Assessment of the utility of dynamically-downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model

Hwang, Syewoon; Graham, Wendy D.; Adams, Alison; Geurink, Jeffrey

doi:10.1007/s10113-013-0406-x

Cited by 25 publications

(13 citation statements)

References 27 publications

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“…The method effectively removes the bias in the temporal statistics (including mean, variance, skewness, kurtosis, etc.) of precipitation and temperature predictions by adjusting the simulated CDF to fit the observed CDF (Hwang et al ., , ).…”

Section: Methodsmentioning

confidence: 99%

“…However, the use of RCMs has significant computational cost and thus their routine application for the generation of ensembles of climate predictions using multiple GCMs and multiple scenarios is limited. Furthermore, RCMs have their own bias in addition to the bias propagated from boundary conditions and thus require bias correction prior to use for hydrologic, agricultural or natural resource impact assessments (Sato et al ., ; Hwang et al ., , ).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Alternative Methods for Statistically Downscaling Daily GCM Precipitation Outputs to Simulate Regional Streamflow

Hwang

Graham

2014

J American Water Resour Assoc

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This study applied three statistical downscaling methods: (1) bias correction and spatial disaggregation at daily time scale (BCSD_daily); (2) a modified version of BCSD which reverses the order of spatial disaggregation and bias correction (SDBC), and (3) the bias correction and stochastic analog method (BCSA) to downscale general circulation model daily precipitation outputs to the subbasin scale for west‐central Florida. Each downscaled climate input dataset was then used in an integrated hydrologic model to examine differences in ability to simulate retrospective streamflow characteristics. Results showed the BCSD_daily method consistently underestimated mean streamflow because the highly spatially correlated small precipitation events produced by this method resulted in overestimation of evapotranspiration. Highly spatially correlated large precipitation events produced by the SDBC method resulted in overestimation of the standard deviation of wet season daily streamflow and the magnitude/frequency of high streamflow events. BCSA showed better performance than the other methods in reproducing spatiotemporal statistics of daily precipitation and streamflow. This study demonstrated differences in statistical downscaling techniques propagate into significant differences in streamflow predictions, and underscores the need to carefully select a downscaling method that reproduces precipitation characteristics important for the hydrologic system under consideration.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Alternative Methods for Statistically Downscaling Daily GCM Precipitation Outputs to Simulate Regional Streamflow

Hwang

Graham

2014

J American Water Resour Assoc

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“…The application of a bias correction procedure generally increases the agreement between RCM‐simulations and the corresponding observations (Chen, St‐Denis, Brissette, & Lucas‐Picher, ; Teutschbein & Seibert, ; Themeßl, Gobiet, & Heinrich, ). For example, Hwang, Graham, Adams, and Geurink () indicated that the bias of RCM simulation in the temporal mean and standard deviation of daily precipitation and temperature could be effectively removed by using a quantile mapping (QM) method. The corrected climate simulation further improved the hydrological modelling over a watershed scale.…”

Section: Introductionmentioning

confidence: 99%

Using reanalysis‐driven regional climate model outputs for hydrology modelling

Chen

Brissette

Chen

2018

Hydrological Processes

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Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale.

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“…It is quite apparent from the diverse set of papers that the demand for ''reliable'' regional-scale climate data over the SUS is of considerable interest for climate impact assessment. As a result, there are several papers in this collection which dwell on developing the regional-scale climate information from dynamic downscaling (e.g., Misra et al 2012), or from statistical downscaling (e.g., Asefa and Adams 2013) or from a combination of statistical and dynamical downscaling (e.g., Hwang et al 2013). Asefa and Adams (2013) introduced in their paper a new statistical bias correction technique for regional climate projections over central Florida based on a Bayesian approach that weights on the reliability of the global climate model in reproducing the observed climate.…”

mentioning

confidence: 99%

“…Asefa and Adams (2013) introduced in their paper a new statistical bias correction technique for regional climate projections over central Florida based on a Bayesian approach that weights on the reliability of the global climate model in reproducing the observed climate. Hwang et al (2013) highlight the merit of using dynamically downscaled and statistically bias-corrected climate data for hydrological applications over the Tampa Bay watershed. Misra et al (2012) show the advantage and fidelity of dynamically downscaling the twentieth-century global atmospheric reanalysis (20CR) to 10 km grid resolution.…”

mentioning

confidence: 99%

A multi-disciplinary assessment of the southeastern United States climate

Misra

2013

Reg Environ Change

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The southeastern United States (SUS) is a region that exhibits considerable climate variability throughout the year along with high-impact weather extremes of severe weather and land-falling Atlantic tropical cyclones in the summer and fall, freeze events in the winter season, and tornadoes in the spring season. The SUS region has rapidly grown in terms of human population for last several decades that further exposes vulnerabilities to the frailties of nature. Furthermore, in the recent times, the agricultural production of the region has grown significantly with the rise in consumption and demand for bio fuels, increase in price and demand for commodity and food crops and increasing irrigation infrastructure that is subsidized by the state governments in the SUS. Similarly, the coastal development and capitalization of natural marine resource environment have also risen with the growing wealth of the population. All of these developments have led to an increasing interest in the understanding of the climate variations and change on the built and the natural environments of the SUS. This special issue is a manifestation of such an interest in the SUS with contributions from several climate-related inter-disciplinary groups including the Southeast Climate Consortium (SECC; http://seclimate. org/), the Florida Climate Institute (FCI; http://floridacli mateinstitute.org/), the University of Florida Water Institute (http://waterinstitute.ufl.edu/), and the Florida Water & Climate Alliance (FloridaWCA; http://www.floridawca.org).There are 15 research papers in this special issue covering climate science and its applications in hydrology, ecology, economy, crop science, and social science. It is quite apparent from the diverse set of papers that the demand for ''reliable'' regional-scale climate data over the SUS is of considerable interest for climate impact assessment. As a result, there are several papers in this collection which dwell on developing the regional-scale climate information from dynamic downscaling (e.g., Misra et al. 2012), or from statistical downscaling (e.g., Asefa and Adams 2013) or from a combination of statistical and dynamical downscaling (e.g., Hwang et al. 2013). Asefa and Adams (2013) introduced in their paper a new statistical bias correction technique for regional climate projections over central Florida based on a Bayesian approach that weights on the reliability of the global climate model in reproducing the observed climate. Hwang et al. (2013) highlight the merit of using dynamically downscaled and statistically bias-corrected climate data for hydrological applications over the Tampa Bay watershed. Misra et al. (2012) show the advantage and fidelity of dynamically downscaling the twentieth-century global atmospheric reanalysis (20CR) to 10 km grid resolution. For example, they demonstrate that data inhomogeneity issues over SUS in the 20CR are greatly ameliorated by the internal variations resolved by the dynamic downscaling to 10-km grid resolution.The interest in regional-scale cli...

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Assessment of the utility of dynamically-downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model

Cited by 25 publications

References 27 publications

Assessment of Alternative Methods for Statistically Downscaling Daily GCM Precipitation Outputs to Simulate Regional Streamflow

Assessment of Alternative Methods for Statistically Downscaling Daily GCM Precipitation Outputs to Simulate Regional Streamflow

Using reanalysis‐driven regional climate model outputs for hydrology modelling

A multi-disciplinary assessment of the southeastern United States climate

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