Statistics

Spatial dependence

Ecology

Electronic engineering

Engineering

Artificial intelligence

Sensitivity (control systems)

Remote sensing

Temporal resolution

Physics

Hydrological modelling

Geology

Meteorology

Mathematics

Biology

Geography

Quantum mechanics

Spatial ecology

Computer science

Climatology

Computer graphics (images)

Animation

Interpolation (computer graphics)

Image resolution

Environmental science

Surface runoff

Hydrology and earth system sciences

Abstract. Rainfall is the most important input for rainfall–runoff models. It is usually measured at specific sites on a daily or sub-daily timescale and requires interpolation for further application. This study aims to evaluate whether a higher temporal and spatial resolution of rainfall can lead to improved model performance. Four different gridded hourly and daily rainfall datasets with a spatial resolution of 1 km × 1 km for the state of Baden-Württemberg in Germany were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of sub-daily stations. Lumped and spatially distributed HBV models were used to investigate the sensitivity of model performance to the spatial resolution of rainfall. The four different rainfall datasets were used to drive both lumped and distributed HBV models to simulate daily discharges in four catchments. The main findings include that (1) a higher temporal resolution of rainfall improves the model performance if the station density is high; (2) a combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement in the tested models; and (3) for the present research, the increase in spatial resolution improves the performance of the model insubstantially or only marginally in most of the study catchments.

Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data