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 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 Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

2012 ◽  
Vol 16 (8) ◽  
pp. 2825-2838 ◽  
Author(s):  
S. Shukla ◽  
N. Voisin ◽  
D. P. Lettenmaier
Keyword(s):  
Weather Forecast ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Medium Range Weather Forecast ◽  
Weather Forecasts ◽  
Medium Range ◽  
Hydrologic Prediction ◽  
Potential Improvement

Abstract. We investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts for runoff [SM] forecasts generally varies from 0 to 0.8 [0 to 0.5] as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

scholarly journals Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

2012 ◽  
Vol 9 (2) ◽  
pp. 1827-1857 ◽  
Author(s):  
S. Shukla ◽  
N. Voisin ◽  
D. P. Lettenmaier
Keyword(s):  
Weather Forecast ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Medium Range Weather Forecast ◽  
Weather Forecasts ◽  
Medium Range ◽  
Hydrologic Prediction ◽  
Potential Improvement

Abstract. We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

scholarly journals Influence of bias correction of meteorological and streamflow forecast on hydrological prediction in India

2021 ◽  
Author(s):  
Amar Deep Tiwari ◽  
Parthasarathi Mukhopadhyay ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Lead Time ◽  
Root Zone ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Post Processing ◽  
Streamflow Prediction ◽  
Streamflow Forecast ◽  
Hydrological Prediction

AbstractThe efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecast and streamflow post-processing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root zone soil moisture in the Narmada basin (drainage area: 97,410 km2), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June-September) season for 2000-2018 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1-3 day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root mean squared error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1-3-day lead time. We used Empirical Quantile Mapping (EQM) to bias correct precipitation forecast. The bias correction of precipitation forecast resulted in significant improvement in the precipitation forecast skill. Runoff and root zone soil moisture forecast was also significantly improved due to bias correction of precipitation forecast where the forecast evaluation is performed against the reference model run. However, bias correction of precipitation forecast did not cause considerable improvement in the streamflow prediction. Bias correction of streamflow forecast performs better than the streamflow forecast simulated using the bias-corrected meteorological forecast. The combination of the bias correction of precipitation forecast and post-processing of streamflow resulted in a significant improvement in the streamflow prediction (reduction in bias from 40% to 5%).

scholarly journals Seasonal Hydrologic Forecasting: Do Multimodel Ensemble Averages Always Yield Improvements in Forecast Skill?

2010 ◽  
Vol 11 (6) ◽  
pp. 1358-1372 ◽  
Author(s):  
Theodore J. Bohn ◽  
Mergia Y. Sonessa ◽  
Dennis P. Lettenmaier
Keyword(s):  
Bias Correction ◽  
Land Surface ◽  
Land Surface Model ◽  
Winter Precipitation ◽  
Forecast Skill ◽  
Surface Model ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Common Input ◽  
Model Average

Abstract Multimodel techniques have proven useful in improving forecast skill in many applications, including hydrology. Seasonal hydrologic forecasting in large basins represents a special case of hydrologic modeling, in which postprocessing techniques such as temporal aggregation and time-varying bias correction are often employed to improve forecast skill. To investigate the effects that these techniques have on the performance of multimodel averaging, the performance of three hydrological models [Variable Infiltration Capacity, Sacramento/Snow-17, and the Noah land surface model] and two multimodel averages [simple model average (SMA) and multiple linear regression (MLR) with monthly varying model weights] are examined in three snowmelt-dominated basins in the western United States. These evaluations were performed for both simulating and forecasting [using the Ensemble Streamflow Prediction (ESP) method] monthly discharge, with and without monthly bias corrections. The single best bias-corrected model outperformed the multimodel averages of raw models in both retrospective simulations and ensemble mean forecasts in terms of RMSE. Forming an MLR multimodel average from bias-corrected models added only slight improvements over the best bias-corrected model. Differences in performance among all bias-corrected models and multimodel averages were small. For ESP forecasts, both bias correction and multimodel averaging generally reduced the RMSE of the ESP ensemble means at lead times of up to 6 months in months when flow is dominated by snowmelt, with the reduction increasing as lead time decreased. The primary reason for this is that aggregating simulated streamflows from daily to monthly time scales increases model cross correlation, which in turn reduces the effectiveness of multimodel averaging in reducing those components of model error that bias correction cannot address. This effect may be stronger in snowmelt-dominated basins because the interannual variability of winter precipitation is a common input to all models. It was also found that both bias correcting and multimodel averaging using monthly varying parameters yielded much greater error reductions than methods using time-invariant parameters.

scholarly journals Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

2019 ◽  
Vol 11 (1) ◽  
pp. 101-110 ◽  
Author(s):  
James W. Roche ◽  
Robert Rice ◽  
Xiande Meng ◽  
Daniel R. Cayan ◽  
Michael D. Dettinger ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sierra Nevada ◽  
Land Surface ◽  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Process Models ◽  
Series Data ◽  
Climate Data ◽  
Data Set ◽  
Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

Long-Term Climate and Derived Surface Hydrology and Energy Flux Data for Mexico: 1925–2004

2007 ◽  
Vol 20 (9) ◽  
pp. 1936-1946 ◽  
Author(s):  
Chunmei Zhu ◽  
Dennis P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
North American ◽  
Land Surface ◽  
Surface Fluxes ◽  
The Other ◽  
Shortwave Radiation ◽  
Infiltration Capacity ◽  
Time Step ◽  
Downward Longwave Radiation

Abstract Studying the role of land surface conditions in the Mexican portion of the North American monsoon system (NAMS) region has been a challenge due to the paucity of long-term observations. A long-term gridded observation-based climate dataset suitable for forcing land surface models, as well as model-derived land surface states and fluxes for a domain consisting of all of Mexico, is described. The datasets span the period of January 1925–October 2004 at 1/8° spatial resolution at a subdaily (3 h) time step. The simulated runoff matches the observations plausibly over most of the 14 small river basins spanning all of Mexico, which suggests that long-term mean evapotranspiration is realistically reproduced. On this basis, and given the physically based model parameterizations of soil moisture and energy fluxes, the other surface fluxes and state variables such as soil moisture should be represented reasonably. In addition, a comparison of the surface fluxes from this study is performed with North American Regional Reanalysis (NARR) data on a seasonal mean basis. The results indicate that downward shortwave radiation is generally smaller than in the NARR data, especially in summer. Net radiation, on the other hand, is somewhat larger in the Variable Infiltration Capacity (VIC) hydrological model than in the NARR data for much of the year over much of the domain. The differences in radiative and turbulent fluxes are attributed to (i) the parameterization used in the VIC forcings for solar and downward longwave radiation, which links them to the daily temperature and temperature range, and (ii) differences in the land surface parameterizations used in VIC and the NCEP–Oregon State University–U.S. Air Force–NWS/Hydrologic Research Lab (Noah) land scheme used in NARR.

scholarly journals Climate, snow, and soil moisture data set for the Tuolumne and Merced River watersheds, California, USA

2018 ◽  
Author(s):  
James W. Roche ◽  
Robert Rice ◽  
Xiande Meng ◽  
Daniel R. Cayan ◽  
Michael D. Dettinger ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sierra Nevada ◽  
Land Surface ◽  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Process Models ◽  
Series Data ◽  
Climate Data ◽  
Data Set ◽  
Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) includes temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8) spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (level 0), data that are serially continuous with noise and non-physical points removed (level 1), and, where possible, data that are gap-filled using linear interpolation or regression with a nearby station record (level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow-depth and co-located soil-moisture measurements (2–6) from four locations spanning the rain-to-snow transition zone in the centre of the domain. Spatial data layers pertinent to snowpack modelling in this data set are basin polygons and 100-m resolution rasters of elevation, vegetation type, forest basal area, tree height, and forest canopy cover, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

scholarly journals Correction of real-time satellite precipitation with satellite soil moisture observations

2015 ◽  
Vol 12 (6) ◽  
pp. 5749-5787 ◽  
Author(s):  
W. Zhan ◽  
M. Pan ◽  
N. Wanders ◽  
E. F. Wood
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Surface Condition ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Normal Surface ◽  
Infiltration Capacity ◽  
Satellite Precipitation ◽  
The Impact

Abstract. Rainfall and soil moisture are two key elements in modeling the interactions between the land surface and the atmosphere. Accurate and high-resolution real-time precipitation is crucial for monitoring and predicting the on-set of floods, and allows for alert and warning before the impact becomes a disaster. Assimilation of remote sensing data into a flood-forecasting model has the potential to improve monitoring accuracy. Space-borne microwave observations are especially interesting because of their sensitivity to surface soil moisture and its change. In this study, we assimilate satellite soil moisture retrievals using the Variable Infiltration Capacity (VIC) land surface model, and a dynamic assimilation technique, a particle filter, to adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) real-time precipitation estimates. We compare updated precipitation with real-time precipitation before and after adjustment and with NLDAS gauge-radar observations. Results show that satellite soil moisture retrievals provide additional information by correcting errors in rainfall bias. High accuracy soil moisture retrievals, when merged with precipitation, generally increase both rainfall frequency and intensity, and are most effective in the correction of rainfall under dry to normal surface condition while limited/negative improvement is seen over wet/saturated surfaces. Errors from soil moisture, mixed among the real signal, may generate a false rainfall signal approximately 2 mm day−1 and thus lower the precipitation accuracy after adjustment.

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.

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