Athanasios Paschalis

A stochastic model for high‐resolution space‐time precipitation simulation

High-resolution space-time stochastic models for precipitation are crucial for hydrological applications related to flood risk and water resources management. In this study, we present a new stochastic space-time model, STREAP, which is capable of reproducing essential features of the statistical structure of precipitation in space and time for a wide range of scales, and at the same time can be used for continuous simulation. The model is based on a three-stage hierarchical structure that mimics the precipitation formation process. The stages describe the storm arrival process, the temporal evolution of areal mean precipitation intensity and wet area, and the evolution in time of the two-dimensional storm structure. Each stage of the model is based on appropriate stochastic modeling techniques spanning from point processes, multivariate stochastic simulation and random fields. Details of the calibration and simulation procedures in each stage are provided so that they can be easily reproduced. STREAP is applied to a case study in Switzerland using 7 years of high-resolution (2 × 2 km2; 5 min) data from weather radars. The model is also compared with a popular parsimonious space-time stochastic model based on point processes (space-time Neyman-Scott) which it outperforms mainly because of a better description of spatial precipitation. The model validation and comparison is based on an extensive evaluation of both areal and point scale statistics at hydrologically relevant temporal scales, focusing mainly on the reproduction of the probability distributions of rainfall intensities, correlation structure, and the reproduction of intermittency and wet spell duration statistics. The results shows that a more accurate description of the space-time structure of precipitation fields in stochastic models such as STREAP does indeed lead to a better performance for properties and at scales which are not used in model calibration.

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.

Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2

Significance Elevated levels of atmospheric carbon dioxide affect plants directly by stimulating photosynthesis and reducing stomatal aperture. These direct effects trigger several more subtle, indirect effects via changes in soil moisture and plant structure. While such effects have been acknowledged, they have never been assessed quantitatively, partly due to the fact they are inseparable in field experiments. Here we show that the indirect effects of elevated CO2 explain, on average, 28% of the total plant productivity response, and are almost equal to the size of direct effects on evapotranspiration. This finding has major implications for our mechanistic understanding of plant response to elevated CO2, forcing us to revisit the interpretation of experimental results as well as simulations of future productivity. Increasing concentrations of atmospheric carbon dioxide are expected to affect carbon assimilation and evapotranspiration (ET), ultimately driving changes in plant growth, hydrology, and the global carbon balance. Direct leaf biochemical effects have been widely investigated, whereas indirect effects, although documented, elude explicit quantification in experiments. Here, we used a mechanistic model to investigate the relative contributions of direct (through carbon assimilation) and indirect (via soil moisture savings due to stomatal closure, and changes in leaf area index) effects of elevated CO2 across a variety of ecosystems. We specifically determined which ecosystems and climatic conditions maximize the indirect effects of elevated CO2. The simulations suggest that the indirect effects of elevated CO2 on net primary productivity are large and variable, ranging from less than 10% to more than 100% of the size of direct effects. For ET, indirect effects were, on average, 65% of the size of direct effects. Indirect effects tended to be considerably larger in water-limited ecosystems. As a consequence, the total CO2 effect had a significant, inverse relationship with the wetness index and was directly related to vapor pressure deficit. These results have major implications for our understanding of the CO2 response of ecosystems and for global projections of CO2 fertilization, because, although direct effects are typically understood and easily reproducible in models, simulations of indirect effects are far more challenging and difficult to constrain. Our findings also provide an explanation for the discrepancies between experiments in the total CO2 effect on net primary productivity.

Abiotic and biotic controls of soil moisture spatiotemporal variability and the occurrence of hysteresis

An expression that separates biotic and abiotic controls on the temporal dynamics of the soil moisture spatial coefficient of variation Cv(?) was explored via numerical simulations using a mechanistic ecohydrological model, Tethys-Chloris. Continuous soil moisture spatiotemporal dynamics at an exemplary hillslope domain were computed for six case studies characterized by different climate and vegetation cover and for three configurations of soil properties. It was shown that abiotic controls largely exceed their biotic counterparts in wet climates. Biotic controls on Cv(?) were found to be more pronounced in Mediterranean climates. The relation between Cv(?) and spatial mean soil moisture inline image was found to be unique in wet locations, regardless of the soil properties. For the case of homogeneous soil texture, hysteretic cycles between Cv(?) and inline image were observed in all Mediterranean climate locations considered here and to a lesser extent in a deciduous temperate forest. Heterogeneity in soil properties increased Cv(?) to values commensurate with field observations and weakened signatures of hysteresis at all of the studied locations. This finding highlights the role of site-specific heterogeneities in hiding or even eliminating the signature of climatic and biotic controls on Cv(?), thereby offering a new perspective on causes of confounding results reported across field experiments.

Cross‐scale impact of climate temporal variability on ecosystem water and carbon fluxes

While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1?h) on the related ecosystem processes remains to be fully understood. Various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water and carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.

Temporal dependence structure in weights in a multiplicative cascade model for precipitation

We investigate the ability of the multiplicative random cascade model to accurately simulate temporal precipitation. Specifically, we explore the effect of the dependence structure in cascade weights due to clustering and within-storm variability on the temporal correlation in simulated precipitation, and we compare the results with data at 69 stations with 10 min precipitation records in Switzerland. Correlation is quantified with the oscillation coefficient, which is a measure of patterns of fluctuations in data. Simulation results show that the assumption of temporal independence in cascade weights is generally not supported by observations of both rainfall and snowfall, which show generally higher correlation (lower fluctuations) at the hourly time resolution. Seasonal signatures are also apparent, with higher correlation in the cold season with dominant stratiform precipitation than in the warm season with convective precipitation. Measurement artifacts caused by the tipping bucket mechanism at high resolutions (10 min) are shown to play a significant role in the estimation of the correlation structure in cascade weights because of the quantization of precipitation intensity by the tip volume and sampling time resolution of the gauge. These effects are smoothed out at resolutions above 1 h when the oscillation coefficients become independent of resolution. Such measurement artifacts may have an important effect on the estimated scaling and correlation behavior in precipitation at high temporal resolutions.

Persistence and memory timescales in root-zone soil moisture dynamics

The memory timescale that characterizes root-zone soil moisture remains the dominant measure in seasonal forecasts of land-climate interactions. This memory is a quasi-deterministic timescale associated with the losses (e.g. evapotranspiration) from the soil column and is often interpreted as persistence in soil moisture states. Persistence, however, represents a distribution of time periods where soil moisture resides above or below some prescribed threshold, and is therefore inherently probabilistic. Using multiple soil moisture datasets collected at high resolution (sub-hourly) across different biomes and climates, this paper explores the differences, underlying dynamics, and relative importance of memory and persistence timescales in root-zone soil moisture. A first-order Markov process, commonly used to interpret soil moisture fluctuations derived from climate simulations, is also used as a reference model. Persistence durations of soil moisture below the plant water-stress level (chosen as the threshold), and the temporal spectrum of up- and down-crossings of this threshold, are compared to the memory timescale and spectrum of the full time series, respectively. The results indicate that despite the differences between meteorological drivers, the spectrum of threshold-crossings is similar across sites, and follows a unique relation with that of the full soil moisture series. The distribution of persistence times exhibits an approximate stretched exponential type and reflects a likelihood of exceeding the memory at all sites. However, the rainfall counterpart of these distributions shows that persistence of dry atmospheric periods is less likely at sites with long soil moisture memory. The cluster exponent, a measure of the density of threshold crossings in a time frame, reveals that the clustering tendency in rainfall events (on-off switches) does not translate directly to clustering in soil moisture. This is particularly the case in climates where rainfall and evapotranspiration are out of phase, resulting in less ordered (more independent) persistence in soil moisture than in rainfall.

An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

Matching ecohydrological processes and scales of banded vegetation patterns in semiarid catchments

While the claim that water-carbon interactions result in spatially coherent vegetation patterning is rarely disputed in many arid and semi-arid regions, the significance of the detailed water pathways and other high frequency variability remain an open question. How the short temporal scale meteorological fluctuations form the long term spatial variability of available soil water in complex terrains due to the various hydrological, land surface and vegetation dynamic feedbacks, frames the scope of the work here. Knowledge of the detailed mechanistic feedbacks between soil, plants and the atmosphere will lead to advances in our understanding of plant water availability in arid and semi-arid ecosystems and will provide insights for future model development concerning vegetation pattern formation. In this study, quantitative estimates of water fluxes and vegetation productivity are provided for a semi-arid ecosystem with established vegetation bands on hillslopes using numerical simulations. A state-of-the-science process based ecohydrological model is used, which resolves hydrological and plant physiological processes at the relevant space and time scales, for relatively small periods (e.g. decades) of mature ecosystems (i.e. spatially static vegetation distribution). To unfold the mechanisms that shape the spatial distribution of soil moisture, plant productivity and the relevant surface/subsurface and atmospheric water fluxes, idealized hillslope numerical experiments are constructed, where the effects of soil-type, slope steepness and overland flow accumulation area are quantified. Those mechanisms are also simulated in the presence of complex topography features on landscapes. The main results are: (a) Short temporal scale meteorological variability and accurate representation of the scales at which each ecohydrological process operates are crucial for the estimation of the spatial variability of soil water availability to the plant root zone; (b) Water fluxes such as evapotranspiration, infiltration, runoff-runon and subsurface soil water movement have a dynamic short temporal scale behavior that determines the long term spatial organization of plant soil water availability in ecosystems with established vegetation patterns; (c) Hypotheses concerning the hydrological responses that can lead to vegetation pattern formation have to accommodate realistic and physically based representations of the fast dynamics of key ecohydrological fluxes.