Locally relevant and accurate climate impact assessments: A case study for Bhutan

Abstract

Understanding the impact of climate change on water resources is crucial for selecting adequate adaptation strategies at a local scale. Global meteorological re-analysis datasets are useful to evaluate current-day climate conditions and trends, as a first step in a climate change assessment in poorly gauged basins. However, these datasets often lack the level of detail to calculate meaningful climate impacts at a local scale, especially in mountainous regions, where topography and orographic effects play a crucial role on the temporal and spatial variability of climate characteristics and change. In this study, we test how the combined use of global, regional and local datasets together with fieldwork result in locally relevant climate change impact assessment. The method is applied in Bhutan, a country with large differences between hydroclimatic zones, caused by the steep topography and the occurrence of the annual Monsoon rains in the Southern half of the country. The results of this study show the large variability between different global datasets in terms of precipitation volumes. The comparison of global, regional and local meteorological datasets in combination with locally observed streamflow data suggest that the regional re-analysis dataset is the most reliable and plausible to use for the climate impact assessment. Interestingly, the two global datasets used in this study, ERA5 (Hersbach et al., 2018) and W5E5 (Lange et al., 2021), seem to either underestimate (W5E5) or overestimate (ERA5) the precipitation considerably. The regional Indian Monsoon Data Assimilation and Analysis (IMDAA, Ashrit, 2020) precipitation re-analysis dataset seems to best represent the current climate conditions in Bhutan. This conclusion was further supported during a field visit, which highlighted that the spatial variability of the precipitation was likely not well captured the local precipitation gauges, which were mostly in the valleys. As this local data is used for the bias correction of the W5E5 dataset, it is likely that W5E5 is also not representative of the spatial variability of the local climate in Bhutan. This study demonstrates the importance of local knowledge, locally observed hydrological data and fieldwork to strengthen the local and regional climate impact assessments.ReferencesAshrit, R., Indira Rani, S., Kumar, S., Karunasagar, S., Arulalan, T., Francis, T., et al. (2020). IMDAA regional reanalysis: Performance evaluation during Indian summer monsoon season. Journal of Geophysical Research: Atmospheres, 125, e2019JD030973. https://doi.org/10.1029/2019JD030973  Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on pressure levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < DD-MMM-YYYY >), https://doi.org/10.24381/cds.bd0915c6Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G.P., Amici, A., Bellouin, N., Schmied, H.M., Hersbach, H., Buontempo, C., Cagnazzo C., 2021. WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.342217