Roberto Deidda

Rainfall downscaling in a space‐time multifractal framework

A space-time multifractal analysis on radar rainfall sequences selected from the Global Atmospheric Research Program Atlantic Tropical Experiment database is presented. It is shown that space-time rainfall can be considered with a good approximation to be a self-similar multifractal process, so that a multifractal analysis can be carried out assuming Taylors hypothesis to hold for rainfall over a wide range of spatial and temporal scales. The advection velocity needed to rescale the time dimension is estimated using different tracking techniques. On each selected rainfall sequence, a very good scaling is observed for spatial scales ranging from 4 to 256 km and for timescales from 15 min to 16 hours. A recently developed scale-covariant multifractal model is then reformulated for numerical simulation of space-time rainfall fields. The two parameters of the log-Poisson distribution used as cascade generator within the model are systematically estimated from each selected rainfall sequence, and the dependence of one of these parameters on the large-scale rain rate is highlighted. The model is then applied to disaggregate large-scale rainfall, and some comparisons between synthetically downscaled and observed rainfall are discussed.

Multifractal modeling of anomalous scaling laws in rainfall

The coupling of hydrological distributed models to numerical weather prediction outputs is an important issue for hydrological applications such as forecasting of flood events. Downscaling meteorological predictions to the hydrological scales requires the resolution of two fundamental issues regarding precipitation, namely, (1) understanding the statistical properties and scaling laws of rainfall fields and (2) validation of downscaling models that are able to preserve statistical characteristics observed in real precipitation. In this paper we discuss the first issue by introducing a new multifractal model that appears particularly suitable for random generation of synthetic rainfall. We argue that the results presented in this paper may be also useful for the solution of the second question. Statistical behavior of rainfall in time is investigated through a high- resolution time series recorded in Genova (Italy). The multifractal analysis shows the presence of a temporal threshold, localized around 15-20 hours, which separates two ranges of anomalous scaling laws. Synthetic time series, characterized by very similar scaling laws to the observed one, are generated with the multifractal model. The potential of the model for extreme rainfall event distributions is also discussed. The multifractal analysis of Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE) radar fields have shown that statistical properties of rainfall in space depend on time durations over which precipitation is accumulated. Further analysis of some rainfall fields produced with a meteorological limited area model exhibited the same anomalous scaling as the GATE fields.

Impact of climate change and water use policies on hydropower potential in the south-eastern Alpine region

Climate change is expected to cause alterations of streamflow regimes in the Alpine region, with possible relevant consequences for several socio-economic sectors including hydropower production. The impact of climate change on water resources and hydropower production is evaluated with reference to the Noce catchment, which is located in the Southeastern Alps, Italy. Projected changes of precipitation and temperature, derived from an ensemble of 4 climate model (CM) runs for the period 2040-2070 under the SRES A1B emission scenario, have been downscaled and bias corrected before using them as climatic forcing in a hydrological model. Projections indicate an increase of the mean temperature of the catchment in the range 2-4K, depending on the climate model used. Projections of precipitation indicate an increase of annual precipitation in the range between 2% and 6% with larger changes in winter and autumn. Hydrological simulations show an increase of water yield during the period 2040-2070 with respect to 1970-2000. Furthermore, a transition from glacio-nival to nival regime is projected for the catchment. Hydrological regime is expected to change as a consequence of less winter precipitation falling as snow and anticipated melting in spring, with the runoff peak decreasing in intensity and anticipating from July to June. Changes in water availability reflect in the Technical Hydropower Potential (THP) of the catchment, with larger changes projected for the hydropower plants located at the highest altitudes. Finally, the impacts on THP of water use policies such as the introduction of prescriptions for minimum ecological flow (MEF) have been analyzed. Simulations indicate that in the lower part of the catchment reduction of the hydropower production due to MEF releases from the storage reservoirs counterbalances the benefits associated to the projected increases of inflows as foreseen by simulations driven only by climate change.

Space‐time scaling in high‐intensity Tropical Ocean Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA‐COARE) storms

Received 8 August 2003; accepted 23 October 2003; published 17 February 2004. [1] A scale-invariance analysis of rainfall retrieved during the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE) campaign is discussed. As already found in the previous Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE) rainfall data set, these new analyses of high-intensity storms confirm the evidence of scale invariance under selfsimilar space-time transformations. A simple interpretation of this space-time selfsimilarity accounting for the hierarchical organization of precipitation patterns is proposed. Finally, a downscaling model based on a log-Poisson generator is calibrated on the results of the multifractal analysis and applied to the generation of synthetic fields, reproducing observed statistical properties over a wide range of space scales and timescales. INDEX TERMS: 1854 Hydrology: Precipitation (3354); 3250 Mathematical Geophysics: Fractals and multifractals; 3354 Meteorology and Atmospheric Dynamics: Precipitation (1854); 1869 Hydrology: Stochastic processes; KEYWORDS: rainfall, scaling processes Citation: Deidda, R., M. G. Badas, and E. Piga (2004), Space-time scaling in high-intensity Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE) storms, Water Resour. Res., 40, W02506,

Multifractal analysis and simulation of rainfall fields in space

Abstract Statistical downscaling of precipitation from the large scales of meteorological models to the characteristic response scales of small catchment basins needs to correctly preserve the anomalous scaling laws observed in real rainfall. Multifractal behaviour of precipitation in space is investigated on a set of rainfall fields obtained by a high resolution simulation with a limited area model for numerical weather prediction and on two sets of radar measures of it rainfall during the GATE campaign. Some sets of synthetic rainfall fields were generated applying a multifractal model based on a wavelet expansion with coefficients extracted by a log-Poisson random cascade, and results of comparisons with the GATE rainfall fields are presented.

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting

Abstract Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal rainfall downscaling model is coupled with a fully distributed hydrological model to create, under controlled conditions, an extensive set of synthetic hydrometeorological events, assumed as observations. Subsequently, for each event, flood hindcasts are simulated by the hydrological model using three ensembles of QPFs—one reliable and the other two affected by different kinds of precipitation forecast errors—generated by the downscaling model. Two verification tools based on the verification rank histogram and the continuous ranked probability score are then used to evaluate the characteristics of the correspondent three sets of ensemble streamflow forecasts. Analyses indicate that the best forecast accuracy of the ensemble streamflows is obtained when the r...

A simple approximation to multifractal rainfall maxima using a generalized extreme value distribution model

Among different approaches that have been proposed to explain the scaling structure of temporal rainfall, a significant body belongs to models based on sequences of independent pulses with internal multifractal structure. Based on a standard asymptotic result from extreme value theory, annual rainfall maxima are typically modelled using a generalized extreme value (GEV) distribution. However, multifractal rainfall maxima converge slowly to a GEV shape, with important shape-parameter estimation issues, especially from short samples. The present work uses results from multifractal theory to propose a solution to the GEV shape-parameter estimation problem, based on an iterative numerical procedure.

Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall downscaling, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed downscaling schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the ENSEMBLES project. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima.

Impacts of climate change on precipitation and discharge extremes through the use of statistical downscaling approaches in a Mediterranean basin.

Mediterranean region is characterized by high precipitation variability often enhanced by orography, with strong seasonality and large inter-annual fluctuations, and by high heterogeneity of terrain and land surface properties. As a consequence, catchments in this area are often prone to the occurrence of hydrometeorological extremes, including storms, floods and flash-floods. A number of climate studies focused in the Mediterranean region predict that extreme events will occur with higher intensity and frequency, thus requiring further analyses to assess their effect at the land surface, particularly in small- and medium-sized watersheds. In this study, climate and hydrologic simulations produced within the Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB) EU FP7 research project were used to analyze how precipitation extremes propagate into discharge extremes in the Rio Mannu basin (472.5km(2)), located in Sardinia, Italy. The basin hydrologic response to climate forcings in a reference (1971-2000) and a future (2041-2070) period was simulated through the combined use of a set of global and regional climate models, statistical downscaling techniques, and a process based distributed hydrologic model. We analyzed and compared the distribution of annual maxima extracted from hourly and daily precipitation and peak discharge time series, simulated by the hydrologic model under climate forcing. For this aim, yearly maxima were fit by the Generalized Extreme Value (GEV) distribution using a regional approach. Next, we discussed commonality and contrasting behaviors of precipitation and discharge maxima distributions to better understand how hydrological transformations impact propagation of extremes. Finally, we show how rainfall statistical downscaling algorithms produce more reliable forcings for hydrological models than coarse climate model outputs.

Threshold detection for the generalized Pareto distribution: Review of representative methods and application to the NOAA NCDC daily rainfall database

In extreme excess modeling, one fits a generalized Pareto (GP) distribution to rainfall excesses above a properly selected threshold u. The latter is generally determined using various approaches, such as nonparametric methods that are intended to locate the changing point between extreme and nonextreme regions of the data, graphical methods where one studies the dependence of GP-related metrics on the threshold level u, and Goodness-of-Fit (GoF) metrics that, for a certain level of significance, locate the lowest threshold u that a GP distribution model is applicable. Here we review representative methods for GP threshold detection, discuss fundamental differences in their theoretical bases, and apply them to 1714 overcentennial daily rainfall records from the NOAA-NCDC database. We find that nonparametric methods are generally not reliable, while methods that are based on GP asymptotic properties lead to unrealistically high threshold and shape parameter estimates. The latter is justified by theoretical arguments, and it is especially the case in rainfall applications, where the shape parameter of the GP distribution is low; i.e., on the order of 0.1–0.2. Better performance is demonstrated by graphical methods and GoF metrics that rely on preasymptotic properties of the GP distribution. For daily rainfall, we find that GP threshold estimates range between 2 and 12 mm/d with a mean value of 6.5 mm/d, while the existence of quantization in the empirical records, as well as variations in their size, constitute the two most important factors that may significantly affect the accuracy of the obtained results.