Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Abstract. Medium-term hydrologic forecast uncertainty is strongly dependent on the forecast quality of meteorological variables. Of these variables, the influence of precipitation has been studied most widely, while temperature, radiative forcing and their derived product potential evapotranspiration (PET) have received little attention from the perspective of hydrological forecasting. This study aims to fill this gap by assessing the usability of potential evaporation forecasts for 10-day-ahead streamflow forecasting in the Rhine basin, Europe. In addition, the forecasts of the meteorological variables are compared with observations. Streamflow reforecasts were performed with the daily wflow_hbv model used in previous studies of the Rhine using the ECMWF 20-year meteorological reforecast dataset. Meteorological forecasts were compared with observed rainfall, temperature, global radiation and potential evaporation for 148 subbasins. Secondly, the effect of using PET climatology versus using observation-based estimates of PET was assessed for hydrological state and for streamflow forecast skill. We find that (1) there is considerable skill in the ECMWF reforecasts to predict PET for all seasons, and (2) using dynamical PET forcing based on observed temperature and satellite global radiation estimates results in lower evaporation and wetter initial states, but (3) the effect on forecasted 10-day streamflow is limited. Implications of this finding are that it is reasonable to use meteorological forecasts to forecast potential evaporation and use this is in medium-range streamflow forecasts. However, it can be concluded that an approach using PET climatology is also sufficient, most probably not only for the application shown here, but also for most models similar to the HBV concept and for moderate climate zones. As a by-product, this research resulted in gridded datasets for temperature, radiation and potential evaporation based on the Makkink equation for the Rhine basin. The datasets have a spatial resolution of 1.2×1.2 km and an hourly time step for the period from July 1996 through 2015. This dataset complements an earlier precipitation dataset for the same area, period and resolution.

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Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

Forecasting ◽

10.3390/forecast2030013 ◽

2020 ◽

Vol 2 (3) ◽

pp. 230-247

Author(s):

Ganesh R. Ghimire ◽

Sanjib Sharma ◽

Jeeban Panthi ◽

Rocky Talchabhadel ◽

Binod Parajuli ◽

...

Keyword(s):

Real Time ◽

Water Policy ◽

Low Complexity ◽

Critical Issue ◽

Forecast Skill ◽

Streamflow Forecasting ◽

Streamflow Forecast ◽

Time Flow ◽

Wide Range ◽

Streamflow Forecasts

Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km2. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas.

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A 20-year reforecast study combining high-resolution hydrological modelling, ensemble forecasting and data assimilation for the 12 largest tributaries of the Rhine

10.5194/egusphere-egu2020-3338 ◽

2020 ◽

Author(s):

Bart van Osnabrugge ◽

Maarten Smoorenburg ◽

Remko Uijlenhoet ◽

Albrecht Weerts

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Hydrological Model ◽

Forecast Skill ◽

Ensemble Forecasting ◽

Added Value ◽

River Rhine ◽

Medium Term ◽

Initial State ◽

The Impact

There is an ongoing trend in hydrological forecasting towards both spatially distributed (gridded) models, ensemble forecasting and data assimilation techniques to improve forecasts&#8217; initial states. While in the last years those different aspects have been investigated separately, there are only few studies where the three techniques are combined: ensemble forecasts with state updating of a gridded hydrological model. Additionally, the studies that have addressed this combination of techniques either focus on a small area, a short study period, or both. We here aim to fill this knowledge gap with a 20-year data assimilation and ensemble reforecast experiment with a high resolution gridded hydrological model (wflow_hbv, 1200x1200m) of the full Rhine basin (160 000 km2). To put the impact of state updating in an operational forecasting context, the data assimilation results were compared with AR post-processing as used by the Dutch Forecasting Centre (WMCN).This data assimilation and reforecast experiment was conducted for the twelve main tributaries of the river Rhine. The effect on forecast skill of state updating with the Asynchronous Ensemble Kalman Filter (AEnKF) and AR error correction are compared for medium-term (15-day) forecasts over a period of 20 years (1996 to 2016). State updating improved the initial state for all subbasins and resulted in lasting skill score increase. AR also improved the forecast skill, but the forecast skill with AR did not always converge towards the uncorrected model skill, and instead can deteriorate for longer lead times. AR correction outperformed the AEnKF state updating for the first two days, after which state updating became more effective and outperformed AR. We conclude that state updating has more potential for medium-term hydrological forecasts than the operational AR procedure.Further research is underway to investigate the importance, or added value, of long-term reforecasts as opposed to studies covering a short time span which are often more feasible and therefore more often found in literature.

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Origin and Impact of Initialization Shocks in Coupled Atmosphere–Ocean Forecasts*

Monthly Weather Review ◽

10.1175/mwr-d-15-0076.1 ◽

2015 ◽

Vol 143 (11) ◽

pp. 4631-4644 ◽

Cited By ~ 41

Author(s):

David P. Mulholland ◽

Patrick Laloyaux ◽

Keith Haines ◽

Magdalena Alonso Balmaseda

Keyword(s):

Data Assimilation ◽

Coupled System ◽

Atmospheric Temperature ◽

Ocean Model ◽

Forecast Skill ◽

Weather Forecasts ◽

Model Component ◽

Medium Range ◽

Series Of Experiments ◽

Coupled Data Assimilation

Abstract Current methods for initializing coupled atmosphere–ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-Range Weather Forecasts coupled system, the magnitude and extent of these so-called initialization shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialized by separate oceanic and atmospheric analyses do exhibit initialization shocks in lower atmospheric temperature, when compared to forecasts initialized using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialization on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialization shocks are found to follow a change in either the atmosphere or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5 K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed.

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Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

Monthly Weather Review ◽

10.1175/mwr-d-17-0157.1 ◽

2018 ◽

Vol 146 (4) ◽

pp. 1157-1180 ◽

Cited By ~ 19

Author(s):

Gregory C. Smith ◽

Jean-Marc Bélanger ◽

François Roy ◽

Pierre Pellerin ◽

Hal Ritchie ◽

...

Keyword(s):

Standard Deviation ◽

Large Scale ◽

Weather Forecasting ◽

Positive Impact ◽

Weather Prediction ◽

Ocean Model ◽

Forecast Skill ◽

Medium Range ◽

Forecasting System ◽

The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

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Contribution of Potential Evaporation Forecasts to 10-day streamflow forecast skill for the Rhine river

10.5194/hess-2018-506 ◽

2018 ◽

Author(s):

Bart van Osnabrugge ◽

Remko Uijlenhoet ◽

Albrecht Weerts

Keyword(s):

Radiative Forcing ◽

Potential Evapotranspiration ◽

Global Radiation ◽

Forecast Skill ◽

Meteorological Variables ◽

Rhine River ◽

Potential Evaporation ◽

Streamflow Forecasting ◽

Streamflow Forecast ◽

Rhine Basin

Abstract. Medium term hydrologic forecast uncertainty is strongly dependent on the forecast quality of meteorological variables. Of these variables, the influence of precipitation has been studied most widely, while temperature, radiative forcing and their derived product potential evapotranspiration (PET) have received little attention from the perspective of hydrological forecasting. This study aims to fill this gap by assessing the usability of potential evaporation forecasts for 10-day-ahead streamflow forecasting in the Rhine basin, Europe. In addition, the forecasts of the meteorological variables are compared with observations. Streamflow reforecasts were performed with the daily wflow_hbv model used in previous studies of the Rhine using the ECMWF 20-year meteorological reforecast dataset. Meteorological forecasts were compared with observed rainfall, temperature, global radiation and potential evaporation for 148 subbasins. Secondly, the effect of using PET climatology versus using observation-based estimates of PET was assessed for hydrological state and for streamflow forecast skill. We find that: (1) there is considerable skill in the ECMWF reforecasts to predict PET for all seasons, (2) using dynamical PET forcing based on observed temperature and satellite global radiation estimates results in lower evaporation and wetter initial states, but (3) the effect on forecasted 10-day streamflow is limited. Implications of this finding are that it is reasonable to use meteorological forecasts to forecast potential evaporation and use this is in medium-range streamflow forecasts. However, it can be concluded that an approach using PET climatology is also sufficient, most probably not only for the application shown here, but for most models similar to the HBV concept and for moderate climate zones. As a by-product, this research resulted in gridded datasets for temperature, radiation and potential evaporation based on the Makkink equation for the Rhine basin. The datasets have a spatial resolution of 1.2 × 1.2 km and an hourly timestep for the period from July 1996 through 2015. This dataset complements an earlier precipitation dataset for the same area, period and resolution.

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A national-scale seasonal hydrological forecast system: development and evaluation over Britain

10.5194/hess-2017-154 ◽

2017 ◽

Cited By ~ 1

Author(s):

Victoria A. Bell ◽

Helen N. Davies ◽

Alison L. Kay ◽

Anca Brookshaw ◽

Adam A. Scaife

Keyword(s):

Lead Time ◽

Hydrological Model ◽

Forecast Skill ◽

Initial Condition ◽

Seasonal Forecasts ◽

National Scale ◽

The North ◽

Seasonal Flow ◽

Ensemble Mean ◽

The Uk

Abstract. Skilful winter seasonal predictions for the North Atlantic circulation and Northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring/Summer seasonal hydrological forecasts, however, Autumn/Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform the most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ~ 70 % of the forecast skill (mostly in areas of high rainfall to the North and West) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, benefitting practitioners interested in predicting hydrological extremes, not only in the UK, but potentially across Europe.

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Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California

Monthly Weather Review ◽

10.1175/mwr-d-20-0096.1 ◽

2020 ◽

Vol 148 (8) ◽

pp. 3489-3506

Author(s):

Michael Scheuerer ◽

Matthew B. Switanek ◽

Rochelle P. Worsnop ◽

Thomas M. Hamill

Keyword(s):

Neural Network ◽

Large Scale ◽

Signal To Noise Ratio ◽

Weather Prediction ◽

Forecast Skill ◽

Lead Times ◽

Probabilistic Forecasts ◽

Medium Range ◽

Artificial Neural ◽

Artificial Neural Network Ann

Abstract Forecast skill of numerical weather prediction (NWP) models for precipitation accumulations over California is rather limited at subseasonal time scales, and the low signal-to-noise ratio makes it challenging to extract information that provides reliable probabilistic forecasts. A statistical postprocessing framework is proposed that uses an artificial neural network (ANN) to establish relationships between NWP ensemble forecast and gridded observed 7-day precipitation accumulations, and to model the increase or decrease of the probabilities for different precipitation categories relative to their climatological frequencies. Adding predictors with geographic information and location-specific normalization of forecast information permits the use of a single ANN for the entire forecast domain and thus reduces the risk of overfitting. In addition, a convolutional neural network (CNN) framework is proposed that extends the basic ANN and takes images of large-scale predictors as inputs that inform local increase or decrease of precipitation probabilities relative to climatology. Both methods are demonstrated with ECMWF ensemble reforecasts over California for lead times up to 4 weeks. They compare favorably with a state-of-the-art postprocessing technique developed for medium-range ensemble precipitation forecasts, and their forecast skill relative to climatology is positive everywhere within the domain. The magnitude of skill, however, is low for week-3 and week-4, and suggests that additional sources of predictability need to be explored.

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