3,134 research outputs found
Urban Tethys-Chloris (UT&C v1.0)
No full text<p>Urban Tethys-Chloris (UT&C v1.0) is a mechanistic urban ecohydrological model combining principles of ecohydrological land surface modelling with urban canopy modelling. UT&C is a fully coupled energy and water balance model that calculates 2 m air temperature, 2 m humidity, and surface temperatures. It explicitly resolves biophysical and ecophysiological characteristics of ground vegetation, urban trees, and green roofs and models all urban water fluxes including evapotranspiration, canopy interception, infiltration, and soil moisture transport. UT&C accounts for variations in urban densities, building properties, and urban irrigation schemes. Hence, the model is able to account for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C is one of the first urban canyon parameterizations to include detailed ecohydrology. Its low computational demand allows for analyses spanning multiple years with an hourly time step, therefore, facilitating long-term and seasonal analysis.</p>
<p>The model development and validation is presented in:</p>
<p>Naika Meili, Gabriele Manoli, Paolo Burlando, Elie Bou-Zeid, Winston T.L. Chow, Andrew<br>
M. Coutts, Edoardo Daly, Kerry A. Nice, Matthias Roth, Nigel J. Tapper, Erik Velasco,<br>
Enrique R. Vivoni, and Simone Fatichi (2019). <strong>An urban ecohydrological model to quantify the effect of vegetation<br>
on urban climate and hydrology (UT&C v1.0)</strong>, <em>Geoscientific Model Development, under review</em></p>Data and codes are open and free for scientific and educational purposes but their use should comply with a fair use policy. Specifically, proper acknowledgment and citations should be given to the model and all data used in a peer reviewed publication.
For any questions, please contact Naika Meili ([email protected]) or Simone Fatichi ([email protected]).
The research was conducted at the Future Cities Laboratory at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and Singapore's National Research Foundation (FI370074016) under its Campus for Research Excellence and Technological Enterprise programme. GM was supported by the "The Branco Weiss Fellowship - Society in Science" administered by ETH Zurich. EV acknowledges a research fellowship granted by the Centre for Urban Greenery and Ecology of Singapore's National Parks Board
Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes
Full text linkWhile 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
On the effects of small scale space-time variability of rainfall on basin flood response
No full textThe spatio-temporal variability of rainfall, especially at fine temporal and spatial scales can significantly affect flood generation, leading to a large variability in the flood response and uncertainty in its prediction. In this study we quantify the impact of rainfall spatial and temporal structure on the catchment hydrological response based on a numerical experiment. Rainfall ensembles generated using a state-of-the-art space–time stochastic model are used as input into a distributed process-based hydrological model. The sensitivity of the hydrograph to several structural characteristics of storm rainfall for three soil moisture initial conditions is numerically assessed at the basin outlet of an Alpine catchment in central Switzerland. The results highlight that the flood response is strongly affected by the temporal correlation of rainfall and to a lesser extent by its spatial variability. Initial soil moisture conditions play a paramount role in mediating the response. We identify the underlying mechanistic explanations in terms of runoff generation and connectivity of saturated areas that determine the sensitivity of flood response to the spatio-temporal variability of rainfall. We show that the element that mostly influences both the flood peak and the time of peak occurrence is the clustering of saturated areas in the catchment which leads to local enhanced runoff
A stochastic model for high-resolution space-time precipitation simulation
No full textHigh-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
On temporal stochastic modeling of precipitation, nesting models across scales
No full textWe analyze the performance of composite stochastic models of temporal precipitation which can satisfactorily reproduce precipitation properties across a wide range of temporal scales. The rationale is that a combination of stochastic precipitation models which are most appropriate for specific limited temporal scales leads to better overall performance across a wider range of scales than single models alone. We investigate different model combinations. For the coarse (daily) scale these are models based on Alternating renewal processes, Markov chains, and Poisson cluster models, which are then combined with a microcanonical Multiplicative Random Cascade model to disaggregate precipitation to finer (minute) scales. The composite models were tested on data at four sites in different climates. The results show that model combinations improve the performance in key statistics such as probability distributions of precipitation depth, autocorrelation structure, intermittency, reproduction of extremes, compared to single models. At the same time they remain reasonably parsimonious. No model combination was found to outperform the others at all sites and for all statistics, however we provide insight on the capabilities of specific model combinations. The results for the four different climates are similar, which suggests a degree of generality and wider applicability of the approach
Rozpor ako východisko, láska ako smer u Simone Weilovej (Contradiction as base, Love as direction in writings of Simone Weil)
No full textArticle is explaining contradiction and love, Simone Weil‘s essential terms of hermeneutics of human Being. It introduces close relation of these terms with her understanding of God as well as with her overall concept of religion. Author also mentions Simone Weil‘s inspirations with philosophical and spiritual concepts of the East
“I beg you to tell me what has become of Djamila”: The Political Mobilization of Simone de Beauvoir’s Readers During the Boupacha Affair
No full textBy Sophia Millman This is a condensed version of a Masters thesis dedicated to the political mobilization of Simone de Beauvoir’s readers. The citations from the letters were translated from French by the author. *** On June 2, 1960, the French government ordered all copies of the daily Algiers edition of Le Monde seized and destroyed to suppress the publication of Simone de Beauvoir’s article “Pour Djamila Boupacha.” Beauvoir, a self-professed “woman of letters”, not “of action[1]”, and one ..
Intensification of convective rain cells at warmer temperatures observed from high-resolution weather radar data
No full textISSN:1525-755XISSN:1525-7541ISSN:1525-754
On the variability of the ecosystem response to elevated atmospheric CO2 across spatial and temporal scales at the Duke Forest FACE experiment
Full text linkWhile the significance of elevated atmospheric CO2 concentration on instantaneous leaf-level processes such as photosynthesis and transpiration is rarely disputed, its integrated effect at ecosystem level and at long-time scales remains a subject of debate. In part, the uncertainty stems from the inherent leaf-to-leaf variability in gas exchange rates. By combining 10 years of leaf gas exchange measurements collected during the Duke Forest Free Air CO2 Enrichment (FACE) experiment and three different leaf-scale stomatal conductance models, the leaf-to-leaf variability in photosynthetic and stomatal conductance properties is examined. How this variability is then reflected in ecosystem water vapor and carbon dioxide fluxes is explored by scaling up the leaf-level process to the canopy using model calculations. The main results are: (a) the space-time variability of the photosynthesis and stomatal conductance response is considerable as expected. (b) Variability of the calculated leaf level fluxes is dependent on both the meteorological drivers and differences in leaf age, position within the canopy, nitrogen and CO2 fertilization, which can be accommodated in model parameters. (c) Meteorological variability is playing the dominant role at short temporal scales while parameter variability is significant at longer temporal scales. (d) Leaf level results do not necessarily translate to similar ecosystem level responses due to indirect effects and other compensatory mechanisms related to long-term vegetation dynamics and ecosystem water balance
Spatial variability of extreme rainfall at radar subpixel scale
No full textExtreme rainfall is quantified in engineering practice using Intensity-Duration-Frequency curves (IDF) that are traditionally derived from rain-gauges and more recently also from remote sensing instruments, such as weather radars. These instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at the point scale while weather radar averages precipitation on a relatively large area, generally around 1 km(2). As such, a radar derived IDF curve is representative of the mean areal rainfall over a given radar pixel and neglects the within-pixel rainfall variability. In this study, we quantify subpixel variability of extreme rainfall by using a novel space-time rainfall generator (STREAP model) that downscales in space the rainfall within a given radar pixel. The study was conducted using a unique radar data record (23 years) and a very dense rain-gauge network in the Eastern Mediterranean area (northern Israel). Radar-1DF curves, together with an ensemble of point-based IDF curves representing the radar subpixel extreme rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions to annual rainfall maxima. It was found that the mean areal extreme rainfall derived from the radar underestimate most of the extreme values computed for point locations within the radar pixel (on average, similar to 70%). The subpixel variability of rainfall extreme was found to increase with longer return periods and shorter durations (e.g. from a maximum variability of 10% for a return period of 2 years and a duration of 4 h to 30% for 50 years return period and 20 min duration). For the longer return periods, a considerable enhancement of extreme rainfall variability was found when stochastic (natural) climate variability was taken into account. Bounding the range of the subpixel extreme rainfall derived from radar-IDF can be of major importance for different applications that require very local estimates of rainfall extremes. (C) 2016 Elsevier B.V. All rights reserved
- …
