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Diego Miralles   Professor  Other 
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Diego Miralles published an article in November 2018.
Top co-authors See all
Niko E. C. Verhoest

95 shared publications

Laboratory of Hydrology and Water Management—Ghent University; Coupure links 653, 9000 Gent, Belgium

Joshua B. Fisher

94 shared publications

Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA

Santiago Beguería

73 shared publications

Estación Experimental de Aula Dei; Consejo Superior de Investigaciones Científicas (EEAD-CSIC); Zaragoza Spain

Fernando Domínguez-Castro

24 shared publications

Instituto Pirenaico de Ecología, Spanish National Research Council (IPE-CSIC), Campus de Aula Dei, P.O. Box 13034, E-50059 Zaragoza, Spain

Brecht Martens

18 shared publications

Laboratory of Hydrology and Water Management—Ghent University; Coupure links 653, 9000 Gent, Belgium

57
Publications
81
Reads
21
Downloads
698
Citations
Publication Record
Distribution of Articles published per year 
(2010 - 2018)
Total number of journals
published in
 
28
 
Publications See all
Article 0 Reads 0 Citations Terrestrial evaporation response to modes of climate variability Brecht Martens, Willem Waegeman, Wouter A. Dorigo, Niko E. C... Published: 15 November 2018
npj Climate and Atmospheric Science, doi: 10.1038/s41612-018-0053-5
DOI See at publisher website
Article 0 Reads 0 Citations Towards Estimating Land Evaporation at Field Scales Using GLEAM Brecht Martens, Richard A. M. De Jeu, Niko E. C. Verhoest, H... Published: 31 October 2018
Remote Sensing, doi: 10.3390/rs10111720
DOI See at publisher website ABS Show/hide abstract
The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.
Article 0 Reads 0 Citations Potential evaporation at eddy-covariance sites across the globe Wouter H. Maes, Pierre Gentine, Niko E. C. Verhoest, Diego G... Published: 25 October 2018
Hydrology and Earth System Sciences Discussions, doi: 10.5194/hess-2018-470
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Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, and these reflect a diverse range of methods to calculate it. As such, a comparison of the performance of these methods against field observations in different global ecosystems is urgently needed. In this study, potential evaporation was defined as the rate of evaporation (or evapotranspiration – sum of transpiration and soil evaporation) that the actual ecosystem would attain if it evaporates at maximal rate. We use eddy-covariance measurements from the FLUXNET2015 database, covering eleven different biomes, to parameterize and inter-compare the most widely used Ep methods and to uncover their relative performance. For each site, we isolate the days for which ecosystems can be considered as unstressed based on both an energy balance approach and a soil water content approach. Evaporation measurements during these days are used as reference to calibrate and validate the different methods to estimate Ep. Our results indicate that a simple radiation-driven method calibrated per biome consistently performs best, with a mean correlation of 0.93, unbiased RMSE of 0.56mmday−1, and bias of −0.02mmday−1 against in situ measurements of unstressed evaporation. A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman, Penman–Monteith-based or temperature-driven approaches. We show that the poor performance of Penman–Monteith-based approaches relates largely to the fact that the unstressed stomatal conductance cannot be assumed to be constant in time at the ecosystem scale. Contrastingly, the biome-specific parameters required for the simple radiation-driven methods are relatively constant in time and per biome type. This makes these methods a robust way to estimate Ep and a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.
Article 0 Reads 0 Citations Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models Carl J. Talsma, Stephen P. Good, Diego G. Miralles, Joshua B... Published: 09 October 2018
Remote Sensing, doi: 10.3390/rs10101601
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Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.
Article 0 Reads 4 Citations Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges Diego G. Miralles, Pierre Gentine, Sonia I. Seneviratne, Adr... Published: 25 June 2018
Annals of the New York Academy of Sciences, doi: 10.1111/nyas.13912
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Droughts and heatwaves cause agricultural loss, forest mortality, and drinking water scarcity, especially when they occur simultaneously as combined events. Their predicted increase in recurrence and intensity poses serious threats to future food security. Still today, the knowledge of how droughts and heatwaves start and evolve remains limited, and so does our understanding of how climate change may affect them. Droughts and heatwaves have been suggested to intensify and propagate via land–atmosphere feedbacks. However, a global capacity to observe these processes is still lacking, and climate and forecast models are immature when it comes to representing the influences of land on temperature and rainfall. Key open questions remain in our goal to uncover the real importance of these feedbacks: What is the impact of the extreme meteorological conditions on ecosystem evaporation? How do these anomalies regulate the atmospheric boundary layer state (event self‐intensification) and contribute to the inflow of heat and moisture to other regions (event self‐propagation)? Can this knowledge on the role of land feedbacks, when available, be exploited to develop geo‐engineering mitigation strategies that prevent these events from aggravating during their early stages? The goal of our perspective is not to present a convincing answer to these questions, but to assess the scientific progress to date, while highlighting new and innovative avenues to keep advancing our understanding in the future.
CONFERENCE-ARTICLE 15 Reads 0 Citations Satellite observed solar induced fluorescence to monitor global plant stress Brianna Pagán, Brecht Martens, Wouter Maes, Diego Miralles Published: 10 November 2017
First International Electronic Conference on the Hydrological Cycle, doi: 10.3390/CHyCle-2017-04874
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Biophysical feedbacks on climate depend on plant responses to stress conditions. Yet current land surface models (LSMs) still treat plant stress rudimentarily, and typically assume the same sensitivity to soil moisture for all vegetation types. There is a need therefore to investigate the dynamics of vegetation stress at the global scale, both to further understand the effect of land feedbacks on climate, as well as to improve the representation of these processes in LSMs. Solar induced fluorescence (SIF) is a subtle glow of energy emitted by vegetation during photosynthesis. Recently, satellite observations of SIF have been shown to closely mimic the spatiotemporal variability of photosynthesis. Given the nexus between photosynthesis and transpiration through the opening and closing of stomata, a link between SIF observations and evaporation can be hypothesised. Here, we introduce a novel index of evaporative stress (i.e. the ratio of actual to potential evaporation) based on satellite SIF observations, and we compare it to the estimates of evaporative stress by various LSMs from the Earth2Observe database (i.e. JULES, HTESSEL, ORCHIDEE). Results of validations against in situ evaporative stress – calculated from the FLUXNET2015 eddy-covariance archive – indicate that our SIF-based stress index outperforms the estimates of the LSMs across the majority of sites, with the exception of regions with sparse vegetation in which bare soil evaporation dominates the flux of vapour from land to atmosphere. SIF derived stress greatly outperforms over densely forested regions, and shows a high skill to capture leaf-out periods. Overall, this novel SIF application provides improvements for large-scale estimates of transpiration and can be used to further understand vegetation–atmosphere feedbacks from different ecosystem types. Furthermore, the implications of this research are relevant to (a) the hydrology and climate modelling communities, given the opportunity to utilise our SIF-based evaporative stress to benchmark model representation of the land control over the atmospheric demand for water, and (b) the remote sensing community, that will see how an observation originally intended for the study of the carbon cycle is valorized through its application to study water cycle dynamics as well.

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