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1
Thirel, Guillaume, Fabienne Rousset-Regimbeau, Eric Martin, and Florence Habets. "On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions." Journal of Hydrometeorology 9, no. 6 (December 1, 2008): 1301–17. http://dx.doi.org/10.1175/2008jhm959.1.
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Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.
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Shrestha, Rajesh R., Markus A. Schnorbus, and Alex J. Cannon. "A Dynamical Climate Model–Driven Hydrologic Prediction System for the Fraser River, Canada." Journal of Hydrometeorology 16, no. 3 (May 27, 2015): 1273–92. http://dx.doi.org/10.1175/jhm-d-14-0167.1.
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Abstract Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.
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Velázquez, J. A., F. Anctil, M. H. Ramos, and C. Perrin. "Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures." Advances in Geosciences 29 (February 28, 2011): 33–42. http://dx.doi.org/10.5194/adgeo-29-33-2011.
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Abstract. An operational hydrological ensemble forecasting system based on a meteorological ensemble prediction system (M-EPS) coupled with a hydrological model searches to capture the uncertainties associated with the meteorological prediction to better predict river flows. However, the structure of the hydrological model is also an important source of uncertainty that has to be taken into account. This study aims at evaluating and comparing the performance and the reliability of different types of hydrological ensemble prediction systems (H-EPS), when ensemble weather forecasts are combined with a multi-model approach. The study is based on 29 catchments in France and 16 lumped hydrological model structures, driven by the weather forecasts from the European centre for medium-range weather forecasts (ECMWF). Results show that the ensemble predictions produced by a combination of several hydrological model structures and meteorological ensembles have higher skill and reliability than ensemble predictions given either by one single hydrological model fed by weather ensemble predictions or by several hydrological models and a deterministic meteorological forecast.
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Yuan, Xing, Joshua K. Roundy, Eric F. Wood, and Justin Sheffield. "Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins." Bulletin of the American Meteorological Society 96, no. 11 (November 1, 2015): 1895–912. http://dx.doi.org/10.1175/bams-d-14-00003.1.
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Abstract Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ensemble forecasts, and better “real time” prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast—a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX RHP basins and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).
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Saleh, F., V. Ramaswamy, N. Georgas, A. F. Blumberg, and J. Pullen. "Inter-comparison between retrospective ensemble streamflow forecasts using meteorological inputs from ECMWF and NOAA/ESRL in the Hudson River sub-basins during Hurricane Irene (2011)." Hydrology Research 50, no. 1 (August 20, 2018): 166–86. http://dx.doi.org/10.2166/nh.2018.182.
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Abstract The objective of this work was to evaluate the benefits of using multi-model meteorological ensembles in representing the uncertainty of hydrologic forecasts. An inter-comparison experiment was performed using meteorological inputs from different models corresponding to Hurricane Irene (2011), over three sub-basins of the Hudson River basin. The ensemble-based precipitation inputs were used as forcing in a hydrological model to retrospectively forecast hourly streamflow, with a 96-hour lead time. The inputs consisted of 73 ensemble members, namely one high-resolution ECMWF deterministic member, 51 ECMWF members and 21 NOAA/ESRL (GEFS Reforecasts v2) members. The precipitation inputs were resampled to a common grid using the bilinear resampling method that was selected upon analysing different resampling methods. The results show the advantages of forcing hydrologic forecasting systems with multi-model ensemble forecasts over using deterministic and single model ensemble forecasts. The work showed that using the median of all 73 ensemble streamflow forecasts relatively improved the Nash–Sutcliffe Efficiency and lowered the biases across the examined sub-basins, compared with using the ensemble median from an individual model. This research contributes to the growing literature that demonstrates the promising capabilities of multi-model systems to better describe the uncertainty in streamflow predictions.
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Franz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences 15, no. 11 (November 15, 2011): 3367–82. http://dx.doi.org/10.5194/hess-15-3367-2011.
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Abstract. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE), and the Shuffle Complex Evolution Metropolis (SCEM). Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
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Franz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences Discussions 8, no. 2 (March 30, 2011): 3085–131. http://dx.doi.org/10.5194/hessd-8-3085-2011.
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Abstract. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences yet, few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from different parameter uncertainty estimation methods. The Generalized Uncertainty Likelihood Estimator (GLUE), a modified version of GLUE, and the Shuffle Complex Evolution Metropolis (SCEM) are used to generate model ensembles for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of parameter uncertainty, one that is commensurate with the dimension of the ensembles themselves. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
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Canli, Ekrem, Martin Mergili, Benni Thiebes, and Thomas Glade. "Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology." Natural Hazards and Earth System Sciences 18, no. 8 (August 16, 2018): 2183–202. http://dx.doi.org/10.5194/nhess-18-2183-2018.
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Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide-triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.
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Ye, Jinyin, Yuehong Shao, and Zhijia Li. "Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast." Advances in Meteorology 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9129734.
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TIGGE (THORPEX International Grand Global Ensemble) was a major part of the THORPEX (Observing System Research and Predictability Experiment). It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.
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Carlberg, Bradley, Kristie Franz, and William Gallus. "A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting." Water 12, no. 12 (December 13, 2020): 3505. http://dx.doi.org/10.3390/w12123505.
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To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members.
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Franz, Kristie J., Terri S. Hogue, and Soroosh Sorooshian. "Snow Model Verification Using Ensemble Prediction and Operational Benchmarks." Journal of Hydrometeorology 9, no. 6 (December 1, 2008): 1402–15. http://dx.doi.org/10.1175/2008jhm995.1.
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Abstract Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling studies, testing in a forecasting mode is seldom undertaken, limiting information derived from these analyses. One can overcome this limitation through generation, and subsequent analysis, of ensemble hindcasts. In this study, long-range ensemble hindcasts are generated for the available period of record for a basin in southwestern Idaho for the purpose of evaluating the Snow–Atmosphere–Soil Transfer (SAST) model against the current operational benchmark, the National Weather Service’s (NWS) snow accumulation and ablation model SNOW17. Both snow models were coupled with the NWS operational rainfall runoff model and ensembles of seasonal discharge and weekly snow water equivalent (SWE) were evaluated. Ensemble predictions from both the SAST and SNOW17 models were better than climatology forecasts, for the period studied. In most cases, the accuracy of the SAST-generated predictions was similar to the SNOW17-generated predictions, except during periods of significant melting. Differences in model performance are partially attributed to initial condition errors. After updating the SWE state in the snow models with the observed SWE, the forecasts were improved during the first 2–4 weeks of the forecast window and the skills were essentially equal in both forecasting systems for the study watershed. Climate dominated the forecast uncertainty in the latter part of the forecast window while initial conditions controlled the forecast skill in the first 3–4 weeks of the forecast. The use of hindcasting in the snow model analysis revealed that, given the dominance of the initial conditions on forecast skill, streamflow predictions will be most improved through the use of state updating.
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Davolio, S., T. Diomede, C. Marsigli, M. M. Miglietta, A. Montani, and A. Morgillo. "Comparing different meteorological ensemble approaches: hydrological predictions for a flood episode in Northern Italy." Advances in Science and Research 8, no. 1 (March 21, 2012): 33–37. http://dx.doi.org/10.5194/asr-8-33-2012.
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Abstract. Within the framework of coupled meteorological-hydrological predictions, this study aims at comparing two high-resolution meteorological ensembles, covering short and medium range. The two modelling systems have similar characteristics, as almost the same number of members, the model resolution (about 7 km), the driving ECMWF global ensemble prediction system, but are obtained through different methodologies: the former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter follows a single-model approach, based on COSMO-LEPS (Limited-area Ensemble Prediction System), the operational ensemble forecasting system developed within the COSMO consortium. Precipitation forecasts are evaluated in terms of hydrological response, after coupling the meteorological models with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno river (Northern Italy), for a severe weather episode. Although a single case study does not allow for robust and definite conclusions, the comparison among different predictions points out a remarkably better performance of mesoscale model ensemble forecasts compared to global ones. Moreover, the multi-model ensemble outperforms the single model approach.
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Paiva, R. C. D., W. Collischonn, M. P. Bonnet, and L. G. G. de Gonçalves. "On the sources of hydrological prediction uncertainty in the Amazon." Hydrology and Earth System Sciences 16, no. 9 (September 5, 2012): 3127–37. http://dx.doi.org/10.5194/hess-16-3127-2012.
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Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.
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Paiva, R. C. D., W. Collischonn, M. P. Bonnet, and L. G. G. Gonçalves. "On the sources of hydrological prediction uncertainty in the Amazon." Hydrology and Earth System Sciences Discussions 9, no. 3 (March 20, 2012): 3739–60. http://dx.doi.org/10.5194/hessd-9-3739-2012.
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Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems (HFSs) using process based models for this region. In this direction, the knowledge of the source of errors in HFSs may guide the choice on improving model structure, model forcings or developing data assimilation (DA) systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions (ICs) and model meteorological forcings (MFs) errors (precisely precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach developed by Wood and Lettenmaier (2008) that contrasts Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. Model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions play an important role for discharge predictability even for large lead times (~1 to 3 months) on main Amazonian Rivers. ICs of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. ICs of groundwater state variables are important mostly during low flow period and southeast part of the Amazon, where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions, may be feasible. Also, development of DA methods is encouraged for this region.
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Davolio, S., M. M. Miglietta, T. Diomede, C. Marsigli, and A. Montani. "A flood episode in Northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions." Hydrology and Earth System Sciences Discussions 9, no. 12 (December 4, 2012): 13415–50. http://dx.doi.org/10.5194/hessd-9-13415-2012.
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Abstract. Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (Limited-area Ensemble Prediction System). The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno River (Northern Italy), for a recent severe weather episode affecting Northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over the entire Northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that both mesoscale model ensembles remarkably outperform the global ensemble for application at basin scale as the horizontal resolution plays a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides more informative probabilistic predictions with respect to COSMO-LEPS, since it is characterized by a larger spread especially at short lead times. A thorough analysis of the multi-model results shows that this behaviour is due to the different characteristics of the involved meteorological models and represents the added value of the multi-model approach. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members, forced by the same large scale conditions, indicates that the systems are governed mainly by the large scale boundary conditions.
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Trambauer, P., M. Werner, H. C. Winsemius, S. Maskey, E. Dutra, and S. Uhlenbrook. "Hydrological drought forecasting and skill assessment for the Limpopo river basin, Southern Africa." Hydrology and Earth System Sciences Discussions 11, no. 8 (August 28, 2014): 9961–10000. http://dx.doi.org/10.5194/hessd-11-9961-2014.
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Abstract. Ensemble hydrological predictions are normally obtained by forcing hydrological models with ensembles of atmospheric forecasts produced by Numerical weather prediction models. To be of practical value to water users, such forecasts should not only be sufficiently skilful, they should also provide information that is relevant to the decisions end users make. The semi-arid Limpopo basin in Southern Africa has experienced severe droughts in the past, resulting in crop failures, high economic losses and the need for humanitarian aid. In this paper we address the seasonal prediction of hydrological drought for the Limpopo river basin by testing three proposed forecasting systems (FS) that can provide operational guidance to dam operators and water managers within the basin at the seasonal time scale. All three FS include a distributed hydrological model of the basin, and are forced with either (i) a global atmospheric model forecast (ECMWF seasonal forecast system – S4), (ii) the commonly applied Ensemble Streamflow Prediction approach (ESP) using resampled historical data, or (iii) a conditional ESP approach (ESPcond), which is conditional on the ENSO signal. We determine the skill of the three systems in predicting drought indices and streamflow. We also assess the skill of the model in predicting indicators that are meaningful to the local end users in the basin. FS_S4 shows moderate skill for all lead times (3, 4, and 5 months) and aggregation periods. FS_ESP also performs better than climatology for the shorter lead times, but with a lower skill than FS_S4. FS_ESPcond shows skill in between the other two FS, though its skill is shown to be more robust. The skills of FS_ESP and FS_ESPcond reduce rapidly with increasing lead time. Both FS_S4 and FS_ESPcond show good potential for seasonal hydrological drought forecasting in the Limpopo river basin, which is encouraging in the context of providing better operational guidance to water users.
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Harrigan, Shaun, Christel Prudhomme, Simon Parry, Katie Smith, and Maliko Tanguy. "Benchmarking ensemble streamflow prediction skill in the UK." Hydrology and Earth System Sciences 22, no. 3 (March 29, 2018): 2023–39. http://dx.doi.org/10.5194/hess-22-2023-2018.
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Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient =0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.
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Cluckie, I. D., Y. Xuan, and Y. Wang. "Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction." Hydrology and Earth System Sciences Discussions 3, no. 5 (October 19, 2006): 3211–37. http://dx.doi.org/10.5194/hessd-3-3211-2006.
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Abstract. Advances in meso-scale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.
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Xuan, Y., I. D. Cluckie, and Y. Wang. "Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction." Hydrology and Earth System Sciences 13, no. 3 (March 6, 2009): 293–303. http://dx.doi.org/10.5194/hess-13-293-2009.
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Abstract. Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.
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Davolio, S., M. M. Miglietta, T. Diomede, C. Marsigli, and A. Montani. "A flood episode in northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions." Hydrology and Earth System Sciences 17, no. 6 (June 5, 2013): 2107–20. http://dx.doi.org/10.5194/hess-17-2107-2013.
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Abstract. Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (limited-area ensemble prediction system). The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno River (northern Italy), for a recent severe weather episode affecting northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that, for the intercomparison undertaken in this specific study, both mesoscale model ensembles outperform the global ensemble for application at basin scale. Horizontal resolution is found to play a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides a better indication concerning the occurrence, intensity and timing of the two observed discharge peaks, with respect to COSMO-LEPS. This seems to be ascribable to the different behaviour of the involved meteorological models. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members forced by the same large-scale conditions indicates that the systems are governed mainly by the boundary conditions, although the different limited area models' characteristics may still have a non-negligible impact.
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Pan, Ming, Eric F. Wood, Dennis B. McLaughlin, Dara Entekhabi, and Lifeng Luo. "A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part I: Implementation and Synthetic Experiment." Journal of Hydrometeorology 10, no. 3 (June 1, 2009): 794–806. http://dx.doi.org/10.1175/2009jhm1088.1.
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Abstract The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A synthetic data assimilation experiment is performed, driven by meteorological forcing fields downscaled from the ensemble forecasts made by the NOAA/National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The classic full-rank ensemble Kalman filter is used as the benchmark to evaluate the multiscale filter performance, and comparisons are also made with a horizontally uncoupled filter. It is demonstrated that the multiscale filter is able to closely approximate the full-rank solution with a low computational cost (∼1/20 of the full-rank filter) in an experiment in which the top-layer soil moisture is assimilated, whereas the horizontally uncoupled filter fails to approximate the full-rank solution.
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Trambauer, P., M. Werner, H. C. Winsemius, S. Maskey, E. Dutra, and S. Uhlenbrook. "Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa." Hydrology and Earth System Sciences 19, no. 4 (April 13, 2015): 1695–711. http://dx.doi.org/10.5194/hess-19-1695-2015.
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Abstract. Ensemble hydrological predictions are normally obtained by forcing hydrological models with ensembles of atmospheric forecasts produced by numerical weather prediction models. To be of practical value to water users, such forecasts should not only be sufficiently skilful, they should also provide information that is relevant to the decisions end users make. The semi-arid Limpopo Basin in southern Africa has experienced severe droughts in the past, resulting in crop failure, economic losses and the need for humanitarian aid. In this paper we address the seasonal prediction of hydrological drought in the Limpopo River basin by testing three proposed forecasting systems (FS) that can provide operational guidance to reservoir operators and water managers at the seasonal timescale. All three FS include a distributed hydrological model of the basin, which is forced with either (i) a global atmospheric model forecast (ECMWF seasonal forecast system – S4), (ii) the commonly applied ensemble streamflow prediction approach (ESP) using resampled historical data, or (iii) a conditional ESP approach (ESPcond) that is conditional on the ENSO (El Niño–Southern Oscillation) signal. We determine the skill of the three systems in predicting streamflow and commonly used drought indices. We also assess the skill in predicting indicators that are meaningful to local end users in the basin. FS_S4 shows moderate skill for all lead times (3, 4, and 5 months) and aggregation periods. FS_ESP also performs better than climatology for the shorter lead times, but with lower skill than FS_S4. FS_ESPcond shows intermediate skill compared to the other two FS, though its skill is shown to be more robust. The skill of FS_ESP and FS_ESPcond is found to decrease rapidly with increasing lead time when compared to FS_S4. The results show that both FS_S4 and FS_ESPcond have good potential for seasonal hydrological drought forecasting in the Limpopo River basin, which is encouraging in the context of providing better operational guidance to water users.
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Mehrparvar, M., and K. Asghari. "Modular optimized data assimilation and support vector machine for hydrologic modeling." Journal of Hydroinformatics 20, no. 3 (February 5, 2018): 728–38. http://dx.doi.org/10.2166/hydro.2018.009.
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Abstract Accurate and reliable simulation models are crucial for the operation and management of systems. Developing a simulation model to forecast future states of a system is generally followed by errors in prediction. Frequently, data-based models such as support vector machines (SVM) are used as forecasting techniques. This paper introduces a modular method which couples the machine learning technique of support vector regression (SVR) as a prediction model and a modified data assimilation (MDA) technique to partially correct the predicted values based on the observation data. To improve the performance and accuracy of the system output, the ensemble Kalman filter (EnKF) as a data assimilation procedure is implemented with an optimization procedure. As a case study, inflow quantities to Zayandehroud reservoir is considered as the state vector in the assimilation process to enhance the system output. Evaluation criteria such as root mean square error (RMSE) and R-squared criteria are implemented to evaluate the performance of the proposed model. The adjusted values of a hybrid model compared to the SVR model and standard DA indicate improved performance of the proposed model.
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Shah, Reepal, Atul Kumar Sahai, and Vimal Mishra. "Short to sub-seasonal hydrologic forecast to manage water and agricultural resources in India." Hydrology and Earth System Sciences 21, no. 2 (February 2, 2017): 707–20. http://dx.doi.org/10.5194/hess-21-707-2017.
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Abstract. Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7–45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all-India median mean absolute error – MAE), while all-India median bias corrected temperature forecast was improved by 2.1 °C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.
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Vié, B., G. Molinié, O. Nuissier, B. Vincendon, V. Ducrocq, F. Bouttier, and E. Richard. "Hydro-meteorological evaluation of a convection-permitting ensemble prediction system for Mediterranean heavy precipitating events." Natural Hazards and Earth System Sciences 12, no. 8 (August 21, 2012): 2631–45. http://dx.doi.org/10.5194/nhess-12-2631-2012.
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Abstract. An assessment of the performance of different convection-permitting ensemble prediction systems (EPSs) is performed, with a focus on Heavy Precipitating Events (HPEs). The convective-scale EPS configuration includes perturbations of lateral boundary conditions (LBCs) by using a global ensemble to provide LBCs, initial conditions (ICs) through an ensemble data assimilation technique and perturbations of microphysical parameterisations to account for part of model errors. A probabilistic evaluation is conducted over an 18-day period. A clear improvement is found when uncertainties on LBCs and ICs are considered together, but the chosen microphysical perturbations have no significant impact on probabilistic scores. Innovative evaluation processes for three HPE case studies are implemented. First, maxima diagrams provide a multi-scale analysis of intense rainfall. Second, an hydrological evaluation is performed through the computation of discharge forecasts using hourly ensemble precipitation forecasts as an input. All ensembles behave similarly, but differences are found highlighting the impact of microphysical perturbations on HPEs forecasts, especially for cases involving complex small-scale processes.
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Wang, Lili, and Hongjun Bao. "Ensemble flood forecasting based on ensemble NWP and the GMKHM distributed hydrological model." MATEC Web of Conferences 246 (2018): 01108. http://dx.doi.org/10.1051/matecconf/201824601108.
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The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a new opportunity for flood forecast. The GMKHM distributed hydrological model, which is based on a mixed runoff generation model and overland flow routing model based on kinematic wave theory, and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the GMKHM model and the TIGGE database gives a promising tool for the anticipation of flood events several days ahead,, comparable with that driven by raingauge observation.
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Jaun, S., and B. Ahrens. "Evaluation of a probabilistic hydrometeorological forecast system." Hydrology and Earth System Sciences Discussions 6, no. 2 (March 6, 2009): 1843–77. http://dx.doi.org/10.5194/hessd-6-1843-2009.
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Abstract. Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with an appropriate spread-skill relationship. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.
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Jaun, S., and B. Ahrens. "Evaluation of a probabilistic hydrometeorological forecast system." Hydrology and Earth System Sciences 13, no. 7 (July 8, 2009): 1031–43. http://dx.doi.org/10.5194/hess-13-1031-2009.
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Abstract. Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with appropriate spread. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.
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Zappa, Massimiliano, Mathias W. Rotach, Marco Arpagaus, Manfred Dorninger, Christoph Hegg, Andrea Montani, Roberto Ranzi, et al. "MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems." Atmospheric Science Letters 9, no. 2 (2008): 80–87. http://dx.doi.org/10.1002/asl.183.
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Kim, Sunghee, Hossein Sadeghi, Reza Ahmad Limon, Manabendra Saharia, Dong-Jun Seo, Andrew Philpott, Frank Bell, James Brown, and Minxue He. "Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas." Journal of Hydrometeorology 19, no. 9 (September 1, 2018): 1467–83. http://dx.doi.org/10.1175/jhm-d-18-0027.1.
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Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.
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Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, F. Regimbeau, and F. Habets. "A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts." Hydrology and Earth System Sciences 14, no. 8 (August 24, 2010): 1639–53. http://dx.doi.org/10.5194/hess-14-1639-2010.
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Abstract. The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.
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Vegad, Urmin, and Vimal Mishra. "Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India." Hydrology and Earth System Sciences 26, no. 24 (December 16, 2022): 6361–78. http://dx.doi.org/10.5194/hess-26-6361-2022.
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Abstract. Developing an ensemble hydrological prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrological ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River Basin as a test bed. We use the variable infiltration capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada Basin. Streamflow prediction skills of the ERFS forecast were examined for the period 2003–2018 at 1–32 d lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1–10 d lead for the summer monsoon (June–September) 2019–2020. The ERFS forecast underestimates extreme precipitation against the observations compared to the GEFS forecast during the summer monsoon of 2019–2020. However, both forecast products show better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performs better than the ERFS during 2019–2020. The performance of GEFS-based ensemble streamflow forecast declines after 5 days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1–5 d lead, which can be utilized in streamflow prediction. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India.
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Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, F. Regimbeau, and F. Habets. "A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts." Hydrology and Earth System Sciences Discussions 7, no. 2 (April 22, 2010): 2455–97. http://dx.doi.org/10.5194/hessd-7-2455-2010.
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Abstract. The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Such systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.
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Siqueira, Vinícius Alencar, Walter Collischonn, Fernando Mainardi Fan, and Sin Chan Chou. "Ensemble flood forecasting based on operational forecasts of the regional Eta EPS in the Taquari-Antas basin." RBRH 21, no. 3 (September 2016): 587–602. http://dx.doi.org/10.1590/2318-0331.011616004.
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ABSTRACT Hydrological Ensemble Prediction Systems (HEPS) play an important role on operational flood forecasting. Unlike in deterministic approach, which relies on a single prediction of future river flows, these systems can represent the forecast uncertainty and provide a better detection of extreme hydro-meteorological events. In this context, the present study aimed to assess both the quality of ensemble flood forecasts on Taquari-Antas basin and its potential to provide additional information to a local Flood Alert System. The hydrological model MGB-IPH was coupled to the high-resolution meteorological EPS Eta model with five members of different parameterization schemes and boundary conditions, as well as to the deterministic version of Eta regional model. On a single event evaluation, the peak discharge was reasonable well predicted by at least one ensemble member, in nearly all forecasts, with a good prediction of the flood timing for the considered lead times. In a comparison with deterministic forecasts, the ensemble ones showed higher accuracy and higher probability of detection (POD) for the reference thresholds, preserving false alarm rates at reasonably low levels. An overall tendency of underestimation was also identified, with most observations falling between the higher ranks of the ensemble. Furthermore, the combination of previous forecasts (t-12h) with the recent ones leads to a slight increase of ensemble spread and POD, despite the performance reduction in terms of accuracy and bias for the ensemble mean. Results suggest that there is a benefit in having hydrological ensemble forecasts obtained from the high resolution EPS Eta model, which can be used as a complementary information to a local Flood Alert System supporting pre-alert issues and Civil Defense internal planning actions.
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Swinbank, Richard, Masayuki Kyouda, Piers Buchanan, Lizzie Froude, Thomas M. Hamill, Tim D. Hewson, Julia H. Keller, et al. "The TIGGE Project and Its Achievements." Bulletin of the American Meteorological Society 97, no. 1 (January 1, 2016): 49–67. http://dx.doi.org/10.1175/bams-d-13-00191.1.
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Abstract The International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research. Their extratropical transition also has a major impact on the skill of midlatitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extratropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally, the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill.
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Grillakis, Manolis, Aristeidis Koutroulis, and Ioannis Tsanis. "Improving Seasonal Forecasts for Basin Scale Hydrological Applications." Water 10, no. 11 (November 7, 2018): 1593. http://dx.doi.org/10.3390/w10111593.
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Seasonal forecasting is a fast-growing climate prediction application that puts into practice the latest improvements in the climate modeling research. Skillful seasonal forecasts can drastically aid practical applications and productive sectors by reducing weather-related risks such as water availability. In this study two operational seasonal forecasting systems are tested in a water resource important watershed on the island of Crete. Hindcast precipitation and temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 and Met Office GloSea5 systems are tested for their forecast skill up to seven months ahead. Data of both systems are downscaled and corrected for biases towards the observations. Different correction methods are applied and evaluated. Post-processed data from these methods are used as an input to the hydrological model HYPE, to provide streamflow forecasts. Results show that a prior adjustment of the two systems’ precipitation and temperature may improve their forecast skill. Adjusted GloSea5 forecasts are slightly better estimates than the corresponding forecasts based on System 4. The results show that both systems provide a skillful ensemble streamflow prediction for one month ahead, with the skill decreasing rapidly beyond that. Update of the initial state of HYPE results in the reduction of the variability of the ensemble flow predictions and improves the skill but only as far as two months of forecast. Finally, the two systems were tested for their ability to capture a limited number of historical streamflow drought events, with indications that GloSea5 has a slightly better skill.
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Xu, Jing, François Anctil, and Marie-Amélie Boucher. "Hydrological post-processing of streamflow forecasts issued from multimodel ensemble prediction systems." Journal of Hydrology 578 (November 2019): 124002. http://dx.doi.org/10.1016/j.jhydrol.2019.124002.
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Brochero, D., F. Anctil, and C. Gagné. "Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria." Hydrology and Earth System Sciences Discussions 8, no. 2 (March 11, 2011): 2739–82. http://dx.doi.org/10.5194/hessd-8-2739-2011.
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Abstract. Hydrological Ensemble Prediction System (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of members) of a HEPS configured with 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Center for Medium-range Weather Forecasts (ECMWF). Here, the selection of the most relevant members is proposed using a Backward greedy technique with k-fold cross-validation, allowing an optimal use of the information. The methodology draws from a multi-criterion score that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).
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Brochero, D., F. Anctil, and C. Gagné. "Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria." Hydrology and Earth System Sciences 15, no. 11 (November 4, 2011): 3307–25. http://dx.doi.org/10.5194/hess-15-3307-2011.
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Abstract. Hydrological Ensemble Prediction Systems (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of the number of hydrological members) that can be achieved with a HEPS configured using 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). Here, Backward Greedy Selection (BGS) is proposed to assess the weight that each model must represent within a subset that offers similar or better performance than a reference set of 800 hydrological members. These hydrological models' weights represent the participation of each hydrological model within a simplified HEPS which would issue real-time forecasts in a relatively short computational time. The methodology uses a variation of the k-fold cross-validation, allowing an optimal use of the information, and employs a multi-criterion framework that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).
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Dietrich, J., S. Trepte, Y. Wang, A. H. Schumann, F. Voß, F. B. Hesser, and M. Denhard. "Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event." Nonlinear Processes in Geophysics 15, no. 2 (March 19, 2008): 275–86. http://dx.doi.org/10.5194/npg-15-275-2008.
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Abstract. Flood forecasts are essential to issue reliable flood warnings and to initiate flood control measures on time. The accuracy and the lead time of the predictions for head waters primarily depend on the meteorological forecasts. Ensemble forecasts are a means of framing the uncertainty of the potential future development of the hydro-meteorological situation. This contribution presents a flood management strategy based on probabilistic hydrological forecasts driven by operational meteorological ensemble prediction systems. The meteorological ensemble forecasts are transformed into discharge ensemble forecasts by a rainfall-runoff model. Exceedance probabilities for critical discharge values and probabilistic maps of inundation areas can be computed and presented to decision makers. These results can support decision makers in issuing flood alerts. The flood management system integrates ensemble forecasts with different spatial resolution and different lead times. The hydrological models are controlled in an adaptive way, mainly depending on the lead time of the forecast, the expected magnitude of the flood event and the availability of measured data. The aforementioned flood forecast techniques have been applied to a case study. The Mulde River Basin (South-Eastern Germany, Czech Republic) has often been affected by severe flood events including local flash floods. Hindcasts for the large scale extreme flood in August 2002 have been computed using meteorological predictions from both the COSMO-LEPS ensemble prediction system and the deterministic COSMO-DE local model. The temporal evolution of a) the meteorological forecast uncertainty and b) the probability of exceeding flood alert levels is discussed. Results from the hindcast simulations demonstrate, that the systems would have predicted a high probability of an extreme flood event, if they would already have been operational in 2002. COSMO-LEPS showed a reasonably good performance within a lead time of 2 to 3 days. Some of the deterministic very short-range forecast initializations were able to predict the dynamics of the event, but others underpredicted rainfall. Thus a lagged average ensemble approach is suggested. The findings from the case study support the often proposed added value of ensemble forecasts and their probabilistic evaluation for flood management decisions.
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Goenner, Andrew R., Kristie J. Franz, William A. Gallus Jr, and Brett Roberts. "Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting." Water 12, no. 10 (October 14, 2020): 2860. http://dx.doi.org/10.3390/w12102860.
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Probabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale for input to the operational Sacramento Soil Moisture Accounting hydrologic model to generate probabilistic streamflow predictions. The technique was tested using both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast version 2.0 (HREF) for 11 river basins across the upper Midwest for 109 cases. The resulting discharges associated with low probability of exceedance values were too large; no events were observed having discharges above the 10% exceedance value predicted from the technique applied to both ensembles, and no events were observed having discharges above the 25% exceedance value from the HREF-based forecast. The large differences are due to using the same precipitation exceedance value at all points; it is unlikely that all watershed points would experience the heavy rainfall associated with the 5% probability of exceedance. The technique likely can be improved through calibration of the basin-average precipitation forecasts based on typical distributions of precipitation within the convective systems that dominate warm-season precipitation events or calibration of the resulting probabilistic discharge forecasts.
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Rathnayake, Namal, Upaka Rathnayake, Tuan Linh Dang, and Yukinobu Hoshino. "Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka." PLOS ONE 18, no. 4 (April 26, 2023): e0282847. http://dx.doi.org/10.1371/journal.pone.0282847.
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Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions.
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Abaza, Mabrouk, François Anctil, Vincent Fortin, and Richard Turcotte. "A Comparison of the Canadian Global and Regional Meteorological Ensemble Prediction Systems for Short-Term Hydrological Forecasting." Monthly Weather Review 141, no. 10 (September 25, 2013): 3462–76. http://dx.doi.org/10.1175/mwr-d-12-00206.1.
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Abstract Meteorological ensemble prediction systems (M-EPS) are generally set up at lower resolution than for their deterministic counterparts. Operational hydrologists are thus more prone to selecting deterministic meteorological forecasts for driving their hydrological models. Limited-area implementation of meteorological models may become a convenient way of providing the sought after higher-resolution meteorological ensemble forecasts. This study aims to compare the Canadian operational global EPS (M-GEPS) and the experimental regional EPS (M-REPS) for short-term operational hydrological ensemble forecasting over eight watersheds, for which performance and reliability was assessed. Higher-resolution deterministic forecasts were also available for the study. Results showed that both M-EPS provided better performance than their deterministic counterparts when comparing their mean continuous ranked probability score (MCRPS) and mean absolute error (MAE), especially beyond a 24-h horizon. The global and regional M-EPS led to very similar performance in terms of RMSE, but the latter produced a larger spread and improved reliability. The M-REPS was deemed superior to its operational global counterpart, especially for its ability to better depict forecast uncertainty.
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Monhart, Samuel, Massimiliano Zappa, Christoph Spirig, Christoph Schär, and Konrad Bogner. "Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach." Hydrology and Earth System Sciences 23, no. 1 (January 28, 2019): 493–513. http://dx.doi.org/10.5194/hess-23-493-2019.
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Abstract. Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.
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Gouweleeuw, B. T., J. Thielen, G. Franchello, A. P. J. De Roo, and R. Buizza. "Flood forecasting using medium-range probabilistic weather prediction." Hydrology and Earth System Sciences 9, no. 4 (October 7, 2005): 365–80. http://dx.doi.org/10.5194/hess-9-365-2005.
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Abstract. Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.
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Courdent, Vianney, Morten Grum, Thomas Munk-Nielsen, and Peter S. Mikkelsen. "A gain–loss framework based on ensemble flow forecasts to switch the urban drainage–wastewater system management towards energy optimization during dry periods." Hydrology and Earth System Sciences 21, no. 5 (May 22, 2017): 2531–44. http://dx.doi.org/10.5194/hess-21-2531-2017.
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Abstract. Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage–wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain–loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial uses.
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Slater, Louise J., Louise Arnal, Marie-Amélie Boucher, Annie Y. Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, et al. "Hybrid forecasting: blending climate predictions with AI models." Hydrology and Earth System Sciences 27, no. 9 (May 15, 2023): 1865–89. http://dx.doi.org/10.5194/hess-27-1865-2023.
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Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.
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Beg, Md Nazmul Azim, Jorge Leandro, Punit Bhola, Iris Konnerth, Winfried Willems, Rita F. Carvalho, and Markus Disse. "Discharge Interval method for uncertain flood forecasts using a flood model chain: city of Kulmbach." Journal of Hydroinformatics 21, no. 5 (July 18, 2019): 925–44. http://dx.doi.org/10.2166/hydro.2019.131.
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Abstract Real-time flood forecasting can help authorities in providing reliable warnings to the public. Ensemble prediction systems (EPS) have been progressively used for operational flood forecasting by European hydrometeorological agencies in recent years. This process, however, is non-deterministic such that uncertainty sources need to be considered before issuing forecasts. In this study, a new methodology for flood forecasting named Discharge Interval method is proposed. This method uses at least one historical event hindcast data, run in several ensembles and selects a pair of best ensemble discharge results for every certain discharge level. Later, the method uses the same parameter settings of the chosen ensemble discharge pair to forecast any certain flood discharge level. The methodology was implemented within the FloodEvac tool. The tool can handle calibration/validation of the hydrological model (LARSIM) and produces real-time flood forecasts with the associated uncertainty of the flood discharges. The proposed methodology is computationally efficient and suitable for real-time forecasts with uncertainty. The results using the Discharge Interval method were found comparable to the 90th percentile forecasted discharge range obtained with the Ensemble method.
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Liechti, K., L. Panziera, U. Germann, and M. Zappa. "The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps." Hydrology and Earth System Sciences 17, no. 10 (October 10, 2013): 3853–69. http://dx.doi.org/10.5194/hess-17-3853-2013.
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Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.
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Hashino, T., A. A. Bradley, and S. S. Schwartz. "Evaluation of bias-correction methods for ensemble streamflow volume forecasts." Hydrology and Earth System Sciences Discussions 3, no. 2 (April 27, 2006): 561–94. http://dx.doi.org/10.5194/hessd-3-561-2006.
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Abstract. Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for the Des Moines River, issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.