Spatio-temporal assessment of WRF, TRMM and in situ precipitation data in a tropical mountain environment (Cordillera Blanca, Peru)

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Abstract

The estimation of precipitation over the broad range of scales of interest for climatologists, meteorologists and hydrologists is challenging at high altitudes of tropical regions, where the spatial variability of precipitation is important while in situ measurements remain scarce largely due to operational constraints. Three different types of rainfall products-ground based (kriging interpolation), satellite derived (TRMM3B42), and atmospheric model outputs (WRF -Weather Research and Forecasting) - are compared for 1 hydrological year in order to retrieve rainfall patterns at timescales ranging from sub-daily to annual over a watershed of approximately 10 000 km2 in Peru. An ensemble of three different spatial resolutions is considered for the comparison (27, 9 and 3 km), as long as well as a range of timescales (annual totals, daily rainfall patterns, diurnal cycle). WRF simulations largely overestimate the annual totals, especially at low spatial resolution, while reproducing correctly the diurnal cycle and locating the spots of heavy rainfall more realistically than either the ground-based KED or the Tropical Rainfall Measuring Mission (TRMM) products. The main weakness of kriged products is the production of annual rainfall maxima over the summit rather than on the slopes, mainly due to a lack of in situ data above 3800 m a. s. l. This study also confirms that one limitation of TRMM is its poor performance over ice-covered areas because ice on the ground behaves in a similar way as rain or ice drops in the atmosphere in terms of scattering the microwave energy. While all three products are able to correctly represent the spatial rainfall patterns at the annual scale, it not surprisingly turns out that none of them meets the challenge of representing both accumulated quantities of precipitation and frequency of occurrence at the short timescales (sub-daily and daily) required for glacio-hydrological studies in this region. It is concluded that new methods should be used to merge various rainfall products so as to make the most of their respective strengths.

Figures

  • Figure 1. Location of the upper Santa watershed (the star marks outlet Condorcerro). Color dots indicate annual precipitation amounts at in situ stations. White dots correspond to stations with missing data. The Huascaran peak is indicated, as well as the Rio Marañon watershed. Topography is from SRTM (http://srtm.csi.cgiar.org/).
  • Table 1. Information on in situ rainfall stations. Under “Location”, CB means Cordillera Blanca, CN means Cordillera Negra, M means Marañon, and C means Costa. [NS] indicates stations used for the study along the Rio Santa Valley. [H] indicates stations used for the transect along the Huascaran peak. ∗ indicates stations used to calculate the precipitation index (data from 1965) (Sect. 2.2). Precipitation (mmyr−1) during the hydrological year 2012–2013 is indicated at each rain gauge station for in situ data (Obs), TRMM and WRF (WRF27, WRF9 and WRF3). Accu indicates the value for glacier accumulation over the year.
  • Figure 2. Annual precipitation (mmyr−1) from TRMM2B31 for the hydrological year 2012–2013 (August 2012–July 2013). The three boxes indicate the WRF simulation domain: box 1 for 27km× 27 km; box 2 for 9km× 9km; box 3 for 3km× 3km. Topography contours are displayed every 500 m.
  • Table 2. Characteristics of the WRF simulations at the three different spatial scales.
  • Table 3. List of the physical parameterizations used in the WRF simulations.
  • Table 4. Precipitation data used in this study, with their spatial and temporal resolutions, and the accumulated amount precipitated over the upper Rio Santa watershed during the hydrological year 2012–2013. WRF and KED (corresponding to kriging data with external drift – daily evolving variogram) are at three different spatial resolutions (27, 9 and 3 km). TRMM is the TRMM3B42 product.
  • Figure 3. (a) Cross-validation residuals with in situ yearly precipitation amount. (b) Annual precipitation amount from KED interpolations at 27 (b1), 9 (b2) and 3 km (b3) spatial resolutions. Delimitation of the upper Rio Santa watershed is indicated in bold gray lines. The coastline is also indicated in black.
  • Table 5. Contingency table used to assess the statistical performances of the 3 km resolution products against punctual in situ data at a daily timescale. The B value corresponds for example to a day with no precipitation in the in situ data and precipitation > threshold mmd−1 in the 3 km grid product.

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APA

Mourre, L., Condom, T., Junquas, C., Lebel, T., E. Sicart, J., Figueroa, R., & Cochachin, A. (2016). Spatio-temporal assessment of WRF, TRMM and in situ precipitation data in a tropical mountain environment (Cordillera Blanca, Peru). Hydrology and Earth System Sciences, 20(1), 125–141. https://doi.org/10.5194/hess-20-125-2016

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