Streamflow seasonality in a snow-dwindling world.

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2-4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5-9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950-2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the 'less snow equals earlier streamflow' heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

A harmonized global gridded transpiration product based on collocation analysis.

Transpiration (T) is pivotal in the global water cycle, responding to soil moisture, atmospheric stress, climate changes, and human impacts. Therefore, establishing a reliable global transpiration dataset is essential. Collocation analysis methods have been proven effective for assessing the errors in these products, which can subsequently be used for multisource fusion. However, previous results did not consider error cross-correlation, rendering the results less reliable. In this study, we employ collocation analysis, taking error cross-correlation into account, to effectively analyze the errors in multiple transpiration products and merge them to obtain a more reliable dataset. The results demonstrate its superior reliability. The outcome is a long-term daily global transpiration dataset at 0.1°from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with inputs. The merged dataset performs well across various vegetation types and is validated against in-situ observations. Incorporating non-zero ECC considerations represents a significant theoretical and proven enhancement over previous methodologies that neglected such conditions, highlighting its reliability in enhancing our understanding of transpiration dynamics in a changing world.