scholarly journals An intercomparison of approaches for improving operational seasonal streamflow forecasts

2017 ◽  
Vol 21 (7) ◽  
pp. 3915-3935 ◽  
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 Prediction ◽  
Ensemble 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 IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US 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 intercompare 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 (hierarchical ensemble streamflow prediction – 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.

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

2017 ◽  
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.

scholarly journals ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction

2016 ◽  
Vol 20 (8) ◽  
pp. 3277-3287 ◽  
Author(s):  
Joost V. L. Beckers ◽  
Albrecht H. Weerts ◽  
Erik Tijdeman ◽  
Edwin Welles
Keyword(s):  
Pacific Northwest ◽  
Southern Oscillation ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
The Us ◽  
Resampling Method ◽  
Streamflow Regime ◽  
Ensemble Streamflow Prediction ◽  
Index Value ◽  
Climate Mode

Abstract. Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A new method is proposed to incorporate climate mode information into the well-known ensemble streamflow prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of skill at forecasting stations that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal hindcasts of streamflows at three test stations in the Columbia River basin in the US Pacific Northwest. An improvement in forecast skill of 5 to 10 % is found for two test stations. The streamflows at the third station are less affected by ENSO and no change in forecast skill is found here.

scholarly journals ENSO-Conditioned Weather Resampling Method for Seasonal Ensemble Streamflow Prediction

2016 ◽  
Author(s):  
Joost V. L. Beckers ◽  
Albrecht H. Weerts ◽  
Erik Tijdeman ◽  
Edwin Welles
Keyword(s):  
Pacific Northwest ◽  
Southern Oscillation ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
The Pacific ◽  
Resampling Method ◽  
Streamflow Regime ◽  
Ensemble Streamflow Prediction ◽  
Index Value ◽  
Climate Mode

Abstract. Oceanic-atmospheric climate modes, such as El Niño Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A method is proposed to incorporate climate mode information into the Ensemble Streamflow Prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of forecast skill in sub-basins that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal reforecasts of streamflows in three sub-basins of the Columbia River basin in the Pacific Northwest. An improvement in forecast skill up to 10% is found.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

2008 ◽  
Vol 9 (1) ◽  
pp. 132-148 ◽  
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.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 10 (2) ◽  
pp. 1987-2013 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Lead Time ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Hydrologic Prediction

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic prediction skill at seasonal lead times (i.e. 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs – primarily the state of initial soil moisture and snow) and seasonal climate forecast skill (FS). In this study we quantify the contributions of IHCs and FS to seasonal hydrologic prediction skill globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the Variable Infiltration Capacity (VIC) macroscale hydrology model, one based on Ensemble Streamflow Prediction (ESP) and another based on Reverse-ESP (rESP), both for a 47 yr reforecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts obtained from each experiment with a control simulation forced with observed atmospheric forcings over the reforecast period and estimate the ratio of Root Mean Square Error (RMSE) of both experiments for each forecast initialization date and lead time. We find that in general, the contributions of IHCs are greater than the contribution of FS over the Northern (Southern) Hemisphere during the forecast period starting in October and January (April and July). Over snow dominated regions in the Northern Hemisphere the IHCs dominate the CR forecast skill for up to 6 months lead time during the forecast period starting in April. Based on our findings we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity

2017 ◽  
Vol 18 (6) ◽  
pp. 1715-1729 ◽  
Author(s):  
Louise Arnal ◽  
Andrew W. Wood ◽  
Elisabeth Stephens ◽  
Hannah L. Cloke ◽  
Florian Pappenberger
Keyword(s):  
Sensitivity Analysis ◽  
Forecast Skill ◽  
Sensitivity Analyses ◽  
Streamflow Prediction ◽  
Streamflow Forecast ◽  
Analysis Technique ◽  
Climate Forecasts ◽  
Modeling And Forecasting ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

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.

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

2014 ◽  
Vol 15 (1) ◽  
pp. 69-88 ◽  
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.

scholarly journals Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill

2011 ◽  
Vol 15 (11) ◽  
pp. 3529-3538 ◽  
Author(s):  
S. Shukla ◽  
D. P. Lettenmaier
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Forecast Skill ◽  
Climate Forecast ◽  
Streamflow Prediction ◽  
Seasonal Climate ◽  
Western Us ◽  
Ensemble Streamflow Prediction ◽  
Hydrologic Forecast ◽  
Hydrologic Conditions

Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.

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