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.

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

2011 ◽  
Vol 8 (4) ◽  
pp. 6565-6592 ◽  
Author(s):  
S. Shukla ◽  
D. P. Lettenmaier
Keyword(s):  
United States ◽  
The United States ◽  
Forecast Skill ◽  
Climate Forecast ◽  
Streamflow Prediction ◽  
Seasonal Climate ◽  
Southeastern Us ◽  
Western Us ◽  
Hydrologic Forecast ◽  
Hydrologic Conditions

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

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 The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)

2020 ◽  
Vol 12 (9) ◽  
pp. 1490 ◽  
Author(s):  
Calum Baugh ◽  
Patricia de Rosnay ◽  
Heather Lawrence ◽  
Toni Jurlina ◽  
Matthias Drusch ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
The United States ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Skill Scores ◽  
High Flows ◽  
The Impact ◽  
Awareness System

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff.

scholarly journals Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons

2013 ◽  
Vol 17 (25) ◽  
pp. 1-22 ◽  
Author(s):  
Satish Bastola ◽  
Vasubandhu Misra ◽  
Haiqin Li
Keyword(s):  
United States ◽  
South Carolina ◽  
Climate Model ◽  
Southeastern United States ◽  
Boreal Summer ◽  
Hydrological Models ◽  
State University ◽  
Streamflow Prediction ◽  
Seasonal Climate ◽  
Hydrological Prediction

Abstract The authors evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts [the Florida Climate Institute–Florida State University Seasonal Hindcasts at 50-km resolution (FISH50)] is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall–runoff models. The hydrological models are forced with rainfall from FISH50, (quantile based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogs of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp). The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, the authors find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root-mean-square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that, over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. The authors contend that, given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.

scholarly journals Benchmarking ensemble streamflow prediction skill in the UK

2018 ◽  
Vol 22 (3) ◽  
pp. 2023-2039 ◽  
Author(s):  
Shaun Harrigan ◽  
Christel Prudhomme ◽  
Simon Parry ◽  
Katie Smith ◽  
Maliko Tanguy
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Base Flow ◽  
Ensemble Prediction ◽  
Flow Index ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Skill Scores ◽  
Ensemble Streamflow Prediction ◽  
The Uk

Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient =0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.

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 CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

2016 ◽  
Author(s):  
Di Tian ◽  
Eric F. Wood ◽  
Xing Yuan
Keyword(s):  
United States ◽  
Southern Oscillation ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Climate Forecast ◽  
Seasonal Forecasts ◽  
Precipitation Indices ◽  
Temperature Indices ◽  
Precipitation And Temperature ◽  
Rainy Days

Abstract. Forecasts from global seasonal climate forecast models can be potentially exploited for sub-seasonal forecasts of precipitation and 2-m temperature. The probabilistic sub-seasonal forecast skill of ten precipitation and temperature indices is investigated using the 28-years’ hindcasts of the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill is highly dependent on the forecast indices, regions, seasons, leads, and methods. Indices characterizing mean precipitation and temperature as well as measuring frequency or duration of precipitation and temperature extremes for 7-, 14-, and 30-day forecasts were skillful depending on seasons, regions, and forecast leads. Forecasts for 7- and 14-day temperature indices showed skill even at weeks 3 and 4, and generally more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region. While the forecast skill related to mean precipitation indices were low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and the number of consecutive dry days showed considerable high skill for the west coast region. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts are less skillful than those calculated from the daily CFSv2 forecasts, suggesting the potential usefulness of the CFSv2 daily forecasts for hydrological applications relative to the temporally disaggregated CFSv2 monthly forecasts. While the presence of active Madden-Julian Oscillation (MJO) events improves CFSv2 weekly mean precipitation forecast skill over major areas of CONUS, MJO or El Niño Southern Oscillation did not have same strong effects on weekly mean temperature forecasts.

scholarly journals Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

2011 ◽  
Vol 15 (11) ◽  
pp. 3399-3410 ◽  
Author(s):  
C. M. DeChant ◽  
H. Moradkhani
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Water Storage ◽  
Initial Condition ◽  
Streamflow Prediction ◽  
Western Us ◽  
Operational Forecasting ◽  
Snow Water ◽  
Ensemble Streamflow Prediction ◽  
Using Data

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

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