The Value of Accurate High-Resolution and Spatially Continuous Snow Information to Streamflow Forecasts

Abstract Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

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Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Hydrology and Earth System Sciences ◽

10.5194/hess-21-1573-2017 ◽

2017 ◽

Vol 21 (3) ◽

pp. 1573-1591 ◽

Cited By ~ 19

Author(s):

Louise Crochemore ◽

Maria-Helena Ramos ◽

Florian Pappenberger ◽

Charles Perrin

Keyword(s):

Long Range ◽

Precipitation Forecast ◽

Low Flow ◽

Drought Risk ◽

Streamflow Forecasting ◽

Streamflow Forecasts ◽

Precipitation Indices ◽

Seasonal Streamflow ◽

Prediction Approach ◽

The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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Quantifying streamflow predictability across North America on sub-seasonal to seasonal timescales

10.5194/egusphere-egu21-8092 ◽

2021 ◽

Author(s):

Louise Arnal ◽

Martyn Clark ◽

Vincent Vionnet ◽

Vincent Fortin ◽

Alain Pietroniro ◽

...

Keyword(s):

North America ◽

Climate Prediction ◽

Forecast Skill ◽

Streamflow Forecasting ◽

Water Sector ◽

Forecast System ◽

Streamflow Forecast ◽

Streamflow Forecasts ◽

Forecasting System ◽

Seasonal Streamflow

<div> <p><span><span>Sub-seasonal to seasonal streamflow forecasts represent critical operational inputs for many water sector applications of societal relevance, such as spring flood early warning, water supply, hydropower generation, and irrigation scheduling. However, the skill of such forecasts has not risen greatly in recent decades despite recognizable advances in many relevant capabilities, including hydrologic modeling and S2S climate prediction. In order to build a continental-scale forecasting system that has value at the local scale, the sources and nature of predictability in the forecasts should be quantified and communicated. This can additionally help to target science investments for tangible improvements in the sub-seasonal to seasonal streamflow forecasting skill.</span></span></p> </div><div> <p><span><span>As part of the Canada-based Global Water Futures (GWF) program, we are advancing capabilities for probabilistic sub-seasonal to seasonal streamflow forecasts over North America. The overall aim is to improve sub-seasonal to seasonal streamflow forecasts for a range of water sector applications. We are implementing an array of forecasting methods that integrate state-of-the-art mechanistic models and statistical methods. These include, for instance, a </span></span><span>probabilistic sub-seasonal to seasonal streamflow forecasting system based on quantile regression of snow water equivalent observations, and a system based on the ESP approach (Day, 1985). </span></p> <p><span><span>To guide forecast system developments over North America, we are currently quantifying streamflow predictability for different hydroclimatic regimes, forecast initialization times, and lead times, against both streamflow simulations and observations to quantify the effect of model errors. Building on the work from Wood et al. (2016) and Arnal et al. (2017), we are disentangling the dominant predictability sources (i.e., initial hydrological conditions and atmospheric forcings) of sub-seasonal to seasonal streamflow across North American watersheds. The results provide insights into the elasticity of predictability, i.e., the increase in streamflow forecast skill possible by improving a specific component of the forecast system, and will inform the forecasting system development.</span></span></p> </div><div> <p><span><span>Arnal Louise, Wood Andrew W., Stephens Elisabeth, Cloke Hannah L., Pappenberger Florian, 2017: An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity. Journal of Hydrometeorology, doi: 10.1175/JHM-D-16-0259.1</span></span></p> </div><div> <p>Day, Gerald N., 1985: Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management, doi:10.1061/(ASCE)0733-9496(1985)111:2(157)</p> </div><p>Wood, Andrew W., Tom Hopson, Andy Newman, Levi Brekke, Jeff Arnold, and Martyn Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology, doi: 10.1175/JHM-D-14-0213.1</p>

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Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

10.5194/hess-2016-78 ◽

2016 ◽

Cited By ~ 2

Author(s):

Louise Crochemore ◽

M.-H. Ramos ◽

Florian Pappenberger

Keyword(s):

Bias Correction ◽

Linear Scaling ◽

Drought Risk ◽

Seasonal Precipitation ◽

Streamflow Forecasting ◽

Seasonal Forecasts ◽

Ensemble Forecasts ◽

Streamflow Forecasts ◽

Distribution Mapping ◽

Seasonal Streamflow

Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias corrected precipitation and streamflow ensemble forecasts in sixteen catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e., ESP method), is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contribute mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

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Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2439-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2439-2459

Author(s):

Katrina E. Bennett ◽

Jessica E. Cherry ◽

Ben Balk ◽

Scott Lindsey

Keyword(s):

Snow Cover ◽

The United States ◽

Remotely Sensed ◽

Streamflow Forecasting ◽

Snow Cover Area ◽

Interior Alaska ◽

Fractional Snow Cover ◽

The Us ◽

Physically Based ◽

Streamflow Forecasts

Abstract. Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal basins and where climate change is leading to rapid shifts in basin function. In this study, the operational framework employed by the United States (US) National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of fractional snow cover area (fSCA) to determine if these data improve streamflow forecasts in interior Alaska river basins. Two versions of MODIS fSCA are tested against a base case extent of snow cover derived by aerial depletion curves: the MODIS 10A1 (MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product over the period 2000–2010. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced simulations have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG fSCA version provides moderate increases in skill but is similar to the MOD10A1 results. The basins with the largest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungauged systems throughout the high-latitude regions of the globe, this result is valuable and indicates the utility of the MODIS fSCA data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snowpack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically based National Water Model. Physically based models may also be more capable of adapting to changing climates than statistical models corrected to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote-sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

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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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Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

Journal of Hydrometeorology ◽

10.1175/2007jhm862.1 ◽

2008 ◽

Vol 9 (1) ◽

pp. 132-148 ◽

Cited By ~ 106

Author(s):

Andrew W. Wood ◽

John C. Schaake

Keyword(s):

River Basin ◽

Hydrological Models ◽

Streamflow Forecasting ◽

Streamflow Prediction ◽

Summer Period ◽

Streamflow Forecast ◽

Ensemble Mean ◽

Ensemble Streamflow Prediction ◽

Deterministic Components ◽

Seasonal Streamflow

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

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Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality?

10.5194/egusphere-egu2020-9149 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ilias Pechlivanidis ◽

Louise Crochemore ◽

Thomas Bosshard

Keyword(s):

A Priori ◽

Hydrological Regime ◽

Climatic Conditions ◽

Streamflow Forecasting ◽

Seasonal Forecasts ◽

Streamflow Forecasts ◽

Key Drivers ◽

Seasonal Streamflow ◽

Forecast Quality

<p>Streamflow information for the months ahead is of great value to existing decision-making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate-related risks. Despite the large effort, there is still limited knowledge of the key drivers controlling the quality of the seasonal streamflow forecasts. In this investigation, we show that the seasonal streamflow predictability can be clustered, and hence regionalised, based on a priori knowledge of local hydro-climatic conditions. To reach these conclusions we analyse the seasonal forecasts of streamflow volumes across about 35400 basins in Europe, which vary in terms of climatology, scale and hydrological regime. We then link the forecast quality to various descriptors including physiography, hydro-climatic characteristics and meteorological biases. This allows the identification of the key drivers along a strong hydro-climatic gradient. Results show that, as expected, the seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. In addition, the predictability deteriorates with increasing lead months particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of drivers, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin&#8217;s hydrological regime, with quickly reacting basins (of low river memory) showing limited predictability. On the contrary, snow and/or baseflow dominated regions with long recessions (and hence high river memory) show high streamflow predictability. Finally, climatology and precipitation biases are also strongly related to streamflow predictability, highlighting the importance of developing robust bias-adjustment methods.</p>

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Skilful seasonal forecasts of streamflow over Europe?

10.5194/hess-2017-610 ◽

2017 ◽

Cited By ~ 3

Author(s):

Louise Arnal ◽

Hannah L. Cloke ◽

Elisabeth Stephens ◽

Fredrik Wetterhall ◽

Christel Prudhomme ◽

...

Keyword(s):

Lead Time ◽

Climate Model ◽

Streamflow Forecasting ◽

Streamflow Prediction ◽

Seasonal Forecasts ◽

Seasonal Climate ◽

Climate Forecasts ◽

Streamflow Forecasts ◽

Meteorological Observations ◽

Seasonal Streamflow

Abstract. This paper presents a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts, benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only. However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making. Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for most of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting.

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Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

10.5194/hess-2016-584 ◽

2016 ◽

Cited By ~ 2

Author(s):

Harm-Jan F. Benninga ◽

Martijn J. Booij ◽

Renata J. Romanowicz ◽

Tom H. M. Rientjes

Keyword(s):

Hydrological Model ◽

Initial Conditions ◽

Snow Accumulation ◽

Lead Times ◽

Model Parameters ◽

Streamflow Forecasting ◽

Weather Forecasts ◽

Streamflow Forecasts ◽

Forecasting System ◽

Precipitation And Temperature

Abstract. The paper presents a methodology to give insight in the performance of ensemble streamflow forecasting systems. We developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times from 1 day to 10 days for low, medium and high streamflow and related runoff generating processes. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts serve as input to a deterministic lumped hydrological (HBV) model. Due to inconsistent bias, the best streamflow forecasts were obtained without pre- and post-processing of the meteorological and streamflow forecasts. Best forecast skill, relative to alternative forecasts based on historical measurements of precipitation and temperature, is shown for high streamflow and for snow accumulation low streamflow events. Forecasts of medium streamflow events and low streamflow events generated by precipitation deficit show less skill. To improve the performance of the forecasting system for high streamflow events, in particular the meteorological forecasts require improvement. For low streamflow forecasts, the hydrological model should be improved. The study recommends improving the reliability of the ensemble streamflow forecasts by including the uncertainties in hydrological model parameters and initial conditions, and by improving the dispersion of the meteorological input forecasts.

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Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5273-2017 ◽

2017 ◽

Vol 21 (10) ◽

pp. 5273-5291 ◽

Cited By ~ 6

Author(s):

Harm-Jan F. Benninga ◽

Martijn J. Booij ◽

Renata J. Romanowicz ◽

Tom H. M. Rientjes

Keyword(s):

Hydrological Model ◽

Initial Conditions ◽

Snow Accumulation ◽

Lead Times ◽

Model Parameters ◽

Streamflow Forecasting ◽

Weather Forecasts ◽

Streamflow Forecasts ◽

Forecasting System ◽

Hydrometeorological Conditions

Abstract. The paper presents a methodology that gives insight into the performance of ensemble streamflow-forecasting systems. We have developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times ranging from 1 to 10 days for low, medium and high streamflow and different hydrometeorological conditions. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts served as inputs to a deterministic lumped hydrological (HBV) model. Due to a non-homogeneous bias in time, pre- and post-processing of the meteorological and streamflow forecasts are not effective. The best forecast skill, relative to alternative forecasts based on meteorological climatology, is shown for high streamflow and snow accumulation low-streamflow events. Forecasts of medium-streamflow events and low-streamflow events under precipitation deficit conditions show less skill. To improve performance of the forecasting system for high-streamflow events, the meteorological forecasts are most important. Besides, it is recommended that the hydrological model be calibrated specifically on low-streamflow conditions and high-streamflow conditions. Further, it is recommended that the dispersion (reliability) of the ensemble streamflow forecasts is enlarged by including the uncertainties in the hydrological model parameters and the initial conditions, and by enlarging the dispersion of the meteorological input forecasts.

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