High resolution spatiotemporal distribution of rainfall seasonality and extreme events based on a 12-year TRMM time series

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1 High resolution spatiotemporal distribution of rainfall seasonality and extreme events based on a 12-year TRMM time series Bodo Bookhagen, Geography Department, UC Santa Barbara, Santa Barbara, CA bodo@icess.ucsb.edu Abstract Seasonal rainfall in the tropics modulates hydrology and vegetation and is ultimately responsible for many mass-transport processes at different spatial and temporal scales. In order to link hydrologic, geomorphic, and ecologic processes with rainfall, a mean-monthly climatology based on high spatial resolution (~5x5 km) TRMM 2B31 data from 1998 to 2009 has been calibrated and validated against GPCC rain gauges. Data calibration reveals that averaging over >6 years provide robust monthly rainfall amounts (r 2 = 0.82). The 12-year time series for rainfall-extreme events (>90 th percentile) shows that more than twice as many events occur in some mountainous settings compared to immediately adjacent lower-elevation areas. Peak extreme events spatially correlate with sites of highest mean annual rainfall, but several extreme events can occur in semi-arid to arid regions leeward of orographic rainfall peaks. An example application for the central Andes demonstrates how contrasting climatic settings impact vegetation, hydrology, and surface-mass transport Introduction The seasonal water cycle is the driver of many processes in hydrology, ecology, and geomorphology. Additionally, some of the most densely populated areas in the tropics are affected by high rainfall seasonality associated for example with the Asian and South American Monsoon [e.g., Vera et al., 2006; Webster, 1987]. During the rainy season, the moisture-soaked, windward sides of mountain ranges, such as the southern Himalaya and eastern Andean flanks, are associated with some of the highest terrestrial rainfall amounts [e.g., Bookhagen and Burbank, 2006; Bookhagen and Strecker, 2008; Nesbitt and Anders, 2009]. In these areas, the interaction of atmospheric processes with steep terrain and high topographic relief leads to high surface-rainfall amounts that exhibit steep orographic gradients. Ultimately, the combination of mountainous landscapes and seasonal, high runoff results in high sediment erosion and transport rates. Thus, seasonal rainfall (e.g., during the monsoon) controls river discharge, vegetation growth, and sediment transport. However, several field studies document that the important, 1

2 landscape-shaping processes that impact fluvial networks, sediment removal, and ecology occur during rare extreme events at relatively small spatial scales [e.g., Douglas et al., 1999; Snyder et al., 2003; Wolman and Miller, 1960]. Many of these events can be associated with abnormal conditions during coupled ocean-atmosphere phenomena (e.g., El Niño/Southern Oscillation) leading to high rainfall amounts in usually arid places [e.g., Bookhagen et al., 2005; Carvalho et al., 2002; Coppus and Imeson, 2002; Craddock et al., 2007; Magilligan et al., 1998; Wulf et al., 2010]. Importantly, these rare events are often not recorded with instruments, but their landscape impact has been well documented [e.g., Coppus and Imeson, 2002]. Here, I present a high-spatial resolution, monthly-averaged climatology for the tropics that helps delineate seasonal gradients and the spatial distribution of extreme-rainfall events at scales of ~5 x 5 km (0.04 x 0.04 º). The time series has been calibrated with and validated against rain-gauge stations and averaged over the 12 year period from 1998 to This dataset has important applications across a broad range of natural science disciplines, including hydrology, geomorphology, and ecology Methods and Data The Tropical Rainfall Measurement Mission (TRMM) platform is equipped with two main instruments used for rainfall detection: (1) the first space-born precipitation radar (PR) and (2) the TRMM microwave imager (TMI) following the legacy of the Special Sensor Microwave/Imager (SSM/I) [e.g., Kummerow et al., 2000]. In this analysis, I use TRMM product 2B31 which derives its rainfall estimates from both instruments [Haddad et al., 1997; Kummerow et al., 2000]. The high resolution, orbital rainfall data have a spatial resolution of ~5 x 5 km (~0.04º) and each location is recorded 1 to 3 times per day, depending on its latitude. A comparison between combined product 2B31 and PR-only product 2A25 [Nesbitt and Anders, 2009] of mean annual surface rainfall amounts from 1998 to 2008 reveals no significant difference at low to moderate annual rainfall amounts (<4 m/y) and some differences at higher rainfall amounts (>6 m/y) (Figure DR1, DR2, DR3). I have applied the following primary processing steps to create a consistent and robust climatology following similar procedures as described in Bookhagen and Burbank [2010]: First, I project each TRMM orbit to an equally spaced grid with 5 x 5 km resolution using a bilinear interpolation while removing erroneous measurements. Second, I correct for the footprint-resolution changes associated with 2

3 the boosting of the TRMM platform during several maneuvers in August Third, I use mean annual and monthly rain gauge data to calibrate the TRMM rainfall intensities and allow conversion to averaged seasonal amounts. In order to perform this last step and quantify uncertainties, I validate the accuracy of the TRMM-based data product 2B31 with the revised, globally-gridded gauge data from the Global Precipitation Climatology Centre (GPCC), e.g., Schneider et al. [2008]. I refrain from comparing TRMM product 2B31 data to other, remotely sensed datasets, because product 2B31 is included in several global climatologies, such as the TRMM multisatellite precipitation analysis (TMPA) [Huffman et al., 2007] and the global precipitation climatology project [Adler et al., 2003]. In contrast to global climatologies based on the combination of direct rainfall measurements and calibration of an infrared signal [e.g., Huffman et al., 2007], the orbital TRMM data products do not provide spatial and temporally continuous measurements, but have significantly higher spatial resolution. The calibration procedure allows conversion of the instantaneous rainfall amounts (measured in mm/hr) to meaningful seasonal amounts (e.g., mm/month or mm/y) that can be more readily used by hydrologists, geomorphologists, and ecologists. This conversion is important, because the diurnal nature and short life time of some convective tropical storm systems may result in discrepancies between remotely sensed and gauge-derived surface rainfall amounts [e.g., Bell and Kundu, 2000]. My validation procedure is twofold. First, I validate mean annual (monthly) TRMM2B31 rainfall intensities with mean annual (monthly) gauge data from 1998 to 2007 (Figure 1, Figure DR5). This validation indicates that averaging TRMM 2B31 measurements over 10 years yields a robust relation (annual average r 2 = 0.82), based on GPCC cells with one or more gauge stations (n= 2576) that vary as a function of latitude (Figure DR4). Second, I quantify the integration time that is needed for a reliable climatology with orbital TRMM data products (Figure 1B). In this step, I calibrate monthly rainfall data with an increasing integration timescale ranging from one to ten years. In order to calculate extreme-rainfall events, I use the entire time series from 1998 to 2009 (12 y), calculate the probability density function of instantaneous rainfall measurement, and determine the 90 th percentile. Several authors have classified all rainfall events above the 90 th percentile as extreme-rainfall events [e.g.,grimm and Tedeschi, 2009; Krishnamurthy et al., 2009]. I have calculated the 90 th percentile based on all wet days (rainfall > 0 mm/hr). I emphasize that the calculation of the 90 th percentile is 3

4 strongly affected by the sampling-frequency distribution and, hence, the number of extreme events are not to be seen as absolute amounts but rather as relative amounts which can be compared from one site to the next (Figure DR6) Results The calibration and validation procedure between TRMM product 2B31 and GPCC rain gauges results in robust surface-rainfall products. The validation indicates that surface rainfall averaged over 10 years in the tropics is underestimated by 13±1% taken from a weighted linear fit (±95% confidence intervals) with r 2 =0.82: y [TRMM2B31 in mm/y] = x [TRMM2B31 in mm/hr] * (1/0.8555) ± (1/0.0090) (Figure 1). The resulting parameters are comparable with previous calibration efforts that rely on regional rain gauges [Bookhagen and Burbank, 2006; Bookhagen and Strecker, 2008; Bookhagen and Burbank, 2010]. The monthly calibration factors suggest that surface rainfall is underestimated between ~13 and ~20% (Figure DR5). The calibration procedure with changing time-series length indicates that the root mean square error is reduced by 40% when including 7 years of TRMM2B31 data (Figure 1B). I emphasize that, despite the time series length of 12 years, considerable errors associated with the sampling frequency still remain, as predicted by previous studies [e.g., Nesbitt and Anders, 2009; Steiner et al., 2003]. However, the calibration coefficients are a valuable step towards adjusting rainfall amounts and converting the instantaneous measurements into meaningful surface rainfall amounts when averaging over monthly time scales Discussion and Application Example The calibrated 12-year-long tropical TRMM rainfall time series with monthly averages provides the opportunity to decipher steep climatic gradients, rainfall seasonality, and extreme-event variations at high spatial scales (Figure 2). In semi-arid to arid regions, landscape-shaping hydrologic events are often associated with extreme rainfall or flooding events [e.g., Bookhagen et al., 2005; Coppus and Imeson, 2002; Douglas et al., 1999; Gabet et al., 2008; Snyder et al., 2003; Wolman and Miller, 1960; Wulf et al., 2010]. For 4

5 example, in the northwestern Himalaya, two large rainfall events identified by a rain-gauge network resulted in 50% of the sediment flux recorded over a five-year interval [Wulf et al., 2010]. Similar observations were made in the central Himalaya, where single storm events caused large erosion amounts [e.g., Craddock et al., 2007; Gabet et al., 2008]. In these regions, the mean annual rainfall amount during this time period did not vary significantly and thus does not serve as a reliable predictor for mass-transport processes. I use an analysis of the 90 th -percentile rainfall threshold (Figure 2B, DR6) and number of rainfall events at or above the 90 th percentile (i.e., extreme events ) (Figure 2C, DR6). I emphasize that the absolute number of extreme events may differ from gauge data, but their relative number or the spatial gradient is representative of natural conditions. In order to document the applicability of the monthly TRMM2B31 climatology, I focus on the steep rainfall gradients flanking the eastern central Andes and the Altiplano-Puna Plateau, the second largest plateau on Earth [e.g., Allmendinger et al., 1997; Strecker et al., 2007]. The meridional striking Andes orogen not only form a major hydrologic boundary and drainage divide on the South American continent, but also impose a significant topographic barrier to atmospheric circulation systems [e.g., Garreaud et al., 2003; Vera et al., 2006]. The central Andes are defined by the internally drained Altiplano-Puna plateau within a latitudinal range roughly between ~15 and 25ºS [e.g., Allmendinger et al., 1997]. The primary water-vapor transport mechanism is the low-level jet that conveys recycled Atlantic moisture from the Amazon Basin along the eastern Andes and is ultimately responsible for heavy rainfall at the central Andean flanks [Vera et al., 2006]. Previous studies that explored the impact of rainfall on surface erosion and mass-transport processes emphasize the difference in mean annual rainfall amounts between the north and south-central Andes [e.g., Barnes and Pelletier, 2006]. In order to document the applicability and importance of high-spatial resolution rainfall data on deciphering climatic gradients and associated surface processes, I compare mean annual rainfall, number of extreme-rainfall events with respect to all wet days, and rainfall seasonality for two swaths with different climatic regimes from the north-central and south-central Andes (Figure 3). In summary, several important aspects arise: First, rainfall in the north-central Andes is focused at orographic barriers and location of peak rainfall correlates with a higher number of extreme rainfall events occurring at or above the 90 th percentile (Figure 3). The extreme-event frequency at the steep orogenic front is high compared 5

6 to other places along the Andes (Figure 3D) and is only nominally exceeded by other continental locations with steep topographic settings, such as the Himalaya and Papua New Guinea (Figure 2C). Second, rainfall seasonality in the south-central Andes is significantly higher than in the northern parts, but rainfall seasonality is similar leewards (westwards) of orographic barriers on the Altiplano-Puna Plateau (Figure 3C). I have compared the 6-month periods between November to April (wet season) with May to October (dry season) to document that the eastern flanks of the north-central (south-central) Andes experience moderate rainfall seasonality, with one to five (ten) times the rainfall amount occurring during the wet austral summer. Third, absolute extreme-rainfall intensity (i.e., the 90 th percentile threshold) is similar between the north- and south-central Andes, but their frequency significantly varies (Figure 3D, E). Extreme events have been shown to be an important agent for erosion and plant ecology where low frequency events occur with large magnitudes [e.g, Douglas et al., 1999; Wolman and Miller, 1960; Wulf et al., 2010]. In order to document the potential impact of extreme rainfall events on the landscape, I use the ratio of number of extreme events to the number of wet days: in the north-central Andes, between 8 to 9% of all rainfall events occur at or above the 90 th percentile. In contrast, in the south-central Andes only 5 to 6% of all rainfall events are extreme events (Figure DR8). A similar pattern emerges when forming the ratio of the 90 th percentile rainfall threshold vs. mean annual rainfall amount: extreme rainfall in the south-central Andes is about 10-times higher with respect to their mean annual amounts than in the north-central Andes (Figure DR9). This analysis suggests that the erosion potential of extreme events in the south-central Andes and on the Altiplano-Puna Plateau is likely to be large and thus extreme events have a significant impact on hydrology and ecology Conclusion I present a calibrated monthly climatology averaged from 1998 to 2009 at high spatial resolutions (~5 km or 0.04º). These new data have potential application in regional climate modeling, hydrology, and ecology as they elucidate the interaction between topography and orographic rainfall and can be used as input parameters for climate, hydrologic, and runoff modeling. I use the 12-year time series to identify extremerainfall amounts (i.e., rainfall amount at and above the 90 th percentile) and reveal a spatial disconnect between mean annual and extreme rainfall amounts especially in semi-arid to arid environments. This 165 disconnect is important for ecology and geomorphologic studies analyzing the relation between rainfall 6

7 and erosion, because extreme-rainfall events are responsible for much of the mass transport and thus may be a better predictor for long-term erosion than mean annual or seasonal-rainfall amounts Acknowledgments All data are available for download at in various formats. The data used in this study were acquired as part of the Tropical Rainfall Measuring Mission (TRMM) sponsored by the Japan National Space Development Agency (NASDA) and the US National Aeronautics and Space Administration (NASA). This work was supported with grants from NASA (NNX08AG05G) and the National Science Foundation (EAR ). I thank D. Burbank, L. Carvalho, and B. Fisher for discussions and suggestions. 175 Bibliography 176 Adler, R. F., et al. (2003), The version-2 global precipitation climatology project (GPCP) monthly precipitation 177 analysis (1979-present), Journal of Hydrometeorology, 4(6), Allmendinger, R. W., et al. (1997), The evolution of the Altiplano-Puna plateau of the Central Andes, Annual 179 Review of Earth and Planetary Sciences, 25, Barnes, J. B., and J. D. Pelletier (2006), Latitudinal variation of denudation in the evolution of the Bolivian 181 Andes, American Journal of Science, 306(1), Bell, T. L., and P. K. Kundu (2000), Dependence of satellite sampling error on monthly averaged rain rates: 183 Comparison of simple models and recent studies, Journal of Climate, 13(2), Bookhagen, B., et al. (2005), Abnormal monsoon years and their control on erosion and sediment flux in the 185 high, and northwest Himalaya, Earth and Planetary Science Letters, 231(1-2), Bookhagen, B., and D. W. Burbank (2006), Topography, relief, and TRMM-derived rainfall variations along 187 the Himalaya, Geophysical Research Letters, 33(8), 2006GL Bookhagen, B., and M. R. Strecker (2008), Orographic barriers, high-resolution TRMM rainfall, and relief 189 variations along the eastern Andes, Geophysical Research Letters, 35(doi: /2007GL032011). 190 Bookhagen, B., and D. W. Burbank (2010), Towards a complete Himalayan hydrological budget: The 191 spatiotemporal distribution of snow melt and rainfall and their impact on river discharge, Journal of 192 Geophysical Research-Earth Surface, doi: /2009jf Carvalho, L. M. V., et al. (2002), Extreme precipitation events in southeastern South America and large-scale 194 convective patterns in the South Atlantic convergence zone, Journal of Climate, 15(17), Coppus, R., and A. C. Imeson (2002), Extreme events controlling erosion and sediment transport in a semi- 196 arid sub-andean valley, Earth Surface Processes and Landforms, 27(13), Craddock, W. H., et al. (2007), Bedrock channel geometry along an orographic rainfall gradient in the upper 198 Marsyandi River valley in central Nepal, Journal of Geophysical Research-Earth Surface, 112(F03007), 199 doi: /2006jf Douglas, I., et al. (1999), The role of extreme events in the impacts of selective tropical forestry on erosion 201 during harvesting and recovery phases at Danum Valley, Sabah, Philosophical Transactions of the Royal 202 Society of London Series B-Biological Sciences, 354(1391),

8 203 Gabet, E. J., et al. (2008), Modem erosion rates in the High Himalayas of Nepal, Earth and Planetary Science 204 Letters, 267(3-4), Garreaud, R., et al. (2003), The climate of the Altiplano: observed current conditions and mechanisms of past 206 changes, Palaeogeography Palaeoclimatology Palaeoecology, 194(1-3), Grimm, A. M., and R. G. Tedeschi (2009), ENSO and Extreme Rainfall Events in South America, Journal of 208 Climate, 22(7), Haddad, Z. S., et al. (1997), The TRMM 'day-1' radar/radiometer combined rain-profiling algorithm, Journal of 210 the Meteorological Society of Japan, 75(4), Huffman, G. J., et al. (2007), The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, 212 combined-sensor precipitation estimates at fine scales, Journal of Hydrometeorology, 8(1), Krishnamurthy, C. K. B., et al. (2009), Changing Frequency and Intensity of Rainfall Extremes over India from to 2003, Journal of Climate, 22(18), Kummerow, C., et al. (2000), The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in 216 orbit, Journal of Applied Meteorology, 39(12), Magilligan, F. J., et al. (1998), Geomorphic and sedimentological controls on the effectiveness of an extreme 218 flood, Journal of Geology, 106(1), Nesbitt, S. W., and A. M. Anders (2009), Very high resolution precipitation climatologies from the Tropical 220 Rainfall Measuring Mission precipitation radar, Geophysical Research Letters, Schneider, U., et al. (2008), Global Precipitation Analysis Products of the GPCC. Global Precipitation 222 Climatology Centre (GPCC), Deutscher Wetter Dienst (DWD), Internet Publikation, 1(12). 223 Snyder, N. P., et al. (2003), Importance of a stochastic distribution of floods and erosion thresholds in the 224 bedrock river incision problem, Journal of Geophysical Research-Solid Earth, 108(B2). 225 Steiner, M., et al. (2003), Comparison of two methods for estimating the sampling-related uncertainty of 226 satellite rainfall averages based on a large radar dataset, Journal of Climate, 16(22), Strecker, M. R., et al. (2007), Tectonics and climate of the southern central Andes, Annual Review of Earth 228 and Planetary Sciences, 35, Vera, C., et al. (2006), Toward a unified view of the American Monsoon Systems, Journal of Climate, 19(20), Webster, P. J. (1987), The Elementary Monsoon, 3-32 pp., Wiley, New York. 232 Wolman, M. G., and J. P. Miller (1960), Magnitude and frequency of forces in geomorphic processes, Journal 233 of Geology, 68, Wulf, H., et al. (2010), Seasonal precipitation gradients and their impact on fluvial sediment flux in the 235 Northwest Himalaya, Geomorphology, doi: /j.geomorph

9 Figure 1: (A) Calibration of TRMM2B31 data and (B) influence of time-series length on calibration. TRMM 2B31 mean annual rates from 1998 to 2007 (measured in m/hr) are calibrated with (1) error-weighted Global Precipitation Climatology Centre (GPCC) rain-gauge stations (n=2576) [Schneider et al., 2008] and (2) with a weighted 0.05 mm/hr binned dataset (n=14) for a more balanced calibration. Both calibrations overlap within their 1-σ error bounds and suggest robust conversion from instantaneous rainfall amounts (mm/hr) to mean annual rainfall rates (m/y). The fit produced by fitting all 2576 data points is used in subsequent analysis. The root mean square error of the TRMM2B31 vs. GPCC gauge data is reduced by 40% when integrating over 7 years of data (B). 9

10 Figure 2: Overview of (A) calibrated mean annual rainfall, (B) 90th-percentile rainfall threshold, and (C) mean annual number of events in the 90th percentile ( extreme events ). There exists a general, first-order correlation between mean annual rainfall and number of extreme events; however, a detailed example from an orographic setting in South America s central Andes suggest a discrepancy between mean and seasonal rainfall as well as occurrences of extreme events (Figure 3). 10

11 Figure 3: Central Andean mean annual rainfall (A), number of extreme events (B) and rainfall seasonality (C) averaged from 1998 to 2009 and two 110-km wide swath profiles from the north- (D) and southcentral Andes (E). In A to C, white polygon outlines the internally drained Altiplano-Puna Plateau, gray lines indicate international borders with Argentina in the southeast, Bolivia in the northeast, and blue lines are major rivers. There are more than twice and up to four times as many extreme events per year in the north than in the south-central Andes (B). High numbers in rainfall seasonality (C) indicate most rain falling during the wet austral summer (Nov-Apr).Swath profiles (D, E) show mean (black) and ±1σ (gray) topography; blue lines indicate mean annual rainfall (±1σ). Red line outlines averaged number of extreme events per year ranging from ~0.25 (every 4 years) to ~0.75 (every 1.3 y) in the north-central Andes (D). 11