Distributed hydrologic modeling of a sparsely monitored basin in Sardinia, Italy, through hydrometeorological downscaling

Abstract

Abstract. The water resources and hydrologic extremes in Mediterranean basins are heavily influenced by climate variability. Modeling these watersheds is difficult due to the complex nature of the hydrologic response as well as the sparseness of hydrometeorological observations. In this work, we present a strategy to calibrate a distributed hydrologic model, known as TIN-based Real-time Integrated Basin Simulator (tRIBS), in the Rio Mannu basin (RMB), a medium-sized watershed (472.5 km 2 ) located in an agricultural area in Sardinia, Italy. In the RMB, precipitation, streamflow and meteorological data were collected within different historical periods and at diverse temporal resolutions. We designed two statistical tools for downscaling precipitation and potential evapotranspiration data to create the hourly, high-resolution forcing for the hydrologic model from daily records. Despite the presence of several sources of uncertainty in the observations and model parameterization, the use of the disaggregated forcing led to good calibration and validation performances for the tRIBS model, when daily discharge observations were available. The methodology proposed here can be also used to disaggregate outputs of climate models and conduct high-resolution hydrologic simulations with the goal of quantifying the impacts of climate change on water resources and the frequency of hydrologic extremes within medium-sized basins.

Quantification of hydrologic impacts of climate change in a Mediterranean basin in Sardinia, Italy, through high-resolution simulations

“…The root mean square error (RMSE) and bias between the observed MAP and the ensemble average from the downscaling model were then calculated. As reported in Mascaro et al (2013a ; Table 7), the RMSE has little interannual variability (average value of 4.38 mm), while the bias is negative (mean of −0.89 mm), indicating that the downscaling procedure tends to slightly underestimate the observed MAP (less than 10 %).…”

“…First, the model capability to capture the small-scale rainfall distribution within the coarse-scale domain was evaluated by visually comparing observed and synthetic empirical cumulative distribution functions for each rainfall event. An example of this comparison is provided in Mascaro et al (2013a;Fig. 6), which shows relatively good skill of the downscaling routine.…”

“…For this aim, four bias-corrected climate forcings are first set up for a reference and a future period, using the best-performing combinations of global (GCM) and regional (RCM) climate models selected by Deidda et al (2013). These climate forcings are used as input for the TIN-based Real-time Integrated Basin Simulator (tRIBS) hydrologic model, which was calibrated and validated with reasonable accuracy as illustrated in a previous study by Mascaro et al (2013a). Since climate model outputs are provided at coarse spatial (∼ 25 km) and temporal (daily) scales while the hydrologic model requires hourly data, proper downscaling tools are applied to increase their spatiotemporal resolution (up to 5 km, 1 h).…”

“…This is achieved by means of a stochastic generator of multiplicative multifractal cascades, whose parameters can be derived from the large-scale rainfall amount, R (mm h −1 ), according to empirical calibration relations. For the RMB, Mascaro et al (2013a) calibrated the algorithm with rainfall observations at 1 min resolution of 204 gages, collected in the period 1986-1996 in the coarse spatial domain of 104 × 104 km 2 shown in Fig. 1b.…”

“…A total of 800 precipitation events were used to estimate the model parameters and identify the calibration relations as a function of R for our study area. As described in detail in Mascaro et al (2013a), two tests were conducted to validate the downscaling method. First, the model capability to capture the small-scale rainfall distribution within the coarse-scale domain was evaluated by visually comparing observed and synthetic empirical cumulative distribution functions for each rainfall event.…”