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

Abstract

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.

Statistical Downscaling Using Localized Constructed Analogs (LOCA)*

“…First, it smears finescale spatial features and increases the spatial coherence of the final downscaled field. This affects flooding, which is influenced by the spatial coherence of the precipitation field (the same problem has been found in BCSD; Zhang and Georgakakos 2012;Hwang and Graham 2014). Second, averaging tends to reduce the temporal variance of the final result (e.g., von Storch 1999).…”

“…There are many different forms of statistical downscaling [reviews can be found in Wilby et al (2004), Fowler et al (2007), and Maraun et al (2010); see also Hwang and Graham (2014)]. These methods include stochastic weather generators (e.g., Wilby et al 1998;Wilks 2012); various approaches that use large-scale fields as predictors for fine-resolution fields through regression or artificial neural networks (e.g., von Storch et al 1993;Schoof and Pryor 2001;Chen et al 2014); weather-type methods that use observed associations between characteristic recurring large-scale weather patterns and local responses typically seen when that weather type is present (e.g., Goodess and Palutikof 1998); and even simple ''delta methods,'' which typically add the time-mean model-predicted change to the sequence of observations.…”

“…Unlike many of the statistical downscaling methods noted above, these procedures use a coarse-resolution depiction of a climate variable to produce a fine-resolution version of the same climate variable. BCSD has proven to be an effective technique (e.g., Maurer et al 2010), but as traditionally implemented it is a monthly method, which limits the possible changes in the shape of daily distributions and does not preserve the GCM's daily weather sequences [although Piani et al (2010) and Thrasher et al (2012) give extensions to the daily time scale, which is evaluated in Hwang and Graham (2014)]. Our interests include impacts that are affected by both the distribution and specific sequences of daily extremes and how those might change in the future, such as energy use during heat waves (e.g., California electricity use rises after the third consecutive day of hot weather) and floods generated by sequential days of heavy precipitation, so we focus on improving the constructed analogs process rather than changes to BCSD.…”

Evaluating climate change impacts on the hydrology of watershed in northwestern China using a stepwise‐clustered downscaling approach

“…an analogue method, a multi‐objective fuzzy‐rule‐based classification and a statistical downscaling model) to investigate the impact of downscaled precipitation on variability of seasonal streamflow for a meso‐scale catchment in southeastern Sweden; results showed that the statistical downscaling model could best downscale the precipitation during winter and spring seasons. Hwang and Graham () employed three statistical methods to downscale daily precipitations and to simulate retrospective streamflow for the sub‐basin scale in west‐central Florida; results showed that the accuracy in reproducing temporal and spatial variability of precipitation could vary with downscaling methods.…”

Future Climate Change Scenarios for Florida

“…In brief, dynamical downscaling translates large-scale GCM data to a finer grid using a regional climate model (Giorgi et al 2001(Giorgi et al , 2009 Statistical and dynamical downscaling of climate projections has often been used over the southeast and Florida. In studying the hydrological system of the Tampa Bay region, Hwang and Graham (2014) emphasized the importance of choosing the correct statistical downscaling that preserves the precipitation characteristics of the region in order to simulate the streamflow variations. Hwang et al (2011) evaluated the fifth-generation Pennsylvania State UniversityNational Center for Atmospheric Research Mesoscale Model (MM5) to dynamically downscale precipitation over the Tampa Bay region, and found the spatial patterns of precipitation to be realistic on daily, seasonal, and inter-annual timescales; they consider the data useful for multidecadal water resource planning in Tampa Bay.…”