1,721,026 research outputs found
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
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
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
Temporal dependence structure in weights in a multiplicative cascade model for precipitation
No full textWe 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
Two-dimensional Hurst-Kolmogorov process and its application to rainfall fields
No full textThe Hurst–Kolmogorov (HK) dynamics has been well established in stochastic representations of the temporal evolution of natural processes, yet many regard it as a puzzle or a paradoxical behaviour. As our senses are more familiar with spatial objects rather than time series, understanding the HK behaviour becomes more direct and natural when the domain of our study is no longer the time but the two-dimensional space. Therefore, here we detect the presence of HK behaviour in spatial hydrological and generally geophysical fields including Earth topography, and precipitation and temperature fields. We extend the one-dimensional HK process into two dimensions and we provide exact relationships of its basic statistical properties and closed approximations thereof. We discuss the parameter estimation problem, with emphasis on the associated uncertainties and biases. Finally, we propose a two-dimensional stochastic generation scheme, which can reproduce the HK behaviour and we apply this scheme to generate rainfall fields, consistent with the observed ones
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
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 km2. 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-IDF 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, ?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 a major importance for different applications that require very local estimates of rainfall extremes
Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2
No full textIncreasing 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, while 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, LAI) effects of elevated CO2 across a variety of ecosystems. We specifically determined which ecosystems and climatic conditions maximise 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, while 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
Matching ecohydrological processes and scales of banded vegetation patterns in semi-arid catchments
No full textWhile 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
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