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Congo Basin Hydrology, Climate, and Biogeochemistry. Группа авторов
Читать онлайн.Название Congo Basin Hydrology, Climate, and Biogeochemistry
Год выпуска 0
isbn 9781119656999
Автор произведения Группа авторов
Жанр География
Издательство John Wiley & Sons Limited
Figure 3.5 Mean vertical motion (omega: hPa/s × 10–2) in SON at 850 hPa during mid‐afternoon and evening (from Jackson et al., 2009; ©American Meteorological Society. Used with permission).
Figure 3.6 Gauge network around the Congo Basin in four time periods (from Nicholson et al., 2018; © American Meteorological Society. Used with permission). Country boundaries are shown. In the map for 1999–2014, the letters DRC and A respectively identify the Democratic Republic of the Congo and Angola.
Gridded rainfall data sets do cover the recent years, despite the paucity of gauge data. Examples are the CRU data set (Harris et al., 2014) and the GPCC data set (Schneider et al., 2015). The gridding is based primarily on techniques that assume linear relationships between available stations, a weak assumption when the gaps are as large as those over the Congo Basin. Nicholson et al. (2018a) created a gridded data set using a spatial reconstruction technique that allows for more complex relationships among the available stations. This data set, which is at 2.5‐degree resolution, has been validated and covers the years 1921 to 2014. Termed NIC131‐gridded, this data set is available at monthly, seasonal, and annual time scales and can be obtained from the author.
Numerous satellite precipitation products cover equatorial Africa. Unfortunately, over the Congo Basin the rainfall estimates of the various products differ greatly, much more so than over other areas of equatorial Africa. This is illustrated in Figure 3.7, which shows rainfall estimates from several products for a single month (March 2001) and Figure 3.8, which depicts interannual variability based on the various products.
The nine satellite estimates presented in Figure 3.7 show some striking contrasts. These are most pronounced over the Democratic Republic of the Congo (DRC), where there are few gauge stations. CMORPH CRT (Xie et al., 2017) and TRMM 3B43 V7 (Huffman et al., 2007, Huffman & Bolvin, 2014) show numerous areas where rainfall is below 80 mm, while PERSIANN CDR (Ashouri et al., 2015), ARC2 (Novella & Thiaw, 2013), and TAMSAT V3 (Maidment et al., 2017) show rainfall on the order of 140 mm or more throughout most of the country. In general, rainfall increases southward, but in RFE (Love et al., 2004) this trend is reversed. The gauge products (NIC131‐gridded, GPCC) highlight drier conditions in the northwest, a feature clearly captured by CHIRPS2 (Funk et al., 2015), GPCP (Adler et al., 2003), and to a lesser extent TRMM 3B43 V7 and CMAP Enhanced (Xie et al. 2003; Xie & Arkin, 1997).
Figure 3.7 Maps of rainfall (mm/mo) for March, based on nine satellite products and three gauge products (GPCC, individual stations, NIC131‐gridded) (from Nicholson et al., 2019; © American Meteorological Society. Used with permission).
Figure 3.8 Interannual variability of rainfall (mm/yr) over the central Congo Basin and over a large portion of the Central African Republic (modified from Nicholson et al., 2019). Rainfall is averaged for March–April (left) and for October–November (right). The number of available gauge stations in each year is indicated below each graph, with the top graph indicate the number in GPCC and the bottom indicating the numbers in the NIC131 archive.
Source: Jackson et al., 2009. © American Meteorological Society. Used with permission.
Figure 3.8 shows the interannual variability of March/April and October/November rainfall over the Congo Basin and over the Central African Republic (CAR) to the north. The gauge network is dense over CAR and the satellite products are in good agreement with each other and with the NIC131‐gridded data set. For the Congo Basin, a fair amount of gauge data was available until the mid‐1990s, after which time there is wide disparity among the estimates and little agreement with the NIC131‐gridded data set. These results suggest that the reason for the poor performance of satellite products in this region is the paucity of gauge data.
When the products shown above were validated against gauge data (Camberlin et al., 2019; Nicholson et al., 2019), the best performing products appeared to be CHIRPS for mean rainfall, TRMM for daily rainfall, and CHIRPS2 and TRMM for interannual variability. Both products show generally good agreement with gauge data over the Congo on monthly time scales and thus are selected for use in this study.
Most rainfall analyses in this chapter are based on the CHIRPS2 satellite product (Funk et al., 2015). It has a spatial resolution of 0.05° × 0.05° and a daily temporal resolution. CHIRPS2 begins in 1981 and extends through 2019. However, TRMM 3B43 Version 7 is used to evaluate rainfall over the Amazon. It runs from 1998 to 2014 and has a spatial resolution of 0.25 degrees of latitude/longitude. Its temporal resolution is monthly. Both CHIRPS2 and TRMM 3B43 Version 7 have been extensively validated over equatorial Africa and show a close relationship to gauge rainfall (e.g., Camberlin et al., 2019; Nicholson et al., 2019). TRMM 3B42 V7 is used here to ascertain the diurnal cycle of rainfall. Its successor from the global precipitation measurement mission (GPM), IMERG, has much higher temporal and spatial resolution and is available since 2014. However, it has not been validated over the Congo Basin and for that reason TRMM is used instead.
3.4. MEAN RAINFALL
3.4.1. Annual Rainfall
Figure 3.9 shows mean annual rainfall based on two products, CHIRPS2 and raw gauge data in the NIC131 archive. The raw gauge data are averaged for the period 1945 to 1984, the period with the greatest number of gauges in the Congo Basin. CHIRPS2 was averaged over the time period 1981 to 2019. A comparison with gauge data during that time period would be desirable, but the station density over the Congo Basin is too low. In that the averaging period for both data sets is roughly four decades, the long‐term means derived from the two should be comparable. In fact, there is excellent agreement between the two analyses, despite the much lower resolution of the gauge data. There is, likewise, excellent agreement with the analysis of Bultot (1971) for 1930–1959, which is based on some 500 stations in the Congo (Camberlin et al., 2019).