Enrica Caporali

Estimation of surface heat flux and an index of soil moisture using adjoint-state surface energy balance

Estimates of surface heat flux and an index of the surface control over evaporation are made based on radiometric observations of ground temperature. A variational data assimilation approach is used to include surface energy balance in the estimation procedure as a physical constraint (the adjoint technique). This technique formulates the estimation problem as a minimization of ground temperature forecast misfits against observations. The surface energy balance equation is incorporated as a physical constraint. Applications to the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) are presented. The procedure estimates of the surface control over latent heat flux match those values based on independent latent heat flux measurements. The estimates of surface heat flux, when compared with measurements, have a root-mean-square error of 20 W m−2. The need to discriminate between soil wetness and aerodynamic contributions to the surface control over evaporation is recognized.

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.

Investigating Interannual Variability of Precipitation at the Global Scale: Is There a Connection with Seasonality?

AbstractInterannual variability of precipitation can directly or indirectly affect many hydrological, ecological, and biogeochemical processes that, in turn, influence climate. Despite the significant importance of the phenomenon, few studies have attempted to elucidate spatial patterns of this variability at the global scale. This study uses land gauge precipitation records of the Global Historical Climatology Network, version 2, as well as reanalysis data to provide an assessment of the spatial organization of characteristics of precipitation interannual variability. The coefficient of variation, skewness, and short- and long-range dependence of the precipitation variability are analyzed. Among the major inferences is that the coefficient of variation of annual precipitation shows a significant correlation with intra-annual seasonality. Specifically, subyearly precipitation anomalies occurring in locations with pronounced seasonality affect the total yearly amount, imposing a higher variability in the a...

Uncertainty partition challenges the predictability of vital details of climate change

Decision makers and consultants are particularly interested in “detailed” information on future climate to prepare adaptation strategies and adjust design criteria. Projections of future climate at local spatial scales and fine temporal resolutions are subject to the same uncertainties as those at the global scale but the partition among uncertainty sources (emission scenarios, climate models, and internal climate variability) remains largely unquantified. At the local scale the uncertainty of the mean and extremes of precipitation is shown to be irreducible for mid and end-of-century projections because it is almost entirely due to internal climate variability (stochasticity). Conversely, projected changes in mean air temperature and other meteorological variables can be largely constrained, even at local scales, if more accurate emission scenarios can be developed. The results were obtained by applying a comprehensive stochastic downscaling technique to climate model outputs for three exemplary locations. In contrast with earlier studies, the three sources of uncertainty are considered as dependent and, therefore, non-additive. The evidence of the predominant role of internal climate variability leaves little room for uncertainty reduction in precipitation projections; however, the inference is not necessarily negative, since the uncertainty of historic observations is almost as large as that for future projections with direct implications for climate change adaptation measures.

Definition of Risk Indicators for Reservoirs Management Optimization

An alternative procedure for drought risk assessment and for the mitigation of drought risk is proposed in the paper. An analysis of the relationship between failure of water supply systems and reservoir volumes for the urban area of Firenze in central Tuscany, in central Italy, is performed. Long term simulations are carried out using the software package WEAP. A simplified model of the water resources system is built to assess the threshold values and the management rules. The probability to have definite degree of shortage in the water supply system is evaluated in function of the volume stored in the reservoir at the beginning of the month with Monte Carlo simulations. The reservoir levels and volumes are simulated using time series of the period 1970–2005. Four scenarios (i.e. normal, pre-alert, alert and emergency) associated with different levels of severity of drought are defined. Threshold values are identified considering the probability to assure a given fraction of the demand in a certain time horizon, and are calibrated with an optimization method, which try to minimize the water shortages, especially the heavier. The critical situations are assessed month by month in order to evaluate optimal management rules during the year and avoid conditions of total water shortage.

Hydrometeorology of Flash Floods

Flash floods are phenomena in which the important hydrologic processes are occurring on the same spatial and temporal scales as the intense precipitation. To date, the time required for appropriate public response has typically been much longer than the time between the causative precipitation and the subsequent flash flood. The impact of a flash flood is primarily related to the sudden increase in level and velocity of floodwater, rather than the peak level and velocity, or the final duration and extent of the floodwater. The impact of rapidly evolving precipitation systems on the complex hydrologic processes of fast-response basins makes the flash flood phenomenon a particularly challenging forecast problem.

Hydrological control of flooding: Tuscany, October 1992

In the study of flash-flood occurrence in small catchments the lack of flow measurements is often one of the main limiting factors. Prior to estimating the forecasting potentialities and techniques for such events, an accurate reconstruction of past event flood dynamics is first required. This issue is here addressed by analyzing, with the use of a distributed hydrological model, the hydrometeorological conditions in which a severe flash-flood occurred, on October 1992, on a 48 square kilometers catchment in the Arno basin. Such an event was caused by the persistence of intense convective clusters on the background of widespread rain bands of frontal origin. The distributed hydrological model here adopted is devoted to simulate the evolution and the variability of the primary processes involved in the runoff cycle. Together with the hydrological model structure, other particular aspects of the event reconstruction procedure are discussed: the managing and processing of the information coming from different sensors, with different temporal and spatial resolutions; the identification of local precipitation dynamics (frontal or convective) within small areas of integrated radar and rain gauges data fields; the interpolation of rain gauge data on the basis of the radar-estimated spatial correlation. The results of the distributed modeling, concerning the estimate of the flood wave at various sites, are compared with analogous results obtained with simpler lumped models.

Hydrological Response to Radar Rainfall Maps through a Distributed Model

Weather radars in investigating physical characteristics of precipitation are becoming essential instruments in the field of short term meteorological investigation and forecasting. To analyze the radar signal impact in hydrological forecasting, precipitation input fields, generated through a statistical mathematical model, are supplied to a distributed hydrological model. Such a model would allow the control of the basin response to precipitation measurements obtained by a meteorological radar and, in the meanwhile, to evaluate the influence of distributed input. The distributed model describes the basin hydrological behavior, subdividing it into distinct geometrical cells and increasing the physical significance by reproducing the distributed hydrographic basins characteristics, such as infiltration capacity, runoff concentration time, network propagation speed, soil moisture influence. Each basin cell is characterized by its geological, pedological and morphological status, and may be considered a unitary hydrological system, linked to the others by geomorphological and hydraulic relationships. To evaluate the dynamics of the flood event a synthetic representation of the channel network is introduced, where each stream branch is modeled as a linear reservoir. Finally, the discharge in the outlet section is derived, taking into account the hydraulic characteristics of the upstream branches.

Analysis of radar and raingauge measurements for a critical meteorological event in Tuscany

In this paper, some considerations are given to the employment of C-band polarimetric weather radars for rainfall estimates. The most common error sources are discussed, such as ground clutter and propagation attenuation effects, together with decorrelation in the sampling at the ground between radar and raingauge measurements, which can be quite significant in radar systems located in hilly regions, as is the case of the Arno basin in Tuscany. Since the main objective from a hydrological point of view is the estimate of rainfall at ground, integrations and comparisons are needed between radar and raingauge data, which are characterized by different time and space sampling. The paper is then focussed mainly on this problem and a technique is presented in order to improve radar based rainfall estimates through the integration with raingauge data, in order to enhance the correlation between the two types of measurements. Such a method is finally applied to a serious meteorological event which affected the Arno basin on October 1992.

Application of Landsat TM data to evaluate soil hydrological status in the Arno basin, Italy: preliminary results

Remote sensing can be a very interesting source of distributed data for large or medium scale hydrological modeling, where soil status and land conditions can be extremely different from one zone to another and a large amount of in-situ measurement would be necessary. In this study two Landsat TM images of the lower part of the Arno basin (Tuscany, Italy) taken in 1991 have been processed using several techniques. Cluster analysis gave interesting results in monitoring the state of soil and vegetation in the two different periods of the year. Clusters obtained have been compared with the distribution of different pedological classes and soil use and with geomorphological information derived from the DTM. Landsat data have been used also to obtain several soil water content indexes, and produce maps of soil moisture. A principal component analysis has been used to obtain data that are directly dependent on soil and as less influenced as possible by other factors like vegetation. Finally, an algorithm to retrieve soil hydraulic properties (permeability, gravitational storage, capillary storage) from geomorphologic data (slope, aspect) and pedological class has been studied, using Monte Carlo simulation and optimization techniques. The spatially distributed hydraulic properties of soil have been applied in a physically based hydrological model. The results have been compared with soil water content indexes obtained from Landsat data analysis on two sub-basins of the Arno river.