Belgium is ranked 23rd out of 164 countries in water scarcity and the third highest in Europe according to the Water Resource Institute. The warm and dry summers of the past few years have made it clear that Flanders has little if any buffer to cope with a sharp increase in water demand or a prolonged period of dry weather. To increase the resilience and preparedness against droughts, we developed the framework named hAIdro: an operational early warning system for low flows that allows to take timely, local and effective measures against water shortages. Data driven rainfall-runoff models are at the core of the forecasting system that allows to forecast droughts up to 30 days ahead.

The architecture of the data driven hydrological models are inspired by the Multi-Timescale Long Short Term Memory (MTS-LSTM, [1]) that allow to integrate past and future data in one prediction pipeline. The model architecture consists of 3 LTSM’s that are organized in a branched structure. The historical branch processes the historical meteorological data, remote sensing data and static catchment features into encoded state vectors. These are passed through fully connected layers to both a daily and an hourly forecasting branch which are used to make runoff predictions on short (72 hours) and long (30 days) time horizons. The forecasting branches are fed with forecasts of rainfall and temperature, static catchment features and discharge observations. The novelty of the proposed model structure lies in the way discharge observations are incorporated. Only the most recent discharge observations are used in the forecasting branches to minimize the consequences of missing discharge observations in an operational context. The models are trained using a weighted Nash-Sutcliffe Efficiency (NSE) as objective function that puts additional emphasis on low flows. Results show that the newly created data driven models perform well compared to calibrated lumped hydrological PDM models [2] for various performance metrics including Log-NSE and NSE.

We developed a custom cloud-based operational forecasting system, called hAIdro to bring the data driven hydrological models in production. hAIdro processes large quantities of local meteorological measurements, radar rainfall data and ECMWF extended range forecasts to make probabilistic forecasts up to 30 days ahead. hAIdro has been forecasting the runoff twice a day for 262 locations spread over Flanders since April 2021. A continuous monitoring and evaluation framework provides valuable insights in the online model performance and the informative value of hAIdro.

[1] M. Gauch, F. Kratzert, D. Klotz, G. Nearing, J. Lin, and S. Hochreiter. “Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network.” Hydrol. Earth Syst. Sci., 25, 2045–2062, 2021 

[2] Moore, R. J. “The PDM rainfall-runoff model.” Hydrol. Earth Syst. Sci., 11, 483–499,  2007

How to cite: Franken, T., Gullentops, C., Wolfs, V., Defloor, W., Cabus, P., and De Jongh, I.: An operational framework for data driven low flow forecasts in Flanders, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6191, https://doi.org/10.5194/egusphere-egu22-6191, 2022.

A drought is a slowly developing natural phenomenon that can occur in all climatic zones and can be defined as a temporary but significant decrease in water availability. Over the past three decades, the cost of droughts in Europe amounted to over 100 billion euros, with the recent summer droughts being unprecedented in the last 2,000 years. Although drought monitoring and management are extensively studied in the literature, capturing the evolution of drought dynamics, and associated impacts across different temporal and spatial scales remains a critical, unsolved challenge.

In this work, we contribute with a Machine Learning procedure named FRIDA (FRamework for Index-based Drought Analysis) for the identification of impact-based drought indexes. FRIDA is a fully automated data-driven approach that relies on advanced feature extraction algorithms to identify relevant drought drivers from a pool of candidate hydro-meteorological predictors. The selected predictors are then combined into an index representing a surrogate of the drought conditions in the considered area, including either observed or simulated water deficits or remotely sensed information on crop status. Notably, FRIDA leverages multi-task learning algorithms to upscale the analysis over a large region where drought impacts might depend on diverse but potentially correlated drivers. FRIDA captures the heterogeneous features of the different sub-regions while efficiently using all available data and exploiting the commonalities across sub-regions. In this way, the accuracy of the resulting prediction benefits from a reduced uncertainty compared to training separate models for each sub-region. Several real-world examples will be used to provide a synthesis of recent applications of FRIDA in case studies featuring diverse hydroclimatic conditions and variable levels of data availability.

How to cite: Giuliani, M., Bonetti, P., Metelli, A. M., Restelli, M., and Castelletti, A.: Advancing drought monitoring via feature extraction and multi-task learning algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5130, https://doi.org/10.5194/egusphere-egu22-5130, 2022.

Improving streamflow forecasts helps in reducing socio-economical impacts of hydrological-related damages. Among them, improving hydropower production is a challenge, even more so in a context of climate change. Deep learning models drew the attention of scientists working on forecasting models based on physical laws, since they got recognition in other domains. Artificial Neural Network (ANN) offer promising performance for streamflow forecasts, including good accuracy and lesser time to run compared to traditional physically-based models. 

The objective of this study is to compare different spatial discretization schemes of inputs in an ANN model for streamflow forecast. The study focuses on the “Au Saumon” watershed in Southern Quebec (Canada) during summer periods, with a forecast window of 7 days at a daily timestep. Parameterization of the ANN was a key preliminary step: the number of neurons in the hidden layer was first optimized, leading to 6 neurons. The model was trained on a 11-year dataset (2000-2005 and 2007-2011) followed by model validation on one dry (2012) and one wet (2006) year to take into account extreme hydrologic regimes. 

To lead this study, the physically-based hydrological ‘Hydrotel’ model is the reference to compare our results. The model defines watershed heterogeneity using hydrological units based on land uses, soil types, and topography, called Relative Homogeneous Hydrological Units (RHHU). The Nash-Sutcliffe Efficiency score (NSE) is the main evaluation criteria calculated. In a preliminary step, we have to ensure the ANN model can satisfactorily mimic Hydrotel. With the same model inputs, that is same variables and same spatial discretizations of variables (total precipitation, daily maximum and minimum temperatures, and soil surface humidity), the ANN forecasts were found to be better than those of Hydrotel for one to 7-day forecasts. 

Three different watershed spatial discretizations were tested: global, fully distributed, and semi-distributed. For the global model, hydrometeorological data used as inputs to the ANN model were averaged across all RHHUs. The complexity is reduced with loss of spatial details and heterogeneity. For the fully distributed model, a regular grid was defined with six cells of 28x28km2 covering all the watershed. For the semi-distributed model, spatial distribution of the input data was that of the RHHUs. For this discretization, the state variables (soil moisture and outflow) were updated at each forecast timestep, whether on all RHHUs, or only on the RHHU of the outlet.

Depending on the spatial discretization of inputs used, the accuracy differed. The fully distributed model offered the least performance, with NSE values of 0.85 ,while the global model surprisingly performed better with a 0.93 NSE. Moreover, updating soil moisture on all the RHHUs of the semi-distributed model improved the NSE across the entire window of forecast.

This research will assess the ANN model performance developed using ERA5-land precipitation and temperature reanalysis and ground observations of soil moisture. Given the promising results obtained with the fully and semi distributed models, our ANN model will be tested with state variables retrieved from satellite data, such as surface soil moisture from SMAP and SMOS missions.

How to cite: Buire, M., Ahlouche, M., Jougla, R., and Leconte, R.: Forecasting streamflow using Artificial Neural Network (ANN) with different spatial discretizations of the watershed : use case on the Au Saumon watershed in Quebec (Canada)., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1767, https://doi.org/10.5194/egusphere-egu22-1767, 2022.

How to cite: Durrant, A., Haro, D., and Leontidis, G.: Graph Neural Networks for Reservoir Level Forecasting and Draught Identification , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3946, https://doi.org/10.5194/egusphere-egu22-3946, 2022.

Reservoirs and dams are essential infrastructures for human utilization and management of water resources; yet modelling real-time reservoir operation and controlled reservoir outflow remains a challenge. Artificial intelligence techniques, especially machine learning and deep learning, have become increasingly popular in hydrological forecasting, including reservoir operation. In this study, we applied a recurrent neural network (RNN) and a long short-term memory (LSTM) to model the reservoir operation and outflow of a large-scale multi-purpose reservoir at the real-time (daily) timescale. This study aims to investigate the capabilities of RNN and LSTM models in simulating and reforecasting the real-time reservoir outflow, considering the uncertainties in model inputs, model training-testing periods, and different model algorithms. The Sirikit reservoir in Thailand was selected as a case study. The main inputs for the RNN and LSTM models were daily reservoir inflow, daily storage, and the month of the year. We applied the distributed wflow_sbm model for reservoir inflow simulation (using MSWEP precipitation data) and ensemble inflow reforecasting (using ECMWF precipitation data). Daily reservoir storage was obtained from observations and real-time recalculation based on the reservoir water balance. The models were trained and tested with 10-fold cross-validation. Results show that both RNN and LSTM models have high accuracies for real-time simulations and reasonable accuracies for multi-step reforecasts, and that LSTM exhibits better model performance in forecasting mode. The performance varied between each cross-validation, being highly related to the extreme events included in either training or test period. With further understanding of the reservoir inflow uncertainty influences on reservoir operation, we conclude that the models can be potentially applicable in real-time reservoir operation and decision-making for operational water management.

How to cite: Wannasin, C., Brauer, C., Uijlenhoet, R., Torfs, P., and Weerts, A.: Simulating and multi-step reforecasting real-time reservoir operation using combined neural network and distributed hydrological model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9771, https://doi.org/10.5194/egusphere-egu22-9771, 2022.

Reliable streamflow predictions are critical for managing water resources for flood warning, agricultural irrigation apportionment, hydroelectric production, to name a few. However, there are geographical heterogeneities in available observed streamflow data, river basin geophysical attributes, and meteorological data to support such predictions. Moreover, in data-sparse regions, both process-based and data-driven models have difficulties in being sufficiently calibrated or trained; increasing the difficulty to achieve satisfactory predictions. That being mentioned, it is possible to transfer knowledge from regions with dense and available measured data to data-sparse regions. In earlier work, we have shown that transfer learning based on a long short-term memory (LSTM) network, pre-trained over the conterminous United States, could improve daily streamflow prediction in Quebec (Canada) when compared to a semi-distributed hydrological model (HYDROTEL). The dataset used for pre-training (source dataset) was the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS), while the data for the basins located at the target locations (local dataset) were extracted from the Hydrometeorological Sandbox-École de Technologie Supérieure (HYSETS). Both datasets provide access to various types of information with different spatial resolutions. While HYSETS is generally spanning from 1950 to 2018, the temporal interval for most of the basins reported in CAMELS goes back to 1980. The types of data included in both CAMELS and HYSETS include daily meteorological data (precipitation, temperature, etc.), streamflow observations, and basins physiographic attributes (i.e., considered time-invariant or static). In this work, the techniques applied to further improve streamflow simulations included the use of: (i) streamflow observations and simulated flows from HYDROTEL as input to the LSTM model, (ii) different forcing (meteorological data) and static attribute data from the source and the local datasets, and (iii) additional basins from HYSETS with similar climatological features for model training. The ultimate goal was to improve the accuracy of the predicted hydrographs with an emphasis on enhancing the prediction of peak flows by transfer learning while using the Kling-Gupta efficiency (KGE) and Nash-Sutcliffe efficiency (NSE) metrics. This investigation has revealed the benefits of using transfer learning techniques based on deep learning models to improve streamflow predictions when compared to the application of a distributed hydrological models in data-sparse regions.

How to cite: Khoshkalam, Y., Rahmani, F., Rousseau, A. N., Abbasnezhadi, K., Shen, C., and Foulon, E.: Assessment of Transfer Learning Techniques to Improve Streamflow Predictions in Data-Sparse Regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3281, https://doi.org/10.5194/egusphere-egu22-3281, 2022.

Deep learning methods have repeatedly proven to outperform conceptual hydrologic models in rainfall-runoff modelling. Although attempts of investigating the internals of such deep learning models are being made, traceability of model states and processes and their interrelations to model input and output is not fully given, yet. Direct interpretability of mechanistic processes has always been considered as asset of conceptual models that helps to gain system understanding aside of predictability. We introduce hydrologic Neural ODE models that perform as well as state-of-the-art deep learning methods in rainfall-runoff prediction while maintaining the ease of interpretability of conceptual hydrologic models. In Neural ODEs, model internal processes that are typically implemented in differential equations by hard-coding are substituted by neural networks. Therefore, Neural ODE models offer a way to fuse deep learning with mechanistic modelling yielding time-continuous solutions. We demonstrate the basin-specific predictive capability for several hundred catchments of the continental US, and exemplarily give insight to what the neural networks within the ODE models have learned about the model internal processes. Further, we discuss the role of Neural ODE models on the middle ground between pure deep learning and pure conceptual hydrologic models.

How to cite: Höge, M., Scheidegger, A., Baity-Jesi, M., Albert, C., and Fenicia, F.: Neural ODEs in Hydrology: Fusing Conceptual Models with Deep Learning for Improved Predictions and Process Understanding, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3661, https://doi.org/10.5194/egusphere-egu22-3661, 2022.

Hydrodynamic models (numerical solutions of the Saint Venant equations) are at the core of simulating water movements in natural streams and drainage systems. They enable realistic simulations of water movement and are directly linked to physical system characteristics such as channel slope and diameter. This feature is important for man-made drainage structures as it enables straightforward testing of the effects of varying channel designs. In cities, models with hundreds up to tens of thousands of pipes are commonly used for drainage infrastructure. Their computational expense remains high and they are not suited for a systematic screening of design options, discussing water management options in workshops, as well as many real-time applications such as data assimilation.

Hydrologists have developed many approaches to enable faster simulations. All of these do, however, compromise on the physical detail of the simulated processes (for example, by simulating only flows using linear reservoirs), and usually also on the spatial and temporal resolution of the models (for example, by simulating only flows between key points in the system). The link to physical system characteristics is thus lost. Therefore, it is challenging to incorporate such approaches into planning workflows where changing city plans require a constant revision of water management options.

Recent advances in scientific machine learning enable the creation of fast machine learning surrogates for complex systems that preserve a high spatio-temporal detail and a physically accurate simulation. We present such an approach that employs generalized residue networks for the simulation of hydraulics in drainage systems. The key concept is to train neural networks that learn how hydraulic states (level, flow and surcharge volume) at all nodes and pipes in the drainage network evolve from one time step to another, given a set of boundary conditions (surface runoff). Training is performed against the output of a hydrodynamic model for a short time series.

Once trained, the surrogates generate the same results as a hydrodynamic model in the same level of detail, and they can be used to quickly simulate the effect of many different rain events and climate scenarios. Considering pipe networks with 50 to 100 pipes, our approach achieves NSE values in the order of 0.95 for the testing dataset. Simulations are performed 10 to 50 times faster than the hydrodynamic model. Training times are in the order of 25 minutes on a single CPU. The surrogates are system specific and need to be retrained when the physical system changes. To minimize this overhead, we train surrogates for small subsystems which can subsequently be linked into a model for a large drainage network.

Our approach is an initial application of scientific machine learning for the simulation of hydraulics that is readily combined with other recent developments. Future research should, in particular, explore the application of physics-informed loss functions for bypassing the generation of training data from hydrodynamic simulations, and of graph neural networks to exploit spatial correlation structures in the pipe network.

How to cite: Löwe, R., Palmitessa, R., Engsig-Karup, A. P., and Grum, M.: Fast and detailed emulation of urban drainage flows using physics-guided machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4303, https://doi.org/10.5194/egusphere-egu22-4303, 2022.

Modeling the rainfall-runoff process has been a key challenge for hydrologists. Multiple modeling frameworks have been introduced with time to understand and predict the runoff generation process, including physics-based models, conceptual models, and data-driven models. In recent years the use of deep learning models like Long Short-Term Memory (LSTM) has increased in hydrology because of its ability to learn information in the sequence of input. Studies report LSTM outperforms the well-established hydrological models (e.g. SAC-SMA), which led authors to question the need for process understanding in the machine learning era. In the current study, we claim that process understanding helps to reduce LSTM model complexity and ultimately improves recession flow prediction. Here, we used past streamflow information as input to LSTM and predicted ten days of recession flow. To reduce LSTM complexity, we used insights from a conceptual hydrological model that accounts for storage-discharge dynamics. Overall, our study re-emphasizes the need to understand hydrological processes.

How to cite: Istalkar, P., Kadu, A., and Biswal, B.: Physics-informed LSTM structure for recession flow prediction    , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11110, https://doi.org/10.5194/egusphere-egu22-11110, 2022.

Drainage ditches are common forestry practice across northern European boreal forests and in some parts of North America. Ditching helps with lowering the groundwater level in the wet parts of the forest to improve soil aeration and to support tree growth. However, the intensive ditching practice pose multidimensional environmental risks, particularly for degradation of wetland and soil, greenhouse gas, increased nutrient and sediment loadings to water bodies, as well as biodiversity loss. At the same time there is a discrepancy between the potential significance of artificial water bodies, such as drainage ditches and their low representation in scientific research and water management policy. A comparison with a national inventory of Sweden showed that only 9 % of drainage ditches are present on the best avalible map of Sweden. The increasing understanding of the environmental risks associated with forest ditches together with the poor representation of ditch networks in existing maps of many forest landscapes makes detailed mapping of these ditches a priority for sustainable land and water management. Here, we combine two state-of-the-art technologies – airborne laser scanning and deep learning - for detecting drainage ditches on a national scale.

A deep neural network was trained on airborne laser scanning data and 1607 km of manually digitized ditch channels from 10 regions spread across Sweden. 20 % of the data was set aside for testing the model.  The model correctly mapped 82 % of all small drainage channels in the test data with a Matthew's correlation coefficient of 0.72. This approach only requires one topographical index, a high pass median filter calculated from a digital elevation model with a 1 m spatial resolution. This made it possible to scale up over large areas with limited computational resources and the trained model was implemented using Microsoft Azure to map ditch channels across all of Sweden. The total mapped channel length was 970 00 km (equivalent to 24 times around the world). Visual inspection indicated that this method also classifies natural stream channels as drainage channels, which suggests that a deep neural network can be trained to detect natural stream channels in addition to drainage ditches. The model only required one topographical index which makes it possible to implement this approach in other areas with access to high resolution digital elevation data.

How to cite: Lidberg, W., Paul, S., Westphal, F., and Ågren, A.: Mapping Sweden’s drainage ditches using deep learning and airborne laser scanning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4639, https://doi.org/10.5194/egusphere-egu22-4639, 2022.