scholarly journals Global land-surface evaporation estimated from satellite-based observations

2010 ◽  
Vol 7 (5) ◽  
pp. 8479-8519 ◽  
Author(s):  
D. G. Miralles ◽  
T. R. H. Holmes ◽  
R. A. M. De Jeu ◽  
J. H. Gash ◽  
A. G. C. A. Meesters ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Surface Conductance ◽  
Vegetation Density ◽  
Climate Conditions ◽  
Eddy Covariance Measurements ◽  
One Year ◽  
Daily Evaporation

Abstract. This paper outlines a new methodology to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale, with a 0.25 degree spatial resolution. Central to this approach is the use of the Priestley and Taylor (PT) evaporation model. Because the PT equation is driven by net radiation, this strategy avoids the need to specify surface fields of variables, such as the surface conductance, which cannot be detected directly from space. Key distinguishing features are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the use of a detailed rainfall interception module. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R = 0.84, N = 43) and show a negligible bias (−1.5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R = 0.84, still lower than the R = 0.91 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

scholarly journals Global land-surface evaporation estimated from satellite-based observations

2011 ◽  
Vol 15 (2) ◽  
pp. 453-469 ◽  
Author(s):  
D. G. Miralles ◽  
T. R. H. Holmes ◽  
R. A. M. De Jeu ◽  
J. H. Gash ◽  
A. G. C. A. Meesters ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Vegetation Density ◽  
Climate Conditions ◽  
New Strategy ◽  
Interception Loss ◽  
One Year ◽  
Daily Evaporation

Abstract. This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R=0.80, N=43) and present a low average bias (−5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R=0.83, still lower than the R=0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

scholarly journals Pan-Antarctic map of near-surface permafrost temperatures at 1 km<sup>2</sup> scale

2020 ◽  
Vol 14 (2) ◽  
pp. 497-519 ◽  
Author(s):  
Jaroslav Obu ◽  
Sebastian Westermann ◽  
Gonçalo Vieira ◽  
Andrey Abramov ◽  
Megan Ruby Balks ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Reanalysis Data ◽  
Strong Wind ◽  
Near Surface ◽  
Permafrost Temperature ◽  
Scale Modelling ◽  
Almost All ◽  
The Antarctic

Abstract. Permafrost is present within almost all of the Antarctic's ice-free areas, but little is known about spatial variations in permafrost temperatures except for a few areas with established ground temperature measurements. We modelled a temperature at the top of the permafrost (TTOP) for all the ice-free areas of the Antarctic mainland and Antarctic islands at 1 km2 resolution during 2000–2017. The model was driven by remotely sensed land surface temperatures and downscaled ERA-Interim climate reanalysis data, and subgrid permafrost variability was simulated by variable snow cover. The results were validated against in situ-measured ground temperatures from 40 permafrost boreholes, and the resulting root-mean-square error was 1.9 ∘C. The lowest near-surface permafrost temperature of −36 ∘C was modelled at Mount Markham in the Queen Elizabeth Range in the Transantarctic Mountains. This is the lowest permafrost temperature on Earth, according to global-scale modelling results. The temperatures were most commonly modelled between −23 and −18 ∘C for mountainous areas rising above the Antarctic Ice Sheet and between −14 and −8 ∘C for coastal areas. The model performance was good where snow conditions were modelled realistically, but errors of up to 4 ∘C occurred at sites with strong wind-driven redistribution of snow.

scholarly journals Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

2019 ◽  
Vol 11 (22) ◽  
pp. 2616 ◽  
Author(s):  
Stefan Mayr ◽  
Claudia Kuenzer ◽  
Ursula Gessner ◽  
Igor Klein ◽  
Martin Rutzinger
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Optical Sensors ◽  
Land Surface ◽  
Global Scale ◽  
Observation Time ◽  
Large Area ◽  
Validation Data ◽  
Information Products ◽  
Global Coverage

Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.

Advancements in the mesoscale Hydrologic Model at the global scale

2021 ◽  
Author(s):  
Oldrich Rakovec ◽  
Maren Kaluza ◽  
Rohini Kumar ◽  
Robert Schweppe ◽  
Pallav Shrestha ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Hydrologic Model ◽  
Seasonal Forecasts ◽  
Ongoing Research ◽  
Surface Models ◽  
Forecasting System ◽  
Best Parameter

&lt;p&gt;This study synthesizes the advancements made in the setup of the mesoscale Hydrologic Model (mHM; [1,2,3]) at the global scale. Underlying vegetation and geophysical characteristics are provided at &amp;#8776;200m, while the mHM simulates water fluxes and states between 10 km and 100 km spatial resolution. The meteorologic forcing data are derived from the readily available, near-real time ERA-5 dataset [4]. The total of 50 global parameters of the Multiscale Parameter Regionalization (MPR) are constrained in two modes: (1) streamflow only across 3054 gauges, and (2) streamflow across 3054 gauges and simultaneously with FLUXNET ET and GRACE TWSA across 258 domains consisting of &amp;#8776;10&amp;#176; x 10&amp;#176; blocks. Model performance is finally evaluated against a range of observed and reference data since 1985.&amp;#160;&lt;/p&gt;&lt;p&gt;The single best parameter set evaluated across 3054 GRDC global streamflow station yield median performance of 0.47 daily KGE (0.55 monthly KGE). This performance varies strongly between continents. For example, median daily KGE across Europe is around 0.55 (N basins=972) and across northern America around 0.5 (N basins=1264). So far, the worst model performance is observed across Africa, with median KGE of 0 (N basins=202), using the same globally constrained parameter set. The deterioration of model performance based on seamless parameterization can be explained by the quality of the underlying data, which corresponds to areas, where water balance closure error is the biggest. Additionally, missed model processes play an important role as well. Finally, there remains a large gap between the onsite calibrations and global calibrations and ongoing research is being done to narrow down these differences. This work is the fundament for building skillful global seasonal forecasting system ULYSSES [6], which provides hindcasts and operational seasonal forecasts of hydrologic variables using four state of the art hydrologic/land surface models with lead time of 6 months.&lt;/p&gt;&lt;ul&gt;&lt;li&gt;[1] https://www.ufz.de/mhm&lt;/li&gt; &lt;li&gt;[2] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327&lt;/li&gt; &lt;li&gt;[3] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195&lt;/li&gt; &lt;li&gt;[4] https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803&lt;/li&gt; &lt;li&gt;[5] https://www.ufz.de/ulysses&lt;/li&gt; &lt;/ul&gt;

scholarly journals Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

2017 ◽  
Vol 10 (4) ◽  
pp. 1403-1422 ◽  
Author(s):  
Christoph Müller ◽  
Joshua Elliott ◽  
James Chryssanthacopoulos ◽  
Almut Arneth ◽  
Juraj Balkovic ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Data ◽  
Model Performance ◽  
Global Scale ◽  
Crop Model ◽  
Yield Variability ◽  
Crop Modeling ◽  
Model Intercomparison ◽  
Crop Models ◽  
Simulation Results

Abstract. Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

scholarly journals Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces

2020 ◽  
Author(s):  
Ren Wang ◽  
Pierre Gentine ◽  
Jiabo Yin ◽  
Lijuan Chen ◽  
Jianyao Chen ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Sensible Heat Flux ◽  
Global Scale ◽  
Surface Conductance ◽  
Land Surfaces

Abstract. Evapotranspiration (ET) accompanied by water and heat transport in the hydrological cycle is a key component in regulating surface aridity. Existing studies on changes in surface aridity have typically estimated ET using semi-empirical equations or parameterizations of land surface processes, which are based on the assumption that the parameters in the equation are stationary. However, plant physiological effects and its response to a changing environment are dynamically modifying ET, thereby challenging this assumption and limiting the estimation of long-term ET. In this study, the latent heat flux (ET in energy units) and sensible heat flux were retrieved for recent decades on a global scale using machine learning approach and driven by ground-based observations from flux towers and weather stations. The study resulted in several findings, namely that the evaporative fraction (EF) – the ratio of latent heat flux to available surface energy – exhibited a relatively decreasing trend on fractional land surfaces; In particular, the decrease in EF was accompanied by an increase in long-term runoff as assessed by precipitation (P) minus ET, accounting for 27.06 % of the global land areas. The signs were indicative of reduced surface conductance, which further emphasized that land-surface vegetation has major impacts on regulating the water and energy cycles, as well as aridity variability.

scholarly journals Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION

2016 ◽  
pp. 1
Author(s):  
A. Verger ◽  
I. Filella ◽  
F. Baret ◽  
J. Peñuelas
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Global Scale ◽  
Latitudinal Gradients ◽  
Area Index ◽  
Climatic Drivers ◽  
Phenological Metrics ◽  
Earth System Models ◽  
Good Agreement ◽  
Birch Forests

<p align="justify">Land surface phenology from time series of satellite data are expected to contribute to improve the representation of vegetation phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from GEOCLIM-LAI, a global climatology of leaf area index (LAI) derived from 1-km SPOT VEGETATION time series for 1999-2010. The calibration with ground measurements showed that the start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The satellite-derived phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests in Europe, cherry in Asia and lilac shrubs in North America showed an overall root mean square error lower than 19 days for the start, end and length of season, and good agreement between the latitudinal gradients of VEGETATION LAI phenology and ground data.</p>

scholarly journals Pan-Antarctic map of near-surface permafrost temperatures at 1 km<sup>2</sup> scale

2019 ◽  
Author(s):  
Jaroslav Obu ◽  
Sebastian Westermann ◽  
Gonçalo Vieira ◽  
Andrey Abramov ◽  
Megan Balks ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Reanalysis Data ◽  
Strong Wind ◽  
Near Surface ◽  
Ground Temperatures ◽  
Permafrost Temperature ◽  
Snow Conditions ◽  
Almost All

Abstract. Permafrost is present under almost all of the Antarctic’s ice-free areas but little is known about spatial variations of permafrost temperatures outside a few areas with established ground temperature measurements. We modelled a temperature at the top of the permafrost (TTOP) for all the ice-free areas of Antarctic mainland and Antarctic Islands at 1 km2 resolution during 2000–2017. The model was driven by remotely-sensed land surface temperatures and down-scaled ERA-Interim climate reanalysis data and subgrid permafrost variability was simulated by variable snow cover. The results were validated against in-situ measured ground temperatures from 40 permafrost boreholes and the resulting root mean square error was 1.9 °C. The lowest near-surface permafrost temperature of −33 °C was modelled at Mount Markham in Queen Elizabeth Range in the Transantarctic Mountains. This is the lowest permafrost temperature on Earth according to the modelling results on global scale. The temperatures were most commonly modelled between −23 and −18 °C for mountainous areas rising above the Antarctic Ice Sheet and between −14 and −8 °C for coastal areas. The model performance was good where snow conditions were modelled realistically but errors of up to 4 °C can occur at sites with strong wind-driven redistribution of snow.

scholarly journals Appraising the capability of a land biosphere model as a tool in modelling land surface interactions: results from its validation at selected European ecosystems

2015 ◽  
Vol 6 (1) ◽  
pp. 217-265 ◽  
Author(s):  
M. R. North ◽  
G. P. Petropoulos ◽  
G. Ireland ◽  
J. P. McCalmont
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Global Scale ◽  
Surface Interactions ◽  
Climatic Conditions ◽  
Model Parameters ◽  
Observation Data ◽  
Ongoing Research ◽  
In Situ Data

Abstract. In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Specifically, SimSphere's performance in predicting Net Radiation (Rnet), Latent Heat (LE), Sensible Heat (H) and Air Temperature (Tair) at 1.3 and 50 m was examined. Model simulations were validated by ground-based measurements of the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation. Overall, model performance can largely be described as satisfactory for most of the experimental sites and evaluated parameters. For all model parameters compared, predicted H fluxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36 W m−2. LE fluxes and Rnet also agreed well with the in-situ data with RSMDs of 62.75 and 64.65 W m−2 respectively. A good agreement between modelled and measured LE and H fluxes was found, especially for smoothed daily flux trends. For both Tair 1.3 m and Tair 50 m a mean RMSD of 4.14 and 3.54 °C was reported respectively. This work presents the first all-inclusive evaluation of SimSphere, particularly so in a European setting. Results of this study contribute decisively towards obtaining a better understanding of the model's structure and its correspondence to the real world system. Findings also further establish the model's capability as a useful teaching and research tool in modelling Earth's land surface interactions. This is of considerable importance in the light of the rapidly expanding use of the model worldwide, including ongoing research by various Space Agencies examining its synergistic use with Earth Observation data towards the development of operational products at a global scale.

scholarly journals Global-scale analysis of satellite-derived time series of naturally inundated areas as a basis for floodplain modeling

2010 ◽  
Vol 27 ◽  
pp. 45-50 ◽  
Author(s):  
L. Adam ◽  
P. Döll ◽  
C. Prigent ◽  
F. Papa
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Water Cycle ◽  
Open Water ◽  
Global Scale ◽  
Irrigated Rice ◽  
Surface Models ◽  
Terrestrial Water ◽  
Inundation Extent ◽  
And Storage

Abstract. Floodplains play an important role in the terrestrial water cycle and are very important for biodiversity. Therefore, an improved representation of the dynamics of floodplain water flows and storage in global hydrological and land surface models is required. To support model validation, we combined monthly time series of satellite-derived inundation areas (Papa et al., 2010) with data on irrigated rice areas (Portmann et al., 2010). In this way, we obtained global-scale time series of naturally inundated areas (NIA), with monthly values of inundation extent during 1993–2004 and a spatial resolution of 0.5°. For most grid cells (0.5°×0.5°), the mean annual maximum of NIA agrees well with the static open water extent of the Global Lakes and Wetlands database (GLWD) (Lehner and Döll, 2004), but in 16% of the cells NIA is larger than GLWD. In some regions, like Northwestern Europe, NIA clearly overestimates inundated areas, probably because of confounding very wet soils with inundated areas. In other areas, such as South Asia, it is likely that NIA can help to enhance GLWD. NIA data will be very useful for developing and validating a floodplain modeling algorithm for the global hydrological model WGHM. For example, we found that monthly NIAs correlate with observed river discharges.

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