Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale

Including the dynamic relationship between climatic variables and leaf area index in a hydrological model to improve streamflow prediction under a changing climate

Abstract. Anthropogenic climate change is projected to enrich the atmosphere with carbon dioxide, change vegetation dynamics and influence the availability of water at the catchment scale. This study combines a nonlinear model for estimating changes in leaf area index (LAI) due to climatic fluctuations with the variable infiltration capacity (VIC) hydrological model to improve catchment streamflow prediction under a changing climate. The combined model was applied to 13 gauged sub-catchments with different land cover types (crop, pasture and tree) in the Goulburn–Broken catchment, Australia, for the "Millennium Drought" (1997–2009) relative to the period 1983–1995, and for two future periods (2021–2050 and 2071–2100) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5) which were compared with the baseline historical period of 1981–2010. This region was projected to be warmer and mostly drier in the future as predicted by 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) runs from 15 global climate models (GCMs) and for two emission scenarios. The results showed that during the Millennium Drought there was about a 29.7–66.3 % reduction in mean annual runoff due to reduced precipitation and increased temperature. When drought-induced changes in LAI were included, smaller reductions in mean annual runoff of between 29.3 and 61.4 % were predicted. The proportional increase in runoff due to modeling LAI was 1.3–10.2 % relative to not including LAI. For projected climate change under the RCP4.5 emission scenario, ignoring the LAI response to changing climate could lead to a further reduction in mean annual runoff of between 2.3 and 27.7 % in the near-term (2021–2050) and 2.3 to 23.1 % later in the century (2071–2100) relative to modeling the dynamic response of LAI to precipitation and temperature changes. Similar results (near-term 2.5–25.9 % and end of century 2.6–24.2 %) were found for climate change under the RCP8.5 emission scenario. Incorporating climate-induced changes in LAI in the VIC model reduced the projected declines in streamflow and confirms the importance of including the effects of changes in LAI in future projections of streamflow