HS4.3

Hydrological processes and phenomena are naturally complex and nonlinear. Many physiographical variables such as those dealing with drainage network characteristics may influence streamflow characteristics and should be considered in regional frequency analysis (RFA). These variables have hence a significant impact on the effectiveness of flood quantile estimation techniques. Although many statistical tools are considered to estimate flood quantiles at ungauged sites in the hydrological literature, little attention has been given to the nonlinearity and to the high-dimensionality of physio-meteorological variable space. In this study, the multivariate adaptive regression splines (MARS) approach is introduced in RFA. This model allows to account simultaneously for non-linearity and interactions between variables hidden in high-dimensional data. MARS is hereby applied on two datasets of 151 hydrometric stations located in the southern part of the province of Quebec (Canada): a standard dataset (STA) including commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. It is then compared to generalized additive models (GAM), a state-of-the-art method for regional estimation. Numerical results show that MARS outperforms GAM, especially with the extensive database EXTD. The study suggests that MARS may be a promising tool to take into account the complexity of the hydrological phenomena involved and the increasing number of variables used in RFA.

How to cite: Msilini, A., Masselot, P., and Ouarda, T. B. M. J.: Accounting for high-dimensional predictors in RFA with MARS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-299, https://doi.org/10.5194/egusphere-egu21-299, 2021.

Hydrological time series modeling is an important task in water resources planning and management. However, time series may include noise, which can result in an inaccurate model. Therefore, removing noise from time series is valuable to obtain accurate predictions. The aims of this study are i) to develop and compare Long-Short Term Memory (LSTM) and Gated Recurring Units (GRU) Deep Learning (DL) models to predict hydrological time series and ii) to integrate a preprocessing method, Gaussian Filter (GF), to smooth out time series and couple it with DL to improve prediction accuracy. Moreover, the DL models are benchmarked against statistical time series models (e.g., Seasonal Autoregressive Integrated Moving Average (SARIMA)) to assess their added value for hydrological time series modeling. To establish predictive models, several monthly hydrological time series including water level (e.g., from the Great Lakes in North America, including Lakes Michigan, Ontario, and Erie (1918-2019)) and streamflow (e.g., gauging stations at Umfreville, along the English River, Ontario, Canada (1921-2019), Rapides Fryers, along the Richelieu River, Quebec, Canada (1937-2020) and near Lethbridge, along the Oldman River, Alberta, Canada (1957-2019)) were explored. For developing non-GF- and GF-DL models, time series were partitioned into training (70% of the data) and testing (the remaining 30% of the data) subsets and the time series’ past measurements up to 12 months (t-1, t-2, ..., t-12) were served to the DL models (LSTM and GRU) to predict the time series at time t. The structure of the DL models was tuned using Bayesian optimization. The SARIMA models (i.e., non-GF- and GF-SARIMA) were also implemented and tuned using pmdarima's auto-arima function. After calibrating the models, the testing step was implemented and the performance of the models was evaluated using statistical indicators including correlation coefficient, root mean square error, mean absolute error, the Nash-Sutcliffe efficiency coefficient, and Willmot’s index. The results of the developed DL models showed that the GRU outperforms the LSTM models. Moreover, both LSTM and GRU have superior performance when compared to the SARIMA models. It is observed that GF preprocessing significantly improves the accuracy of the developed DL and SARIMA models. It is concluded that coupling GF preprocessing with DL, due to capturing both linear and nonlinear features of the time series, represents a promising tool for obtaining accurate hydrological time series predictions.

How to cite: Barzegar, R., Adamowski, J., and Quilty, J.: Improving Deep Learning hydrological time series modeling using Gaussian Filter preprocessing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1644, https://doi.org/10.5194/egusphere-egu21-1644, 2021.

Sub-seasonal streamflow forecasts (with lead times of 1-30 days) provide valuable information for many consequential water resource management decisions, including reservoir operation to meet environmental flow and irrigation demands, issuance of early flood warnings, and others. A key aim is to produce “seamless” forecasts, with high quality performance across the full range of lead times and time scales.  

This presentation introduces the Multi-Temporal Hydrological Residual Error model (MuTHRE) to address the challenge of obtaining “seamless” sub-seasonal forecasts, i.e., daily forecasts with consistent high-quality performance over multiple lead times (1-30 days) and aggregation scales (daily to monthly).

The model is designed to overcome common errors in streamflow forecasts:

• Seasonality
• Dynamic biases due to hydrological non-stationarity
• Extreme errors poorly represented by the common Gaussian distribution.

The model is evaluated comprehensively over 11 catchments in the Murray-Darling Basin, Australia, using multiple performance metrics to scrutinize forecast reliability, sharpness and bias, across a range of lead times, months and years, at daily and monthly time scales.

The MuTHRE model provides ”high” improvements, in terms of reliability for

• Short lead times (up to 10 days), due to representing non-Gaussian errors
• Stratified by month, due to representing seasonality in hydrological errors
• Dry years, due to representing dynamic biases in hydrological errors.

Forecast performance also improved in terms of sharpness, volumetric bias and CRPS skill score; Importantly, improvements are consistent across multiple time scales (daily and monthly).

This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.

How to cite: Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Tuteja, N., and Kuczera, G.: Do you want Seamless Subseasonal Streamflow Forecasts?    Ask MuTHRE!, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3714, https://doi.org/10.5194/egusphere-egu21-3714, 2021.

This work presents the development of the inflow ensemble forecasting system for Itaipu Dam. The system is based on combination of twelve Quantitative Precipitation Forecast (QPF) with three hydrological models and one hydrodynamic one-dimensional model. The QPF are provided by different meteorological institutions based in the results of the global Numerical Weather Prediction (NWP) models GFS and ECMWF, as well as of the regional NWP models WRF and COSMO executed by the Brazilian and Paraguay meteorological services (SIMEPAR, INMET and DINAC). The semi-distributed model MGB – Large Basin Model, the lumped models SMAP and HEC-HMS are considered as the hydrological models. Furthermore, the computed flows are propagated in a HEC-RAS scheme designed with extensive field data from bathymetry of Parana River and tributaries. The daily results are presented as fifteen inflow scenarios that are considered for the definition of a unique flow forecast. Finally, that forecast is used for the electric generation scheduling of the power plant. Each of these fifteen methods performance was evaluated as well as the suitability of the system for it purposes. For a short-term forecast horizon (less than 4 days), the performances of the hydrological models forced by the different rain forecast are quite similar. However, it is remarkable the difference between the results of the three hydrological models for the same horizon. On the other hand, for medium-term horizon (more than 4 days) both hydrological and meteorological models have diverse behavior and contribute for a wide representation of the possible scenarios. Overall, it has been showed that the simulations are complementary and provides to the forecaster a general overview of the hydrologic situation. Nevertheless, at this moment, further analysis of accuracy and reliability of the prediction have not been realized, so the forecaster needs to appeal to its own expertise to assure the consistence of the scenarios for decision-making.

How to cite: Quevedo, J., Firmo Kazay, D., Maria Werlang, M., Gomes, G., Takahashi, R., Zaicovski, M., Villanueva Aguero, T., and Fariña Jara, J. M.: Coupling meteorological forecasts with hydrologic and hydraulic models: The Itaipu Dam ensemble inflow forecasting system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6228, https://doi.org/10.5194/egusphere-egu21-6228, 2021.

The quality of water level predictions is highly dependent on the success of the flow forecasts that inform the hydraulic model. Ensemble predictions, by considering several sources of uncertainty, provide more accurate and reliable forecasts. In this project, we aim to evaluate a water level ensemble prediction system coupling a hydraulic model to an ensemble streamflow prediction system accounting for 3 sources of uncertainty: meteorological data, hydrological processing (multimodel) and data assimilation to update the initial conditions. The hydraulic model is previously calibrated and validated and the roughness coefficients are adapted as a function of flow according to predefined relationships developed for several river segments. The forecasts reliability and accuracy are then assessed at each layer of the forecasting system and the outcomes are illustrated comparing the ensembles skills and reliability for the considered events. Overall, the results show that accounting of the hydrometeorological uncertainty improves the performances of the water level forecasts for different lead times.

How to cite: Bessar, M. A., Anctil, F., and Matte, P.: Evaluation of a short-term ensemble water level forecasting system: case of the Chaudière River, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8000, https://doi.org/10.5194/egusphere-egu21-8000, 2021.

Wood, Andrew W., Tom Hopson, Andy Newman, Levi Brekke, Jeff Arnold, and Martyn Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology, doi: 10.1175/JHM-D-14-0213.1

How to cite: Arnal, L., Clark, M., Vionnet, V., Fortin, V., Pietroniro, A., and Wood, A.: Quantifying streamflow predictability across North America on sub-seasonal to seasonal timescales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8092, https://doi.org/10.5194/egusphere-egu21-8092, 2021.

Flooding is New Zealand’s most frequent natural disaster with an average annual cost of approximately NZ$51 million. Accurately forecasting convective and orographically enhanced precipitation for hydrometeorological ensemble prediction systems is challenging in Aotearoa New Zealand’s complex topographic regions with fast-responding and mostly ungauged catchments. Globally, designing convection-permitting ensemble flood forecasting chains is still a work in progress, with errors in the forecast rainfall amount and the location or timing of storm events a significant contributor to uncertainties in river flow forecasts. Given operational, computational and model representation constraints, compromises are often required on ensemble size, frequency of forecast issue times, NWP model resolution, domain size and data assimilation strategies. This research aims to design an optimal operational forecasting chain for convective-scale flood forecasting in New Zealand.  In doing so, our goal is to improve uncertainty representation in hydrometeorological forecasts during flood events by understanding the impact of convective-scale ensemble strategies.

The NWP model used is a local implementation of the UK Met Office-developed Unified Model.  The New Zealand Convective-Scale Model (NZCSM) is NIWA’s 1.5km high-resolution operational forecast model, configured such that convective processes develop explicitly. The New Zealand Ensemble (NZENS) is configured with similar convection-permitting model physics but operates with a 4.5km horizontal resolution and features up to 18 members.  Flood forecasts were produced by coupling several weather ensemble configurations with the semi-distributed hydrological model TopNet and its built-in statistical ensemble generation tool. TopNet is based on TOPMODEL concepts of runoff generation controlled by sub-surface water storage.

In this study, we evaluated three ensemble strategies for flood forecasting. The experiment design allowed for the effect of model horizontal resolution (and thus the representation of orography) to be investigated using ensemble forecasts from consecutive initialization times (a “lagged ensemble”), and from the same initialisation time (a “dynamical ensemble”). The third forecasting chain is a “statistical ensemble” generated by perturbing the deterministic 1.5km NWP model and hydrological states. For recent flood events across multiple case study catchments, we evaluated the impact of each approach on flood forecast performance. Flood forecasts were most sensitive to convective-scale forecasts with consecutive issue time initialisations (lagged ensemble) over other hydrometeorological ensemble configurations considered. Given dynamical ensembles are computationally expensive, the study suggests an optimal strategy might be to produce a small ensemble pool of dynamical forecasts at more frequent issue times combined with statistically post-processed ensembles rather than a larger ensemble pool generated less frequently.

How to cite: Cattoën, C., Moore, S., and Carey-Smith, T.: Designing an optimal flood forecasting chain using convective-scale ensembles: a sensitivity study., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8098, https://doi.org/10.5194/egusphere-egu21-8098, 2021.

The skill of seasonal hydro-meteorological forecasts with a lead time of up to six months is currently limited, since they frequently exhibit random but also systematic errors. Bias correction algorithms can be applied and provide an effective approach in removing historical biases relative to observations. Systematic errors in hydrology model outputs can be consequence of different sources: i) errors in meteorological data used as input data, ii) errors in the hydrological model response to climate forcings, iii) unknown/unobservable internal states and iv) errors in the model parameterizations, also due to unresolved subgrid scale variability.

Normally, bias correction techniques are used to correct meteorological, e.g. precipitation data, provided by climate models. Only few studies are available applying these techniques to hydrological model outputs. Standard bias correction techniques used in literature can be classified into scaling-, and distributional-based methods. The former consists of using multiplicative or additive scaling factors to correct the modeled simulations, while the later methods are quantile mapping techniques that fit the distribution of the simulation to fit to the observations. In this study, the impact of different bias correction techniques on the seasonal discharge forecasts skill is assessed.

As a case study, a seasonal discharge forecasting system developed for the Danube basin upstream of Vienna, is used. The studied basin covers an area of around 100 000 km² and is subdivided in 65 subbasins, 55 of them gauged with a long historical record of observed discharge. The forecast system uses the calibrated hydrological model, COSERO, which is fed with an ensemble of seasonal temperature and precipitation forecasts. The output of the model provides an ensemble of seasonal discharge forecasts for each of the (gauged) subbasins. Seasonal meteorological forecasts for the past (hindcast), together with historical discharge observations, allow to assess the quality of the seasonal discharge forecasting system, also including the effects of different bias correction methods. The corrections applied to the discharge simulations allow to eliminate potential systematic errors between the modeled and observed values.

Our findings generally suggest that the quality of the seasonal forecasts improve when applying bias correction. Compared to simpler methods, which use additive or multiplicative scaling factors, quantile mapping techniques tend to be more appropriate in removing errors in the ensemble seasonal forecasts.

How to cite: Martin Santos, I., Herrnegger, M., and Holzmann, H.: Impact of bias correction techniques on an ensemble of seasonal discharge forecast for the Danube upstream of Vienna, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12145, https://doi.org/10.5194/egusphere-egu21-12145, 2021.

The estimation of the impacts of climate change on hydrology at the local level comprises various sources of uncertainty. Especially, global climate models (GCMs) are found to be one of the major sources of uncertainty at the local level and it is important to identify for robust water resource planning and management. Therefore, this study demonstrates the separate and multi-model ensemble GCMs uncertainty for the surface runoff projections for near, mid, and far future under representative concentration pathway (RCP) 4.5 (present condition) and RCP 8.5 (worst condition) at medium level river sub-basins scale in the Western Ghats region of India. The results indicate that considered GCMs are not appropriate for use to prediction of peak surface runoff in the wet season. In addition, uncertainty from ensemble GCMs is closer to actual data than individual GCM because of closely associated with ensemble rainfall data which is maximum influencing the peak surface runoff for the near mid, and far future. Furthermore, findings also suggest that the selection of appropriate GCMs for the study of peak flow analysis at the local level is important for the projection of future surface runoff. Therefore, it is also important to make attention to rainfall data while projecting surface runoff for future time periods in the humid tropic regions.

How to cite: Sinha, R. K. and Eldho, T. I.: Estimation of Uncertainty Contribution of Multiple Sources of GCMs in Hydrological Prediction., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12896, https://doi.org/10.5194/egusphere-egu21-12896, 2021.

How to cite: Bent, O., Kuehnert, J., Remy, S., Jones, A., and Edwards, B.: Machine learning approaches to parameter calibration and uncertainty propagation for seasonal flood risk prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13262, https://doi.org/10.5194/egusphere-egu21-13262, 2021.

Information about monitoring of hydrological extremes, agricultural yields and irrigation may be informed by early warning, forecasts and flood management advice through appropriate modelling skills. However hydrological modelling is a challenging task in poorly gauged catchments, especially in developing countries like Kenya. Open access global precipitation and temperature reanalysis datasets with different spatial and temporal resolutions provide alternative sources in data-scarce regions but, individual reanalysis precipitation datasets have significant uncertainties. Inspired by data scarcity issues, significant spatial and temporal gaps in gauge observations, and poor performance of individual reanalysis in hydrological models, this study assess the performance of five new-era reanalysis datasets (ERA5, ERA-Interim, Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR) and Japanese 55-year Reanalysis Project(JRA55)) to simulate daily streamflow using the GR4J model across the 20 catchments in Kenya. Deviating from the modelling normality of calculating the model performance statistics for the calibration and validation periods to investigate whether a model serves as satisfactory representations of the natural hydrologic phenomenon, we couple with sensitivity analysis (SA) to unveil model structural uncertainty and suitability when forced with the different reanalysis products. In this study we use the reanalysis precipitation, maximum (T max) and minimum (T min) temperatures against the observations from the Climate Hazards group Precipitation (CHIRPS) for 1981–2016 to calculate performance statistics, streamflow simulations and sensitivity analysis. In addition, we develop model suitability index (MSI) by coupling the performance statistics with the sensitivity results across the different reanalysis products for our study catchments. Our results show that ERA5 performs better than other reanalysis products in terms of performance statistics and streamflow simulations at catchments scale. MSI results were suitable with ERA5 and lower in JRA55 across most of the Kenyan catchments, with 0.8 and 0.4 MSI respectively. MSI developed in this study is a quantitative measure that can be used for the comparison of reanalysis products for different catchments, thus useful for application to modelling to assess the suitability of both the modelling tools and catchment response to alternative forcings for early warning and inform early action.

How to cite: Wanzala, M., Ficchi, A., Cloke, H., and Stephens, E.: Assessment of Suitability of Global Reanalysis for Hydrological Applications by Coupling Performance Statistics and Sensitivity Analysis in Kenya, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14311, https://doi.org/10.5194/egusphere-egu21-14311, 2021.

Ensemble Streamflow Prediction (ESP) is a widely used method in forecasting streamflow, particularly for extremely low or high flows. However, the incorporation of reservoir operations in using ensemble streamflow prediction has not been investigated till yet. We calibrated Variable Infiltration Capacity (VIC) model for daily streamflow for Narmada river basin at four stations (Sandia, Handia, Mandleshwar and Garudeshwar) considering the effect of four reservoirs (Bargi, Tawa, Indira Sagar and Sardar Sarovar). The model is well-calibrated for the selected river basin (R2>0.55) at all locations. Further, routing of streamflow is done considering the reservoir storage dynamics and operating rules. Input data for ensemble prediction is taken from all 16 members of the Extended Range Forecast System (ERFS) developed by Indian Institute of Tropical Meteorology (IITM) and implemented by India Meteorological Department (IMD). Post-processing of the results gave us probabilities of uncertainties associated with streamflow prediction using ERFS members. This study provides key information in predictions of streamflow by incorporating the reservoirs based on the ERFS ensemble members, which can be used to effectively mitigate life and property losses associated with extreme flows in rivers.

How to cite: Vegad, U. and Mishra, V.: Probabilistic Streamflow forecast for Narmada River Basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15745, https://doi.org/10.5194/egusphere-egu21-15745, 2021.

Global flood forecasting systems rely on definition of flood thresholds for identifying upcoming flood events. Existing methods for flood threshold definition can often be based on reanalysis datasets and single thresholds, used for all forecast lead times, but this leads to inconsistencies between how the extreme flood events are represented in the flood thresholds and the ensemble forecasts.

This paper explores the potential benefits of using river flow ensemble reforecasts to generate flood thresholds that can deliver improved reliability and skill. Using the Copernicus Emergency Management Service’s Global Flood Awareness System, the impact of the dataset and the method used to sample the annual maxima to define flood thresholds, are analysed in terms of threshold magnitude, forecast reliability and skill for different flood severity levels and lead times.

It was found that the variability of the threshold magnitudes, when estimated from the different annual maxima samples, can be extremely large, as can the subsequent impact on forecast skill. It was also found that reanalysis-based thresholds should only be used for the first few days, after which ensemble-reforecast-based thresholds, that vary with forecast lead time and can account for the forecast bias trends, provide more reliable and skilful flood forecasts.

How to cite: Zsoter, E., Prudhomme, C., Stephens, E., and Cloke, H.: Using ensemble reforecasts to generate flood thresholds for improved global flood forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16303, https://doi.org/10.5194/egusphere-egu21-16303, 2021.