An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity

Abstract Seasonal streamflow prediction skill can derive from catchment initial hydrological conditions (IHCs) and from the future seasonal climate forecasts (SCFs) used to produce the hydrological forecasts. Although much effort has gone into producing state-of-the-art seasonal streamflow forecasts from improving IHCs and SCFs, these developments are expensive and time consuming and the forecasting skill is still limited in most parts of the world. Hence, sensitivity analyses are crucial to funnel the resources into useful modeling and forecasting developments. It is in this context that a sensitivity analysis technique, the variational ensemble streamflow prediction assessment (VESPA) approach, was recently introduced. VESPA can be used to quantify the expected improvements in seasonal streamflow forecast skill as a result of realistic improvements in its predictability sources (i.e., the IHCs and the SCFs)—termed “skill elasticity”—and to indicate where efforts should be targeted. The VESPA approach is, however, computationally expensive, relying on multiple hindcasts having varying levels of skill in IHCs and SCFs. This paper presents two approximations of the approach that are computationally inexpensive alternatives. These new methods were tested against the original VESPA results using 30 years of ensemble hindcasts for 18 catchments of the contiguous United States. The results suggest that one of the methods, end point blending, is an effective alternative for estimating the forecast skill elasticities yielded by the VESPA approach. The results also highlight the importance of the choice of verification score for a goal-oriented sensitivity analysis.

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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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Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-050.1 ◽

2014 ◽

Vol 15 (1) ◽

pp. 69-88 ◽

Cited By ~ 9

Author(s):

Randal D. Koster ◽

Gregory K. Walker ◽

Sarith P. P. Mahanama ◽

Rolf H. Reichle

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Large Scale ◽

Forecast Skill ◽

Surface Model ◽

Streamflow Forecast ◽

Continental Scale ◽

Streamflow Forecasts ◽

Seasonal Streamflow ◽

Soil Moisture Initialization

Abstract Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.

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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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Why is large sample hydrology important in hydrological forecasting?

10.5194/egusphere-egu21-4871 ◽

2021 ◽

Author(s):

Ilias Pechlivanidis ◽

Louise Crochemore ◽

Marc Girons Lopez

Keyword(s):

River Flow ◽

Forecast Skill ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

River Systems ◽

Local Conditions ◽

Large Sample ◽

Streamflow Forecasts ◽

Seasonal Streamflow

<p>The scientific community has made significant progress towards improving the skill of hydrological forecasts; however, most investigations have normally been conducted at single or in a limited number of catchments. Such an approach is indeed valuable for detailed process investigation and therefore to understand the local conditions that affect forecast skill, but it is limited when it comes to scaling up the underlying hydrometeorological hypotheses. To advance knowledge on the drivers that control the quality and skill of hydrological forecasts, much can be gained by comparative analyses and from the availability of statistically significant samples. Large-scale modelling (at national, continental or global scales) can complement the in-depth knowledge from single catchment modelling by encompassing many river systems that represent a breadth of physiographic and climatic conditions. In addition to large sample sizes which cover a gradient in terms of climatology, scale and hydrological regime, the use of machine learning techniques can contribute to the identification of emerging spatiotemporal patterns leading to forecast skill attribution to different regional physiographic characteristics.</p><p>Here, we draw on two seasonal hydrological forecast skill investigations that were conducted at the national and continental scales, providing results for more than 36,000 basins in Sweden and Europe. Due to the large generated samples, we are capable of demonstrating that the quality of seasonal streamflow forecasts can be clustered and regionalized, based on a priori knowledge of the local hydroclimatic conditions. We show that the quality of seasonal streamflow forecasts is linked to physiographic and hydroclimatic descriptors, and that the relative importance of these descriptors varies with initialization month and lead time. In our samples, hydrological similarity, temperature, precipitation, evaporative index, and precipitation forecast biases are strongly linked to the quality of streamflow forecasts. This way, while seasonal river flow can generally be well predicted in river systems with slow hydrological responses, predictability tends to be poor in cold and semiarid climates in which river systems respond immediately to precipitation signals.</p>

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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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An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting

10.5194/hess-2017-60 ◽

2017 ◽

Cited By ~ 2

Author(s):

Pablo A. Mendoza ◽

Andrew W. Wood ◽

Elizabeth Clark ◽

Eric Rothwell ◽

Martyn P. Clark ◽

...

Keyword(s):

Water Supply ◽

Pacific Northwest ◽

Land Surface ◽

Forecast Skill ◽

Climate Indices ◽

Statistical Regression ◽

Climate Information ◽

Streamflow Forecasting ◽

Streamflow Prediction ◽

Seasonal Streamflow

Abstract. For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHC and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the U.S. Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs, and model-based ensemble streamflow prediction (ESP) – and then systematically inter-compare WSFs across a range of lead times. Additional methods include: (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables; and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are: (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0210.1 ◽

2019 ◽

Vol 20 (4) ◽

pp. 731-749 ◽

Cited By ~ 5

Author(s):

Dongyue Li ◽

Dennis P. Lettenmaier ◽

Steven A. Margulis ◽

Konstantinos Andreadis

Keyword(s):

High Resolution ◽

Sierra Nevada ◽

Snow Accumulation ◽

Forecast Errors ◽

Streamflow Forecasting ◽

Streamflow Forecast ◽

Physically Based ◽

Streamflow Forecasts ◽

Seasonal Streamflow ◽

The Impact

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 forecasts using mixture-kernel GPR and advanced methods of input variable selection

Hydrology Research ◽

10.2166/nh.2018.023 ◽

2018 ◽

Vol 50 (1) ◽

pp. 200-214 ◽

Cited By ~ 12

Author(s):

Shuang Zhu ◽

Xiangang Luo ◽

Zhanya Xu ◽

Lei Ye

Keyword(s):

Variable Selection ◽

Gaussian Process Regression ◽

Quadratic Term ◽

Streamflow Prediction ◽

Factors Analysis ◽

Streamflow Forecasts ◽

Random Components ◽

Flexible Framework ◽

High Flows ◽

Seasonal Streamflow

Abstract Gaussian Process Regression (GPR) is a new machine-learning method based on Bayesian theory and statistical learning theory. It provides a flexible framework for probabilistic regression and uncertainty estimation. The main effort in GPR modelling is determining the structure of the kernel function. As streamflow is composed of trend, period and random components. In this study, we constructed a mixture-kernel composed of squared exponential kernel, periodic kernel and a rational quadratic term to reflect different properties of streamflow time series to make streamflow forecasts. A relevant feature-selection wrapper algorithm was used, with a top-down search for relevant features by Random Forest, to offer a systematic factors analysis that can potentially affect basin streamflow predictability. Streamflow prediction is evaluated by putting emphasis on the degree of coincidence, the deviation on low flows, high flows and the error level. The objective of this study is to construct a seasonal streamflow forecasts model using mixture-kernel GPR and the advanced input variable selection method. Results show that the mixture-kernel GPR has good forecasting quality, and top importance predictors are streamflow at 12, 6, 5, 1, 11, 7, 8, 4 months ahead, Nino 1 + 2 at 11, 5, 12, 10 months ahead.

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Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall–Runoff Regime

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0155.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2470-2483 ◽

Cited By ~ 16

Author(s):

Tushar Sinha ◽

A. Sankarasubramanian ◽

Amirhossein Mazrooei

Keyword(s):

Real Time ◽

Lead Time ◽

General Circulation ◽

Streamflow Forecasting ◽

Climate Forecasts ◽

Monthly Streamflow ◽

Streamflow Forecasts ◽

Temporal Disaggregation ◽

Seasonal Streamflow ◽

Spatial Downscaling

Abstract Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.

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