Corrado Camera

Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980–2010

High-resolution gridded daily data sets are essential for natural resource management and the analyses of climate changes and their effects. This study aims to evaluate the performance of 15 simple or complex interpolation techniques in reproducing daily precipitation at a resolution of 1 km2 over topographically complex areas. Methods are tested considering two different sets of observation densities and different rainfall amounts. We used rainfall data that were recorded at 74 and 145 observational stations, respectively, spread over the 5760 km2 of the Republic of Cyprus, in the Eastern Mediterranean. Regression analyses utilizing geographical copredictors and neighboring interpolation techniques were evaluated both in isolation and combined. Linear multiple regression (LMR) and geographically weighted regression methods (GWR) were tested. These included a step-wise selection of covariables, as well as inverse distance weighting (IDW), kriging, and 3D-thin plate splines (TPS). The relative rank of the different techniques changes with different station density and rainfall amounts. Our results indicate that TPS performs well for low station density and large-scale events and also when coupled with regression models. It performs poorly for high station density. The opposite is observed when using IDW. Simple IDW performs best for local events, while a combination of step-wise GWR and IDW proves to be the best method for large-scale events and high station density. This study indicates that the use of step-wise regression with a variable set of geographic parameters can improve the interpolation of large-scale events because it facilitates the representation of local climate dynamics.

Physically based dynamic run-out modelling for quantitative debris flow risk assessment: a case study in Tresenda, northern Italy

Quantitative landslide risk assessment requires information about the temporal, spatial and intensity probability of hazardous processes both regarding their initiation as well as their run-out. This is followed by an estimation of the physical consequences inflicted by the hazard, preferentially quantified in monetary values. For that purpose, deterministic hazard modelling has to be coupled with information about the value of the elements at risk and their vulnerability. Dynamic run-out models for debris flows are able to determine physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk can suffer an impact. These results can then be applied for vulnerability and risk calculations. Debris flow risk has been assessed in the area of Tresenda in the Valtellina Valley (Lombardy Region, northern Italy). Three quantitative hazard scenarios for different return periods were prepared using available rainfall and geotechnical data. The numerical model FLO-2D was applied for the simulation of the debris flow propagation. The modelled hazard scenarios were consequently overlaid with the elements at risk, represented as building footprints. The expected physical damage to the buildings was estimated using vulnerability functions based on flow depth and impact pressure. A qualitative correlation between physical vulnerability and human losses was also proposed. To assess the uncertainties inherent in the analysis, six risk curves were obtained based on the maximum, average and minimum values and direct economic losses to the buildings were estimated, in the range of 0.25–7.7 million €, depending on the hazard scenario and vulnerability curve used.

High-Resolution Simulations of Recent Past Extreme Precipitation Events Over Cyprus

Besides global warming, climate change is expected to influence precipitation amounts and distribution. While global climate projections typically address the long-term, and weather forecasts the short to medium range up to weeks, decision-makers and stakeholders also need guidance on inter-annual to decadal time scales. In this context, the BINGO H2020 project aims both at reducing the uncertainty of near-term climate predictions and developing response strategies. One of the main objectives is to provide decadal predictions with a specific focus on extreme events. The projected precipitation distribution will eventually drive hydrological impact models. In this study we present the dynamical downscaling of the ERA-Interim (EI) dataset for validation purposes. Extreme rainfall periods were identified and simulated in very high horizontal resolution (up to 4 km) using the WRF model. In a later stage, future periods of extreme precipitation or droughts will be identified from the output of the MiKlip decadal prediction system and will be downscaled in order to assess the climate change impact on water resources in Cyprus. Our simulations seem to capture reasonably well rainfall during an extreme event (November 2014) over the eastern Mediterranean. It is also found to improve the EI precipitation that was found to be underestimated.

Impact of precipitation variability on the performance of a rainfall–runoff model in Mediterranean mountain catchments

ABSTRACT Losses in performance of a low-dimensional rainfall–runoff model resulting from changes in the annual precipitation are assessed for five catchments located in a mountainous Mediterranean environment, the Troodos Mountains, Cyprus. For an increase of 25% or a decrease of 15% in the cumulative precipitation depth of a five-year period, model performance losses become significant: the model robustness criterion reaches 13% and simulated cumulative runoff is under- or overestimated by up to 20%. However, only the 25% decrease in the precipitation depth influences the ability of the model to predict runoff dynamics (10% decrease in the Nash-Sutcliffe coefficient). This indicates the occurrence of either non-stationary effects or temporary secondary hydrological processes, which tend to amplify the decrease (or increase) in runoff production during the drier (or wetter) period. In addition, losses in model performance are analysed between one catchment and another. High performance losses despite close physical catchment properties show the poor transferability of model parameters in this environment. Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei