Multi-decadal trends in global terrestrial evapotranspiration and its components

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 (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. 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

Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types

© 2016 Elsevier Ltd Terrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle. The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. Here we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: ln(GPP)=a×ln(iEVI)+b. iEVI was computed from MODIS monthly EVI products following removal of values affected by snow or cold temperature and without calculating growing season duration. Through categorisation of flux sites into 12 land cover types, the ability of iEVI to estimate GPP was considerably improved (R2 from 0.62 to 0.74, RMSE from 454.7 to 368.2 g C m−2 yr−1). The biome-specific GPP-iEVI formulae generally showed a consistent performance in comparison to a global benchmarking dataset (R2 = 0.79, RMSE = 387.8 g C m−2 yr−1). Specifically, iEVI performed better in cropland regions with high productivity but poorer in forests. The ability of iEVI in estimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreen due to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was in a closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are more common and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant and negative correlation (R2 = 0.37, p < 0.05) was observed between the strength (R2) of GPP-iEVI relationships and mean annual maximum leaf area index (LAImax), and the relationship between the strength and mean annual precipitation followed a similar trend. LAImax also revealed a scaling effect on GPP-iEVI relationships. Our results suggest that iEVI provides a very simple but robust approach to estimate spatial patterns of global annual GPP whereas its effect is comparable to various light-use-efficiency and data-driven models. The impact of vegetation structure on accuracy and sensitivity of EVI in estimating spatial GPP provides valuable clues to improve EVI-based models

Parameterization of an ecosystem light-use-efficiency model for predicting savanna GPP using MODIS EVI

© 2014 Elsevier Inc. Accurate estimation of carbon fluxes across space and time is of great importance for quantifying global carbon balances. Current production efficiency models for calculation of gross primary production (GPP) depend on estimates of light-use-efficiency (LUE) obtained from look-up tables based on biome type and coarse-resolution meteorological inputs that can introduce uncertainties. Plant function is especially difficult to parameterize in the savanna biome due to the presence of varying mixtures of multiple plant functional types (PFTs)with distinct phenologies and responses to environmental factors. The objective of this study was to find a simple and robust method to accurately up-scale savanna GPP fromlocal, eddy covariance (EC) flux tower GPP measures to regional scales utilizing entirely remote sensing oservations. Here we assessed seasonal patterns of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation productswith seasonal EC tower GPP (GPPEC) at four sites along an ecological rainfall gradient (the North Australian Tropical Transect, NATT) encompassing tropical wet to dry savannas. The enhanced vegetation index (EVI) tracked the seasonal variations of GPPEC well at both site- and cross-site levels (R2= 0.84). The EVI relationship with GPPEC was further strengthened through coupling with ecosystem light-use-efficiency (eLUE), defined as the ratio of GPP to photosynthetically active radiation (PAR). Two savanna landscape eLUEmodels, driven by top-of-canopy incident PAR (PARTOC) or top-of-atmosphere incident PAR (PARTOA) were parameterized and investigated. GPP predicted using the eLUE models correlated well with GPPEC, with R2 of 0.85 (RMSE = 0.76 g C m-2 d-1) and 0.88 (RMSE = 0.70 g C m-2 d-1) for PARTOC and PARTOA, respectively, and were significantly improved compared to the MOD17 GPP product (R2 = 0.58, RMSE= 1.43 g C m-2 d-1). The eLUE model also minimized the seasonal hysteresis observed between greenup and brown-down in GPPEC and MODIS satellite product relationships, resulting in a consistent estimation of GPP across phenophases. The eLUE model effectively integrated the effects of variations in canopy photosynthetic capacity and environmental stress on photosynthesis, thus simplifying the up-scaling of carbon fluxes from tower to regional scale. The results fromthis study demonstrated that region-wide savanna GPP can be accurately estimated entirely with remote sensing observations without dependency on coarse-resolution ground meteorology or estimation of light-use-efficiency parameters

Development and Extrapolation of a General Light Use Efficiency Model for the Gross Primary Production

The global carbon cycle is one of the large biogeochemical cycles spanning all living and non-living compartments of the Earth system. Against the background of accelerating global change, the scientific community is highly interested in analyzing and understanding the dynamics of the global carbon cycle and its complex feedback mechanism with the terrestrial biosphere. The international network FLUXNET was established to serve this aim with measurement towers around the globe. The overarching objective of this thesis is to exploit the powerful combination of carbon flux measurements and satellite remote sensing in order to develop a simple but robust model for the gross primary production (GPP) of vegetation stands. Measurement data from FLUXNET sites as well as remote sensing data from the NASA sensor MODIS are exploited in a data-based model development approach. The well-established concept of light use efficiency is chosen as modeling framework. As a result, a novel gross primary production model is established to quantify the carbon uptake of forests and grasslands across a broad range of climate zones. Furthermore, an extrapolation scheme is derived, with which the model parameters calibrated at FLUXNET sites can be regionalized to pave the way for spatially continuous model applications

Enhancing regional estimates of evapotranspiration with earth observation data

Food security and food sustainability are high on the global policy agenda. Reliable information on crop water use and terrestrial water stress are important to ensure an optimal use of available water resources and for enhancing crop production. Remote sensing provides a feasible avenue to estimate regional evapotranspiration (ET), which can be employed to assess terrestrial water stress. However, the heterogeneity of land surfaces and the accumulated errors from various inputs often result in substantial biases in most global or regional ET models across different landscapes. Reducing uncertainties in available ET products or remote sensing (RS)-based models and obtaining regional ET estimates with improved accuracy is important for effectively using ET to support agricultural monitoring and water resources managements. This thesis first compared different Priestly-Taylor (PT)-based methods that use three Earth observation-based alternatives - apparent thermal inertia (ATI), microwave soil moisture (SM), and optical spectral indices based on shortwave infrared (SWIR) to assess soil evaporation over cropland and grassland regions. Using FLUXNET data as ET reference, the results illustrated that the incorporation of the SWIR-based soil moisture divergence index (SMDI) and microwave-based SM into monthly soil evaporation led to 6% and 5% increase in explained ET variances and reduced RMSE by 23.2% and 13.1% for cropland and grassland, respectively, as compared to PT-JPL using atmospheric reanalysis data only. The results suggested that a combination of optical SWIR and microwave SM has good potential to improve the PT-JPL model accuracy for agricultural landscapes. Based on the performance of different PT-based methods, ET estimates derived from the revised PT method were used to assess water budgets across 53 catchments in central-western Europe with a humid climate and were compared with three additional ET data sources (MOD16, GLEAM, and PT-JPL). Surprisingly, all RS-based ET estimates significantly diverged from water balance-based ET (ETWB) in 45 humid catchments, whereas most previous studies that focussed on arid catchments or on the global scale found significantly less divergence. Using ET retrievals from the Budyko framework and upscaled ET from FLUXCOM as references, the closure errors of water budgets were sensitive to errors arising from precipitation data in humid regions and the water balance approach was found to overestimate ET during heavy rainfall events. Instead, the Budyko framework that describes the partitioning of precipitation to ET as a functional balance between atmospheric water supply (precipitation, P) and demand (potential evapotranspiration, PET) had good correlation with ET ensemble from multiple data sources and upscaled ET from FLUXCOM product. 161 Summary The results indicated that errors from precipitation and terrestrial water storage anomalies introduce large uncertainties in ETWB, thereby complicating water balance validation in humid regions across multiple timesteps. To improve the application of ETWB for benchmarking ETEB in humid regions, high-quality input data should be used or – like the Budyko framework – energy constraints should be considered. The thesis then proceeds to explore causes for the notable deviations between observed and Budyko-predicted water balances in certain catchments. The results revealed that for humid catchments, topography and seasonal cumulative moisture surplus can explain the spatial distributions of Budyko scatter with r higher than 0.65, whereas soil properties and vegetation indices explained little of the variance (r≤0.30). Temporally, the interannual variability of Budyko scatter was negatively correlated with annual average vegetation indices, particularly for catchments with relatively low vegetation cover. This thesis provides valuable insights to the interpretation of the Budyko framework and offers possible solutions to improve its performance to predict the spatiotemporal variability of water balances. Lastly, to address the deviations from the predictive Budyko curve, additional controls of hydrological partitioning were introduced to correct Budyko scatter between catchments and between years. The results illustrated that the use of catchment climatic seasonality properties and topography attributes is effective in reproducing the Budyko parameter (w) with an r of 0.76 and RMSE of 0.49 for all 45 catchments in central-western Europe. After the correction of temporal Budyko scatter using interannual variability of vegetation information and the fraction of precipitation falling as snow, the performance of the modified Budyko-type equation improves with respect to the original Budyko framework, in comparison to ETWB at catchment scale (∆r of 0.26 and ∆RMSE of 19.19 mm/yr). When compared with the gridded ET ensemble using energy balance, the enhanced Budyko framework is generally effective to reproduce the spatial distribution of ET with good similarity, even in ungauged regions. Overall, the revised Budyko framework shows improved performance in predicting water balances and can be applied to assess crop water use and terrestrial water stress at regional scale, particularly in ungauged areas. Overall, this thesis contributes significantly to the enhancement of regional ET estimation using Earth observation. It proposes a novel blended parameterization for soil moisture constraints in the modified PT-JPL model, which is capable of capturing the soil evaporation more accurately within agroecosystems. Meanwhile, this thesis proposes a new water balance-based validation method that uses the Budyko framework integrated with environmental parameters. By developing improved RS-based models and water balance-based validation methods, this thesis provides valuable insights into the complexities of ET 162 Summary estimation at the regional scale. These findings are expected to advance the application of ET in decision-making regarding the management of agriculture and water resources

Comparison of MODIS-Algorithms for Estimating Gross Primary Production from Satellite Data in semi-arid Africa

The climatic patterns of the world are changing and with them the spatial distribution of global terrestrial carbon; the food and fiber of the world and in itself an important factor in the changing climate. Knowledge of how the terrestrial carbon stock is changing, its distribution and quantity, is important in understanding how the patterns of the world are changing and large scale models using remotely sensed data have emerged for this purpose. This study compares four vegetation related MODIS (Moderate Resolution Imaging Spectroradiometer) products, derived from MODIS satellite data using algorithms which calculates the two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and the two biophysical factors, Leaf Area Index (LAI) and absorbed Fraction of Photosynthetically Active Radiation (FPAR). The comparison is in their ability to estimate intra-annual variations of Gross Primary Production (GPP); this is done using the time-series data of quality screened eddy covariance (EC) Flux Tower stations from the Carbo Africa network as truth data. The results show a modest agreement between the different vegetation metrics and EC Flux Tower derived GPP, with an overall average coefficient of determination (R2) of 0.63 for LAI, R2 of 0.51 for NDVI, R2 of 0.52 FPAR and a R2 of 0.49 for EVI, using all stations and years of data. When each station received the same weight, i.e. using the correlation of all observation for each station and then calculating the average, the overall correlation improved, still showing LAI as the best predictor of Flux Tower GPP with a R2 of 0.62, but with an improved EVI with a R2 of 0.61, while NDVI and FPAR had an R2 of 0.57 and 0.59 respectively. This result and the observed large variation in between stations, e.g. NDVI between an R2 of 0.62 and 0.83 for the station Demokeya compared to an R2 of 0.32 and 0.49 of NDVI for the station Tchizalamou, may indicate a site specific proficiency of the vegetation metrics. When the observations within the growing period were tested separately a strong decrease in correlation was observed, with an average R2 between 0.41 – 0.56 for all station and years and an average R2 between 0.36 – 0.45 for all sites using all observations for each station regardless of year, lending strength to the assumption that the non-vegetation period observations affect the correlation greatly. The study concludes that up scaling of an intra-annual standardized major axis regression model based solely on the relationship between any of these metrics and Flux Tower estimated GPP is inadvisable due to the modest overall intra-annual agreement between the metrics and GPP. It is also concluded that since the vegetation metrics display site specific proficiency, models of GPP would benefit from site specific ancillary data that describes vegetation-limiting factors, e.g. water availability.Klimatmönstren världen över förändras och med dessa den globala distributionen och mängden av landbundet kol, dvs vegetationen som bland annat nyttjas som mat och fiber. Också I sig självt en viktig faktor i klimatets utveckling genom dess roll i energi- och vattenkretsloppen. Vetskap om kvantitet och distribution av landbundet kol och hur detta förändras är en viktigt del av arbetet i att förstå hur de globala mönster förändras, och för denna avsikt har bredskaliga modeller som nyttjar satellit data framtagits. Denna studie jämför fyra vegetations relaterade MODIS (Moderate Resolution Imaging Spectroradiometer) produkter, som erhålls från MODIS satellit data genom algoritmer som kalkylerar de två vegetation indexen, Normalized Vegetation Index (NDVI) och Enhanced Vegetation Index (EVI), och de två biofysiska faktorerna, Leaf Area Index (LAI) och absorbed Fraction of Photosynthetically Active Radiation (FPAR). Deras förmåga att uppskatta variationen av den totala primär produktionen (Gross Primary Production, GPP) över året jämförs, genom tidsserier av eddy kovarians (EC) data från flux torn ur Carbo Africa nätverket, vars tidsserie-utveckling används som sanningspunkter varmot variationen från de motsvarande tidsserierna av algoritmerna jämförs. Resultatet visar en blygsam korrelation mellan de olika vegetations algoritmernas reslutat och EC flux torn uppskattat GPP, med ett medel av determinationskoefficienter (R2) på 0.49 för EVI, 0.51 för NDVI,0.52 för FPAR och ett R2 på 0.63 för LAI, då data från alla stationer och år användes. När var station erhöll lika stor vikt, dvs då korrelationen kalkylerades för samtliga observationer från var station, varpå medel togs fram, förbättrades korrelationen över lag. fLAI visades fortfarande som den bästa prediktorn av flux-torns uppskattad GPP med ett R2 på 0.62, ett starkt förbättrat R2 för EVI på 0.61erhölls, medans NDVI och FPAR visa ett R2 på 0.57 respektive 0.59. Detta resultat och en stundtals stor variation mellan stationer, t.ex. NDVI med ett R2 mellan 0.62 och 0.83 för stationen Demokeya jämfört med ett R2 mellan 0.32 och 0.49 för NDVI och stationen Tchizalamou, visar kanske på plats specifika förmågor hos vegetations algoritmerna. När observationer inom vegetationsperioden testades separat observerades en starkt minskad korrelation, med ett medel R2 mellan 0.41 – 0.56 för alla stationer och år, och ett R2 mellan 0.36 – 0.45 för alla platser vid användning av samtliga observationer för varje station oberoende av år, vilket indikerar att observationerna utanför växtperioden har stort inflytande på korrelationen. En slutsats av studien är att uppskalning av en standrardiserad storaxels regressions modell för inom annuell variation baserad endast på relationen mellan en av dessa vegetations algoritmer och flux-torns uppskattad GPP ej är att rekommendera med tanke på den blygsamma överrensstämmelsen mellan vegetationsalgoritmerna och flux torn uppskattat GPP. En annan slutsats är att eftersom dessa vegetations algoritmer uppvisar plats specifika förmågor skulle modeller av GPP ha fördel av plats specifik stöd-data som beskriver faktorer som begränsar vegetation, t.ex. vattentillgänglighet

Soil drought anomalies in MODIS GPP of a Mediterranean broadleaved evergreen forest

The Moderate Resolution Imaging Spectroradiometer (MODIS) yields global operational estimates of terrestrial gross primary production (GPP). In this study, we compared MOD17A2 GPP with tower eddy flux-based estimates of GPP from 2001 to 2010 over an evergreen broad-leaf Mediterranean forest in Southern France with a significant summer drought period. The MOD17A2 GPP shows seasonal variations that are inconsistent with the tower GPP, with close-to-accurate winter estimates and significant discrepancies for summer estimates which are the least accurate. The analysis indicated that the MOD17A2 GPP has high bias relative to tower GPP during severe summer drought which we hypothesized caused by soil water limitation. Our investigation showed that there was a significant correlation (R-2 = 0.77, p < 0.0001) between the relative soil water content and the relative error of MOD17A2 GPP. Therefore, the relationship between the error and the measured relative soil water content could explain anomalies in MOD17A2 GPP. The results of this study indicate that careful consideration of the water conditions input to the MOD17A2 GPP algorithm on remote sensing is required in order to provide accurate predictions of GPP. Still, continued efforts are necessary to ascertain the most appropriate index, which characterizes soil water limitation in water-limited environments using remote sensing

Contributions of natural and human factors to increases in vegetation productivity in China

Increasing trends in vegetation productivity have been identified for the last three decades for many regions in the northern hemisphere including China. Multiple natural and human factors are possibly responsible for the increases in vegetation productivity, while their relative contributions remain unclear. Here we analyzed the long-term trends in vegetation productivity in China using the satellite-derived normalized difference vegetation index (NDVI) and assessed the relationships of NDVI with a suite of natural (air temperature, precipitation, photosynthetically active radiation (PAR), atmospheric carbon dioxide (CO2) concentrations, and nitrogen (N) deposition) and human (afforestation and improved agricultural management practices) factors. Overall, China exhibited an increasing trend in vegetation productivity with an increase of 2.7%. At the provincial scale, eleven provinces exhibited significant increases in vegetation productivity, and the majority of these provinces are located within the northern half of the country. At the national scale, annual air temperature was most closely related to NDVI and explained 36.8% of the variance in NDVI, followed by afforestation (25.5%) and crop yield (15.8%). Altogether, temperature, total forest plantation area, and crop yield explained 78.1% of the variance in vegetation productivity at the national scale, while precipitation, PAR, atmospheric CO2 concentrations, and N deposition made no significant contribution to the increases in vegetation productivity. At the provincial scale, each factor explained a part of the variance in NDVI for some provinces, and the increases in NDVI for many provinces could be attributed to the combined effects of multiple factors. Crop yield and PAR were correlated with NDVI for more provinces than were other factors, indicating that both elevated crop yield resulting from improved agricultural management practices and increasing diffuse radiation were more important than other factors in increasing vegetation productivity at the provincial scale. The relative effects of the natural and human factors on vegetation productivity varied with spatial scale. The true contributions of multiple factors can be obscured by the correlation among these variables, and it is essential to examine the contribution of each factor while controlling for other factors. Future changes in climate and human activities will likely have larger influences on vegetation productivity in China

The impacts of climate change and agricultural activities on water cycling of Northern Eurasia

The ecosystems in Northern Eurasia (NE) are important due to their vast land coverage, high rate of observed and projected warming, and the potential feedbacks they can cause on the global climate system. To understand the impacts of climate change and agricultural activities on water cycling in NE, I analysed a variety of datasets and conducted series of studies by applying a combination of modeling, in-situ observations and remote sensing data, uncertainty analysis, and model-data fusion.^ Long-term unique in-situ measurements on soil moisture across multiple stations and discharge records at the outflow basins in Northern China (NC) provide us robust evidence to assess the trends of soil moisture and discharge in this region (Chapter 2). NC overlaps with NE and is one of the hot-spots experiencing the most severe water shortage in the world. Declines in soil moisture and stream flow detected via in-situ measurements in the last three decades indicate that water scarcity has been exacerbated. Multiple linear regression results indicate that intensification of agricultural activities including increase in fertilizer use, prevalence of water-expensive crops and cropland expansion appear to have aggravated these declines in this region.^ Scarce evapotranspiration (ET) measurements make ET estimation via model a necessary step for better regional-scale water management. Penman–Monteith based algorithms for plant transpiration and soil evaporation were introduced into the Terrestrial Ecosystem Model (TEM) to calculate ET (Chapter 3). I then examined the response of ET and water availability to changing climate and land cover on the Mongolian Plateau during the 21st century. It is shown that use of the Penman–Monteith based algorithms in the TEM substantially improved ET estimation on the Mongolia Plateau. Results show that regional annual ET varies from 188 to 286 mm yr−1 – with an increasing trend – across different climate change scenarios during the 21st century. Meanwhile, the differences between precipitation and ET suggest that the available water for human use will not change significantly during the 21st century. In addition, analyses also suggest that climate change is more important than land cover change in determining changes in regional ET.^ Improvement in the accuracy of ET estimation by introducing Penman–Monteith based algorithms into the TEM motivated me to further improve the model representation of ET processes. I further modified the TEM to incorporate more detailed ET processes including canopy interception loss, ET (evaporation) from wetland surfaces, wetlands and water bodies, and snow sublimation to examine spatiotemporal variation of ET in NE from 1948 to 2009 (Chapter 4). Those modifications lead to substantial enhancement in the accuracy of estimation of ET and runoff. The consideration of snow sublimation substantially improved the ET estimates and highlighted the importance of snow in the hydrometeorology of NE. The root mean square error of discharge from the six largest watersheds in NE decreased from 527.74 km 3 yr-1 to 126.23 km3 yr-1. Meanwhile, a systematic underestimation of river discharge after 1970 indicates that other water sources or dynamics not considered in the model (e.g., melting glaciers, permafrost thawing and fires) or bias in the precipitation forcing may also be important for the hydrology of the region.^ To better understand the possible causes of systematic bias in discharge estimates, I examined the impacts of forcing data uncertainty on ET and runoff estimation in NE by driving the modified TEM with five widely-used forcing data sets (Chapter 5). Estimates of regional ET vary between 263.5-369.3 mm yr-1 during 1979-2008 depending on the choice of forcing data, while the spatial variability of ET appears more consistent. Uncertainties in ETforcing propagate as well to estimates of runoff. Independent of the forcing dataset, the climatic variables that dominate ET temporal variability remain the same among all the five TEM simulated ET products. ET is dominated by air temperature in the north and by precipitation in the south during the growing season, and solar radiation and vapour pressure deficit explain the dynamics of ET for the rest of the year. While the Climate Research Unit (CRU) TS3.1 dataset of the University of East Anglia appears as a better choice of forcing via our assessment, the quality of forcing data remains a major challenge to accurately quantify the regional water balance in NE