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Using a gridded global data set to characterize regional hydroclimate in central Chile
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E.M.C. Demaria1, E. Maurer2*, J. Sheffield3, E. Bustos1, D. Poblete1, S. Vicuña1, F. Meza1
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Chile
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Civil Engineering Department, Santa Clara University, Santa Clara, CA, USA
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Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Centro Interdisciplinario de Cambio Global, Pontificia Universidad Católica de Chile, Santiago,
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*Corresponding author, emaurer@engr.scu.edu, 408-554-2178.
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Proposed submission to J. Hydrometeorology
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Abstract
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Central Chile is facing dramatic projections of climate change, with a consensus for declining
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precipitation, negatively affecting hydropower generation and irrigated agriculture. Rising from
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sea level to 6,000 meters within a distance of 200 kilometers, precipitation characterization is
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difficult due to a lack of long-term observations, especially at higher elevations. For
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understanding current mean and extreme conditions and recent hydroclimatological change, as
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well as to provide a baseline for downscaling climate model projections, a temporally and
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spatially complete data set of daily meteorology is essential. We use a gridded global daily
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meteorological data set at 0.25 degree resolution for the period 1948-2008, and adjust it using
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monthly precipitation observations interpolated to the same grid using a cokriging method with
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elevation as covariate. For validation, we compare daily statistics of the adjusted gridded
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precipitation to station observations. For further validation we drive a hydrology model with the
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gridded 0.25-degree meteorology and compare stream flow statistics with observed flow. We
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validate the high elevation precipitation by comparing the simulated snow extent to MODIS
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images. Results show that the daily meteorology with the adjusted precipitation can accurately
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capture the statistical properties of extreme events as well as the sequence of wet and dry events,
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with hydrological model results displaying reasonable agreement for observed flow statistics and
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snow extent. This demonstrates the successful use of a global gridded data product in a relatively
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data-sparse region to capture hydroclimatological characteristics and extremes.
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1. Introduction
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Whether exploring teleconnections for enhancing flood and drought predictability or assessing
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the potential impacts of climate change on water resources, understanding the response of the
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land surface hydrology to perturbations in climate is essential. This has inspired the development
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and assessment of many large scale hydrologic models for simulating land-atmosphere
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interactions over regional and global scales [e.g., Lawford et al., 2004; Milly and Shmakin, 2002;
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Nijssen et al., 2001a; Sheffield and Wood, 2007].
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A prerequisite to regional hydroclimatological analyses is a comprehensive, multi-decadal,
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spatially and temporally complete data set of observed meteorology, whether for historic
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simulations or as a baseline for downscaling future climate projections. In response to this need,
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data sets of daily gridded meteorological observations have been generated, both over
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continental regions [e.g.,Cosgrove et al., 2003; Maurer et al., 2002] and globally [Adam and
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Lettenmaier, 2003; Sheffield et al., 2006]. These have benefited from work at coarser time scales
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[Chen et al., 2002; Daly et al., 1994; Mitchell and Jones, 2005; New et al., 2000; Willmott and
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Matsuura, 2001], with many products combining multiple sources, such as station observations,
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remotely sensed images, and model reanalyses.
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While these large-scale gridded products provide opportunities for hydrological simulations for
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land areas around the globe, they are inevitably limited in their accuracy where the underlying
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density of available station observations is low, the station locations are inadequate to represent
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complex topography, or where the gridded spatial resolution is too large for the region being
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studied. Central Chile is an especially challenging environment for characterizing climate and
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hydrology since the terrain exhibits dramatic elevation changes over short distances, and the
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orographic effects produce high spatial heterogeneity in precipitation in particular. In general, the
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observation station density in South America is inadequate for long-term hydroclimate
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characterization [de Goncalves et al., 2006]. While some of South America is relatively well
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represented by global observational datasets [Silva et al., 2007], regions west of the Andes are
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much less so [Liebmann and Allured, 2005].
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In this study, we utilize a new high-resolution global daily gridded dataset of temperature and
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precipitation, adjust it with available local climatological information, and assess its utility for
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representing river basin hydrology. Recognizing the value in simulating realistic extreme events,
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we assess the new data product for its ability to produce reasonable daily streamflow statistics.
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We evaluate the potential to reproduce climate and hydrology in a plausible manner, such that
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historical statistics are reproduced.
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The principal aim of this study is to produce a gridded representation of the climate and
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hydrology of central Chile, and demonstrate a methodology for producing a reasonable set of
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data products that can be used for future studies of regional hydrology or climate. Given these
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regional results, we assess the potential to export the method to other relatively data-sparse
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regions, where representative climatological average information is available but long-term daily
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data are inadequate. The paper is organized as follows: Section 2 describes the study area. In
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Section 3 we describe the data, the hydrological model, and the methodological approach.
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Results of the adjusted data set validation and model simulations are discussed in Section 4.
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Finally, the main conclusions of the study are presented in Section 5.
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2. Region
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The focus area of this study is the region in central Chile, encompassing the four major river
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basins (from north to south, the Rapel, Mataquito, Maule, and Itata Rivers) between latitudes
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35.25º S and 37.5º S (Figure 1). The climate is Mediterranean, with 80% of the precipitation
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falling in the rainy season from May-August [Falvey and Garreaud, 2007]. The terrain is
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dramatic, rising approximately 6000 meters within a horizontal distance of approximately 200
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km, producing sharp gradients in climate [Falvey and Garreaud, 2009].
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Mean precipitation is approximately 500 mm per year at the North end of the study domain, and
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as much as 3000 mm per year in the high elevations at the Southern end of the domain. Although
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climate information in the valley or mountain foothills is well represented by meteorological
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stations it is evident from Figure 1 that the high elevation areas are under-represented by any of
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the observation stations.
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Our study region in Central Chile is especially important from a hydroclimatological standpoint,
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as
it
contains
more
than
75%
of
the
Country’s
total
irrigated
agriculture
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[www.censoagropecuario.cl/index2.html] and reservoir storage of any region in the country, and
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provides water supply for some of Chile's largest cities. A changing climate is evident in recent
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hydroclimate records [Rubio-Álvarez and McPhee, 2010], and future climate projections for the
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region indicate the potential for very large impacts [Bradley et al., 2006]. The vulnerability of
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Central Chile to projected climate change is high, with robust drying trends in General
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Circulation Model (GCM) projections, and a high sensitivity to changing snow melt patterns
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[Vicuna et al., 2011], who also discuss the challenges in characterizing climate in a Chilean
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catchment with few precipitation observations, and none at high elevations.
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3. Methods and data
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3.1 Gridded data set development
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We begin with a gridded global (land surface) forcing dataset of daily precipitation and
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minimum and maximum temperatures at 0.25º spatial resolution (approximately 25 km),
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prepared following Sheffield et al. [2006]. To summarize, the forcing dataset is based on the
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NCEP–NCAR reanalysis [Kalnay et al., 1996] for 1948-2008, from which daily maximum and
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minimum temperature and daily precipitation are obtained at approximately 2º spatial resolution.
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Reanalysis temperatures are based on upper-air observations, though precipitation is a model
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output and thus exhibits significant biases.
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The reanalysis temperatures are interpolated to a 0.25º spatial resolution, using lapsing
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temperatures of -6.5ºC/km based on the elevation difference between the large reanalysis spatial
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scale and the elevation in each 0.25º grid cell. Precipitation is interpolated to 0.25º using a
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product of the Tropical Rainfall Measuring Mission (TRMM) [Huffman et al., 2007] following
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the methods outlined by Sheffield et al. [2006]. The daily statistics of precipitation (number of
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rain days, wet/dry day transition probabilities) are corrected by resampling to match the
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observationally-based monthly 0.5º rain days data from the Climate Research Unit [CRU,
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Mitchell and Jones, 2005] and the daily statistics of the Global Precipitation Climatology Project
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[Huffman et al., 2001] and the global forcing dataset of Nijssen et al. [2001a]. To ensure large-
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scale correspondence between this data set and the CRU monthly data set, precipitation is scaled
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so the monthly totals match the CRU monthly values at the CRU spatial scale. Maximum and
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minimum temperatures are also scaled to match the CRU time series, using CRU monthly mean
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temperature and diurnal temperature range.
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While the incorporation of multiple sources of extensively reviewed data provides an invaluable
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data product for global and continental scale analyses, as discussed by Mitchell and Jones [2005]
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ultimately much of the local characterization is traceable to a common network of land surface
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observations [Peterson et al., 1998], which is highly variable in station density for different
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regions. For example, for the region of study shown in Figure 1, an average of 3-4 observation
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stations are included in the CRU precipitation data product, and none are in high-elevation areas.
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This results in a few low elevation meteorological stations in Chile on the western side of the
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Andes, and the next observation station to the east is in a more arid area in Argentina. Thus, the
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resulting precipitation fields in the gridded product for this region show a spatial gradient
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opposite to that published by the Dirección General de Aguas [DGA, 1987]. Figure 2a shows the
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spatial distribution of gridded global total annual precipitation that displays a notable decrease of
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rainfall with elevation. Conversely the DGA precipitation map is able to capture the
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climatological orographic enhancement of precipitation by the Andes (Figure 2b). The
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precipitation lapse rates for the latitudinal bands -35.125º S and -36.125º S show a negative
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gradient of precipitation with elevation in the global gridded data set whereas the DGA
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precipitation shows a positive gradient for the period 1951-89 (Figures 2c and 2d, respectively).
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Local data from the DGA of Chile, some monthly and some daily, were obtained to characterize
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better the local climatology. While still biased toward low elevation areas, the stations (Figure 1)
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do cover a wider range and include altitudes up to 2400 m. These stations were filtered to include
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those that had at least 90% complete monthly records for a 25-year period, with the period
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containing the most complete data coverage identified as 1983-2007. Since the climate of Central
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Chile is also modulated by interannual variability linked to El Nino and the Pacific Decadal
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Oscillation [Garreaud et al., 2009] a 25-year period was chosen to limit the influence of a single
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predominant phase of low-frequency oscillations during the climatological period. The monthly
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average precipitation for the selected DGA stations was interpolated onto the same 0.25º grid
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using cokriging, with elevation being the covariate. This method of cokriging has been shown to
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improve kriging interpolation to include orographic effects induced by complex terrain [Diodato
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and Ceccarelli, 2005; Hevesi et al., 1992].
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This process produced twelve monthly mean precipitation maps for the region. The same 1983-
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2007 period was extracted from the daily gridded data set, and monthly average values were
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calculated for each grid cell. Ratios (twelve, one for each month) of observed climatology
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divided by the gridded data set average were then calculated for each grid cell. Daily values in
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the gridded data set were adjusted to create a new set of daily precipitation data, Padj, which
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matches the interpolated observations produced with cokriging, using a simple ratio:
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Padj i, j,t   Pgrid i, j,t  
Pobs,mon i, j 
Pgrid,mon i, j 
(1)
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where Pgrid is the original daily gridded 0.25º data at location (i,j), Pobs is the interpolated
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observed climatology, overbars indicate the 25-year mean, and the subscript “mon” indicates the
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month from the climatology in which day t falls.
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This same method was applied to a global dataset of daily meteorology in a data sparse region in
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Central America, resulting in improved characterization of precipitation and land surface
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hydrology [Maurer et al., 2009]. In addition, this new adjusted data set includes the full 1948-
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2008 period, despite the fact that local observations are very sparse before 1980.
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To validate the adjusted precipitation data set, we computed a set of statistical parameters widely
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used to describe climate extremes [dos Santos et al., 2011; X. Zhang and Yang, 2004].
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Additionally to evaluate the temporal characteristics of rainfall events we computed the
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probability of occurrence of wet and dry days, and the transition probabilities between wet and
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dry states [Wilks and Wilby, 1999]. Table 1 shows a description of the statistics used.
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To evaluate if the adjusted precipitation data set was capturing the orographic gradient of
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precipitation we compared model simulated Snow Water Equivalent (SWE) to the MODIS/Terra
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Snow Cover data set, which is available at 0.05 degree resolution for 8-day periods starting from
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the year 2000. MODIS snow cover data are based on a snow mapping algorithm that employs a
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Normalized Difference Snow Index [Hall et al., 2006]. To estimate snow cover from the
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meteorological data, the Variable Infiltration Capacity (VIC) model was employed (se model
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description in Section 3.2). The accuracy of simulated snow cover relative to that of random
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chance was measured with the Heidke Skill Score [HSS, Wilks, 2006]. A score equal to one
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would indicate perfect agreement between VIC simulated snow cover and observations; a value
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greater than zero indicates some predictive skill. Thus, the closer the HSS is to one, the less
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likely it is that the agreement between observations and simulations has been obtained by
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chance. The score is computed as:
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HSS 
2ad  bc)
a  c c  d  a  bb  d
(2)
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where a and d are the numbers of hits (i.e., pairs of successful MODIS-VIC no snow and snow
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estimates, respectively), b is the number of cases when VIC simulates snow but MODIS does not
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measure it, and c is the number of events when snow is observed by MODIS but not simulated
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by VIC.
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3.2 Hydrologic Model Simulations
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To assess the ability of the daily gridded meteorology developed in this study to capture daily
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climate features across the watersheds, we simulate the hydrology of river basins in the region to
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obtain streamflow and snow cover estimates. The hydrologic model used is the Variable
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Infiltration Capacity (VIC) model [Cherkauer et al., 2003; Liang et al., 1994]. The VIC model is
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a distributed, physically-based hydrologic model that balances both surface energy and water
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budgets over a grid mesh. The VIC model uses a “mosaic” scheme that allows a statistical
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representation of the sub-grid spatial variability in topography, infiltration and vegetation/land
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cover, an important attribute when simulating hydrology in heterogeneous terrain. The resulting
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runoff at each grid cell is routed through a defined river system using the algorithm developed by
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Lohmann et al. [1996]. The VIC model has been successfully applied in many settings, from
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global to river basin scale [e.g., Maurer et al., 2002; Nijssen et al., 2001b; Sheffield and Wood,
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2007].
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For this study, the model was run at a daily time step at a 0.25º resolution (approximately 630
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km2 per grid cell for the study region). Elevation data for the basin routing were based on the 15-
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arc-second Hydrosheds dataset [Lehner et al., 2006], derived from the Shuttle Radar Topography
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Mission (SRTM) at 3 arc-second resolution. Land cover and soil hydraulic properties were based
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on values from Sheffield and Wood [2007], though specified soil depths and VIC soil parameters
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were modified during calibration. The river systems contributing to selected points were defined
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at a 0.25º resolution, following the technique outlined by O’Donnell et al. [1999].
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4. Results and Discussion
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The adjusted data set was validated in several ways. First, daily statistics were compared
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between the adjusted global daily data set and local observations, where available. Second,
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hydrologic simulation outputs were compared to observations to investigate the plausibility of
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using the new data set as an observational baseline for studying climate impacts on hydrology.
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4.1 Gridded meteorological data development and assessment
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The quality of daily gridded precipitation fields was improved using available monthly observed
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precipitation. Rain gauge records from DGA were selected using two criteria: stations with
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records of twenty-five years and with no more than 10% missing daily measurements. Based on
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those two constraints the period 1983-2007 was identified as that with the largest number of
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reporting stations. From the pool of 70 available stations, 40 stations met the two criteria (Figure
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1). Except for the Itata river basin, which had two stations located at 1200 and 2400 meters
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above sea level, most of the selected stations were located in the central part of the region at
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elevations below 500 meters. Mean precipitation was computed for each month and for each
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selected station, resulting in 12 mean values for the 25-year climatological period.
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Cokriging was then applied to produce a set of 12 maps of climatological precipitation at 0.25º
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spatial resolution. A scatter plot between observed and predicted average (1983-2007) monthly
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precipitation for July, the middle of the rainy season, is shown in Figure 3. Cokriged monthly
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totals match observations quite closely for the region with a bias equal to -0.8 % with respect to
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the observed values and a relative RMSE of 0.50 %, which is expected since the stations were
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included in the kriging process.
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Figure 4 shows the adjusted gridded annual precipitation fields and the difference from the
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original gridded observed data set for the period 1950-2006. It is evident that in the more humid
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southern mountainous portion of the study area there has been a marked increase in precipitation
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with the adjustment, incorporating the more detailed information embedded in the rain gauge
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observations. Differences between original and adjusted gridded precipitation indicates the
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existence of a band along the Andes where annual precipitation is greater in the adjusted
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precipitation data set (Figure 4b).
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To verify how the adjusted daily precipitation relates to observations, we compared daily rainfall
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at selected 0.25º grid points with the day-by-day means of three rain gauge stations located in
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approximately a 50 km diameter circle (Figure 5). Rain gauge stations were selected from the
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pool of 40 stations used to perform the cokriging interpolation, hence they had a record of 25
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years with not more than 10% missing values. Selected stations were located, when possible, not
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more that 50% higher (maximum elevation difference was 150 meters except at Loc3 where it
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was 500 meters) or lower elevations than that of the 0.25º grid cell. Four 0.25º grid points were
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selected for the comparison. The locations of the four grid points are listed in Table 2. For these
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four locations, we computed basic statistics, bias, RMSE and correlation coefficient for daily
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observed (OBS) and daily adjusted gridded precipitation (ADJ) for Austral summer (DJF) and
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Austral winter (JJA) for the period 1983-2007. Summary statistics are shown in Table 3. The
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bias is defined as the sum of the differences between ADJ and OBS, and the RMSE is equal to
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the root mean squared error between daily ADJ and OBS precipitation values.
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Mean daily values are very close for the observed and adjusted datasets for both seasons, which
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is expected given the adjustment process. The variability of daily precipitation within each
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season, represented by the standard deviation, also compares relatively well, though the adjusted
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gridded data show greater variability than the observations during the rainy winter season. A
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high RMSE and low correlation values indicate that temporal sequencing differs between the two
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data sets. This is not unexpected, since the daily precipitation in the original 0.25º gridded data
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was derived from reanalysis, and as such it is a model output that does not incorporate station
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observations [Kalnay et al., 1996], and is resampled on a daily basis to correct the daily statistics
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of precipitation. Thus, while important characteristics of daily precipitation variability are
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represented in the 0.25º gridded data, and monthly totals should bear resemblance to
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observations (at least as represented by the underlying monthly data such as CRU),
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correspondence with observed daily precipitation events is not anticipated.
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With this limitation in mind, we focus on the statistics of daily hydroclimatology, rather than
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event-based statistics. Especially given the rising interest in characterizing extreme events in the
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context of a changing climate [IPCC, 2011], the ability of the adjusted daily gridded dataset to
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characterize extreme statistics is important. We compute a set of statistical variables frequently
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used to describe climate extremes, using the RClimDex software [X. Zhang and Yang, 2004;
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Xuebin Zhang et al., 2005]. Additionally we compute the unconditional probability of
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occurrence of wet and dry days and the corresponding transition probabilities. Figure 6 shows
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boxplots of six statistical parameters listed in Table 1 for the four locations. Extreme
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precipitation events (R95p) are well captured in the adjusted gridded data set at most locations.
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The agreement between adjusted total annual precipitation (PRCPTOT) and observations is good
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with an average bias of -9% from the observed station mean (not shown), although this is
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constrained by design, as noted above. The Simple Daily Intensity Index (SDII), which is a
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measure of the mean annual intensity of rainfall, also shows good agreement at the four
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locations, indicating that the number of rainy days is well represented in the adjusted dataset.
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The number of days with intensities larger than the 20 mm (R20mm) compares well between
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observations and adjusted gridded precipitation, though the adjusted gridded data slightly
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underestimate observations. The maximum consecutive number of dry days and wet days in a
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year is lower for the adjusted gridded observations compared to observations suggesting the
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durations of wet and dry events are shorter in the adjusted gridded data set.
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Figure 7 shows that the probabilities of a day being wet or dry are comparable between both
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datasets (panels 7a and 7b). Conversely the adjusted precipitation data shows an average
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transition probability of a wet day followed by a wet day of 0.21 compared to 0.50 obtained for
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the observations suggesting that the duration of storm events is shorter in the adjusted gridded
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dataset. This could partially explain the underestimation of maximum consecutive wet days
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(CWD) in the adjusted gridded precipitation as well.
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The statistical quantities presented in Figures 6 and 7 were compared statistically using the
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correlation coefficient and a two-sample unpaired Student’s t-test. These are summarized in
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Table 4. Statistics linked to high intensity events (R99p and R95p and maximum 1-day
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precipitation (RX1day) have statistically indistinguishable means for all four locations. The
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mean annual precipitation and intensity parameters (Prcptot and SDII) show equal means for
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three out of the four locations. Conversely the parameters, R5mm R20mm, albeit strongly
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correlated, were found to have statistically different mean values. This phenomenon of a gridded
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precipitation data set having lower extreme precipitation values than station observations was
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also noted in the South American study of Silva et al. [2007] and is consistent with the effect of
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spatial averaging, i.e., comparing the average of a 630 km2 0.25º grid cell to the smaller, more
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discrete area represented by the three averaged stations [Yevjevich, 1972]. The statistics related
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to duration of wet and dry spells showed statistically different population means at all 4
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locations.
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4.2 Hydrologic Model Validation of Adjusted Meteorology
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To assess the representation in the new meteorological data set of basin-wide and high elevation
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areas, the adjusted gridded data developed and assessed in the previous sections were then used
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to drive the VIC hydrologic model. Since the precipitation was shown to be comparable to
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observations (where available) in many important respects, another validation of the driving
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meteorology would be the successful simulation of observed streamflow and snow cover.
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Records of observed streamflow in the region tend to be incomplete or for short periods, and
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since most of the rivers are affected by reservoirs and diversions the flows often do not reflect
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natural streamflow as simulated by the VIC model. For this project, we focused on three sites,
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shown in Figure 1, which have more complete records and were judged to be relatively free of
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anthropogenic influences.
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For the site on the Mataquito River, the VIC model was calibrated to monthly stream flows for
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the period 1990-1999 using the Multi-Objective Complex Evolution (MOCOM-UA) algorithm
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[Yapo et al., 1998]. The three optimization criteria used in this study were the Nash-Sutcliff
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model efficiency [NSE, Nash and Sutcliffe, 1970] using both flow (NSE) and the logarithm of
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flow (NSElog), and the bias, expressed as a percent of observed mean flow. This provides a
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balance between criteria that penalize errors at high flows and others that are less sensitive to a
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small number of large errors at high flows [Lettenmaier and Wood, 1993]. Figure 8 shows the
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VIC simulation results for the calibration period and for the validation period of 2000-2007. The
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flows for both periods generally meet the criteria for “satisfactory” calibration based on the
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criteria of Moriasi et al. [2007], with a NSE > 0.50 and absolute bias < 25% (the third criterion
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of Moriasi was not calculated for this experiment). While during the validation period several of
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the maximum annual flow peaks are overestimated, resulting in a lower NSE score compared to
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the calibration period, the reasonable peaks, low flows, and satisfactory calibration and
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validation do serve to provide further validation of the driving meteorology as plausible.
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Despite the highly variable precipitation across the study region, we applied the same VIC
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calibrated parameters from the Mataquito basin to the entire domain and used the VIC model to
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generate streamflow at the other two gage sites. This avoids the possibility of allowing extensive
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calibration to hide meteorological data deficiencies. The simulated flows for the period 2000-
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2007 for each site, and the associated statistics, are in Figures 9 and 10. The simulated flows on
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average show little bias in both locations. The Claro River NSElog value is low, reflecting the
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underestimation of low flows and overestimation of peak flows during the simulation period,
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though the higher NSE value suggests the errors at the high flows are not as systematic. The
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Loncomilla River displays a general overestimation by VIC of low flows, though both NSE and
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NSElog are above the “satisfactory” threshold. While these are not demonstrations of the best
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hydrologic model that could be developed for each basin, or the best that the VIC model could
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produce (since no calibration was performed for two of the three basins), they do provide some
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further validation that the driving meteorology appears plausible, and does not appear to show
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any systematic biases.
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Additionally, a comparison of four streamflow properties is shown in Figure 11 for the three
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simulated basins. We calculate the center timing (CT), defined as the day when half the annual
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(water year) flow volume has passed a given point [Stewart et al., 2005], where the water year
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runs from April 1 through March 31. CT values lie within the -11 to 17 day window compared to
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observed values, indicating the snow melting season is reasonably captured by the model (Figure
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11a). The unpaired Student’s t-test indicates the distributions have equal means at a 5%
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significance level. The water year volume and the 3-day peak flow are systematically
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overestimated by VIC simulations, however their means are found to statistically equal with the
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exception of the Rio Claro 3-day peak flow. Low flows are over and underestimated by VIC
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simulations but only the Loncomilla River has means that are statistically different (Figure 11d).
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Recognizing the high dependence of this region on snow melt and thus the importance of this
379
process being well represented, we validate the high elevation meteorology of the new data set
380
by comparing VIC simulated SWE to MODIS 8-day snow coverage for the six events between
381
2002 and 2007 (one image per year). The satellite images were selected to capture the snow
382
cover in mid to late August in each year, approximating the maximum snow accumulation in the
383
region. Following Maurer et al. [2003] a snow depth of 25.4 mm (1 inch) was used as threshold
384
to indicate the presence of snow on the ground. MODIS snow coverage was interpolated to a
385
0.25º grid using triangle-based cubic interpolation. VIC simulated SWE was averaged to match
386
the MODIS eight-day period. Strong similarities in the spatial extent are found between MODIS
387
and VIC simulated snow coverage for the period August 21-28, 2002 (Figure 12). The average
388
area covered by snow in the six years is 172,320 and 167,050 km2 in VIC simulations and
18
389
MODIS, respectively. This represents only a 3% error in the snow-covered area simulated by the
390
VIC model.
391
392
Table 5 is a contingency table of relative frequencies of snow/no snow in MODIS and VIC
393
simulated SWE. We include all of the pixels for the six selected periods (total 1170). The
394
number of pixels classified as snow or no snow is similar in VIC and MODIS with frequencies
395
of 0.58 and 0.29 for no snow and snow classification, respectively. Conversely the occurrence of
396
misclassified snow/no snow events is quite low, on the order of 6% indicating an excellent
397
agreement between both data sources. The Heidke Skill Score for this data is 0.72, showing that
398
the agreement between observed and VIC simulated snow cover is unlikely to be due to chance.
399
400
Finally, the successful validation of the streamflow and simulated snow cover with observations
401
also implicitly supports the gridded temperatures in the data set. Reasonable end of season snow
402
extent and well simulated timing of flows in snow-dominated streams indicates that the
403
temperatures are not likely to be greatly in error.
404
405
5. Conclusions
406
In this study an adjusted gridded daily precipitation data set is developed for Central Chile for
407
the period 1948-2008. Rain gauge data are used to correct the inaccuracies in the representation
408
of orographic distribution of precipitation existent in the available global gridded data set.
409
Adjusted gridded data are validated using station observations and hydrological model
410
simulations.
19
411
412
In data-sparse regions, a simple cokriging method that incorporates topographic elevation as
413
covariate can be successfully used to improve the spatial representation of gridded precipitation
414
in areas with complex terrain. A month-to-month adjustment can effectively remove biases in
415
precipitation values hailing from few or nonexistent rain gauge observations.
416
417
The adjusted gridded precipitation is able to capture precipitation enhancement due to orography
418
in the region with a good representation of annual totals and precipitation intensity. However the
419
duration of storm events is slightly shorter than observed perhaps as a result of comparing a 630
420
km2 grid cell to the smaller, more discrete, areal precipitation represented by three averaged rain
421
gauges. The statistics of extreme precipitation events are well captured by the adjusted gridded
422
data set, which encourages its use for climate change applications.
423
424
Streamflow simulations in three basins realistically capture high and low flows statistical
425
properties indicating that the driving meteorology in the adjusted gridded data set is well
426
represented. Simulated SWE closely resembles satellite observations which can be linked to a
427
good depiction of winter precipitation at higher elevations, despite the driving meteorological
428
dataset not including high elevation station observations. While not explicitly tested here,
429
successful simulation of snow cover and flow in snow-dominated streams indicates that
430
temperatures in the gridded data set are also reasonable, and do not require adjustment.
431
432
Based on our results, the adjusted daily gridded precipitation data set can be successfully used
433
for hydrologic simulations of climate variability and change in Central Chile. The methodology
20
434
presented in this paper can be implemented in numerous data-sparse basins located in
435
mountainous regions around the globe with one caveat. The sensitivity of the results to the
436
number of rain gauges used to obtain plausible adjusted values was not determined; therefore the
437
quality of the adjusted data set will be constrained by the density of the local observation
438
network.
439
440
441
Acknowledgements
442
This study was funded by CORFO-INNOVA grant 2009-5704 to the Centro Interdisciplinario de
443
Cambio Global at the Pontificia Universidad Católica de Chile. A Fulbright Visiting Scholars
444
Grant also provided partial support to the second author. The authors are grateful to Paul
445
Nienaber and Markus Schnorbus of the Pacific Climate Impacts Consortium, University of
446
Victoria, BC, Canada, and Katrina Bennett at the U. of Alaska, Fairbanks, for providing updated
447
and improved code for the MOCOM/VIC application.
448
21
449
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Table 1 - List of statistical quantities and descriptions.
Name
Description
R95p
Annual total precipitation when rainfall > 95th percentile
R99p
Annual total precipitation when rainfall > 99th percentile
PRCPTOT
Annual total precipitation in wet days (with daily rainfall, RR >=1mm)
CWD
Consecutive wet days: largest number of consecutive wet days with RR >=1mm
CDD
Consecutive dry days: largest number of consecutive dry days with RR <=1mm
SDII
Simple daily intensity index: mean annual intensity for RR > = 1 mm
R5mm
Annual count of days with Precipitation >= 5 mm
R20mm
Annual count of days with Precipitation >= 20 mm
RX1d
Maximum 1 day precipitation in the year
RX5d
Maximum 5 days precipitation in the year
PW
Probability of Wet days
PD
Probability of Dry days
PWW/PDD
Probability of a wet /dry day followed by a wet/dry day
PWD/PDW
Probability of a wet/dry day following a dry/wet day
585
586
587
28
588
589
Table 2 - Location of adjusted gridded precipitation grid cells used in daily precipitation validation.
0.25º Grid Cell Abbreviation
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
29
Grid Cell Center Latitude
Grid Cell Center Longitude
Loc1
-34.375
-70.875
Loc2
-34.875
-71.125
Loc3
-35.875
-71.125
Loc4
-36.125
-71.625
605
Table 3 - Daily precipitation statistics for summer (DJF) and winter (JJA) periods, 1983-2007. OBS are
606
observations, ADJ are adjusted gridded meteorology.
Summer (DJF) Statistics
OBS Mean
ADJ Mean
OBS Std
ADJ Std
Mean Bias
RMSE
Correlation
(mm)
(mm)
(mm)
(mm)
(mm)
(mm)
Loc1
0.08
0.08
1.21
0.63
0.00
1.37
-0.01
Loc2
0.14
0.11
1.61
0.59
-0.03
1.71
0.02
Loc3
0.64
0.60
5.44
2.29
-0.04
5.88
0.01
Loc4
0.60
0.54
4.38
2.34
-0.07
4.99
-0.01
Winter (JJA) Statistics
607
608
609
610
611
612
613
614
615
616
30
Loc1
3.80
3.56
10.88
13.96
-0.23
17.16
0.06
Loc2
5.64
4.72
14.17
16.72
-0.92
20.99
0.09
Loc3
13.63
12.58
30.46
37.39
-1.05
46.18
0.08
Loc4
7.24
7.36
14.51
22.08
0.12
25.64
0.06
617
Table 4 - Correlation coefficients between observed and adjusted daily precipitation statistical parameters.
618
Bold values indicate the null hypothesis of equal means cannot be rejected at the 5% level based on a t-test.
R99p
R95p
Loc1
0.10
0.70
0.96
0.76
0.58
Loc2
0.40
0.65
0.92
0.65
Loc3
0.18
0.47
0.83
Loc4
0.31
0.29
0.87
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
31
PRCTOT
SDII
R5mm
R20mm
CWD
CDD
RX1d
RX5d
0.82
0.37
0.45
0.78
0.61
0.61
0.84
0.43
0.38
0.79
0.53
0.53
0.61
0.65
0.25
0.73
0.45
0.48
0.57
0.71
0.73
0.00
0.28
0.56
0.37
634
Table 5 - Contingency table summarizing the comparisons of MODIS and VIC simulated snow cover. Values
635
are relative frequencies calculated as the total number of occurrences in each category divided by the number
636
of pixels (1170).
VIC
MODIS
637
638
32
No Snow
Snow
Total
No Snow
0.58
0.06
0.64
Snow
0.07
0.29
0.36
Total
0.65
0.35
1.0
639
List of Figures
640
641
Figure 1 - Geographic location of the study area in Central Chile. From north to south the basins
642
are: Rapel, Mataquito (Mataquito river at Licanten), Maule (Claro river at Rauquen and
643
Loncomilla river at Bodega) and Itata river basins. Circles indicate the location of DGA rain
644
gauges and stars the location of the three stream gauges used in VIC simulations
645
Figure 2 - Maps of annual precipitation for the period 1951-1980. Source a) gridded global
646
observations and b) DGA. Precipitation lapse rates for latitudinal bands -35.125 S and -36.125 S
647
for c) global gridded precipitation data set and d) DGA data set.
648
Figure 3 - Scatterplots of observed and predicted average monthly precipitation for the month of
649
July. Each point represents one observation station (abscissa) and the 0.25 grid cell in which the
650
grid cell lies (ordinate).
651
Figure 4 - a) Annual adjusted global precipitation for the period 1950-2006 and b) differences
652
between the original global gridded and the adjusted global precipitation data sets.
653
Figure 5 - Location of DGA rain gauge stations and adjusted global precipitation grid points used
654
for validation of daily rainfall.
655
Figure 6 - Boxplots of statistical parameters, green represents observations and purple represents
656
adjusted precipitation for each geographic location. The bottom and top lines represent the 25th
657
and 75th percentiles and the middle line represents the median. Whiskers extend from each end
658
of the box to the adjacent values in the data within 1.5 times the Inter Quartile Range. The Inter
659
Quartile Range is the difference between the third and the first quartile, i.e., 25th and 75th
660
percentiles. Outliers are displayed with a plus sign.
33
661
Figure 7 - Probabilities of a) wet and b) dry days, and transition probabilities c) and d). Daily
662
observed (black) and adjusted gridded (grey) precipitation for the four selected locations.
663
Figure 8 - Observed and Simulated monthly flows for the Mataquito river at Licanten for the
664
calibration period (top panel) and validation period (bottom panel). Summary statistics are
665
shown in each panel.
666
Figure 9 - Monthly observed and simulated flows for the Claro river at Rauquen.
667
Figure 10 - Same as Figure 9 but for the Loncomilla river at Bodega.
668
Figure 11 - Statistical properties of observed and VIC simulated stream flows in three basins:
669
Mataquito river, Claro river and Loncomilla river. (a) Center timing, (b) water year volume, (c)
670
3-day peak flows and (d) 7-day low flows.
671
Figure 12 - Comparison of snow coverage for the period August 21-28, 2002. Shaded areas
672
indicate snow coverage. a) MODIS and b) VIC simulated Snow Water Equivalent.
673
34
674
675
Figure 1 - Geographic location of the study area in Central Chile. From north to south the basins are: Rapel,
676
Mataquito (Mataquito river at Licanten), Maule (Claro river at Rauquen and Loncomilla river at Bodega)
677
and Itata river basins. Circles indicate the location of DGA rain gauges and stars the location of the three
678
stream gauges used in VIC simulations
679
35
680
681
Figure 2 - Maps of annual precipitation for the period 1951-1980. Source a) gridded global observations and
682
b) DGA. Precipitation lapse rates for latitudinal bands -35.125 S and -36.125 S for c) global gridded
683
precipitation data set and d) DGA data set.
684
685
686
36
687
688
Figure 3 - Scatterplots of observed and predicted average monthly precipitation for the month of July. Each
689
point represents one observation station (abscissa) and the 0.25 grid cell in which the grid cell lies (ordinate).
690
691
37
692
693
Figure 4 - a) Annual adjusted global precipitation for the period 1950-2006 and b) differences between the
694
original global gridded and the adjusted global precipitation data sets.
695
696
38
697
698
Figure 5 - Location of DGA rain gauge stations and adjusted global precipitation grid points used for
699
validation of daily rainfall.
700
701
39
702
703
Figure 6 - Boxplots of statistical parameters, green represents observations and purple represents adjusted
704
precipitation for each geographic location. The bottom and top lines represent the 25th and 75th percentiles
705
and the middle line represents the median. Whiskers extend from each end of the box to the adjacent values
706
in the data within 1.5 times the Inter Quartile Range. The Inter Quartile Range is the difference between the
707
third and the first quartile, i.e., 25th and 75th percentiles. Outliers are displayed with a plus sign.
708
40
709
710
Figure 7 - Probabilities of a) wet and b) dry days, and transition probabilities c) and d). Daily observed
711
(black) and adjusted gridded (grey) precipitation for the four selected locations.
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Figure 8 - Observed and Simulated monthly flows for the Mataquito river at Licanten for the calibration
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period (top panel) and validation period (bottom panel). Summary statistics are shown in each panel.
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Figure 9 - Monthly observed and simulated flows for the Claro river at Rauquen.
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Figure 10 - Same as Figure 9 but for the Loncomilla river at Bodega.
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Figure 11 - Statistical properties of observed and VIC simulated stream flows in three basins: Mataquito
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river, Claro river and Loncomilla river. (a) Center timing, (b) water year volume, (c) 3-day peak flows and
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(d) 7-day low flows.
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Figure 12 - Comparison of snow coverage for the period August 21-28, 2002. Shaded areas indicate snow
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coverage. a) MODIS and b) VIC simulated Snow Water Equivalent.
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