Using a gridded global data set to characterize regional hydroclimate in central Chile

Results showthat the daily meteorology with the adjusted precipitation can accurately capture the statisticalproperties of extreme events as well as the sequence of wet and dry events, w

Using a gridded global data set to characterize regional hydroclimate in central Chile

E.M.C Demaria1, E Maurer2*, J Sheffield3, E Bustos1, D Poblete1, S Vicuña1, F Meza1

1Centro de Cambio Global, Pontificia Universidad Católica de Chile, Santiago, Chile

2Civil Engineering Department, Santa Clara University, Santa Clara, CA, USA

3Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

*Corresponding author, emaurer@engr.scu.edu, 408-554-2178

Proposed submission to J Hydrometeorology

Central Chile is facing dramatic projections of climate change, with a consensus for decliningprecipitation, negatively affecting hydropower generation and irrigated agriculture Rising fromsea level to 6,000 meters within a distance of 200 kilometers, precipitation characterization isdifficult due to a lack of long-term observations, especially at higher elevations Forunderstanding current mean and extreme conditions and recent hydroclimatological change, aswell as to provide a baseline for downscaling climate model projections, a temporally andspatially complete data set of daily meteorology is essential We use a gridded global dailymeteorological data set at 0.25 degree resolution for 1948-2008, and adjust it using monthlyprecipitation observations interpolated to the same grid using a cokriging method with elevation

as covariate For validation, we compare daily statistics of the adjusted gridded precipitation tostation observations For further validation we drive a hydrology model with the gridded 0.25-degree meteorology and compare stream flow statistics with observed flow We validate the highelevation precipitation by comparing the simulated snow extent to MODIS images Results showthat the daily meteorology with the adjusted precipitation can accurately capture the statisticalproperties of extreme events as well as the sequence of wet and dry events, with hydrologicalmodel results displaying reasonable agreement for observed flow statistics and snow extent Thisdemonstrates the successful use of a global gridded data product in a relatively data-sparseregion to capture hydroclimatological characteristics and extremes

Whether exploring teleconnections for enhancing flood and drought predictability or assessingthe potential impacts of climate change on water resources, understanding the response of theland surface hydrology to perturbations in climate is essential This has inspired the developmentand assessment of many large scale hydrology models for simulating land-atmosphere

interactions over regional and global scales [e.g.,Lawford et al., 2004; Milly and Shmakin, 2002;

Nijssen et al., 2001a; Sheffield and Wood, 2007].

A prerequisite to regional hydroclimatological analyses is a comprehensive, multi-decadal,spatially and temporally complete data set of observed meteorology, whether for historicsimulations or as a baseline for downscaling future climate projections In response to this need,data sets of daily gridded meteorological observations have been generated, both over

continental regions [e.g.,Cosgrove et al., 2003; Maurer et al., 2002] and globally [Adam and

Lettenmaier, 2003; Sheffield et al., 2006] These have benefited from work at coarser time scales [Chen et al., 2002; Daly et al., 1994; Mitchell and Jones, 2005; New et al., 2000; Willmott and Matsuura, 2001], with many products combining multiple sources, such as station observations,

remotely sensed images, and model reanalyses

While these large-scale gridded products provide opportunities for hydrological simulations forland areas around the globe, they are inevitably limited in their accuracy where the underlyingstation observation density is low, the station locations are inadequate to represent complex

Central Chile is an especially challenging environment for characterizing climate and hydrologysince the terrain exhibits dramatic elevation changes over short distances, and the orographiceffects this drives produce high spatial heterogeneity in precipitation in particular In general, theobservation station density in South America is inadequate for long-term hydroclimate

characterization [de Goncalves et al., 2006] While some of South America is relatively well represented by global observational datasets [Silva et al., 2007], regions west of the Andes are much less so [Liebmann and Allured, 2005]

In this study, we utilize a new high-resolution global daily gridded dataset of temperature andprecipitation, adjust it with available local climatological information, and assess its utility forrepresenting river basin hydrology Recognizing the value in simulating realistic extreme events,

we assess the new data product for its ability to produce reasonable daily streamflow statistics

We evaluate the potential to reproduce climate and hydrology in a plausible manner, such thathistorical statistics are reproduced

The principal aim of this study is to produce a gridded representation of the climate andhydrology of central Chile, are demonstrate a methodology for producing a reasonable set of dataproducts that can be used for future studies of regional hydrology or climate Given theseregional results, we assess the potential to export the method to other relatively data-sparseregions, where representative climatological average information is available but long-term dailydata are inadequate The paper is organized as follows: Section 2 describes the study area InSection 3 we describe the data, the hydrological model and the methodological approach Results

of the adjusted data set validation and model simulations are discussed in Section 4 Finally, themain conclusions of the study are presented in Section 5.

Region

The focus area of this study is central Chile (Figure 1), encompassing the four major river basins(from north to south, the Rapel, Mataquito, Maule, and Itata Rivers) between latitudes 35.25º Sand 37.5º S The climate is Mediterranean, with 80% of the precipitation falling in the rainy

season from May-August [Falvey and Garreaud, 2007] The terrain is dramatic, rising

approximately 6000 meters within a horizontal distance of approximately 200 km, producing

sharp gradients in climate [Falvey and Garreaud, 2009].

Driven by the terrain, the area exhibits a dramatic climate gradient, with mean precipitation ofapproximately 500 mm per year at the North end of the study domain, and as much as 3000 mmper year in the high elevations at the Southern end of the domain It is evident from Figure 1 thatthe high elevation areas are under-represented by any of the observation stations

The region of Central Chile is especially important from a hydroclimatological standpoint, as itcontains the largest proportion of irrigated agriculture and reservoir storage of any region in thecountry and provides water supply for some of Chile's largest cities A changing climate is

evident in recent hydroclimate records [Rubio-Álvarez and McPhee, 2010], and future climate projections for the region indicate the potential for very large impacts [Bradley et al., 2006] The

General Circulation Model (GCM) projections, and a high sensitivity to changing snow melt

patterns [Vicuna et al., 2010], who also discuss the challenges in characterizing climate in a

Chilean catchment with few precipitation observations, and none at high elevations

Methods and data

Gridded data set development

We begin with a gridded global (land surface) dataset of daily precipitation and minimum andmaximum temperatures at 0.25º spatial resolution (approximately 25 km), prepared followingSheffield et al [2006] To summarize, the forcing dataset is based on the NCEP–NCAR

reanalysis [Kalnay et al., 1996] for 1948-2008, from which daily maximum and minimum

temperature and daily precipitation are obtained at approximately 2º spatial resolution.Reanalysis temperatures are based on observations, though precipitation is a model output andthus exhibits significant biases

The reanalysis temperatures are interpolated to a 0.25º spatial resolution, lapsing temperatures by-6.5ºC/km based on the elevation difference between the large reanalysis spatial scale and theelevation in each 0.25º grid cell Precipitation is interpolated to 0.25º using a product of the

Tropical Rainfall Measuring Mission (TRMM) [Huffman et al., 2007] following the methods

outlined by Sheffield et al [2006] To ensure large-scale correspondence between this data set

and the observationally-based monthly 0.5º data from the Climate Research Unit [CRU, Mitchell

and Jones, 2005], precipitation is scaled so the monthly totals match the CRU monthly values at

the CRU spatial scale Maximum and minimum temperatures are also scaled to match the CRUtime series, using CRU monthly mean temperature and diurnal temperature range.

While the incorporation of multiple sources of extensively reviewed data provides an invaluabledata product for global and continental scale analyses, as discussed by Mitchell and Jones [2005]ultimately much of the local characterization is traceable to a common network of land surface

observations [Peterson et al., 1998], which is highly variable in station density for different

regions For example, for the region of study shown in Figure 1, an average of 3-4 observationstations are included in the CRU precipitation data product, and none are in high-elevation areas.This results in a few low elevation meteorological stations in Chile on the western side of theAndes, and the next observation station to the east is in a more arid area in Argentina Thus, theresulting precipitation fields in the gridded product for this region showed a spatial gradient

opposite to that published by the Dirección General de Aguas [DGA, 1987] Figure 2a shows the

spatial distribution of gridded global total annual precipitation that displays a notable decrease ofrainfall with a elevation Conversely the DGA precipitation map is able to capture theclimatological orographic enhancement of precipitation by the Andes (Figure 2b) Theprecipitation lapse rates for the latitudinal bands -35.125º S and -36.125º S show a negativegradient of precipitation with elevation in the global gridded data set whereas the DGAprecipitation shows a positive gradient for the period 1951-89 (Figures 2c and 2d, respectively)

Local data from the DGA of Chile, some monthly and some daily, were obtained to characterizebetter the local climatology While still biased toward low elevation areas, the stations (Figure 1)

those that had at least 90% complete monthly records for the 25-year period 1983-2007 Themonthly average precipitation for the 25-year period for these 40 DGA stations was interpolatedonto the same 0.25º grid using cokriging, with elevation being the covariate This method ofcokriging has been shown to improve kriging interpolation to include orographic effects induced

by complex terrain [Diodato and Ceccarelli, 2005; Hevesi et al., 1992]

This process produced 12 monthly mean precipitation maps for the region The same 1983-2007period was extracted from the daily gridded data set, and monthly average values were calculatedfor each grid cell Ratios (12, one for each month) of observed climatology divided by thegridded data set average were then calculated for each grid cell Daily values in the gridded dataset were adjusted to create a new set of daily precipitation data, Padj, which matches theinterpolated observations produced with cokriging, using a simple ratio:

 i j P

j i P t j i P t

j

i

P

mon grid

mon obs grid

adj

,

,,

,,

where Pgrid is the original daily gridded 0.25º data at location (i,j), Pobs is the interpolated

observed climatology, overbars indicate the 25-year mean, and the subscript “mon” indicates the month from the climatology in which day t falls.

This same method was applied to a global dataset of daily meteorology in a data sparse region inCentral America, resulting in improved characterization of precipitation and land surface

hydrology [Maurer et al., 2009] In addition, this new adjusted data set includes the full

1948-2008 period, despite the fact that local observations are very sparse before 1980

To validate the adjusted precipitation data set, we computed a set of statistical parameters widely

used to describe climate extremes [dos Santos et al., 2011; X Zhang and Yang, 2004].

Additionally to evaluate the temporal characteristics of rainfall events we computed the wet, dryand transition probabilities Table 1 shows a description of the statistics used

To evaluate if the adjusted precipitation data set was capturing the orographic gradient ofprecipitation we compared VIC simulated Snow Water Equivalent (SWE) to the MODIS/TerraSnow Cover data set, which is available at 0.05 degree resolution for 8-day periods starting fromthe year 2000 MODIS snow cover data are based on a snow mapping algorithm that employs a

Normalized Difference Snow Index [Hall et al., 2006] To estimate snow cover from the

meteorological data, a hydrological model was employed

Hydrologic Model Simulations

To assess the ability of the daily gridded meteorology developed in this study to capture dailyclimate features across the watersheds, we simulate the hydrology of river basins in the region toobtain streamflow and snow cover estimates The hydrologic model used is the Variable

Infiltration Capacity (VIC) model [Cherkauer et al., 2003; Liang et al., 1994] The VIC model is

a distributed, physically-based hydrologic model that balances both surface energy and waterbudgets over a grid mesh The VIC model uses a “mosaic” scheme that allows a statisticalrepresentation of the sub-grid spatial variability in topography, infiltration and vegetation/landcover, an important attribute when simulating hydrology in heterogeneous terrain The resulting

Lohmann et al [1996] The VIC model has been successfully applied in many settings, from

global to river basin scale [e.g.,Maurer et al., 2002; Nijssen et al., 2001b; Sheffield and Wood,

2007]

For this study, the model was run at a daily time step at a 0.25º resolution (approximately 630

km2 per grid cell for the study region) Elevation data for the basin routing are based on the

15-arc-second Hydrosheds dataset [Lehner et al., 2006], derived from the Shuttle Radar Topography

Mission (SRTM) at 3 arc-second resolution Land cover and soil hydraulic properties were based

on values from Sheffield and Wood [2007], though specified soil depths and VIC soil parameterswere modified during calibration The river systems contributing to selected points were defined

at a 0.25º resolution, following the technique outlined by O’Donnell et al [1999]

Results and Discussion

The adjusted data set was validated in several ways First, daily statistics were comparedbetween the adjusted global daily data set and local observations, where available Second,hydrologic simulation outputs were compared to observations to investigate the plausibility ofusing the new data set as an observational baseline for studying climate impacts on hydrology

Gridded meteorological data development and assessment

The quality of daily gridded precipitation fields was improved using available monthly observedprecipitation Rain gauge records from DGA were selected using two criteria: stations with

records of twenty-five years and with no more than 10% missing daily measurements Based onthose two constraints the period 1983-2007 was identified as that with the largest number ofreporting stations From the pool of 70 available stations, 40 stations met the two criteria (Figure1) Except for the Itata river basin, which had two stations located at 1200 and 2400 metersabove sea level, most of the selected stations were located in the central part of the region atelevations below 500 meters Mean precipitation was computed for each month and for eachselected station, resulting in 12 mean values for the 25-year climatological period.

Cokriging was then applied to produce a set of 12 maps of climatological precipitation at 0.25ºspatial resolution A scatter plot between observed and predicted monthly precipitation for July,the middle of the rainy season, is shown in Figure 3 Cokriged monthly totals matchobservations quite closely for the region with a bias equal to -0.8 % with respect to the observedvalues and a relative RMSE of 0.50 %

Figure 4 shows the adjusted gridded annual precipitation fields and the difference from theoriginal gridded observed data set for the period 1950-2006 It is evident that in the more humidsouthern mountainous portion of the study area there has been a marked increase in precipitationwith the adjustment, incorporating the more detailed information embedded in the rain gaugeobservations Differences between original and adjusted gridded precipitation indicates theexistence of a band along the Andes where annual precipitation is greater in the adjustedprecipitation data set (Figure 4b)

To verify how the adjusted daily precipitation relates to observations, we compared daily rainfall

at selected 0.25º grid points with the day-by-day means of three rain gauge stations located inapproximately a 50 km diameter circle (Figure 5) Rain gauge stations were selected from thepool of 40 stations used to perform the cokriging interpolation, hence they had record of 25 yearswith not more than 10% missing values Selected stations were located, when possible, not morethat 50% higher or lower elevations than that of the 0.25º grid cell Four 0.25º grid points wereselected for the comparison The locations of the four grid points are listed in Table 2 For thesefour locations, we computed basic statistics, bias, RMSE and correlation coefficient for dailyobserved (OBS) and daily adjusted gridded precipitation (ADJ) for Austral summer (DJF) andAustral winter (JJA) for the period 1983-2007 Summary statistics are shown in Table 3 The bias

is defined as the sum of the differences between ADJ and OBS and the RMSE is equal to the rootmean squared error between the ADJ and OBS daily precipitation values

Mean daily values are very close for the observed and adjusted datasets for both seasons, which

is expected given the adjustment process The variability of daily precipitation within eachseason, represented by the standard deviation, also compares relatively well, though the adjustedgridded data show greater variability than the observations during the rainy winter season A highRMSE and low correlation values indicate that temporal sequencing differs between the two datasets This is not unexpected, since the daily precipitation in the original 0.25º gridded data wasderived from reanalysis, and as such it is a model output that does not incorporate station

observations [Kalnay et al., 1996] Thus, while important characteristics of daily precipitation

variability are represented in the 0.25º gridded data, and monthly totals should bear resemblance

to observations (at least as represented by the underlying monthly data such as CRU),correspondence with observed daily precipitation events is not anticipated.

With this limitation in mind, we focus on the statistics of daily hydroclimatology, rather thanevent-based statistics Especially given the rising interest in characterizing extreme events in the

context of a changing climate [IPCC, 2011], the ability of the adjusted daily gridded dataset to

characterize extreme statistics is important We compute a set of statistical variables frequently

used to describe climate extremes, using the RClimDex software [X Zhang and Yang, 2004;

Xuebin Zhang et al., 2005] Additionally we computed the probability of occurrence of wet and

dry days and transition probabilities Figure 6 shows box plots of six statistical parameters listed

in Table 1 for the four locations Extreme precipitation events (R95p) are well captured in theadjusted gridded data set at most locations The agreement between adjusted total annualprecipitation (PRCPTOT) and observations is good with an average bias of -9% from theobserved station mean (not shown), although this is constrained by design, as noted above TheSimple Daily Intensity Index (SDII), which is a measure of the mean annual intensity of rainfall,also shows good agreement at the four locations, indicating that the number of rainy days is wellrepresented in the adjusted dataset The number of days with intensities larger than the 20 mm(R20mm) compares well between observations and adjusted gridded precipitation, though theadjusted gridded data slightly underestimate observations The maximum consecutive number ofdry days and wet days in a year is lower for the adjusted gridded observations compared toobservations suggesting the durations of wet and dry events are shorter in the adjusted griddeddata set

Figure 7 shows that the probabilities of a day being wet or dry are comparable between bothdatasets (panels 7a and 7b) Conversely the adjusted precipitation data shows an averagetransition probability of a wet day followed by a wet day of 0.21 compared to 0.50 obtained forthe observations suggesting that the duration of storm events is shorter in the adjusted griddeddataset This could partially explain the underestimation of maximum consecutive wet days(CWD) in the adjusted gridded precipitation as well.

The statistical quantities presented in Figures 6 and 7 were compared statistically using thecorrelation coefficient and a two-sample unpaired Student’s t-test These are summarized inTable 4 Statistics linked to high intensity events (R99p and R95p and maximum 1-dayprecipitation (RX1day) have statistically indistinguishable means for all four locations Themean annual precipitation and intensity parameters (Prcptot and SDII) show equal means forthree out of the four locations Conversely the parameters, R5mm R20mm, albeit stronglycorrelated, were found to have statistically different mean values This phenomenon of a griddedprecipitation data produce having lower extreme precipitation values than station observationswas also noted in the South American study of Silva et al [2007] and is consistent with the effect

of spatial averaging, i.e., comparing the average of a 630 km2 0.25º grid cell to the smaller, more

discrete area represented by the three averaged stations [Yevjevich, 1972] The statistics related to

duration of wet and dry spells showed statistically different population means at all 4 locations

Hydrologic Model Validation of Adjusted Meteorology

To assess the representation in the new meteorological data set of basin-wide and high elevationareas, the adjusted gridded data developed and assessed in the previous sections were then used

to drive the VIC hydrologic model Since the precipitation was shown to be comparable toobservations (where available) in many important respects, another validation of the drivingmeteorology would be the successful simulation of observed streamflow and snow cover.Records of observed streamflow in the region tend to be incomplete or for short periods, andsince most of the rivers are affected by reservoirs and diversions the flows often do not reflectnatural streamflow as simulated by the VIC model For this project, we focused on three sites,which are shown in Figure 1

For the site on the Mataquito River, the VIC model was calibrated to monthly stream flows forthe period 1990-1999 using the Multi-Objective Complex Evolution (MOCOM-UA) algorithm

[Yapo et al., 1998] The three optimization criteria used in this study were the Nash-Sutcliff

model efficiency [Nash and Sutcliffe, 1970] using both flow (NSE) and the logarithm of flow

(NSElog), and the bias, expressed as a percent of observed mean flow This provides a balancebetween criteria that penalize errors at high flows and others that are less sensitive to a small

number of large errors at high flows [Lettenmaier and Wood, 1993] Figure 8 shows the VIC

simulation results for the calibration period and for a validation period of 2000-2007 The flowsfor both periods generally meet the criteria for “satisfactory” calibration based on the criteria ofMoriasi et al [2007], with a NSE > 0.50 and absolute bias < 25% (the third criterion of Moriasiwas not calculated for this experiment) While during the validation period several of themaximum annual flow peaks are overestimated, resulting in a lower NSE score compared to the

calibration period, the reasonable peaks, low flows, and satisfactory calibration and validation doserve to provide further validation of the driving meteorology as plausible.

Despite the highly variable precipitation across the study region, we applied the same VICcalibrated parameters from the Mataquito basin to the entire domain and used the VIC model togenerate streamflow at the other two gage sites This avoids the possibility of allowing extensivecalibration to hide meteorological data deficiencies The simulated flows for the period 2000-

2007 for each site, and the associated statistics, are in Figures 9 and 10 The simulated flows onaverage show little bias in both locations The Claro River NSElog value is low, reflecting theunderestimation of low flows and overestimation of peak flows during the simulation period,though the higher NSE value suggests the errors at the high flows are not as systematic TheLoncomillo River displays a general overestimation by VIC of low flows, though both NSE andNSElog are above the “satisfactory” threshold While these are not demonstrations of the besthydrologic model that could be developed for each basin, or the best that the VIC model couldproduce (since no calibration was performed for two of the three basins), they do provide somefurther validation that the driving meteorology appears plausible, and does not appear to showany systematic biases

A comparison of four streamflow properties are shown in Figure 11 for the three simulatedbasins We calculate the center timing (CT), defined as the day when half the annual (water year)

flow volume has passed a given point [Stewart et al., 2005], where the water year runs from

April 1 through March 31 CT values lie within the -11 to 17 day window compared to observedvalues, indicating the snow melting season is reasonably captured by the model (Figure 11a) The

unpaired Student’s t-test indicates the distributions have equal means at a 5% significance level.The water year volume and the 3-day peak flow are systematically overestimated by VICsimulations, however their means are found to statistically equal with the exception of the RioClaro 3-day peak flow Low flows are over and underestimated by VIC simulations but only theLoncomillo River has means that are statistically different (Figure 11d).

Recognizing the high dependence of this region on snow melt and thus the importance of thisprocess being well represented, we validate the high elevation meteorology of the new data set

by comparing VIC simulated SWE to MODIS 8-day global snow coverage for six eventsbetween 2002 and 2007 The satellite images were selected in mid August to capture themaximum snow accumulation in the region Following Maurer et al [2003] a snow depth of25.4 mm was used as threshold to indicate the presence of snow on the ground MODIS snowcoverage was interpolated to a 0.25º grid using triangle-based cubic interpolation VIC simulatedSWE was averaged to match the MODIS eight-day period Strong similarities in the spatialextent is found between MODIS and VIC simulated the snow coverage for the period August21-28, 2002 (Figure 12) The average area covered by snow in the six years is 172,320 and167,050 km2 in VIC simulations and MODIS, respectively This represents a 3% error in theSCA simulated by the VIC model, which is very small

Table 5 is a contingency table of relative frequencies of snow/no snow in MODIS and VICsimulated SWE We include all the pixels for the six selected periods (total 1530) The number ofpixels classified as snow or no snow are similar in VIC and MODIS with frequencies of 0.65 and

misclassified snow/no snow events is quite low, in the order of 0.06% indicating an excellentagreement between both data sources.

Conclusions

In this study an adjusted gridded daily precipitation data set is developed for Central Chile for

the period 1948-2008 Rain gauge data are used to correct the inaccuracies in the representation

of orographic distribution of precipitation existent in the available global gridded data set.Adjusted gridded data are validated using station observations and hydrological modelsimulations

In data-sparse regions, a simple cokriging method that incorporates topographic elevation ascovariate can be successfully used to improve the spatial representation of gridded precipitation

in areas with complex terrain A month-to-month adjusting can effectively remove biases inprecipitation values hailing from few or nonexistent rain gauge observations

The adjusted gridded precipitation is able to capture precipitation enhancement due to orography

in the region with a good representation of annual totals and precipitation intensity However theduration of storm events is slightly shorter than observed perhaps as a result of comparing a 630

km2 grid cell to the smaller, more discrete, areal precipitation represented by three averaged raingauges The statistics of extreme precipitation events are well captured by the adjusted griddeddata set which encourages its use for climate change applications

Streamflow simulations in three basins realistically capture high and low flows statisticalproperties indicating that the driving meteorology in the adjusted gridded data set is wellrepresented Simulated SWE closely resembles satellite observations which can be linked to agood depiction of winter rainfall at higher elevations, despite the driving meteorological datasetincluding no high elevation station observations.

Based on our results, the adjusted daily gridded precipitation data set is very useful forhydrologic simulations of climate variability and change in Central Chile However there are twocaveats First, we assume the period 1983-2007 is representative of a longer time period, hencelong term variability of precipitation is assumed properly captured Second, the sensitivity of theresults to the number of rain gauges used to obtain plausible adjusted values was not determined.Despite those, the methodology presented in this paper can be implemented in numerous data-sparse basins located in mountainous regions around the globe

Acknowledgements

This study was funded by CORFO INNOVA grant to the Centro de Cambio Global and theDepartamento de Ingeniería Hidráulica y Ambiental at the Pontificia Universidad Católica deChile A Fulbright Visiting Scholars Grant also provided partial support to the second author

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