Congo Basin Hydrology, Climate, and Biogeochemistry. Группа авторов

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Название Congo Basin Hydrology, Climate, and Biogeochemistry
Автор произведения Группа авторов
Жанр География
Серия
Издательство География
Год выпуска 0
isbn 9781119656999



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In‐situ River Discharge

      Observed river discharge data for the Congo Kinshasa station was accessed from the GRDC (www.bafg.de/GRDC) archives and used to assess hydrological response of the Congo River to climatic fluctuations. The Congo River is one of the key rivers in the region, as multiple sources of discharge from other tributaries within the Congo Basin connect with this channel before reaching the Atlantic Ocean. While the Congo River discharge encapsulates most of the flows within the basin (Ndehedehe et al., 2019), this river largely modulates the surface water hydrology of the Congo Basin (e.g., Alsdorf et al., 2016; Ndehedehe et al., 2018b). The monthly river discharge data of the Congo River in Kinshasa station covering the period between 1980 and 2010 was used in combination with sea‐surface temperature to model the impacts of the surrounding oceans on temporal dynamics of Congo River discharge. But in assessing climate influence on surface water hydrology (i.e., TWS) over the Congo Basin, the data covering the period during 20022010 was used.

      5.2.3. Tropical Rainfall Measuring Mission

      The TRMM (Tropical rainfall measuring mission) 3B43 (Huffman et al., 2007; Kummerow et al., 2000) provides monthly precipitation estimates on a 0.25° × 0.25° spatial grid across the globe. The data were used in this study to assess the leading driver of GRACE‐derived TWS and the spatial and temporal distributions of rainfall over the Congo Basin.

      5.2.4. Sea‐Surface Temperature Products

      This study used the global sea‐surface temperature (SST) data (Reynolds et al., 2002) covering the period between 1982 and 2015 and was accessed from NOAA’s official earth system research laboratory portal (http://www.esrl.noaa.gov/psd/data/gridded/data. noaa.oisst.v2.html). Given that the influence of global SST anomalies on precipitation over tropical central Africa has been reported (see, e.g., Farnsworth et al., 2011; Ndehedehe et al., 2019), SST over the Atlantic, Pacific, and Indian oceans were used in this study to model climate influence on discharge. The global oceans modulate the zonal and local circulation patterns over Equatorial Africa (Nicholson & Dezfuli, 2013; Pokam et al., 2014), thus our motivation to examine the impact of SST on discharge.

      5.2.5. Standardized Precipitation Evapotranspiration Index

      The SPEI combines precipitation and temperature data in a water balance framework (see Vicente‐Serrano et al., 2010a,b). The SPEI used here was estimated based on a water balance approach as the difference between precipitation (P) and PET (potential evapotranspiration), i.e., δ = P‐PET. As detailed by Vicente‐Serrano et al. (2010b), the computed values of δ are cumulated on different time scales,

      (5.1)delta Subscript n Superscript k Baseline equals sigma-summation Underscript i equals 0 Overscript k minus 1 Endscripts left-parenthesis upper P Subscript n minus 1 Baseline minus upper P upper E upper T Subscript n minus i Baseline right-parenthesis n greater-than-or-equal-to k

      where k is the cumulated time scale and n is the calculation number. This cumulated time series are thereafter fitted with a log‐logistic probability distribution function. The SPEI drought characterization here follows the thresholds defined by McKee et al. (1993), in which a drought condition is assumed to occur when the SPEI is consistently negative and reaches a value of –1. On a 12‐month cumulation, this threshold supports hydrological drought characterization in the Congo Basin.

      5.2.6. Statistical Analysis and Modeling

      (5.3)sigma equals StartFraction upper C Over n EndFraction sigma-summation Underscript i equals 0 Overscript n Endscripts upper L Subscript epsilon Baseline left-parenthesis p Subscript i Baseline minus f left-parenthesis x right-parenthesis right-parenthesis one half one half left-parenthesis normal w right-parenthesis squared

      where the compound risk caused by training errors and model complexity is given as ς. Equation 5.2 provides the estimated values for w and b and comprises the empirical risk measured by the ε‐insensitive loss function, Lε, and the regularization term ½ ‖w‖2, which describes the model complexity (Cortes & Vapnik, 1995; Wauters & Vanhoucke, 2014). Prior to modeling the response of discharge to climate using the SVMR, a regularization approach where the SST is compressed through a PCA‐based orthogonalization was employed (e.g., Barnett & Preisendorfer, 1987; Bretherton et al., 1992; Ndehedehe et al., 2018b). This resulted in significant modes of SST variability from the respective oceans, which were then used as predictands in the SVMR model. Specifically, a linear SVM regression model was trained to fit the data. The SVMR technique evaluates each run of the experiment using regression, by partitioning the data internally into training, validation, and