Search

The hydrological response to climate change in mountainous basins manifests itself at varying spatial and temporal scales, ranging from catchment to large river basin scale and from sub-daily to decade and century scale. To robustly assess the 21st century climate change impact for hydrology in entire High Mountain Asia (HMA) at a wide range of scales, we use a high resolution cryospheric-hydrological model covering 15 upstream HMA basins to quantify the compound effects of future changes in precipitation and temperature based on the range of climate change projections in the Coupled Model Intercomparison Project Phase 6 climate model ensemble. Our analysis reveals contrasting responses for HMA's rivers, dictated by their hydrological regimes. At the seasonal scale, the earlier onset of melting causes a shift in the magnitude and peak of water availability, to earlier in the year. At the decade to century scale, after an initial increase, the glacier melt declines by the mid or end of the century except for the Tarim river basin, where it continues to increase. Despite a large variability in hydrological regimes across HMA's rivers, our results indicate relatively consistent climate change responses across HMA in terms of total water availability at decadal time scales. Although total water availability increases for the headwaters, changes in seasonality and magnitude may diverge widely between basins and need to be addressed while adapting to future changes in a region where food security, energy security as well as biodiversity, and the livelihoods of many depend on water from HMA.

Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.

Multiple states of woody cover under similar climate conditions are found in both conceptual models and observations. Due to the limitation of the observed woody cover data set, it is unclear whether the observed bimodality is caused by the presence of multiple stable states or is due to dynamic growth processes of vegetation. In this study, we combine a woody cover data set with an above ground biomass data set to investigate the simultaneous occurrences of savanna and forest states under different precipitation forcing. To interpret the results we use a recently developed vegetation dynamics model (the Balanced Optimality Structure Vegetation Model), in which the effect of fires is included. Our results show that bimodality also exists in above ground biomass and retrieved vegetation structure. In addition, the observed savanna distribution can be understood as derived from a stable state and a slightly drifting (transient) state, the latter having the potential to shift to the forest state. Finally, the results indicate that vegetation structure (horizontal vs. vertical leaf extent) is a crucial component for the existence of bimodality.