Please login first
Diego Miralles   Professor  Other 
Timeline See timeline
Diego Miralles published an article in February 2019.
Top co-authors See all
Joshua B. Fisher

157 shared publications

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

Niko E. C. Verhoest

128 shared publications

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

Santiago Beguería

115 shared publications

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

Tim R. McVicar

69 shared publications

CSIRO Land and Water Canberra Australian Capital Territory Australia

Fernando Domínguez-Castro

53 shared publications

Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE-CSIC), Zaragoza, Spain

77
Publications
118
Reads
21
Downloads
639
Citations
Publication Record
Distribution of Articles published per year 
(2010 - 2019)
Total number of journals
published in
 
29
 
Publications See all
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: 18 February 2019
Hydrology and Earth System Sciences, doi: 10.5194/hess-23-925-2019
DOI See at publisher website ABS Show/hide abstract
Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, which reflect a diverse range of methods to calculate it. 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 terrestrial evaporation (or evapotranspiration) that the actual ecosystem would attain if it were to evaporate at maximal rate for the given atmospheric conditions. We use eddy-covariance measurements from the FLUXNET2015 database, covering 11 different biomes, to parameterise and inter-compare the most widely used Ep methods and to uncover their relative performance. For each of the 107 sites, we isolate days for which ecosystems can be considered unstressed, based on both an energy balance 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 against in situ measurements (mean correlation of 0.93; unbiased RMSE of 0.56 mm day−1; and bias of −0.02 mm day−1). A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman-based, Penman–Monteith-based or temperature-driven approaches. We show that the poor performance of Penman–Monteith-based approaches largely relates to the fact that the unstressed stomatal conductance cannot be assumed to be constant in time at the ecosystem scale. On the contrary, the biome-specific parameters required by simpler 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 Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates Brianna R. Pagán, Wouter H. Maes, Pierre Gentine, Brecht Mar... Published: 18 February 2019
Remote Sensing, doi: 10.3390/rs11040413
DOI See at publisher website ABS Show/hide abstract
The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF)—normalized by photosynthetically-active radiation (PAR)—to diagnose the ratio of transpiration to potential evaporation (‘transpiration efficiency’, τ). This potential is validated at 25 eddy-covariance sites from seven biomes worldwide. The skill of the state-of-the-art land surface models (LSMs) from the eartH2Observe project to estimate τ is also contrasted against eddy-covariance data. Despite its relatively coarse (0.5°) resolution, SIF/PAR estimates, based on data from the Global Ozone Monitoring Experiment 2 (GOME-2) and the Clouds and Earth’s Radiant Energy System (CERES), correlate to the in situ τ significantly (average inter-site correlation of 0.59), with higher correlations during growing seasons (0.64) compared to decaying periods (0.53). In addition, the skill to diagnose the variability of in situ τ demonstrated by all LSMs is on average lower, indicating the potential of SIF data to constrain the formulations of transpiration in global models via, e.g., data assimilation. Overall, SIF/PAR estimates successfully capture the effect of phenological changes and environmental stress on natural ecosystem transpiration, adequately reflecting the timing of this variability without complex parameterizations.
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 1 Citation 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
DOI See at publisher website ABS Show/hide abstract
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 1 Read 1 Citation Global hydro-climatic biomes identified via multitask learning Christina Papagiannopoulou, Diego G. Miralles, Matthias Demu... Published: 12 October 2018
Geoscientific Model Development, doi: 10.5194/gmd-11-4139-2018
DOI See at publisher website ABS Show/hide abstract
The most widely used global land cover and climate classifications are based on vegetation characteristics and/or climatic conditions derived from observational data. However, these classification schemes do not directly stem from the characteristic interaction between the local climate and the biotic environment. In this work, we model the dynamic interplay between vegetation and local climate in order to delineate ecoregions that share a coherent response to hydro-climate variability. Our novel framework is based on a multitask learning approach that discovers the spatial relationships among different locations by learning a low-dimensional representation of predictive structures. This low-dimensional representation is combined with a clustering algorithm that yields a classification of biomes with coherent behaviour. Experimental results using global observation-based datasets indicate that, without the need to prescribe any land cover information, the identified regions of coherent climate–vegetation interactions agree well with the expectations derived from traditional global land cover maps. The resulting global hydro-climatic biomes can be used to analyse the anomalous behaviour of specific ecosystems in response to climate extremes and to benchmark climate–vegetation interactions in Earth system models.
Top