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. One of the main purposes of detrital thermochronology is to provideconstraints on the regional-scale exhumation rate and its spatial variability inactively eroding mountain ranges. Procedures that use cooling agedistributions coupled with hypsometry and thermal models have been developedin order to extract quantitative estimates of erosion rate and its spatialdistribution, assuming steady state between tectonic uplift and erosion. Thishypothesis precludes the use of these procedures to assess the likelytransient response of mountain belts to changes in tectonic or climaticforcing. Other methods are based on an a priori knowledge of the in situdistribution of ages to interpret the detrital age distributions. In thispaper, we describe a simple method that, using the observed detrital mineralage distributions collected along a river, allows us to extract informationabout the relative distribution of erosion rates in an eroding catchmentwithout relying on a steady-state assumption, the value of thermalparameters or an a priori knowledge of in situ age distributions. The modelis based on a relatively low number of parameters describing lithologicalvariability among the various sub-catchments and their sizes and only usesthe raw ages. The method we propose is tested against synthetic agedistributions to demonstrate its accuracy and the optimum conditions for ituse. In order to illustrate the method, we invert age distributions collectedalong the main trunk of the Tsangpo–Siang–Brahmaputra river system in theeastern Himalaya. From the inversion of the cooling age distributions wepredict present-day erosion rates of the catchments along theTsangpo–Siang–Brahmaputra river system, as well as some of its tributaries.We show that detrital age distributions contain dual information aboutpresent-day erosion rate, i.e., from the predicted distribution of surfaceages within each catchment and from the relative contribution of any givencatchment to the river distribution. The method additionally allows comparingmodern erosion rates to long-term exhumation rates. We provide a simpleimplementation of the method in Python code within a Jupyter Notebook thatincludes the data used in this paper for illustration purposes.

Extracting information on the spatial variability in erosion rate stored in detrital cooling age distributions in river sands