below normal) of the ECMWF S2S precipitation forecast. In the first approach, the ensemble mean was used as the input, and five ML techniques were trained and compared: k-nearest neighbours (k-NN), logistic regression (LR), artificial neural network multilayer perceptron (ANN-MLP), random forest (RF) and long–short-term memory (LSTM). Here, we have proposed a gridded spatial and temporal correlation analysis (autocorrelation, cross-correlation and semivariogram) for the input variable selection, allowing us to explore neighbours’ time series and their lags as inputs. These results provided the final data sets that were used for the training and validation of the machine learning models. The total precipitation (tp), two-metre temperature (t2m) and time series with a resolution of 1.5 by 1.5 degrees were the main variables used, and these two variables were provided as the global ECMWF S2S real-time forecasts, ECMWF S2S reforecasts/hindcasts and observation data from the National Oceanic and Atmospheric Administration (Climate Prediction Centre, CPC). The forecasting skills of the ML models were compared against a reference model (ECMWF S2S precipitation hindcasts and climatology) using RPSS, and the results from the first approach showed that LR and MLP were the best ML models in terms of RPSS values. In addition, a positive RPSS value with respect to climatology was obtained using MLP. It is important to highlight that LSTM models performed quite similarly to MLP yet had slightly lower scores overall. In the second approach, the committee model (CM) was used, in which, instead of using one ECMWF hindcast (ensemble mean), the problem is divided into many ANN-MLP models (train each ensemble member independently) that are later combined in a smart ensemble model (trained with LR). The cross-validation and testing of the CMs showed positive RPSS values regarding climatology, which can be interpreted as improved ECMWF on the three climatological regions. In conclusion, ML models have very low – if any – improvement, but by using a CM, the RPSS values are all better than the reference forecast. This study was done only on random samples over three global regions; a more comprehensive study should be performed to explore the whole range of possibilities.

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Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Machine Learning and Committee Models for Improving ECMWF Subseasonal to Seasonal (S2S) Precipitation Forecast

Elbasheer

Corzo

Solomatine

et al. 2023

Preprint

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“…Recent studies applying deep learning to rainfall-runoff simulation indicate that there is significantly more information in large-scale hydrological data sets than hydrologists have been able to translate into theory or models [129]. GeoAI has also revealed new hydrological patterns and trends, using heterogeneous data from different sources and quality [244,245]. Therefore, novel data-driven modeling provides the potential to gain new information and knowledge and a better understanding of the hydrological system and its changes [129,235].…”

Section: Geoai Capacity To Provide Novel Physical Insightsmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Gonzales‐Inca

Calle

Croghan

et al. 2022

Water

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This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

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“…Only a few studies have been conducted worldwide wherein these models were applied to predict long-term streamflow for future periods under the context of climate change (Das & Nanduri, 2018;Thapa et al, 2021;Adib & Harun, 2022). This limitation can be linked to the difficulties in data assimilation brought on by the use of scenario data from the general circulation models (GCMs) with coarse resolution, which prevents their direct application in the regional impact assessment (Hagen et al, 2021;Adib & Harun, 2022).…”

Section: Introductionmentioning

confidence: 99%

Futuristic Streamflow Prediction Based on Cmip6 Scenarios Using Machine Learning Models

Ullah,

Fawad,

Khan

et al. 2023

Preprint

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Accurate streamflow estimation is vital for effective water resources management, including flood mitigation, drought warning, and reservoir operation. This research assesses the predictive performance of popular machine learning algorithms (LSTM, Regression Tree, AdaBoost, and Gradient Boosting) for daily streamflow forecasting in the Swat River basin. Three key predictor variables (maximum temperature, minimum temperature, and precipitation) are utilized. The study evaluates and compares the effectiveness of ML models (Gradient Boosting, Regression Tree, AdaBoost, and LSTM) during two timeframes (2021–2050 and 2051–2100). Bias-corrected data from ten general circulation models, considering greenhouse gas trajectories (SSP245 and SSP585), are used. Statistical metrics like Coefficient of determination (R2), Mean square Error (MSE), Mean Absolute error (MAE), and Root Mean Square Error (RMSE) are employed for evaluation. Regression Tree exhibits exceptional performance (R2: 0.88 during training, 0.78 during testing). Ensembling Regression Tree, AdaBoost, and Gradient Boosting, future daily streamflow projections are made for SSP245 and SSP585 scenarios. Bias correction enhances reliability, with the ensemble mean indicating an increase in mean annual streamflow between the 2050s and 2080s (3.26–7.52% for SSP245, 3.77–13.55% for SSP585).

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Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

Cited by 29 publications

References 35 publications

Machine Learning and Committee Models for Improving ECMWF Subseasonal to Seasonal (S2S) Precipitation Forecast

Machine Learning and Committee Models for Improving ECMWF Subseasonal to Seasonal (S2S) Precipitation Forecast

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Futuristic Streamflow Prediction Based on Cmip6 Scenarios Using Machine Learning Models

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