scholarly journals A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation

2020 ◽  
Vol 12 (5) ◽  
pp. 878
Author(s):  
Xinlei He ◽  
Tongren Xu ◽  
Youlong Xia ◽  
Sayed M. Bateni ◽  
Zhixia Guo ◽  
...  
Keyword(s):  
Mississippi River ◽  
Land Surface ◽  
Vegetation Index ◽  
A Priori ◽  
Influential Factors ◽  
Surface Models ◽  
Evapotranspiration Estimation ◽  
The Individual ◽  
Gravity Recovery

In this study, a Bayesian-based three-cornered hat (BTCH) method is developed to improve the estimation of terrestrial evapotranspiration (ET) by integrating multisource ET products without using any a priori knowledge. Ten long-term (30 years) gridded ET datasets from statistical or empirical, remotely-sensed, and land surface models over contiguous United States (CONUS) are integrated by the BTCH and ensemble mean (EM) methods. ET observations from eddy covariance towers (ETEC) at AmeriFlux sites and ET values from the water balance method (ETWB) are used to evaluate the BTCH- and EM-integrated ET estimates. Results indicate that BTCH performs better than EM and all the individual parent products. Moreover, the trend of BTCH-integrated ET estimates, and their influential factors (e.g., air temperature, normalized differential vegetation index, and precipitation) from 1982 to 2011 are analyzed by the Mann–Kendall method. Finally, the 30-year (1982 to 2011) total water storage anomaly (TWSA) in the Mississippi River Basin (MRB) is retrieved based on the BTCH-integrated ET estimates. The TWSA retrievals in this study agree well with those from the Gravity Recovery and Climate Experiment (GRACE).

Increased vulnerability of European ecosystems to two consecutive extreme summers

2021 ◽  
Author(s):  
Ana Bastos ◽  
René Orth ◽  
Markus Reichstein ◽  
Philippe Ciais ◽  
Nicolas Viovy ◽  
...  
Keyword(s):  
Central Europe ◽  
Land Surface ◽  
Vegetation Index ◽  
Legacy Effects ◽  
The Past ◽  
Surface Models ◽  
Summer Temperatures ◽  
The Impact ◽  
Observational Record

<p>Extreme summer temperatures in western and central Europe have become more frequent and heatwaves more prolonged over the past decades. The summer of 2018 was one of the driest and hottest in the observational record and led to losses in vegetation productivity in central Europe by up to 50%. Legacy effects from such extreme summers can affect ecosystem functioning over several years, as vegetation slowly recovers. In 2019 an extremely dry and hot summer was registered again in the region, imposing stress conditions at a time when ecosystems were still recovering from summer 2018.</p><p>Using Enhanced Vegetation Index (EVI) fields from MODIS, we evaluate how ecosystems in central Europe responded to the occurrence of two consecutive extreme summers. We find that only ca. 21% of the area negatively impacted by drought in summer 2018 fully recovered in 2019.</p><p>We find that the strongest EVI anomalies in 2018/19 diverge from the long-term relationships between EVI and climate, indicating an increase in ecosystem vulnerability to heat and drought events. Furthermore, 18% of the area showed a worsening of plant status during summer 2019 in spite of drought alleviation, which could be explained by interannual legacy effects from 2018, such as impaired growth and increased biotic disturbances.</p><p>Land-surface models do not simulate interannual legacy effects from summer 2018 and thereby underestimate the impact of drought in 2019 on ecosystems. The poor representation of drought-induced damage and mortality and lack of biotic disturbances in these models may result in an overestimation of the resilience and stability of temperate ecosystems in the future.</p>

scholarly journals Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018

2019 ◽  
Vol 11 (19) ◽  
pp. 2329 ◽  
Author(s):  
Leon Nill ◽  
Tobias Ullmann ◽  
Christof Kneisel ◽  
Jennifer Sobiech-Wolf ◽  
Roland Baumhauer
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Google Earth ◽  
Influential Factors ◽  
The Arctic ◽  
Moisture Regime ◽  
Mackenzie Delta ◽  
Delta Region

Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land–atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings.

scholarly journals Estimating long-term groundwater storage and its controlling factors in Alberta, Canada

2018 ◽  
Vol 22 (12) ◽  
pp. 6241-6255 ◽  
Author(s):  
Soumendra N. Bhanja ◽  
Xiaokun Zhang ◽  
Junye Wang
Keyword(s):  
Groundwater Recharge ◽  
Environmental Sustainability ◽  
Land Surface ◽  
Water Withdrawal ◽  
Groundwater Storage ◽  
Budget Analysis ◽  
Terrestrial Water ◽  
The Individual ◽  
Gravity Recovery

Abstract. Groundwater is one of the most important natural resources for economic development and environmental sustainability. In this study, we estimated groundwater storage in 11 major river basins across Alberta, Canada, using a combination of remote sensing (Gravity Recovery and Climate Experiment, GRACE), in situ surface water data, and land surface modeling estimates (GWSAsat). We applied separate calculations for unconfined and confined aquifers, for the first time, to represent their hydrogeological differences. Storage coefficients for the individual wells were incorporated to compute the monthly in situ groundwater storage (GWSAobs). The GWSAsat values from the two satellite-based products were compared with GWSAobs estimates. The estimates of GWSAsat were in good agreement with the GWSAobs in terms of pattern and magnitude (e.g., RMSE ranged from 2 to 14 cm). While comparing GWSAsat with GWSAobs, most of the statistical analyses provide mixed responses; however the Hodrick–Prescott trend analysis clearly showed a better performance of the GRACE-mascon estimate. The results showed trends of GWSAobs depletion in 5 of the 11 basins. Our results indicate that precipitation played an important role in influencing the GWSAobs variation in 4 of the 11 basins studied. A combination of rainfall and snowmelt positively influences the GWSAobs in six basins. Water budget analysis showed an availability of comparatively lower terrestrial water in 9 of the 11 basins in the study period. Historical groundwater recharge estimates indicate a reduction of groundwater recharge in eight basins during 1960–2009. The output of this study could be used to develop sustainable water withdrawal strategies in Alberta, Canada.

scholarly journals Climatology and Forest Phenology during 1984-2013 around Western Lake Superior, USA.

2020 ◽  
Author(s):  
Matthew Garcia
Keyword(s):  
Remote Sensing ◽  
Interannual Variability ◽  
Land Surface ◽  
Vegetation Index ◽  
Lake Superior ◽  
Growing Season ◽  
Western Lake ◽  
Phenological Cycle ◽  
The Mean

Landsat has a history of use in the diagnosis of land surface phenology, vegetation disturbance, and their impacts on numerous forest biological processes. Studies have connected remote sensing-based phenology to surface climatological patterns, often using average temperatures and derived growing degree day accumulations. I present a detailed examination of remotely sensed forest phenology in the region of western Lake Superior, USA, based on a comprehensive climatological assessment and 1984-2013 Landsat imagery. I use this climatology to explain both the mean annual land surface phenological cycle and its interannual variability in temperate mixed forests. I assess long-term climatological means, trends, and interannual variability for the study period using available weather station data, focusing on numerous basic and derived climate indicators: seasonal and annual temperature and precipitation, the traditionally defined frost-free growing season, and a newly defined metric of the climatological growing season: the warm-season plateau in accumulated chilling days. Results indicate +0.56°C regional warming during the 30-year study period, with cooler springs (–1.26°C) and significant autumn warming (+1.54°C). The duration of the climatological growing season has increased +0.27 days/y, extending primarily into autumn. Summer precipitation in my study area declined by an average –0.34 cm/y, potentially leading to moisture stress that can impair vegetation carbon uptake rates and can render the forest more vulnerable to disturbance. Many changes in temperature, precipitation, and climatological growing season are most prominent in locations where Lake Superior exerts a strong hydroclimatological influence, especially the Minnesota shoreline and in forest areas downwind (southeast) of the lake. I then develop and demonstrate a novel phenoclimatological modeling method, applied over five Landsat footprints across my study area, that combines (1) diagnosis of the mean phenological cycle from remote sensing observations with (2) a partial-least-squares regression (PLSR) approach to modeling vegetation index residuals using these numerous meteorological and climatological observations. While the mean phenology often used to inform land surface models in meteorological and climate modeling systems may explain 50-70% of the observed phenological variability, this mixed modeling approach can explain more than 90% of the variability in phenological observations based on long-term Landsat records for this region.

The ILRS Contribution to ITRF2020

2021 ◽  
Author(s):  
Vincenza Luceri ◽  
Erricos C. Pavlis ◽  
Antonio Basoni ◽  
David Sarrocco ◽  
Magdalena Kuzmicz-Cieslak ◽  
...  
Keyword(s):  
Time Series ◽  
Tracking System ◽  
A Priori ◽  
Systematic Errors ◽  
Novel Approach ◽  
Priori Information ◽  
The Stability ◽  
The Individual ◽  
Stability And Consistency

<p>The International Laser Ranging Service (ILRS) contribution to ITRF2020 has been prepared after the re-analysis of the data from 1993 to 2020, based on an improved modeling of the data and a novel approach that ensures the results are free of systematic errors in the underlying data. This reanalysis incorporates an improved “target signature” model (CoM) that allows better separation of true systematic error of each tracking system from the errors in the model describing the target’s signature. The new approach was developed after the completion of ITRF2014, the ILRS Analysis Standing Committee (ASC) devoting almost entirely its efforts on this task. The robust estimation of persistent systematic errors at the millimeter level permitted the adoption of a consistent set of long-term mean corrections for data collected in past years, which are now applied a priori (information provided by the stations from their own engineering investigations are still taken into consideration). The reanalysis used these corrections, leading to improved results for the TRF attributes, reflected in the resulting new time series of the TRF origin and especially in the scale. Seven official ILRS Analysis Centers computed time series of weekly solutions, according to the guidelines defined by the ILRS ASC. These series were combined by the ILRS Combination Center to obtain the official ILRS product contribution to ITRF2020.</p><p>The presentation will provide an overview of the analysis procedures and models, and it will demonstrate the level of improvement with respect to the previous ILRS product series; the stability and consistency of the solution are discussed for the individual AC contributions and the combined SLR time series.</p>

scholarly journals Towards a public, standardized, diagnostic benchmarking system for land surface models

2012 ◽  
Vol 5 (3) ◽  
pp. 819-827 ◽  
Author(s):  
G. Abramowitz
Keyword(s):  
Land Surface ◽  
A Priori ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Land Surface Models ◽  
Surface Models ◽  
Definition Of ◽  
Benchmarking System

Abstract. This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an example of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS) is introduced – a free, online land surface model benchmarking application that is structured to meet both of these goals.

scholarly journals A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

2020 ◽  
Vol 12 (8) ◽  
pp. 3227 ◽  
Author(s):  
Anjar Dimara Sakti ◽  
Wataru Takeuchi
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Time Series Data ◽  
Global Analysis ◽  
Local Level ◽  
Series Data ◽  
Pixel Resolution ◽  
Cropping Intensity

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.

scholarly journals Simplified Priestley–Taylor Model to Estimate Land-Surface Latent Heat of Evapotranspiration from Incident Shortwave Radiation, Satellite Vegetation Index, and Air Relative Humidity

2021 ◽  
Vol 13 (5) ◽  
pp. 902
Author(s):  
Yunjun Yao ◽  
Zhenhua Di ◽  
Zijing Xie ◽  
Zhiqiang Xiao ◽  
Kun Jia ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Latent Heat ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mainland China ◽  
Shortwave Radiation ◽  
Land Cover Types

An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation (Rs), satellite vegetation index, and air relative humidity (RH). Ground-measured ET for 2000–2009 collected by 100 global FLUXNET eddy covariance (EC) sites was used to calibrate and evaluate the SPT model. A series of cross-validations demonstrated the reasonable performance of the SPT model to estimate seasonal and spatial ET variability. The coefficients of determination (R2) of the estimated versus observed daily (monthly) ET ranged from 0.42 (0.58) (p < 0.01) at shrubland (SHR) flux sites to 0.81 (0.86) (p < 0.01) at evergreen broadleaf forest (EBF) flux sites. The SPT model was applied to estimate agricultural ET at high spatial resolution (16 m) from Chinese Gaofen (GF)-1 data and monitor long-term (1982–2018) ET variations in the Three-River Headwaters Region (TRHR) of mainland China using the Global LAnd-Surface Satellite (GLASS) normalized difference vegetation index (NDVI) product. The proposed SPT model without Rn provides an alternative model for estimating regional terrestrial ET across different land-cover types.

scholarly journals Response of vegetation to the 2003 European drought was mitigated by height

2013 ◽  
Vol 10 (10) ◽  
pp. 16075-16100
Author(s):  
S. L. Bevan ◽  
S. O. Los ◽  
P. R. J. North
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Diurnal Temperature Range ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Changes ◽  
High Latitudes ◽  
Vegetation Height ◽  
Direct Measurements ◽  
Surface Models

Abstract. The effects on climate of land-cover change, predominantly from forests to crops or grassland, are reasonably well understood for low and high latitudes but are largely unknown for temperate latitudes. The main reason for this gap in our knowledge is that there are compensating effects on the energy and water balance when land cover changes. To obtain a better understanding of the direction of this response, we analyse the differential response of tall and short vegetation to the 2003 European drought. We analyse precipitation, temperature and normalized difference vegetation index data and compare these with direct measurements of vegetation height. At the height of the 2003 drought we find for tall vegetation a significantly smaller decrease in vegetation index and a smaller diurnal temperature range, indicating less water stress on tall vegetation, which can be explained by access of tall vegetation to deeper soil water. Based on these results we question the current parameterizations of short and tall vegetation in some land-surface models.

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