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Estimates of present and future extreme sub-hourly rainfall are derived from a daily spatial followed by a sub-daily temporal downscaling, the latter of which incorporates a novel, and crucial, temperature sensitivity. Specifically, daily global climate fields are spatially downscaled to local temperature T and precipitation P, which are then disaggregated to a temporal resolution of 10 min using a multiplicative random cascade model. The scheme is calibrated and validated with a group of 21 station records of 10-min resolution in Germany. The cascade model is used in the classical (denoted as MC) and in the new T-sensitive (MC+) version, which respects local Clausius-Clapeyron (CC) effects such as CC scaling. Extreme P is positively biased in both MC versions. Observed T sensitivity is absent in MC but well reproduced by MC+. Long-term positive trends in extreme sub-hourly P are generally more pronounced and more significant in MC+ than in MC. In units of 10-min rainfall, observed centennial trends in annual exceedance counts (EC) of P > 5 mm are +29% and in 3-yr return levels (RL) +27%. For the RCP4.5-simulated future, higher extremes are projected in both versions MC and MC+: per century, EC increases by 30% for MC and by 83% for MC+; the RL rises by 14% for MC and by 33% for MC+. Because the projected daily P trends are negligible, the sub-daily signal is mainly driven by local temperature.
Niche-based species distribution models (SDMs) play a central role in studying species response to environmental change. Effective management and conservation plans for freshwater ecosystems require SDMs that accommodate hierarchical catchment ordering and provide clarity on the performance of such models across multiple scales. The scale-dependence components considered here are: (a) environment spatial structure, represented by hierarchical catchment ordering following the Strahler system; (b) analysis grain, that included 1st to 5th order catchments; and (c) response grain, the grain at which species respond most, represented by local and upstream catchment area effects. We used fish occurrence data from the Danube River Basin and various factors representing climate, land cover and anthropogenic pressures. Our results indicate that the choice of response grain local vs. upstream area effects and the choice of analysis grain, only marginally influence the performance of SDMs. Upstream effects tend to better predict fish distributions than corresponding local effects for anthropogenic and land cover factors, in particular for species sensitive to pollution. Key predictors and their relative importance are scale and species dependent. Consequently, choosing proper species dependent spatial scales and factors is imperative for effective river rehabilitation measures.