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- Author or Editor: Satish Bastola x
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Abstract
This study investigates the sensitivity of the performance of hydrological models to certain temporal variations of precipitation over the southeastern United States (SEUS). Because of observational uncertainty in the estimates of rainfall variability at subdaily scales, the analysis is conducted with two independent rainfall datasets that resolve the diurnal variations. In addition, three hydrological models are used to account for model uncertainty. Results show that the temporal aggregation of subdaily rainfall can translate into a markedly higher volume error in flow simulated by the hydrological models. For the selected watersheds in the SEUS, the volume error is found to be high (~35%) for a 30-day aggregation in some of the selected watersheds. Hydrological models tend to underestimate flow in these watersheds with a decrease in temporal variability in precipitation. Furthermore, diminishing diurnal amplitude by removing subdaily rainfall corresponding to times of climatological daily maximum and minimum has a detrimental effect on the hydrological simulation. This theoretical experiment resulted in the underestimation of flow, with a disproportionate volume error (of as high as 77% in some watersheds). Observations indicate that over the SEUS variations of diurnal variability of rainfall explain a significant fraction of the seasonal variance throughout the year, with especially strong fractional variance explained in the boreal summer season. The results suggest that, should diurnal variations of precipitation get modulated either from anthropogenic or natural causes in the SEUS, there will be a significant impact on the streamflow in the watersheds. These conclusions are quite robust since both observational and model uncertainties have been considered in the analysis.
Abstract
This study investigates the sensitivity of the performance of hydrological models to certain temporal variations of precipitation over the southeastern United States (SEUS). Because of observational uncertainty in the estimates of rainfall variability at subdaily scales, the analysis is conducted with two independent rainfall datasets that resolve the diurnal variations. In addition, three hydrological models are used to account for model uncertainty. Results show that the temporal aggregation of subdaily rainfall can translate into a markedly higher volume error in flow simulated by the hydrological models. For the selected watersheds in the SEUS, the volume error is found to be high (~35%) for a 30-day aggregation in some of the selected watersheds. Hydrological models tend to underestimate flow in these watersheds with a decrease in temporal variability in precipitation. Furthermore, diminishing diurnal amplitude by removing subdaily rainfall corresponding to times of climatological daily maximum and minimum has a detrimental effect on the hydrological simulation. This theoretical experiment resulted in the underestimation of flow, with a disproportionate volume error (of as high as 77% in some watersheds). Observations indicate that over the SEUS variations of diurnal variability of rainfall explain a significant fraction of the seasonal variance throughout the year, with especially strong fractional variance explained in the boreal summer season. The results suggest that, should diurnal variations of precipitation get modulated either from anthropogenic or natural causes in the SEUS, there will be a significant impact on the streamflow in the watersheds. These conclusions are quite robust since both observational and model uncertainties have been considered in the analysis.
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.
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.
Abstract
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
Abstract
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
