Abstract:
Accurate weather and climate data play an important role in enhancing agricultural productivity and resilience. However, the reliability of agrometeorological data in Rwanda is undermined by complex terrain, microclimatic variability, and a poorly distributed network of stations, many of which are suboptimal located in environmental unsuitable areas. Despite the importance of accurate data for agricultural planning and disaster management, no prior study has addressed the spatial optimization of agrometeorological weather station placement, revealing a critical gap in both research and infrastructure planning.
This study aimed to optimize the spatial distribution of agrometeorological weather stations in Rwanda using a Spatial Multi-Criteria Evaluation (SMCE) approach. Nine suitability criteria were defined based on World Meteorological Organization (WMO) standards and expert consultation, including slope, elevation, hillshade, proximity to water bodies and infrastructure, land use/land cover, and existing station distribution. The Analytic Hierarchy Process (AHP) was employed to assign weights to these criteria, which were integrated through a weighted overlay to produce a national suitability map for guiding the relocation and installation of weather stations.
Results indicated that 17.8% of Rwanda’s land area (4,487.08 km²) is highly suitable for agrometeorological station placement, represented by 13,146 polygons. Slope and proximity to water bodies were identified as the most influential factors affecting suitability. The analysis further revealed that several existing stations are suboptimally located due to steep terrain, closeness to roads or rivers, and nearby obstructions that may compromise data accuracy. The generated suitability map facilitated a spatial gap analysis, identifying underserved area and supporting recommendations for the relocation of four existing stations and the establishment of three new stations. These interventions are expected to enhance network coverage, data representativeness, and the overall reliability of Rwanda’s agrometeorological monitoring system.
This study provides a replicable methodology for optimizing station placement and offers a strategic framework to guide future investments in meteorological infrastructure, thereby supporting climate-smart agriculture and national food security initiatives.
