ORCID Profile
0000-0001-6186-5751
Current Organisations
Vrije Universiteit Amsterdam
,
Ghent University
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Publisher: American Meteorological Society
Date: 07-2015
DOI: 10.1175/2015BAMSSTATEOFTHECLIMATE.1
Abstract: Editors note: For easy download the posted pdf of the State of the Climate for 2014 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.
Publisher: Copernicus GmbH
Date: 12-02-2018
Publisher: Authorea, Inc.
Date: 27-12-2022
DOI: 10.22541/ESSOAR.167214223.33785715/V1
Abstract: The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons — long and short rains — exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather forecast and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modelling and observation-based studies, here we analyze the HAD rainfall from an observationally-constrained Lagrangian perspective. We quantify and map the major oceanic and terrestrial sources of moisture driving the variability in the long and short rains. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low level jet) contribute ~80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ~20% from the second month onwards, i.e., when the oceanic-origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, which has important implications for (sub-)seasonal forecasts and long-term projections of the HAD rainfall.
Publisher: Copernicus GmbH
Date: 19-05-2020
Abstract: Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as open-loop models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo and six estimates from the HBV model with three precipitation inputs (ERA5, IMERG, and MSWEP) and with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5-cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. The median R ± interquartile range across all sites and products in each category was 0.66 ± 0.30 for the satellite products, 0.69 ± 0.25 for the open-loop models, and 0.72 ± 0.22 for the models with satellite data assimilation. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E, SMOS, AMSR2, and ASCAT, with the L-band-based SMAPL3E (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI), MeMo performed better on average (median R of 0.72 versus 0.67), mainly due to the inclusion of SMAPL3E. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.
Publisher: Copernicus GmbH
Date: 12-03-2019
DOI: 10.5194/BG-2019-85
Abstract: Abstract. Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. Our ability to partition evapotranspiration (ET) into E and T is limited at the ecosystem scale, which renders the validation of satellite data and land surface models incomplete. Here, we review current progress in partitioning E and T, and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques provide additional opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. Many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency (EWUE) exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but the conditions under which this assumption holds require further analysis. Another critical assumption for many ET partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T frequently exceeds 95 % of ET from some ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon-water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water flux and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.
Publisher: Copernicus GmbH
Date: 10-2013
DOI: 10.5194/HESS-17-3707-2013
Abstract: Abstract. Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these in idual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the in idual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr−2 in our merged product) followed by a significant decrease in this trend (−0.18 mm yr−2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr−1 (1.35 mm d−1) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr−1 (34 406 km3 yr−1) for a total land area of 130 922 000 km2. Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.
Publisher: Springer Science and Business Media LLC
Date: 24-07-2017
DOI: 10.1038/S41467-017-00114-5
Abstract: Quantifying the responses of the coupled carbon and water cycles to current global warming and rising atmospheric CO 2 concentration is crucial for predicting and adapting to climate changes. Here we show that terrestrial carbon uptake (i.e. gross primary production) increased significantly from 1982 to 2011 using a combination of ground-based and remotely sensed land and atmospheric observations. Importantly, we find that the terrestrial carbon uptake increase is not accompanied by a proportional increase in water use (i.e. evapotranspiration) but is largely (about 90%) driven by increased carbon uptake per unit of water use, i.e. water use efficiency. The increased water use efficiency is positively related to rising CO 2 concentration and increased canopy leaf area index, and negatively influenced by increased vapour pressure deficits. Our findings suggest that rising atmospheric CO 2 concentration has caused a shift in terrestrial water economics of carbon uptake.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-5330
Abstract: & & The Horn of Africa drylands (HAD) are highly vulnerable to hydroclimatic extremes, with droughts and floods frequently leading to famines, crop losses, and significant humanitarian crises. However, development of robust mitigation measures has been hindered by the lack of understanding of the drivers of the two main rainfall seasons in the region: the long (March& #8211 May) and short (October& #8211 December) rains. In particular, the inter-annual variability of the long rains has been subject of much debate a significant amount of research has attempted to diagnose the drivers of the observed decline in the long rains. Given the ecological and socio-economic importance of the two rain seasons for the HAD region, understanding the major moisture sources and their variability in both space and time is essential. Such an analysis can help disentangle the causes of temporal variability in rainfall, especially the long rains, improve forecasts, and build ecosystem and community resilience against hydroclimatic extremes.& & & & To trace the origin of rainfall over the HAD region, we use global simulations of the FLEXPART version 9.01, forced with the ERA-Interim reanalysis for a period of 37 years (1980& #8211 ). The FLEXPART outputs include the properties of the air parcels at 3-hourly time steps, which are then post-processed to identify the source regions of rainfall using the Heat and Moisture Tracking Framework (HAMSTER v1.2.0) described by Keune et al. (2021). Using this framework, we first trace the rainfall occurring over the HAD region during the long and short rain seasons to their terrestrial and oceanic sources spatially. Then, we track the changes in the contributions of ocean and land evaporation to HAD rainfall in time over the 37-year period.& & & & & Preliminary results show that around 80% of HAD rainfall originates from Indian Ocean evaporation, for both seasons. For both seasons the contribution of evaporation from land is relatively low compared to the oceanic contribution. For the long rains, a similar amount of moisture originates from recycling (local) and remote sources (10.9% and 10.5% respectively). On the other hand the short rains show a larger proportion of local recycling (13.8%) relative to remote land evaporation (9.4%). The larger contribution of remote land sources for the long rains arises from the Indian subcontinent and Southeast Asia. Further, we shed light on the trends and anomalies in source regions for the two rain seasons, with particular focus on the anomalies in moisture sources that are characteristic of extreme dry and wet conditions.& & & & & strong& References:& /strong& & & & & Keune, J., Schumacher, D. L., and Miralles, D. G.: A holistic framework to estimate the origins of atmospheric moisture and heat using a Lagrangian model, Geosci. Model Dev. Discuss. [preprint], in review, 2021.& &
Publisher: Springer Science and Business Media LLC
Date: 24-08-2021
DOI: 10.1038/S41597-021-01003-9
Abstract: Challenges exist for assessing the impacts of climate and climate change on the hydrological cycle on local and regional scales, and in turn on water resources, food, energy, and natural hazards. Potential evapotranspiration (PET) represents atmospheric demand for water, which is required at high spatial and temporal resolutions to compute actual evapotranspiration and thus close the water balance near the land surface for many such applications, but there are currently no available high-resolution datasets of PET. Here we develop an hourly PET dataset (hPET) for the global land surface at 0.1° spatial resolution, based on output from the recently developed ERA5-Land reanalysis dataset, over the period 1981 to present. We show how hPET compares to other available global PET datasets, over common spatiotemporal resolutions and time frames, with respect to spatial patterns of climatology and seasonal variations for selected humid and arid locations across the globe. We provide the data for users to employ for multiple applications to explore diurnal and seasonal variations in evaporative demand for water.
Publisher: Wiley
Date: 19-12-2019
DOI: 10.1002/WCC.632
Abstract: This review examines the role of the atmospheric evaporative demand (AED) in drought. AED is a complex concept and here we discuss possible AED definitions, the subsequent metrics to measure and estimate AED, and the different physical drivers that control it. The complex influence of AED on meteorological, environmental/agricultural and hydrological droughts is discussed, stressing the important spatial differences related to the climatological conditions. Likewise, AED influence on drought has implications regarding how different drought metrics consider AED in their attempts to quantify drought severity. Throughout the article, we assess literature findings with respect to: (a) recent drought trends and future projections (b) the several uncertainties related to data availability (c) the sensitivity of current drought metrics to AED and (d) possible roles that both the radiative and physiological effects of increasing atmospheric CO 2 concentrations may play as we progress into the future. All these issues preclude identifying a simple effect of the AED on drought severity. Rather it calls for different evaluations of drought impacts and trends under future climate scenarios, considering the complex feedbacks governing the climate system. This article is categorized under: Paleoclimates and Current Trends Earth System Behavior
Publisher: American Meteorological Society
Date: 03-2019
Abstract: We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans) ii) high spatial (0.1°) and temporal (3 hourly) resolution iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)] iv) distributional bias corrections, mainly to improve the P frequency v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe vi) incorporation of daily observations from 76,747 gauges worldwide and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr −1 , respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P , improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.
Publisher: Copernicus GmbH
Date: 20-10-2015
DOI: 10.5194/HESSD-12-10739-2015
Abstract: Abstract. The WACMOS-ET project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run 4 established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODIS evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in-situ meteorological data from 24 FLUXNET towers was used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed across several time scales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement to the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs re-s led to a common grid to facilitate global estimates) confirmed the original findings.
Publisher: Elsevier BV
Date: 04-2018
Publisher: American Geophysical Union (AGU)
Date: 05-2016
DOI: 10.1002/2015WR018247
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-9418
Abstract: & & The increasing risk of dry extremes and droughts and their further projected exacerbation due to climate change urges the development of reliable risk assessments and mitigation pathways on a regional and global scale. This foremost requires accurate and unambiguous model predictions of dry extremes, as this underpins the effectiveness of the proposed strategies. At present, however, the confidence in regional drought projections is defined as & #8216 medium to low' by the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6), and reducing this uncertainty remains one of the main goals in coming years. & br& In this study, the bias in future projected changes in annual meteorological drought duration (hereafter, longest annual drought, LAD) is assessed in the ensemble of CMIP5 and CMIP6 models. The analyses show that it is the present-day inter-model spread in LAD climatology that largely determines the inter-model uncertainty in future predicted LAD changes. Hereby, both CMIP5 and CMIP6 model ensembles indicate a robust & #8220 dry-model-gets-drier& #8221 relationship in future LAD projections on a global and regional scale. Correcting for this bias using emerging constraint principles and past observational LAD information, we find that nearly half of the world's land area with projected increases in drought duration is underestimating the predicted model ensemble mean change, imposing higher-than-expected risks to the societies and ecosystems. Analysis of physical mechanisms that could underlie this emergent & #8220 resent-future relationship& #8221 points to differences in the responses of & #8220 dry models& #8221 and & #8220 wet models& #8221 to CO2 forcing. Dry and wet models show differences in climate states, which support the role of land& #8211 atmosphere feedbacks and convective scheme sensitivity to atmospheric moisture in the spread of future LAD change projections. & br& In conclusion, the study reveals world regions where climate change may cause stronger drought duration aggravation than expected, and emphasizes the importance of reducing systematic model errors, which are presently largely owed to rainfall biases. Correcting these biases will increase the confidence of future dry extremes predictions, a prerequisite for the effective drought risk reduction in the near future with direct benefits for human and natural systems.& &
Publisher: Springer Science and Business Media LLC
Date: 23-10-2023
Publisher: Copernicus GmbH
Date: 15-10-2014
Abstract: Abstract. Stomatal conductance (gs) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between gs model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We therefore constrained the key model parameter "g1" which represents a plants water use strategy by PFT based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g1 parameter can be estimated using two long-term average (1960–1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal models in conjunction with PFT parameterisations resulted in a large reduction in annual fluxes of transpiration (~ 30% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance when compared to upscaled data products was not degraded, though the new stomatal conductance scheme did not noticeably change existing model-data biases. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.
Publisher: Copernicus GmbH
Date: 09-02-2017
Publisher: Copernicus GmbH
Date: 23-12-2021
Publisher: Copernicus GmbH
Date: 12-02-2018
Abstract: Abstract. Potential evaporation (Ep) is a crucial variable for hydrological forecast and in drought monitoring systems. However, multiple interpretations of Ep exist, and these reflect a erse range of methods to calculate Ep. As such, a comparison of the performance of these methods against field observations in different global ecosystems is badly needed. In this study, we used eddy-covariance measurements from 107 sites of the FLUXNET2015 database, covering 11 different biomes, to parameterize and compare the main Ep methods and uncover their relative performance. For each site, we extracted the days for which ecosystems are unstressed based on both an energy balance approach and on a soil water content approach. The evaporation measurements during these days were used as reference to validate the different methods to estimate Ep. Our results indicate that a simple radiation-driven method calibrated per biome consistently performed best, with a mean correlation of 0.93, an unbiased RMSE of 0.56 mm day−1, and a bias of −0.02 mm day−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-based approaches. We show that the poor performance of Penman-Monteith based approaches relates largely to the fact that the unstressed stomatal conductance was assumed constant. Further analysis showed that the biome-specific parameters required for the simple radiation-driven methods are relatively constant per biome. This makes this simple radiation-driven method calibrated per biome a robust method that can be incorporated into models for improving our understanding of the impact of global warming on future global water use and demand, drought severity and ecosystem productivity.
Publisher: Copernicus GmbH
Date: 30-01-2017
Abstract: Abstract. Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments ( 50 000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29–0.39 for the other P datasets. MSWEP is available via www.gloh2o.org.
Publisher: Copernicus GmbH
Date: 29-07-2022
DOI: 10.5194/HESS-26-3921-2022
Abstract: Abstract. Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.
Publisher: Authorea, Inc.
Date: 25-05-2023
DOI: 10.22541/ESSOAR.168500285.54555879/V1
Abstract: Understanding the partitioning of runoff into baseflow and quickflow is crucial for informed decision-making in water resources management, guiding the implementation of flood mitigation strategies, and supporting the development of drought resilience measures. Methods that combine the physically-based Budyko framework with machine learning (ML) have shown promise in estimating global runoff. However, such ‘hybrid’ approaches have not been used for baseflow estimation. Here, we develop a Budyko-constrained ML approach for baseflow estimation by incorporating the Budyko-based baseflow coefficient (BFC) curve as a physical constraint. We estimate the parameters of the original Budyko curve and the newly developed BFC curve based on 13 climatic and physiographic characteristics using boosted regression trees (BRT). BRT models are trained and tested in 1226 catchments worldwide and subsequently applied to the entire global land surface at a 0.25° grid scale. The catchment-trained models exhibit strong performance during the testing phase, with R2 values of 0.96 and 0.88 for runoff and baseflow, respectively. Results reveal that, on average, 30.3% (spatial standard deviation std=26.5%) of the continental precipitation is partitioned into runoff, of which 20.6% (std=22.1%) is baseflow and 9.7% (std=10.3%) is quickflow. Among the 13 climatic and physiographic characteristics, topography and soil-related characteristics generally emerge as the most important drivers, although significant regional variability is observed. Comparisons with previous datasets suggest that global runoff partitioning is still highly uncertain and warrants further research.
Publisher: American Geophysical Union (AGU)
Date: 12-2013
DOI: 10.1002/2013WR013918
Publisher: Copernicus GmbH
Date: 24-02-2015
Abstract: Abstract. Stomatal conductance (gs) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between gs model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "g1", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g1 parameter can be estimated using two long-term average (1960–1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration (~ 30% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model–data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.
Publisher: Copernicus GmbH
Date: 23-02-2016
Abstract: Abstract. The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs res led to a common grid to facilitate global estimates) confirmed the original findings.
Publisher: Springer Science and Business Media LLC
Date: 22-08-2023
Publisher: Copernicus GmbH
Date: 24-08-2015
Abstract: Abstract. Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the GEWEX LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman-Monteith based Mu model (PM-Mu) and the Global Land Evaporation: the Amsterdam Methodology (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from forty-five globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overally statistical performance (0.72 61 W m−2 0.65), followed closely by GLEAM (0.68 64 W m−2 0.62), with values in parenthesis representing the R2, RMSD and Nash-Sutcliffe Efficiency (NSE) and respectively. PM-Mu (0.51 78 W m−2 0.45) tended to underestimate fluxes, while SEBS (0.72 101 W m−2 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global scale LandFlux applications. Challenges related to the robust assessment of the LandFlux product are also discussed.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-13975
Abstract: & & The evaporation of rainfall intercepted by canopies back into the atmosphere & #8211 often referred to as rainfall interception loss & #8211 is a significant component of terrestrial evaporation in many ecosystems. The physical process of rainfall interception loss can usually be broken down into three phases: (1) wetting up of the canopy, (2) saturated canopy conditions, and (3) drying out after rainfall has ceased. During each of these phases, the process is affected by many factors, including rainfall characteristics, such as gross rainfall, rainfall intensity and rainfall duration, vegetation characteristics such as canopy height, leaf area and the orientation of branches and leaves, and meteorological& conditions such as temperature, wind speed and relative humidity. The Global Land Evaporation Amsterdam Model (GLEAM Miralles et al. 2011) estimates terrestrial evaporation, including forest rainfall interception loss, at the global scale mostly from satellite data. However, the model estimation of interception loss has not been updated since its release almost 10 years ago (Miralles et al. 2010).& & & & In this regard, improving the estimation of interception loss in the model remains a priority. In GLEAM, rainfall interception is estimated using the revised Gash analytical model by Valente et al. (1997), in which the canopy storage and mean wet canopy evaporation rate are both considered constants in both space and time. In addition, only tall-canopy interception is considered. Here we explore the potential of the modified Gash's model by Van Dijk and Bruijnzeel (2001), which uses time variant canopy storage and evaporation functions dependent on leaf area index, for its application at global scales. In addition, due to its dependency on leaf area index, the model is applied to the estimation of rainfall interception loss of low vegetation types such as shrubs and grasses. An extensive meta-analysis of previous interception loss field c aigns provides an extensive archive of data to parameterize and/or validate model estimates over multiple ecosystem types. This presentation provides a general overview of the challenges in rainfall interception loss modelling at global scales and the first results of the global benchmarking of the Valente et al. (1997) and the Van Dijk and Bruijnzeel (2001) formulations against & em& in situ& /em& data.& & & & & strong& References & /strong& & & & & Miralles D G, Gash J H, Holmes T R H, et al. Global canopy interception from satellite observations[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D16).& & & & Miralles D G, Holmes T R H, De Jeu R A M, et al. Global land-surface evaporation estimated from satellite-based observations[J]. Earth Syst. Sci., 2011, 15(2): 453& #8211 .& & & & Valente F, David J S, Gash J H C. Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models[J]. Journal of Hydrology, 1997, 190(1-2): 141-162.& & & & Van Dijk A, Bruijnzeel L A. Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description[J]. Journal of Hydrology, 2001, 247(3-4): 230-238.& &
Publisher: Copernicus GmbH
Date: 19-10-2015
DOI: 10.5194/HESSD-12-10651-2015
Abstract: Abstract. The WACMOS-ET project aims to advance the development of land evaporation estimates at global and regional scales. Its main objective is the derivation, validation and inter-comparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley and Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2015) inspect the performance of these three models at local scales using measurements from eddy-covariance towers, and include the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results here indicate that the Priestley and Taylor based products (PT-JPL and GLEAM) perform overall best for most ecosystems and climate regimes. While all three products adequately represent the expected average geographical patterns and seasonality, there is a tendency from PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared against surface water balances from 837 globally-distributed catchments, and against separate evaporation estimates from ERA-Interim and the Model Tree Ensemble (MTE). Nonetheless, all products manifest large dissimilarities during conditions of water stress and drought, and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, implies caution in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Yet, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 11-01-2016
DOI: 10.1038/SREP19124
Abstract: Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012 and its three components: transpiration from vegetation (E t ), direct evaporation from the soil (E s ) and vaporization of intercepted rainfall from vegetation (E i ). During this period, ET over land has increased significantly ( p 0.01), caused by increases in E t and E i , which are partially counteracted by E s decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in E t over land is about twofold of the decrease in E s . These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.
Publisher: MDPI AG
Date: 18-02-2019
DOI: 10.3390/RS11040413
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.
Publisher: Copernicus GmbH
Date: 18-02-2019
Abstract: Abstract. Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, which reflect a erse 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.
Publisher: Copernicus GmbH
Date: 26-01-2016
Abstract: Abstract. Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72 61 W m−2 0.65), followed closely by GLEAM (0.68 64 W m−2 0.62), with values in parentheses representing the R2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51 78 W m−2 0.45) tended to underestimate fluxes, while SEBS (0.72 101 W m−2 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Challenges related to the robust assessment of the LandFlux product are also discussed.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-2246
Abstract: We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1& #176 resolution, near-real-time updates (& #8764 -h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within & #8764 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at& swx.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Copernicus GmbH
Date: 04-01-2021
Abstract: Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.
Publisher: Copernicus GmbH
Date: 09-02-2017
DOI: 10.5194/HESS-2017-54
Abstract: Abstract. In just the past five years, the field of Earth observation has evolved from the relatively staid approaches of government space agencies into a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that have been embraced by both for-profit companies and in idual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing 3–5 m resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency and at costs that are a mere fraction of an agency mission. With these advances come new space-borne measurements, such as high-definition video for understanding real-time cloud formation, storm development, flood propagation, precipitation tracking, or for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolution, pushing back on spatiotemporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizenscience to record photos of environmental conditions, estimate daily average temperatures from battery state, and enable the measurement of other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as a new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is not clear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms presents our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observation platforms to enhance our understanding of the Earth system.
Publisher: American Meteorological Society
Date: 2017
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10577
Abstract: & & Land use and land cover change significantly influence regional energy budgets, and hydrological and biogeochemical cycles which may occur from both anthropogenic and natural disturbances. Likewise, vegetation may also respond dynamically to climate. In the past decades, the Horn of Africa has been hit by several droughts and heatwaves causing severe economic, environmental, and social damage. To evaluate and mitigate such impacts, it is necessary to establish and quantify the linkage between land cover change and regional climate. This study presents an observational analysis of recent (2001& #8211 ) historical changes in land cover and land use and their relation to climate in the Horn of Africa.& & & & & Firstly, we evaluate changes in land cover using the Moderate Resolution Imaging Spectrometer (MODIS) dataset. Results indicate steady expansion of grasslands (net gain is 1.2% of total area) and an opposite pattern for open shrublands during the period 2001& #8211 . Importantly, deforestation of evergreen broadleaf forest (0.3% of the total area) is also noticeable in continuous fractional vegetation cover (FVC) analysis. Next, the Global Database of Historical Yields (GDHY) is explored to identify the yield trends for two main cereals: maize and wheat.& Wheat yield shows increasing trends in the northern and southern parts, while maize yields increase in Ethiopia and mildly decrease in Kenya. To quantify the adverse impact of drought on yields, three drought indices are used: (a) Standardized Precipitation Evapotranspiration Index (SPEI), (b) self-calibrating Palmer Drought Severity Index (scPDSI), and (c) Standard Evapotranspiration Deficit Index (SEDI). The analysis identifies SPEI12 as arguably the best performing drought index for monitoring and forecasting impacts on yields in this region.& & & & & Finally, a Conditional Spectral Granger Causality (CSGC) algorithm is employed for understanding the influence of climate variability on vegetation dynamics. Although the influence of climatic factors (i.e., precipitation, temperature, and solar energy radiation) on vegetation dynamics is heterogeneous, given the wide spectrum of climate regimes in the region, an overall increased influence of temperature on vegetation dynamics is revealed. In conclusion, the observational evidence indicates that climate plays an important role as a driver of both crop and natural vegetation change in the Horn of Africa.& & &
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10572
Abstract: & & Water scarcity is a major challenge for effective agricultural water management in semi-arid regions. The lack of water resources often requires irrigation (e.g., surface and sprinkler irrigation), providing crops with sufficient soil moisture to maintain photosynthesis and transpiration. To improve crop yields and simultaneously minimise water usage, accurate monitoring of crop evaporation, the primary indicator of plant water consumption, is essential. Given the heterogeneity inherent to semi-arid croplands,& hyper-resolution images can enhance the quality and accuracy of monitoring(& m). Such monitoring systems necessitate the development of remote sensing-based models capable of resolving processes at hyper-resolution and providing spatio-temporally consistent estimates of evaporation.& br& In this study, we estimate daily crop evaporation of wheat in the experimental site of the Agriculture College of Shiraz University (Shiraz, Iran) over four years (2016& #8211 ). As a first step, we drive the Global Land Evaporation Amsterdam Model (GLEAM) with Landsat 8 data to generate evaporation at hyper-resolution (30 m). The GLEAM model, originally designed to estimate evaporation at ecosystem-to-global scales, is adapted to consider both surface and sprinkler irrigation in water balance calculation, a common feature in irrigated agriculture. The additional water through surface irrigation is introduced into the system via the soil water balance module, whereas the sprinkler irrigation is introduced as additional precipitation into the interception module. In a second step, we execute an energy balance model, the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), using Landsat 8 data. When appropriate extreme pixels (hot and cold pixels) are specified, METRIC can calculate advection, and also METRIC performance is accurate under heterogeneous land use. The results of these two distinct approaches are intercompared and validated against in situ data.& &
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-20451
Abstract: & & & Summer weather in Europe has become more extreme in recent years. Several studies have focused on unraveling the influence that this extreme weather may have on ecosystem dynamics. However, traditional optical indices characterise the state of vegetation in terms of greenness or structure, but fail to capture short term impacts on vegetation activity caused by water or heat stress. Being a byproduct of photosynthesis, solar induced fluorescence (SIF) represents an exception, since its dynamics may reflect an integral of the environmental stressors that have immediate influence on ecosystem water, energy and carbon exchanges during droughts or heatwaves. Spaceborne datasets of SIF have not only been used to monitor crop photosynthetic activity and GPP at global scales, but also as a proxy of transpiration dynamics, or even biogenic volatile organic compound emissions. Additionally, numerous case studies have indicated the potential of using SIF for early drought detection and monitoring of ecosystem impacts.& & & & & However, as with most earth science applications, the majority of previous studies rely on correlations or linear regressions to establish these cause& #8211 effect relationships, which implies that the actual drivers of drought and periods of vegetation stress remain largely unresolved.& & & & Here we examine the underlying causality and interactions between vegetation activity (represented by changes in SIF) and potential environmental drivers of vegetation stress over Europe during the summer months. Using satellite observations of& photosynthetically active radiation (PAR) and the fraction of absorbed PAR (fPAR), the SIF signal is decomposed into the component that relates to fPAR and the component that relates to the fluorescence yield, which represent different physical and biochemical responses to vegetation stress. Using recently developed methods for causal inference applications in Earth science (causeme.uv.es/), the dynamics of SIF and its deconstructed components are evaluated against satellite observations of soil moisture, vapor pressure deficit and temperatures. Common causal relationships and dynamics are observed when grouping regions by aridity index and fractions of vegetation cover. Results help establish direct and indirect links of potential drivers of vegetation activity during periods of heat and water stress.& &
Publisher: American Geophysical Union (AGU)
Date: 23-06-2023
DOI: 10.1029/2022JD038408
Abstract: The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons—long and short rains—exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modeling and observation‐based studies, here we analyze the HAD rainfall from an observationally‐constrained Lagrangian perspective. We quantify and map the region's major oceanic and terrestrial sources of moisture. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low‐level jet) contribute ∼80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ∼20% from the second month onwards, that is, when the oceanic‐origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, and have important implications for (sub‐)seasonal forecasts and long‐term projections of the HAD rainfall.
Publisher: Wiley
Date: 03-11-2022
Publisher: American Meteorological Society
Date: 08-2016
DOI: 10.1175/2016BAMSSTATEOFTHECLIMATE.1
Abstract: Editor’s note: For easy download the posted pdf of the State of the Climate for 2016 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.
Publisher: American Geophysical Union (AGU)
Date: 04-2017
DOI: 10.1002/2016WR020175
Publisher: MDPI AG
Date: 13-05-2019
DOI: 10.3390/RS11091138
Abstract: Characterizing the terrestrial carbon, water, and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for a number of decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented.
Publisher: Copernicus GmbH
Date: 10-2019
Abstract: Abstract. Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. It is difficult to partition ecosystem-scale evapotranspiration (ET) measurements into E and T, which makes it difficult to validate satellite data and land surface models. Here, we review current progress in partitioning E and T and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques create new opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. For ex le, many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but more analysis is necessary to determine the conditions for which this assumption holds. Another critical assumption for many partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T can exceed 95 % of ET from certain ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon–water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water fluxes and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.
Publisher: Elsevier BV
Date: 06-2018
Publisher: Copernicus GmbH
Date: 23-02-2016
Abstract: Abstract. The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.
Publisher: Copernicus GmbH
Date: 28-07-2017
DOI: 10.5194/HESS-21-3879-2017
Abstract: Abstract. In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and in idual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3–5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the internet of things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems.
Publisher: Copernicus GmbH
Date: 19-05-2020
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-5646
Abstract: & & Different land covers present contrasting changes in energy budgets as a response to heatwaves and droughts and thus the land feedback is expected to vary over the landscape. To date, the study of the biotic determinants of land-atmosphere feedbacks during heatwaves has been restricted to the consideration of different plant functional types. We used improved vegetation structural measurements at organizational levels lower than plant functional types (inter& #8211 and intra& #8211 specific) to estimate the impact of forests on the surface thermal balance.& & & & We combined space-borne measurements of the temperature of plants (ECOSTRESS) and the land surface (MODIS) with ground-based meteorological data to estimate the thermal balance of the surface (& #8710 T) at a resolution of 70x70m in 615 forest plots, dominated by 28 different species. In each plot, forest structural variables were determined through LiDAR. We then analysed the spatiotemporal drivers of & #8710 T by quantifying the contribution of topographical, landscape, meteorological and forest structural variables on & #8710 T both during normal conditions and heatwave episodes.& & & & Canopy temperatures fluctuated according to changes in air temperature and were on average 1& #730 C warmer than the air. During heatwaves, canopies were relatively cooler than the air, compared to normal conditions in all but Mediterranean coniferous forests. The thermal response of canopies to heatwaves strongly varied as a function of environmental variables. Forests in rainy areas and in steep slopes presented the lowest & #8710 T, whereas forests in arid areas and flat terrain had the highest & #8710 T. Interestingly, there was a strong effect of forest structure, since forests with larger biomass kept a cooler thermal balance (lower & #8710 T). Indeed, the total effect of forest structural variables on & #8710 T was of equal magnitude as that of topography or meteorological conditions.& & & & The thermal balance of the surface (& #8710 T) was not only different among the main forest types, but also, it strongly varied within forests dominated by the same species. Because & #8710 T is an important component of the surface energy budget, our results on its dependence on forest structure imply that forest management could be employed to modify the surface energy budget to promote negative (mitigating) feedbacks of forests during heatwave episodes. Further efforts concentrate on estimating changes in aerodynamic conductance between forests and their surroundings, and their potential influence on the land& #8211 atmosphere coupling and the feedback of forests on local temperatures.& &
Publisher: Copernicus GmbH
Date: 25-10-2018
Publisher: Copernicus GmbH
Date: 25-10-2018
Abstract: Abstract. Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, and these reflect a erse 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 0.56 mm day−1, and bias of −0.02 mm day−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.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-8896
Abstract: Partitioning runoff accurately into baseflow and quickflow is crucial for our understanding of the water cycle and for the management of droughts and floods. However, global datasets of long-term mean runoff partitioning are rare, even more so datasets relying on physically-based methods. Here, we present a new global 0.25& #176 dataset of runoff, baseflow and quickflow using a hybrid approach of Budyko-based methods and machine learning (ML). The parameters in the Budyko curve and Budyko-based baseflow curve (BFC curve) are estimated with ML (boosted regression trees, BRT) as a function of catchment characteristics. The BRT models are trained and tested in 1226 catchments worldwide, and then applied globally at grid scale. The catchment-trained models show good performance during the testing phase with R2 equal to 0.96 and 0.87 for runoff and baseflow, respectively. The dataset developed in this study shows that 30.3& #177 .5% (mean & #177 standard deviation) of the precipitation is partitioned into runoff of which 20.6& #177 .1% is baseflow and 9.7& #177 .3% is quickflow. The global long-term mean baseflow in this study (151& #177 mm yr& #8211 ) is lower than that from the Global Streamflow Characteristics Dataset (GSCD, 241& #177 mm yr& #8211 ) and higher than that from ERA5-Land (79& #177 mm yr& #8211 ). This study provides a unique, physically and observationally constrained global dataset of the long-term runoff partitioning. The large differences among different datasets suggest that global runoff partitioning is highly uncertain and requires further investigation.
Publisher: American Meteorological Society
Date: 07-2018
Abstract: This article developed and implemented a new methodology for calculating the standardized evapotranspiration deficit index (SEDI) globally based on the log-logistic distribution to fit the evaporation deficit (ED), the difference between actual evapotranspiration (ETa) and atmospheric evaporative demand (AED). Our findings demonstrate that, regardless of the AED dataset used, a log-logistic distribution most optimally fitted the ED time series. As such, in many regions across the terrestrial globe, the SEDI is insensitive to the AED method used for calculation, with the exception of winter months and boreal regions. The SEDI showed significant correlations ( p 0.05) with the standardized precipitation evapotranspiration index (SPEI) across a wide range of regions, particularly for short ( month) SPEI time scales. This work provides a robust approach for calculating spatially and temporally comparable SEDI estimates, regardless of the climate region and land surface conditions, and it assesses the performance and the applicability of the SEDI to quantify drought severity across varying crop and natural vegetation areas.
Publisher: Copernicus GmbH
Date: 23-12-2021
Abstract: Abstract. Satellite Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolution, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based Earth observation data in hydrological modelling. In a set of experiments, the distributed hydrological model Continuum is set up for the Po River Basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling-Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean = 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite dataset on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-2271
Abstract: We present Version 2 of our widely used 1-km K& #246 pen-Geiger climate classification maps for historical and future climate conditions. The historical maps (1901& #8211 , 1931& #8211 , 1961& #8211 , 1991& #8211 ) are based on high-resolution, observation-based climatologies, while the future maps (2041& #8211 and 2071& #8211 ) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 64 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 40 with the most plausible CO2-induced warming rates. Under the & #8220 middle of the road& #8221 scenario SSP2-4.5, the global land surface area (excluding Antarctica) with suitable climatic conditions for tropical, arid, temperate, cold, and polar vegetation is projected to show a net change of +9 %, +3 %, & #8722 %, & #8722 %, & #8722 %, respectively, in 2071& #8211 (with respect to 1991& #8211 ). The K& #246 pen-Geiger maps, including associated confidence estimates, the underlying monthly air temperature and precipitation data, and sensitivity metrics for CMIP6 climate models are available at oppen.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
No related grants have been discovered for Diego Miralles.