94 Tapley, B., Bettadpur, S., Watkins, M., & Reigber, C. (2004). The Gravity Recovery and Climate Experiment: Mission overview and early results. Geophysical Research Letters, 31, 1–4. doi: 10.1029/ 2004GL019920
95 Thomas, B. F., Famiglietti, J. S., Landerer, F. W., Wiese, D. N., Molotch, N. P., & Argus, D. F. (2017). GRACE groundwater drought index: Evaluation of California Central Valley groundwater drought. Remote Sensing of Environment, 198(Supplement C), 384–392. doi: 10.1016/j.rse.2017.06.026
96 Tockner, K., Lorang, M. S., & Stanford, J. A. (2010). River flood plains are model ecosystems to test general hydrogeomorphic and ecological concepts. River Research and Applications, 26(1), 76–86. doi: 10.1002/rra.1328
97 Tshimanga, R. M., & Hughes, D. A. (2014). Basin‐scale performance of a semi‐distributed rainfall‐runoff model for hydrological predictions and water resources assessment of large rivers: The Congo River. Water Resources Research, 50(2), 1174–1188. doi: 10.1002/2013WR014310
98 Van Loon, A. F., Kumar, R., & Mishra, V. (2017). Testing the use of standardised indices and GRACE satellite data to estimate the European 2015 groundwater drought in near‐real time. Hydrology and Earth System Sciences, 21(4), 1947–1971. doi: 10.5194/hess‐21‐1947‐2017
99 Van Loon, A. F., Stahl, K., Di Baldassarre, G., Clark, J., Rangecroft, S., Wanders, N., et al. (2016). Drought in a human‐modified world: reframing drought definitions, understanding, and analysis approaches. Hydrology and Earth System Sciences, 20(9), 3631–3650. doi: 10.5194/hess‐20‐3631‐2016
100 Van Loon, A. F., Tijdeman, E., Wanders, N., Van Lanen, H. A., Teuling, A. J., & Uijlenhoet, R. (2014). How climate seasonality modifies drought duration and deficit. Journal of Geophysical Research: Atmospheres, 119(8), 4640–4656. doi: 10.1002/2013JD020383
101 Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York, NY: Springer.
102 Verhegghen, A., Mayaux, P., de Wasseige, C., & Defourny, P. (2012). Mapping Congo Basin vegetation types from 300 m and 1 km multi‐sensor time series for carbon stocks and forest areas estimation. Biogeosciences, 9(12), 5061–5079. doi: 10.5194/bg‐9‐5061‐2012
103 Vicente‐Serrano, S. M., Beguería, S., & López‐Moreno, J. I. (2010a). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718. doi: 10.1175/2009JCLI2909.1
104 Vicente‐Serrano, S. M., Beguería, S., López‐Moreno, J. I., Angulo, M., & El Kenawy, A. (2010b). A new global 0.5° gridded dataset (1901–2006) of a multiscalar drought index: Comparison with current drought index datasets based on the palmer drought severity index. Journal of Hydrometeorology, 11(4), 1033–1043. doi: 10.1175/2010JHM1224.1
105 Wada, Y., Bierkens, M. F. P., de Roo, A., Dirmeyer, P. A., Famiglietti, J. S., Hanasaki, N., et al. (2017). Human‐water interface in hydrological modelling: Current status and future directions. Hydrology and Earth System Sciences, 21(8), 4169–4193. doi: 10.5194/hess‐21‐4169‐2017
106 Washington, R., James, R., Pearce, H., Pokam, W. M., & Moufouma‐Okia, W. (2013). Congo Basin rainfall climatology: can we believe the climate models? Philosophical Transactions of the Royal Society of London B: Biological Sciences, 368(1625). doi: 10.1098/rstb.2012.0296
107 Watkins, M. M., Wiese, D. N., Yuan, D., Boening, C., & Landerer, F. W. (2015). Improved methods for observing earth’s time variable mass distribution with GRACE using spherical cap mascons. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671. doi: 10.1002/2014JB011547
108 Wauters, M., & Vanhoucke, M. (2014). Support vector machine regression for project control forecasting. Automation in Construction, 47, 92–106. doi: 10.1016/j.autcon.2014.07.014.
109 Wiese, D. N., Landerer, F. W., & Watkins, M. M. (2016). Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resources Research, 52(9), 7490–7502. https://doi.org/10.1002/2016WR019344
110 Zhao, D., Jiang, H., Yang, T., Cai, Y., Xu, D., & An, S. (2012). Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds. Journal of Environmental Management, 95(1), 98–107. doi: 10.1016/j.jenvman.2011.10.007
111 Zhou, L., Tian, Y., Myneni, R. B., Ciais, P., Saatchi, S., Liu, Y. Y., et al. (2014). Widespread decline of congo rainforest greenness in the past decade. Nature, 509(7498), 86–90. doi: 10.1038/nature13265
6 Hydroclimatic Dynamics of Upstream Ubangi River at Mobaye, Central African Republic: Comparative Study of the Role of Savannah and Equatorial Forest
Cyriaque-Rufin Nguimalet1, Didier Orange2, Jean-Pascal Waterendji1, and Athanase Yambele3
1 Department of Geography, University of Bangui, Bangui, Central African Republic
2 Joint Research Unit “Eco&Sols” (UMR Eco&Sols), INRA, IRD, Montpellier SupAgro and CIRAD, Montpellier, France
3 National Directorate of Meteorology, Ministry of Transport and Civil Aviation, Bangui, Central African Republic
ABSTRACT
The rainfall reduction in the 1970s, less marked in Central Africa than in West Africa, still had a major impact on the hydrological regimes of the region’s large rivers. The study of the hydropluviometric behavior of the Ubangi River at Mobaye has the advantage of being a study of a basin excluding anthropogenic impact. Forest cover and population density have not changed since at least 1970. Statistical analysis of the breaks in the long rainfall time series to Mobaye (1938–2015) confirms a long period of drought from 1969 to 2006, corresponding to a reduction of 8% in rainfall. Also, the study of the corresponding hydrological series indicates a second downward break in 1981, marking an exceptional hydrological drought. Flows increased in 2013, a few years after the rainfall increase. The statistical study of the annual rainfall/flow series of the upstream basins over the period 1951–1995 (the Kotto River in Kembe and Bria, the Mbomu River in Bangassou and Zemio, and the Uele River + Bili hydrographic system) highlights different hydrological behaviors related to the vegetation cover. On the one hand, the savannah basins show a continuous hydrological deficit marked by a runoff coefficient (CE) that fell to only 5% from the 1990s. On the other hand, the basins under forest show a runoff increase since 1990, marked by a CE above 10%. Under savannah, the part of the flow infiltrating to recharge the aquifer would have decreased faster than under forest, which results in a runoff CE very significantly negatively correlated with the savannah area present in the studied watershed.
6.1. INTRODUCTION
The rainfall break of the 1970s was less marked in Central Africa than in West Africa. Nevertheless, it has largely impacted the hydrological regimes of the region’s major rivers (Laraque et al., 1998, 2001; Nguimalet & Orange, 2013; Olivry et al., 1998; Orange et al., 1997; Paturel et al., 1998, 2007; Servat et al., 1999; Sighomnou et al., 2007; Wesselink et al., 1996). On the Ubangi River at Bangui, a large savannah‐dominated basin, the slight drop in rainfall of 5 to 6% resulted in a 21% runoff deficit over the period 1983–2013 (Orange et al., 1994). On the Congo River, Laraque et al. (2013) noted that after a hydrological deficit observed in the 1980s, the runoff has returned to normal since 1990. According to Nguimalet & Orange (2019), the rainfall amount in the Ubangi basin at Bangui has shown a significant increase since 2009, but still without any real hydrological impact on the course of the Ubangi River at Bangui. Recently, Nguimalet and Orange (2020) showed that hydrological behaviors in small basins of 2,500 to 5,000 km2 in the north of the Central African Republic, in the Sudanian savannah zone, were all impacted by the 1970 drought, at different levels and without any common hydroclimatic period. It remains to be seen whether this is specific to the