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 scale

Modellierung des hydrologischen Kreislaufs und der Interaktion mit Vegetation im Zusammenhang mit dem Klimawandel

There is a growing interest to extend climate change predictions to smaller, catchment-size scales and identify their implications on hydrological and ecological processes. This thesis presents a blueprint methodology for studying climate change impacts on eco-hydrological dynamics at the plot and catchment scales. A weather generator, AWE-GEN, is developed to produce input meteorological variables to eco-hydrological models. The weather generator is also used for the simulation of future climate scenarios, as inferred from climate models. Using a Bayesian technique, a stochastic downscaling procedure derives the distributions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The time series obtained for present and future climates serve as input to a newly developed eco-hydrological model Tethys-Chloris. The methodology is applied to simulate the present (1961-2000) and future (2081-2100) hydrological regimes for the area of Tucson (AZ, U.S.A.). A general reduction of precipitation and a significant increase of air temperature are inferred with the downscaling procedure. The eco-hydrological model is successively used to detect changes in the surface water partition and vegetation dynamics for a desert shrub ecosystem, typical of the semi-arid climate of southern Arizona. An appreciable effect of climate change can be observed in metrics of vegetation performance. The negative impact on vegetation due to amplification of water stress in a warmer and dryer climate is partially offset by the effect of the augment of carbon dioxide concentration. Additionally, an increase of runoff and a depletion of soil moisture with consequence in deep recharge are detected. Such an outcome might affect water availability and risk management in semi-arid systems.Es besteht derzeit ein wachsendes wissenschaftliches Interesse daran, Vorhersagen zum Klimawandel auch auf eine kleinere Skala zu übertragen. Diese Arbeit präsentiert eine Vorgehensweise um Einflüsse des Klimawandels auf ökologisch-hydrologische Dynamiken auf der Einzugsgebietskala nachzuvollziehen. Dazu wurde ein Wettergenerator, AWEGEN, entwickelt, der meteorologische Variablen ausgibt. Der Wettergenerator wird darüber hinaus für die Simulation zukünftiger Klimaszenarien genutzt, die aus den Klimamodellen hervorgehen. Mittels einer Bayes-Technik werden stochastische Downscaling-Prozeduren zur Verteilung der Wechselfaktoren für verschiedene Klimastatistiken aus einem Multimodell-Ensemble ermittelt, die auf Daten des Globalen Klimamodells beruhen. Die Wechselfaktoren werden danach auf die aus Beobachtungen erhaltenen Statistiken angewendet, um die Parameter des Wettergenerators zu überprüfen. Die Zeitreihen dienen als Ausgangsdaten für das neu entwickelte öko-hydrologische Modell Tethys-Chloris. Diese Methode wird angewendet, um die momentanen (1961-2000) sowie zukünftigen (2081-2100) hydrologischen Regime im Gebiet von Tucson (Arizona, U.S.A.) zu simulieren. Dabei ließen sich eine generelle Reduzierung des Niederschlags und eine Zunahme der Lufttemperatur feststellen. Das öko-hydrologische Modell wurde im Anschluss genutzt, um Änderungen in der Verteilung der Oberflächengewässer und der Vegetationsdynamik für ein Wüsten-Buschland Ökosystems nachzuweisen, wie es für das semi-aride Klima typisch ist. Ein nennenswerter Effekt des Klimawandels kann in den Metriken der Vegetationsleistung beobachtet werden. Der negative Einfluss auf die Vegetation aufgrund von Wassermangel in einem wärmeren und trockeneren Klima wird teilweise ausgeglichen durch den Effekt einer verbesserten Kohlendioxidversorgung. Zusätzlich wird eine Erhöhung des (Oberflächen-)Abflusses beobachtet. Diese Ergebnisse beeinflussen die Wasserverfügbarkeit und das Risikomanagement im semi-ariden System

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

Abstract Interannual 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 annual precipitation fluctuations. Furthermore, the study illustrates that a positive skewness of the distribution of annual precipitation is a robust property worldwide and its magnitude is related to the coefficient of variation. Additionally, annual precipitation exhibits very weak small-lag autocorrelation. Conversely, the intensity of long-memory–long-range dependence is significantly larger than zero, hinting that organized long-term variations are an important feature of the interannual variability of precipitation

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

Using a physically-based model, tRIBS-Erosion, for investigating the effects of climate change in semi-arid headwater basins.

Soil erosion due to rainfall detachment and flow entrainment of soil particles is a physical process responsible for a continuous evolution of landscapes. The rate and spatial distribution of this phenomenon depend on several factors such as climate, hydrologic regime, geomorphic characteristics, and vegetation of a basin. Many studies have demonstrated that climate-erosion linkage in particular influences basin sediment yield and landscape morphology. Although soil erosion rates are expected to change in response to climate, these changes can be highly non-linear and thus require mechanistic understanding of underlying causes. In this study, an integrated geomorphic component of the physically-based, spatially distributed hydrological model, tRIBS, the TIN-based Real-time Integrated Basin Simulator, is used to analyze the sensitivity of semi-arid headwater basins to climate change. Downscaled outputs of global circulation models are used to inform a stochastic weather generator that produces an ensemble of climate scenarios for an area in the Southwest U.S. The ensemble is used as input to the integrated model that is applied to different headwater basins of the Walnut Gulch Experimental Watershed to understand basin response to climate change in terms of runoff and sediment yield. Through a model application to multiple catchments, a scaling relationship between specific sediment yield and drainage basin area is also addressed and probabilistic inferences on future changes in catchment runoff and yield are drawn. Geomorphological differences among catchments do not influence specific changes in runoff and sediment transport that are mostly determined by precipitation changes. Despite a large uncertainty dictated by climate change projections and stochastic variability, sediment transport is predicted to decrease despite a non-negligible possibility of larger runoff rates

Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response

Heavy rainfall is expected to intensify with increasing temperatures, which will likely affect rainfall spatial characteristics. The spatial variability of rainfall can affect streamflow and sediment transport volumes and peaks. Yet, the effect of climate change on the small-scale spatial structure of heavy rainfall and subsequent impacts on hydrology and geomorphology remain largely unexplored. In this study, the sensitivity of the hydro-morphological response to heavy rainfall at the small-scale resolution of minutes and hundreds of metres was investigated. A numerical experiment was conducted in which synthetic rainfall fields representing heavy rainfall events of two types, stratiform and convective, were simulated using a space-time rainfall generator model. The rainfall fields were modified to follow different spatial rainfall scenarios associated with increasing temperatures and used as inputs into a landscape evolution model. The experiment was conducted over a complex topography, a medium-sized (477 km2) Alpine catchment in central Switzerland. It was found that the responses of the streamflow and sediment yields are highly sensitive to changes in total rainfall volume and to a lesser extent to changes in local peak rainfall intensities. The results highlight that the morphological components are more sensitive to changes in rainfall spatial structure in comparison to the hydrological components. The hydro-morphological features were found to respond more to convective rainfall than stratiform rainfall because of localized runoff and erosion production. It is further shown that assuming heavy rainfall to intensify with increasing temperatures without introducing changes in the rainfall spatial structure might lead to overestimation of future climate impacts on basin hydro-morphology

Impacts of fertilization on grassland productivity and water quality across the European Alps under current and warming climate: Insights from a mechanistic model

Alpine grasslands sustain local economy by providing fodder for livestock. Intensive fertilization is common to enhance their yields, thus creating negative externalities on water quality that are difficult to evaluate without reliable estimates of nutrient fluxes. We apply a mechanistic ecosystem model, seamlessly integrating land-surface energy balance, soil hydrology, vegetation dynamics, and soil biogeochemistry, aiming at assessing the grassland response to fertilization. We simulate the major water, carbon, nutrient, and energy fluxes of nine grassland plots across the broad European Alpine region. We provide an interdisciplinary model evaluation by confirming its performance against observed variables from different datasets. Subsequently, we apply the model to test the influence of fertilization practices on grassland yields and nitrate (NO$_{3}$$^{-}$) losses through leaching under both current and modified climate scenarios. Despite the generally low NO$_{3}$$^{-}$ concentration in groundwater recharge, the variability across sites is remarkable, which is mostly (but not exclusively) dictated by elevation. In high-Alpine sites, short growing seasons lead to less efficient nitrogen (N) uptake for biomass production. This combined with lower evapotranspiration rates results in higher amounts of drainage and NO$_{3}$$^{-}$ leaching to groundwater. Scenarios with increased temperature lead to a longer growing season characterized by higher biomass production and, consequently, to a reduction of water leakage and N leaching. While the intersite variability is maintained, climate change impacts are stronger on sites at higher elevations. The local soil hydrology has a crucial role in driving the NO$_{3}$$^{-}$ use efficiency. The commonly applied fixed threshold limit on fertilizer N input is suboptimal. We suggest that major hydrological and soil property differences across sites should be considered in the delineation of best practices or regulations for management. Using distributed maps informed with key soil and climatic attributes or systematically implementing integrated ecosystem models as shown here can contribute to achieving more sustainable practices

Covariation of vegetation and climate constrains present and future T/ET variability

The reliable partitioning of the terrestrial latent heat flux into evaporation (E) and transpiration (T) is important for linking carbon and water cycles and for better understanding ecosystem functioning at local, regional and global scales. Previous research revealed that the transpiration-to-evapotranspiration ratio (T/ET) is well constrained across ecosystems and is nearly independent of vegetation characteristics and climate. Here we investigated the reasons for such a global constancy in present-day T/ET by jointly analysing observations and process-based model simulations. Using this framework, we also quantified how the ratio T/ET could be influenced by changing climate. For present conditions, we found that the various components of land surface evaporation (bare soil evaporation, below canopy soil evaporation, evaporation from interception), and their respective ratios to plant transpiration, depend largely on local climate and equilibrium vegetation properties. The systematic covariation between local vegetation characteristics and climate, resulted in a globally constrained value of T/ET = ~70 ± 9% for undisturbed ecosystems, nearly independent of specific climate and vegetation attributes. Moreover, changes in precipitation amounts and patterns, increasing air temperatures, atmospheric CO2 concentration, and specific leaf area (the ratio of leaf area per leaf mass) was found to affect T/ET in various manners. However, even extreme changes in the aforementioned factors did not significantly modify T/ET

An ecohydrological journey of 4500 years reveals a stable but threatened precipitation–groundwater recharge relation around Jerusalem

Groundwater is a key water resource in semiarid and seasonally dry regions around the world, which is replenished by intermittent precipitation events and mediated by vegetation, soil, and regolith properties. Here, a climate reconstruction of 4500 years for the Jerusalem region was used to determine the relation between climate, vegetation, and groundwater recharge. Despite changes in air temperature and vegetation characteristics, simulated recharge remained linearly related to precipitation over the entire analyzed period, with drier decades having lower rates of recharge for a given annual precipitation due to soil memory effects. We show that in recent decades, the lack of changes in the precipitation–groundwater recharge relation results from the compensating responses of vegetation to increasing CO2, i.e., increased leaf area and reduced stomatal conductance. This multicentury relation is expected to be modified by climate change, with changes up to −20% in recharge for unchanged precipitation, potentially jeopardizing water resource availability

Seasonal hysteresis of surface urban heat islands

Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences (ΔTs) exhibits distinctive hysteretic cycles whose shape and looping direction vary across climatic zones. These observations highlight possible delays underlying the dynamics of the coupled urban–biosphere system. However, a general argument explaining the observed hysteretic patterns remains elusive. A coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrate that the time lags between radiation forcing, air temperature, and rainfall generate a rate-dependent hysteresis, explaining the observed seasonal variations of ΔTs. If solar radiation is in phase with water availability, summer conditions cause strong SUHI intensities due to high rural evaporative cooling. Conversely, cities in seasonally dry regions where evapotranspiration is out of phase with radiation show a summertime oasis effect controlled by background climate and vegetation properties. These seasonal patterns of warming and cooling have significant implications for heat mitigation strategies as urban green spaces can reduce ΔTs during summertime, while potentially negative effects of albedo management during winter are mitigated by the seasonality of solar radiation