ECOHYDROLOGY IN MEDITERRANEAN AREAS: A NUMERICAL MODEL TO DESCRIBE GROWING SEASONS OUT OF PHASE WITH PRECIPITATIONS

The probabilistic description of soil moisture dynamics is a relatively new topic in hydrology. The most common ecohydrological models start from a stochastic differential equation describing the soil water balance, where the unknown quantity, the soil moisture, depends both on spaces and time. Most of the solutions existing in literature are obtained in a probabilistic framework and under steady-state condition; even if this last condition allows the analytical handling of the problem, it has considerably simplified the same problem by subtracting generalities from it. The steady-state hypothesis, appears perfectly applicable in arid and semiarid climatic areas like those of African's or middle American's savannas, but it seems to be no more valid in areas with Mediterranean climate, where, notoriously, the wet season foregoes the growing season, recharging water into the soil. This moisture stored at the beginning of the growing season (known as soil moisture initial condition) has a great importance, especially for deep-rooted vegetation, by enabling survival in absence of rainfalls during the growing season and, however, keeping the water stress low during the first period of the same season. The aim of this paper is to analyze the soil moisture dynamics using a simple non-steady numerical ecohydrological model. The numerical model here proposed is able to reproduce soil moisture probability density function, obtained analytically in previous studies for different climates and soils in steady-state conditions; consequently it can be used to compute both the soil moisture time-profile and the vegetation static water stress time-profile in non-steady conditions. Here the differences between the steady-analytical and the non-steady numerical probability density functions are analyzed, showing how the proposed numerical model is able to capture the effects of winter recharge on the soil moisture. The dynamic water stress is also numerically evaluated, implicitly taking into account the soil moisture condition at the beginning of the growing season. It is also shown the role of different annual climatic parameterizations on the soil moisture probability density function and on the vegetation water stress evaluation

Daily rainfall statistics in Sicily (1920-2000)

Rainfall characteristics are crucial for vegetation patterns formation and evolution in Mediterranean ecosystems. Changes in rainfall frequency and intensity could cause vegetation water stress for some plant species and benefit, at the same time, other species, driving coexistence and competition dynamics. The changes in the precipitation characteristics are sometimes more important than the changes in the total amount of precipitation in determining the partitioning between green and blue water with several implications for both the vegetation communities health and water resource management. Decreasing rainfall is a clear signature of climate change in Mediterranean countries. Annual and winter totals have been demonstrated to decrease in the past century and GCMs forecast a progressive worsening of the current situation even if it is still not clear if and how rainfall could be modified in its temporal and seasonal patterns. This study aims to analyze daily rainfall properties in Sicily in the last century. Namely the daily depths and interarrival times between events are investigated in about 50 stations, also characterizing seasonal rainfall features. The presence of significant trend has been detected using the non parametric Mann Kendall test

Climate changes effects on vegetation in Mediterranean areas

The Mediterranean ecosystems evolved under climatic conditions characterized by precipitations markedly out of phase with the growing period for the vegetation there established. In such environments, deep and shallow rooted species cohabit and compete each other. The formers, being characterized by deeper root, are able to utilize the water stored during the dormant season, while the conditions of shallow rooted plant are closely related to the intermittence of the precipitations. A numerical model has been here used in order to carry out an analysis of the potential climate changes influence on the vegetation state in a typical Mediterranean environment, such as Sicilian one. The most important consequences arising from climate changes in the Mediterranean area, due to the CO2 increase, are the temperatures raise and the contemporaneous rainfall reduction. Probably, this reduction could be accompanied by an increase in events intensity and, at the same time, by a decrease in the number of annual events. There are very few information about possible changes in the distribution of the rainfall events over the year. However, according to the analysis of the recorded trend, it is possible to predict that the rainfall reduction will be mainly concentrated during the autumnal and wintry months. The goal of this work is a quantitative evaluation of the effects due to the climatic forcing changes, on vegetation water stress. In particular, great attention is paid to the effects that rainfall decrease may have on vegetation, by itself or coupled with the temperature increase. A detailed investigation on the influence of the variations in rainfall seasonality, frequency and intensity is carried out. In this work two vegetation covers, with shallow and deep rooting depth (grass and tree) laying on three different soil types (loamy sand, sandy loam and clay) are considered. Simulations on Mediterranean ecosystems have lead to recognize the role of the rainfall amount, frequency and temporal distribution. Rainfall decrease increases the vegetation water stress much more than temperature increase do. Intense and rare rainfall events, as they are expected to be, could attenuate the effects of rainfall reduction because of the less interception correlated to them. The future rainfall distribution over the year is also crucial for vegetation water stress. If the current ratio between the growing season and the dormant season rainfall will be kept, trees and grasses will suffer a common increase of water stress, which seems more severe for trees than for grasses. Otherwise, if the rainfall reduction will be concentrated during the wintry periods, as emerges from literature, grasses will have some advantages over the trees species. In this conditions grasses will keep the water stress similar to the nowadays value, while trees will suffer for the lack of the winter recharge increasing their water stress

Ecohydrology in Mediterranean areas: a numerical model to describe growing seasons out of phase with precipitations

International audienceThe probabilistic description of soil moisture dynamics is a relatively new topic in hydrology. The most common ecohydrological models start from a stochastic differential equation describing the soil water balance, where the unknown quantity, the soil moisture, depends both on spaces and time. Most of the solutions existing in literature are obtained in a probabilistic framework and under steady-state condition; even if this last condition allows the analytical handling of the problem, it has considerably simplified the same problem by subtracting generalities from it. The steady-state hypothesis, appears perfectly applicable in arid and semiarid climatic areas like those of African's or middle American's savannas, but it seems to be no more valid in areas with Mediterranean climate, where, notoriously, the wet season foregoes the growing season, recharging water into the soil. This moisture stored at the beginning of the growing season (known as soil moisture initial condition) has a great importance, especially for deep-rooted vegetation, by enabling survival in absence of rainfalls during the growing season and, however, keeping the water stress low during the first period of the same season. The aim of this paper is to analyze the soil moisture dynamics using a simple non-steady numerical ecohydrological model. The numerical model here proposed is able to reproduce soil moisture probability density function, obtained analytically in previous studies for different climates and soils in steady-state conditions; consequently it can be used to compute both the soil moisture time-profile and the vegetation static water stress time-profile in non-steady conditions. Here the differences between the steady-analytical and the non-steady numerical probability density functions are analyzed, showing how the proposed numerical model is able to capture the effects of winter recharge on the soil moisture. The dynamic water stress is also numerically evaluated, implicitly taking into account the soil moisture condition at the beginning of the growing season. It is also shown the role of different annual climatic parameterizations on the soil moisture probability density function and on the vegetation water stress evaluation. The proposed model is applied to a case study characteristic of Mediterranean climate: the watershed of Eleuterio in Sicily (Italy)

Modelling the shrub encroachment in a grassland with a Cellular Automata Model

Abstract. Arid and semi-arid grasslands of southwestern North America have changed dramatically over the last 150 years as a result of shrub encroachment, i.e. the increase in density, cover and biomass of indigenous shrubby plants in grasslands. Numerous studies have documented the expansion of shrublands in the southwestern American grasslands; in particular shrub encroachment has occurred strongly in part of the northern Chihuahuan desert since 1860. This encroachment has been simulated using an ecohydrological Cellular Automata model, CATGraSS. It is a spatially distributed model driven by spatially explicit irradiance and runs on a fine-resolution gridded domain. Plant competition is modelled by keeping track of mortality and establishment of plants; both are calculated probabilistically based on soil moisture stress. For this study CATGraSS has been improved with a stochastic fire module and a grazing function. The model has been implemented in a small area in Sevilleta National Wildlife Refuge (SNWR), characterized by two vegetation types (grass savanna and creosote bush shrub), considering as encroachment causes the fire return period increase, the grazing increase, the seed dispersal caused by animals, the role of wind direction and plant type competition. The model is able to reproduce the encroachment that has occurred in SNWR, simulating an increase of the shrub from 2% in 1860 to the current shrub percentage, 42%, and highlighting among the most influential factors the reduced fire frequency and the increased grazing intensity

Studying the relationships between hourly precipitation extremes and dewpoint temperature in Sicily.

According to the well-known Clausius-Clapeyron relation, the maximum moisture content of the atmosphere increases with approximately 7% per degree temperature raise (CC-scaling rate). Under the hypothesis that relative humidity does not change, an increase in the water vapor should occur at the same rate. For this reason we could expect for the coming years an increase in the intensity of extreme precipitation as a consequence of the global climate warming. Trend on precipitation extremes and possible links to changes in atmospheric temperature and moisture are investigated in different parts of the world, and a number of observational studies has exhibited scaling rates that are either higher (super-CC) or lower (sub-CC) than CC scaling rate depending on the climatic areas under analysis. One of the most common approaches consists in a regression analysis to interpret the relationships between extreme percentiles of rainfall and surface temperature, and this is often due to the lack of availability of consistent historical data series for other variables of interest, such as the relative air humidity. In some applications, combined temperature-humidity measures, such as the dew point temperature, have been used as proxy measures. In this study we investigate, at the regional scale (Sicily, Italy), the scaling rate between hourly precipitation extremes and dew point temperature. This last is then used as a measure of near surface absolute humidity and is computed for each rainfall event at the same time (T0) and, 2 (T2) and 4 (T4) hours before the event occurrence. The scaling rate is studied at both the level of entire hydrological year and the seasonal level, dividing the calendar year in a wet, colder and more rainy, season and a dry warmer season. The high-resolution dataset from the regional agency SIAS (Agro-meteorological Information Service of Sicily) has been used and it is constituted by data of 10-min rainfall, hourly temperature and maximum hourly relative air humidity, collected by 107 gauges from 2003 to 2015. The hourly temperature and maximum hourly relative air humidity data are combined to provide hourly time-series of dew-point temperature at each gauge. The samples from the different gauges are pooled together forming six different samples relative to six different sub-regions defined within the Sicilian island and at the level of entire region (unique regional sample). At the level of single sub-region a binning procedure is used, investigating the suitability of exponential regression models for interpreting the relationships between dew point temperature (median for bin) and extreme rainfall intensity (95th percentile for bin). A LOESS (LOcally-wEighted Scatter-plot Smoothing) regression analysis is considered for the study of the regional sample. Similar results are obtained from the analysis at the annual level and for the wet season, with high coefficients of determination (R2 >0.94) for all the sub-regions, demonstrating the appropriateness of the used regression models, and with sub-CC scaling rate (4-5%°C1). For the dry season, both the R2 and the rates (especially for T2 and T4) are lower; moreover, the LOESS analysis highlights a decreasing scaling rate at higher dew point temperatures

Modeling the role of climate change on small-scale vegetation patterns in a Mediterranean basin using a Cellular Automata model

Predicting vegetation response in regions of ecotone transition under a changing climate is a among grand challenges in ecohydrology. In a small basin (1.3 sq km) in Sicily, Italy, where north-facing slopes are characterized by Quercus (tree), and south-facing slopes by Opuntia ficus-indaca (evergreen perennial species drought tolerant) and grasses we use an ecohydrological Cellular-Automaton model (CATGraSS) of vegetation coexistence driven by rainfall and solar radiation with downscaled future climate to examine the role of climate change on vegetation patterns. In the model, each cell can hold a single plant type or can be bare soil. Plant competition is modeled explicitly by keeping track of mortality and establishment of plants, both calculated probabilistically based on soil moisture stress. Topographic influence on incoming shortwave radiation is treated explicitly, which leads to spatial variations in potential evapotranspiration and resulting soil moisture and plant distribution. The influence of the soil thickness on the vegetation distribution is also introduced. The model is calibrated first using a representation of the current climate as a forcing and comparing the vegetation obtained from the model with the actual vegetation through statistical techniques.. The calibrated model is then forced with future climate scenarios generated using a stochastic downscaling technique based on the weather generator, AWE-GEN. This methodology allows for the downscaling of an ensemble of climate model outputs deriving the frequency distribution functions of factors of change for several statistics of temperature and precipitation from outputs of General Circulation Models. The stochastic downscaling is carried out using simulations of twelve General Circulation Models adopted in the IPCC 4AR, A1B emission scenario, for the future periods of 2046-2065 and 2081-2100. A high sensitivity of the vegetation distribution to variation of rainfall and temperature has been observed. The simulations suggest that the observed vegetation pattern can exist only in the current climate while the changes in the future storm characteristics could lead to a dramatic reorganization of the plant composition based mainly on the topography. Moreover the model analysis underscores the importance of solar irradiance in determining vegetation composition over complex terrain

Numerical experiments for defining criteria for the use of LS-PIV based techniques in river monitoring.

The developing of new image-based techniques for environmental monitoring is opening new frontiers for remote streamflow measurements in natural environments, since they allow for non-intrusive measurements even in adverse circumstances, such as high flow conditions that often hinder the use of traditional approaches and instruments. Methods based on the acquisition, analysis and elaboration of images for streamflow observation, such as the large scale particle image velocimetry (LSPIV) and the particle tracking velocimetry (PTV) techniques, are rapidly evolving also in consideration of the growing availability of a new generation of optical sensors, digital cameras and methodologies. A number of free and easy software based on LSPIV and PTV allows for a complete characterization of the instantaneous surface velocity field of a river and the assessment of the discharge at specific cross-sections, when the cross-section geometry is known. This kind of software usually requires a sequence of images that can be captured by digital cameras, which can be permanent gauge-cams installed close to the river, mobile-devices with operators standing on the banks or on bridges, or even cams installed on unmanned aerial vehicle (UAV). Despite the great accessibility of cost-effective devices and the simplicity of the free availability of software for image processing, LSPIV and PTV techniques are rarely systematically implemented in practical applications, probably due to the lack of consistent image processing protocols. In this work the performance and the sensitivity of free software based on LSPIV to some factors, such as the seeding density, the frame to frame displacement of tracer, the number of elaborated frames, tracers geometry, are analyzed. In particular, difference sequences of images with known tracers (in size and density) moving at know velocity are created under different configurations (i.e. considering different combinations of the above mentioned factors) and the error in the evaluation of the instantaneous surface velocity field is assessed for each configuration. The different configurations are created considering three possible schemes: ideal (tracers constituted by white disks of equal size and uniformly distributed on a black background), semi-real (tracers constituted by disks of equal size, colored by a white color disturbed by a white noise, and uniformly distributed on a real background), real (real tracers on a real background). Real images, captured by a cam installed on UAV flying on a real river, have been used for the generation of the image-sequences generation under the schemes semi-real and real. Results can be considered extremely useful in defining criteria for guidelines for practical real applications

Detecting precipitation trend using a multiscale approach based on quantile regression over a Mediterranean area

One of the most relevant and debated topics related to the effects of the climate change is whether intense rainfall events have become more frequent over the last decades. It is a crucial aspect, since an increase in the magnitude and frequency of occurrence of heavy rainfall events could result in a dramatic growth of floods and, in turn, human lives losses and economic damages. Because of its central position in the Mediterranean area, Sicily has been often screened with the aim to capture some trends in precipitation, potentially related to climate change. While Mann-Kendall test has been largely used for the rainfall trend detection, in this work a different procedure is considered. Precipitation trends are here investigated by processing the whole rainfall time-series, provided by the regional agency SIAS at a 10-min resolution, through the quantile regression method by aggregating precipitation across a wide spectrum of durations and considering different quantiles. Results show that many rain gauges are characterized by an increasing trend in sub-hourly precipitation intensity, especially at the highest quantiles, thus suggesting that, from 2002 to 2019, sub-hourly events have become more intense in most of the island. Moreover, by analysing some spatial patterns, it has been revealed that the south and the east of Sicily are more interested in significant increasing rainfall trends, especially at the 10-min duration. Finally, the comparison between the two procedures revealed a stronger reliability of the quantile regression in the trend analysis detection, mainly due to the possibility of investigating the temporal variation of the tails of precipitation distribution

Rainfall depth-duration-frequency curves for short-duration precipitation events in Sicily (Italy)

The design criteria of the hydraulic infrastructures, including, for instance, those for flood defense, urban drainage systems, reservoirs spillways and bridges, are based on the coupled analysis of the magnitude of rainfall events for a fixed duration and their estimated annual exceedance probability. The well-known rainfall depth-duration-frequency (DDF) curves, typically derived from the analysis of long historical annual maxima data series, synthesize the relationships between rainfall depth, duration and exceedance probability which is usually expressed as a return period. The time-resolution of rainfall data typically available for the construction of DDF curves and provided by gauges having large sample size, is hourly or coarser; this has allowed the definition of statistically consistent and reliable curves, suitable for rainfall duration hourly or longer, while, for shorter duration, empirical relationships with a high degree of approximation are generally used. Small river basins and plot-size areas with short response time, as well as urban drainage systems, are expected to be particularly vulnerable to sub-hourly intense rainfall events. Many practical applications, design procedures and mathematical models indeed require a finer time-resolution (i.e. sub - hourly). Moreover, in many regions of the world, such as the Sicily (Italy), an intensification of short-duration rainfall events is observed probably in response to the ongoing climate changes. This work proposes an approach for estimating the distribution of sub-hourly extreme rainfalls and extending depth–duration–frequency (DDF) curves derived for duration over the hourly also to sub-hourly durations. The approach is applied in Sicily starting from the coupled analysis of two different databases. The former (OA-ARRA database) contains long series of annual maxima for the fixed duration of 1, 3, 6, 12 and 24 hours for about 250 gauges, while the latter (SIAS database), include 10-minutes rainfall data series for about 100 gauges collected during the last 15 years (from 2003 to now), form which annual maxima time-series for fixed sub-hourly duration are derived. The approach includes a procedure for pairing raingauges, provided from the two databases, according to a distance- and elevation-based criterion and consolidated inference statistical techniques for the coupled analysis of the data-series from the two gauges