Climate Trends, Hydrologic Modeling, and Land Use Analysis in Malawi

This finding is based on the analysis of United States Agency for International Development (USAID) funded Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) datasets, which show a close correlation with observed weather station data. The delayed onset of the rainy season has significant implications for agricultural production in Malawi. Five new weather stations have been established in agricultural sites to improve the understanding of spatial relationships between weather, climate, and food production in Malawi. These weather stations will help reduce uncertainty in climate projections and improve the accuracy of agricultural predictions. Watershed models have been calibrated for all of Malawi's major watersheds, but further validation is needed to capture peak flow. The existing datasets on land use in Malawi disagree significantly, which is a problem for integrating models. Net primary productivity (NPP) estimates show declines in productivity over Malawi's recent history. Extensification is poorly characterized among several independent datasets, and there is little understanding of what locations are marginal for agriculture and why assessments disagree. Biophysical patterns, such as land use, greenness, rainfall, and crop yield, are poorly measured in Malawi in terms of spatial and temporal resolution, as well as coherent and consistent indicators of land cover. The analysis of weather data, land cover data, and crop yield across scales can improve agricultural predictions of yield and nutrient stress significantly. However, there is disagreement even in the sign of change, and more data at finer scales is needed to reduce uncertainty. Significant effort is required to describe where processes are inconsistent at different scales and how models can make useful projections in the face of disagreement between various data sources. The implications of these results are significant, as crop yields may be declining despite the Food and Agricultural Organization (FAO) estimates. New methods are being developed to better characterize the biophysical changes in recent decades. These methods can be used and improved by Malawi researchers and field teams of MSU researchers to improve predictions. Training sessions, short courses, and other capacity-building activities are needed to develop the scientific expertise for doing integrated modeling and assessment. The report is arranged in three sections: Climate Trends, Hydrologic Modeling, and Land Use Analysis. The Climate Trends section provides an overview of the analysis of climate data, including the delayed onset of the rainy season and the implications for agricultural production. The Hydrologic Modeling section discusses the calibration of watershed models for all of Malawi's major watersheds and the need for further validation to capture peak flow. The Land Use Analysis section examines the existing datasets on land use in Malawi and the need for more accurate and consistent indicators of land cover. The analysis of climate trends, hydrologic modeling, and land use analysis in Malawi has significant implications for agricultural production and food security in the region. The findings of this report highlight the need for more accurate and consistent indicators of land cover, as well as the need for training and capacity-building activities to develop the scientific expertise for doing integrated modeling and assessment.