Runoff regime estimation at high-elevation sites: a parsimonious water balance approach

摘要

We develop a water balance model, parsimonious both in terms ofparameterization and of required input data, to characterize theaverage runoff regime of high-elevation and scarcely monitoredbasins. The model uses a temperature threshold to partitionprecipitation into rainfall and snowfall, and to estimateevapotranspiration volumes. The role of snow in the transformationof precipitation into runoff is investigated at the monthly timescale through a specific snowmelt module that estimates meltedquantities by a non-linear function of temperature. Aprobabilistic representation of temperature is also introduced, inorder to mimic its sub-monthly variability. To account for thecommonly reported rainfall underestimation at high elevations, atwo-step precipitation adjustment procedure isimplemented to guarantee the closure of the water balance.The model is applied to a group of catchments in the North-WesternItalian Alps, and its performances are assessed by comparingmeasured and simulated runoff regimes both in terms of total biasand anomalies, by means of a new metric, specifically conceived tocompare the shape of the two curves. The obtained resultsindicates that the model is able to predict the observed runoffseasonality satisfactorily, notwithstanding its parsimony (themodel has only two parameters to be estimated). In particular,when the parameter calibration is performed separately for eachbasin, the model proves to be able to reproduce the runoffseasonality. At the regional scale (i.e., with uniform parametersfor the whole region), the performance is less positive, but themodel is still able to discern among different mechanisms ofrunoff formation that depend on the role of the snow storage.Because of its parsimony and the robustness in the approach, themodel is suitable for application in ungauged basins and for largescale investigations of the role of climatic variables on wateravailability and runoff timing in mountainous regions.