Impact Assessment of Climate Change on Climate Potential Productivity in Central Africa Based on High Spatial and Temporal Resolution Data
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Preprocessing
- (1)
- Missing Data HandlingCRU TS v.4.04 employs spatial interpolation to fill missing station data, as described in Harris et al. [39]. This paper further excluded grids with >20% missing values in the study period to minimize uncertainty.
- (2)
- Bias Correction
- (3)
- Reprojection and Spatial AggregationOriginal 0.5° grids were reprojected to an equal-area projection, Mollweide, before zonal statistics to minimize area distortion in Africa’s low latitudes. Monthly NetCDF files were batch-converted to GeoTIFF in MATLAB 2022b, then masked with African boundaries in ArcMap ModelBuilder. In the Model Builder of ArcMap, this study performed batch clipping, averaging, and summing of raster images to obtain the raster data of mean annual temperature and precipitation in the Central African region.
- (4)
- CPP CalculationZero-precipitation grids were set to 0.01 mm to avoid computational errors in the Thornthwaite model. Annual CPP was computed in MATLAB 2022b using reprojected, bias-corrected temperature and precipitation inputs.
- (5)
- Temporal and Spatial AnalysisTo analyze both spatial and temporal patterns of climate variables across Central Africa, this study first extracted mean annual temperature and precipitation from the reprojected rasters. These spatial trend analyses were complemented by a comprehensive time-series examination, according to which this study applied zonal statistics to derive key climatic indicators, including annual means, maxima, and minima, for each subregion over the 1901–2019 study period. This dual analytical approach enabled us to simultaneously capture the geographical distribution of climate parameters and their long-term temporal variations across Central Africa.
2.3. Methodology
2.3.1. Climate Tendency Rate
2.3.2. Thornthwaite Memorial Model
2.3.3. Mann–Kendall Trend Test
2.3.4. Mann–Kendall–Sneyers Trend Test
3. Results
3.1. Spatio-Temporal Climate Patterns
3.2. Spatio-Temporal Patterns of CPP in Central Africa
- Pre-1936: Declining CPP (UF < 0), particularly significant during 1908–1922;
- Period of 1936–2006: Increasing CPP (UF > 0), with significant rise during 1950–1982;
- Post-2006: Non-significant trends.
3.3. Impacts of Climate Change on Central Africa’s CPP
4. Discussion
5. Conclusions and Implications
5.1. Main Conclusions
5.2. Policy Implications
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Temperature (°C) | Proportion of Study Area (%) | Precipitation (mm) | Proportion of Study Area (%) | CPP (kg/hm2) | Proportion of Study Area (%) |
---|---|---|---|---|---|
0−20 | 1.5 | 0−1000 | 2.7 | 10,000−15,000 | 0.6 |
20−25 | 73.0 | 1000−2000 | 93.4 | 15,000−20,000 | 48.0 |
25−30 | 25.5 | 1000−3000 | 3.9 | 20,000− | 51.4 |
Significance | Temperature | Precipitation | CPP |
---|---|---|---|
Proportion of Study Area (%) | Proportion of Study Area (%) | Proportion of Study Area (%) | |
Extremely significant decrease | 0 | 4.2 | 0.9 |
Significant decrease | 0 | 9.8 | 1.7 |
Non-significant decrease | 0 | 45.3 | 26.4 |
Non-significant increase | 0 | 31.7 | 41.9 |
Significant increase | 0 | 4.2 | 10.4 |
Extremely significant increase | 100 | 4.8 | 18.7 |
Correlation | Temperature | Precipitation |
---|---|---|
Proportion of Zoning Area (%) | Proportion of Zoning Area (%) | |
WNC | 0.5 | 0 |
VWNC | 16.7 | 0 |
VWPC | 39.5 | 0 |
WPC | 38.0 | 0 |
MPC | 5.1 | 0 |
SPC | 0.2 | 0.4 |
VSPC | 0 | 99.6 |
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Bi, M.; Ren, F.; Xu, Y.; Guo, X.; Zhou, X.; van den Bersselaar, D.; Li, X.; Ren, H. Impact Assessment of Climate Change on Climate Potential Productivity in Central Africa Based on High Spatial and Temporal Resolution Data. Land 2025, 14, 1535. https://doi.org/10.3390/land14081535
Bi M, Ren F, Xu Y, Guo X, Zhou X, van den Bersselaar D, Li X, Ren H. Impact Assessment of Climate Change on Climate Potential Productivity in Central Africa Based on High Spatial and Temporal Resolution Data. Land. 2025; 14(8):1535. https://doi.org/10.3390/land14081535
Chicago/Turabian StyleBi, Mo, Fangyi Ren, Yian Xu, Xinya Guo, Xixi Zhou, Dmitri van den Bersselaar, Xinfeng Li, and Hang Ren. 2025. "Impact Assessment of Climate Change on Climate Potential Productivity in Central Africa Based on High Spatial and Temporal Resolution Data" Land 14, no. 8: 1535. https://doi.org/10.3390/land14081535
APA StyleBi, M., Ren, F., Xu, Y., Guo, X., Zhou, X., van den Bersselaar, D., Li, X., & Ren, H. (2025). Impact Assessment of Climate Change on Climate Potential Productivity in Central Africa Based on High Spatial and Temporal Resolution Data. Land, 14(8), 1535. https://doi.org/10.3390/land14081535