Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This ...parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs.
Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a ...unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (ΔT
) worldwide and find a nonlinear increase in ΔT
with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of ΔT
with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban-rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.
An increase in urban vegetation is an often proposed mitigation strategy to reduce urban heat and improve outdoor thermal comfort (OTC). Vegetation can alter urban microclimate through changes in air ...temperature, mean radiant temperature, humidity, and wind speed. In this study, we model how street tree and ground vegetation cover and their structural, optical, interception, and physiological traits control the diurnal cycle of OTC in different urban densities in a tropical city (Singapore). For this purpose, we perform a variance based sensitivity analysis of the urban ecohydrological model UT&C. Model performance is evaluated through a comparison with local microclimate measurements and OTC is assessed with the Universal Thermal Climate Index (UTCI).
We find a pronounced daily cycle of vegetation effects on UTCI. Tree cover fraction is more efficient in decreasing UTCI during daytime, while a higher vegetated ground fraction provides more cooling during night. Generally, increasing vegetation cover fractions do not deter OTC, except in certain urban densities during some periods of the day. An increase in tree and ground vegetation fractions provides a higher average UTCI reduction compared to a change in vegetation traits (0.9 – 2.9 °C vs. 0.7 – 1.1 °C during midday, 10 month average). The increase in humidity related to plant transpiration prevents further reduction of UTCI. However, the choice of vegetation traits enhancing tree transpiration can decrease UTCI during hot periods. These results can inform urban planners on the selection of vegetation amount and traits to achieve feasible OTC improvements in tropical cities.
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•Sensitivity analysis is used to quantify the effects of vegetation properties on UTCI.•There is a pronounced daily cycle of vegetation effects on UTCI.•Trees decrease UTCI during daytime, while ground vegetation decreases UTCI at night.•Increasing humidity due to vegetation constrains UTCI decrease on average to < 3 °C.
Plant trait diversity in many vegetation models is crudely represented using a discrete classification of a handful of ‘plant types’ (named plant functional types; PFTs). The parameterization of PFTs ...reflects mean properties of observed plant traits over broad categories ignoring most of the inter- and intraspecific plant trait variability.
Taking advantage of a multivariate leaf-trait distribution (leaf economics spectrum), as well as documented plant drought strategies, we generate an ensemble of hypothetical species with coordinated attributes, rather than using few PFTs. The behavior of these proxy species is tested using a mechanistic ecohydrological model that translates plant traits into plant performance. Simulations are carried out for a range of climates representative of different elevations and wetness conditions in the European Alps. Using this framework we investigate the sensitivity of ecosystem response to plant trait diversity and compare it with the sensitivity to climate variability.
Plant trait diversity leads to highly divergent vegetation carbon dynamics (fluxes and pools) and to a lesser extent water fluxes (transpiration). Abiotic variables, such as soil water content and evaporation, are only marginally affected.
These results highlight the need for revising the representation of plant attributes in vegetation models. Probabilistic approaches, based on observed multivariate whole-plant trait distributions, provide a viable alternative.
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.
Key Points
We develop a high‐resolution space‐time precipitation model (STREAP)
STREAP reproduces all essential statistical properties of precipitation
STREAP has a reproducible calibration procedure for use with radar data
Abstract
Urban heat islands (UHIs) are a widely studied phenomenon, while research on urban-rural differences in humidity, the so called urban dry or moisture islands (UDIs, UMIs), is less common and ...a large-scale quantification of the seasonal and diurnal patterns of the UDI is still lacking. However, quantification of the UDI/UMI effect is essential to understand the impacts of humidity on outdoor thermal comfort, building energy consumption, and urban ecology in cities worldwide. Here, we use a set of globally distributed air temperature and humidity measurements (1089 stations) to quantify diurnal and seasonal patterns of UHI and UDI resulting from rapid urbanization over many regions of the world. The terms ‘absolute UDI’ and ‘relative UDI’ are defined, which quantify urban–rural differences in actual and relative humidity metrics, respectively.
Results show that absolute UDI is largest during daytime with the peak humidity decrease in urban areas occurring during late afternoon hours. In contrast, relative UDI is largest during night and the peak urban relative humidity (RH) decrease and vapor pressure deficit (VPD) increase occurs in the late evening hours with values of around −10% to −11% for RH and 2.9–3.6 hPa for VPD between 20–00 local time during summer. Relative and absolute UDIs are largest during the warm season, except for daytime RH UDI, which does not show any seasonal pattern. In agreement with literature, canopy air UHI is shown to be a nighttime phenomenon, which is larger during summer than winter. Relative UDI is predominantly caused by changes in actual humidity during day and UHI during nighttime.
•Radar subpixel variability of extreme rainfall was quantify by a stochastic model.•Radar extreme rainfall was found to underestimate most of the point extreme values.•Subpixel variability of ...rainfall extreme increase with longer return periods.•It was also found to increase with shorter durations.
Extreme 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 1km2. 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 (23years) 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 2years and a duration of 4h to 30% for 50years return period and 20min 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.
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.
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