Regional climate sensitivity of wetland environments in Rwanda: the need for a location-specific approach

Abstract

Wetlands are sustaining large communities of people in Rwanda where 10 % of its land surface consists of many local wetlands. Sustainable future management of these numerous wetlands requires a reliable inventory of their location and a dynamic quantitative characterization that allows assessment of their climate change sensitivity. The aim of this study was to assess the importance of climatic factors for determining wetland location at different regional scales. Wetland locations were analyzed and statistically modeled using their location factors with logistic regression. Wetland location probability was determined using topographic (elevation, slope), hydrological (contributing area) and climatic (temperature and rainfall) location factors. A wetland location probability map was made that demonstrated a calibration accuracy of 87.9 % correct at national level compared to an existing inventory, displaying even better fits at subnational level (reaching up to 98 % correct). A validation accuracy of 86.2 % was obtained using an independently collected dataset. A sensitivity analysis was applied to the threshold values used as cutoff value between wetland/non-wetland, demonstrating a robust performance. The developed models were used in a sensitivity scenario analysis to assess future wetland location probability to changes in temperature and rainfall. In particular, wetlands in the central regions of Rwanda demonstrate a high sensitivity to changes in temperature (1 % increase causes a net probable wetland area decline by 12.4 %) and rainfall (?1 % causes a net increase by 1.6 %). This potentially significant impact on wetland number and location probability indicates that climate-sensitive future planning of wetland use is required in Rwanda.

Sixty percent of small coffee farms have suitable socio-economic and environmental locations in Rwanda

“…Coffee competes with food crops for the limited land available. Agricultural land (including coffee) take up 54.2 % of the total country territory, the remaining 45.8 % comprise of forest (28.8 %), wetlands and water bodies (11 %) (Nyandwi et al 2016) and urban areas (6 %). As with other cropping systems, the spatial distribution of coffee is a function of environmental drivers (soil, climate and topography) and human drivers (demography, infrastructure, technology and institutions) Veldkamp & Fresco 1997;Verbist et al 2005;Hernandez-Martinez et al 2009).…”

“…Both approaches did not include neither socio-economic factors nor the potential dynamic climate factors making both approach not sensitive for the rapid changing Rwandan socio-economic conditions and the expected climate change. The later can be significant in the nearby future as illustrated by Nyandwi et al (2016) who demonstrated that an increase of 1 % in annual temperature can cause a net wetland area decline by 12 %, making these areas suitable for other land use. It is our objective to analyse the spatial coffee field distribution using the new high resolution coffee map (Mukashema et al 2014) and using available biophysical and socioeconomic factors that co-determine coffee cultivation locations in Rwanda.…”

Smallholder coffee terroirs of Rwanda : linking local to global trade through geographical characterization

“…Spatial variables used for mapping coffee fields are the average diameter of the tree crown and the minimum size of the field. The diameter of the coffee tree crown is set to 2 m based on our knowledge of the coffee trees and their spacing across Rwanda whereas the size of the field is set to a minimum of 200 coffee trees following Nsengiyumva (2009) giving it the same minimum criterion as used in the national coffee census. Land use and topography variables were also considered to optimize the location of coffee within the landscape.…”

“…(6) Reported coffee areas by the national coffee census of 2009. The census was conducted in all 30 districts of Rwanda by OCIR-CAFÉ together with Statistics Officers at the district and sector level (Nsengiyumva, 2009). …”

“…Coffee tree probability output is aggregated into a coffee field probability. We set the threshold to 3200 pixels corresponding to the average minimum field size of 200 trees as reported by (Nsengiyumva, 2009). The field size rule therefore filters out coffee fields that are below the threshold and recomputed the new probability of coffee field object in the conditional probability attribute table.…”