Using a Parsimonious Rainfall-Runoff Model to Detect Non-stationarities in the Hydrological Behaviour of Watersheds

The detection of non-stationarities (trends) in the hydrological behaviour of watersheds affectedby environmental change has traditionally been achieved through the comparison of “control” (reference) and“modified” watersheds. These comparisons are probably the most efficient solution for trend detection, andare extensively documented in the hydrological literature. Outside experimental watersheds however, controlwatersheds are seldom available, and another approach is needed to assess the evolution of watershedbehaviour. In this paper, we present a methodology using a parsimonious 4-parameter rainfall-runoff model(GR4J) to detect non-stationarities. The parsimony of the model makes it relatively easy to identify stablerepresentative parameter sets over short time periods, and to quantify the calibration uncertainty for theseparameters. Using this uncertainty knowledge, we generate equi-probable parameter quadruplets forsuccessive periods of time, from which we derive through simulation a distribution of a hydrological variable(e.g. total runoff), representative of the watershed behaviour during this period. We then propose a nonparametricstatistical test to identify non-stationarities from the distributions, and we validate this test on adeforested experimental watershed.