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  • Open Access English
    Authors: 
    J. Janapati; B. K. Seela; P.-L. Lin; P.-L. Lin; P.-L. Lin; M.-T. Lee; E. Joseph; E. Joseph;
    Publisher: Copernicus Publications

    Information about the raindrop size distribution (RSD) is vital for comprehending the precipitation microphysics, improving the rainfall estimation algorithms, and appraising the rainfall erosivity. Previous research has revealed that the RSD exhibits diversity with geographical location and weather type, which leads to the assessment of the region and weather-specific RSDs. Based on long-term (2004 to 2016) disdrometer measurements in northern Taiwan, this study attempts to demonstrate the RSD aspects of summer seasons that were bifurcated into two weather conditions, namely typhoon (TY) and non-typhoon (NTY) rainfall. The results show a higher concentration of small drops and a lower concentration of large-sized drops in TY compared to NTY rainfall, and this behavior persisted even after characterizing the RSDs into different rainfall rate classes. RSDs expressed in gamma parameters show higher mass-weighted mean diameter (Dm) and lower normalized intercept parameter (Nw) values in NTY than TY rainfall. Moreover, sorting these two weather conditions (TY and NTY rainfall) into stratiform and convective regimes revealed a larger Dm in NTY than in TY rainfall. The RSD empirical relations used in the valuation of rainfall rate (Z–R, Dm–R, and Nw–R) and rainfall kinetic energy (KE–R and KE–Dm) were enumerated for TY and NTY rainfall, and they exhibited profound diversity between these two weather conditions. Attributions of RSD variability between the TY and NTY rainfall to the thermodynamical and microphysical processes are elucidated with the aid of reanalysis, remote sensing, and ground-based data sets.

  • Open Access English
    Authors: 
    C. Scudeler; C. Scudeler; L. Pangle; D. Pasetto; G.-Y. Niu; G.-Y. Niu; T. Volkmann; C. Paniconi; M. Putti; P. Troch; +1 more
    Publisher: Copernicus Publications
    Countries: Italy, Switzerland
    Project: NSF | COLLABORATIVE RESEARCH: C... (1417097), NSF | Collaborative Research: H... (1344552)

    Abstract. This paper explores the challenges of model parameterization and process representation when simulating multiple hydrologic responses from a highly controlled unsaturated flow and transport experiment with a physically based model. The experiment, conducted at the Landscape Evolution Observatory (LEO), involved alternate injections of water and deuterium-enriched water into an initially very dry hillslope. The multivariate observations included point measures of water content and tracer concentration in the soil, total storage within the hillslope, and integrated fluxes of water and tracer through the seepage face. The simulations were performed with a three-dimensional finite element model that solves the Richards and advection–dispersion equations. Integrated flow, integrated transport, distributed flow, and distributed transport responses were successively analyzed, with parameterization choices at each step supported by standard model performance metrics. In the first steps of our analysis, where seepage face flow, water storage, and average concentration at the seepage face were the target responses, an adequate match between measured and simulated variables was obtained using a simple parameterization consistent with that from a prior flow-only experiment at LEO. When passing to the distributed responses, it was necessary to introduce complexity to additional soil hydraulic parameters to obtain an adequate match for the point-scale flow response. This also improved the match against point measures of tracer concentration, although model performance here was considerably poorer. This suggests that still greater complexity is needed in the model parameterization, or that there may be gaps in process representation for simulating solute transport phenomena in very dry soils.

  • Open Access
    Authors: 
    Chunwei Liu; Ge Sun; Steven G. McNulty; Asko Noormets; Yuan Fang;
    Publisher: Copernicus GmbH
    Project: NSERC

    The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient (Kc) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, Kc has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. This study aimed at deriving monthly Kc for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly Kc data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), Kc values had large seasonal variation across all land covers. The spatial variability of Kc was well explained by latitude, suggesting site factors are a major control on Kc. Seasonally, Kc increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly Kc in all land covers, except in EBF. During the peak growing season, forests had the highest Kc values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for Kc by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. The Kc models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.

  • Open Access English
    Authors: 
    Anaïs Barella-Ortiz; Jan Polcher; Patricia de Rosnay; Maria Piles; Emiliano Gelati;
    Publisher: Copernicus Publications
    Countries: Spain, France, Spain
    Project: EC | EARTH2OBSERVE (603608)

    L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies This work contributes to the FP7 Earth2Observe project under grant agreement no. 603608 19 pges, 10 figures, 6 tables Peer Reviewed

  • Open Access English
    Authors: 
    Bernd R. Schöne; Aliona E. Meret; Sven Baier; Jens Fiebig; Jan Esper; Jeffrey J. McDonnell; Laurent Pfister;

    The stable isotope composition of lacustrine sediments is routinely used to infer Late Holocene changes in precipitation over Scandinavia and, ultimately, atmospheric circulation dynamics in the North Atlantic realm. However, such archives only provide a low temporal resolution (ca. 15 years), precluding the ability to identify changes on inter-annual and quasi-decadal timescales. Here, we present a new, high-resolution reconstruction using shells of freshwater pearl mussels, Margaritifera margaritifera, from three streams in northern Sweden. We present seasonally to annually resolved, calendar-aligned stable oxygen and carbon isotope data from 10 specimens, covering the time interval from 1819 to 1998. The bivalves studied formed their shells near equilibrium with the oxygen isotope signature of ambient water and, thus, reflect hydrological processes in the catchment as well as changes, albeit damped, in the isotope signature of local atmospheric precipitation. The shell oxygen isotopes were significantly correlated with the North Atlantic Oscillation index (up to 56 % explained variability), suggesting that the moisture that winter precipitation formed from originated predominantly in the North Atlantic during NAO+ years but in the Arctic during NAO− years. The isotope signature of winter precipitation was attenuated in the stream water, and this damping effect was eventually recorded by the shells. Shell stable carbon isotope values did not show consistent ontogenetic trends, but rather oscillated around an average that ranged from ca. −12.00 to −13.00 ‰ among the streams studied. Results of this study contribute to an improved understanding of climate dynamics in Scandinavia and the North Atlantic sector and can help to constrain eco-hydrological changes in riverine ecosystems. Moreover, long isotope records of precipitation and streamflow are pivotal to improve our understanding and modeling of hydrological, ecological, biogeochemical and atmospheric processes. Our new approach offers a much higher temporal resolution and superior dating control than data from existing archives.

  • Open Access English
    Authors: 
    Shufen Pan; Naiqing Pan; Hanqin Tian; Pierre Friedlingstein; Stephen Sitch; Hao Shi; Vivek K. Arora; Vanessa Haverd; Atul K. Jain; Etsushi Kato; +7 more
    Publisher: HAL CCSD
    Countries: Switzerland, France
    Project: NSF | INFEWS: U.S.-China: Integ... (1903722), NSF | Collaborative Research: E... (1243232), SNSF | Die Entmündigung wegen ps... (20020), EC | 4C (821003)

    Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1(454 mm yr−1)to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p  0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

  • Open Access
    Authors: 
    Pengsen Sun; Zhen Yu; Shirong Liu; Xiaohua Wei; Jingxin Wang; Nicolas Zegre; Ning Liu;
    Publisher: Copernicus GmbH

    Considerable work has been done to examine the relationship between environmental constraints and vegetation activities represented by the remote sensing-based normalized difference vegetation index (NDVI). However, the relationships along either environmental or vegetational gradients are rarely examined. The aim of this paper was to identify the vegetation types that are potentially susceptible to climate change through examining their interactions between vegetation activity and evaporative water deficit. We selected 12 major vegetation types along the north–south transect of eastern China (NSTEC), and tested their time trends in climate change, vegetation activity and water deficit during the period 1982–2006. The result showed significant warming trends accompanied by general precipitation decline in the majority of vegetation types. Despite that the whole transect increased atmospheric evaporative demand (ET<sub>0</sub>) during the study period, the actual evapotranspiration (ET<sub>a</sub>) showed divergent trends with ET<sub>0</sub> in most vegetation types. Warming and water deficit exert counteracting controls on vegetation activity. Our study found insignificant greening trends in cold temperate coniferous forest (CTCF), temperate deciduous shrub (TDS), and three temperate herbaceous types including the meadow steppe (TMS), grass steppe (TGS) and grassland (TG), where warming exerted more effect on NDVI than offset by water deficit. The increasing growing season water deficit posed a limitation on the vegetation activity of temperate coniferous forest (TCF), mixed forest (TMF) and deciduous broad-leaved forest (TDBF). Differently, the growing season brownings in subtropical or tropical forests of coniferous (STCF), deciduous broad-leaved (SDBF), evergreen broad-leaved (SEBF) and subtropical grasslands (STG) were likely attributed to evaporative energy limitation. The growing season water deficit index (GWDI) has been formulated to assess ecohydrological equilibrium and thus indicating vegetation susceptibility to water deficit. The increasing GWDI trends in CTCF, TCF, TDS, TG, TGS and TMS indicated their rising susceptibility to future climate change.

  • Open Access
    Authors: 
    Michael J. Lathuillière; Michael T. Coe; Mark S. Johnson;
    Publisher: Copernicus GmbH
    Project: NSERC

    Abstract. The Amazon Basin is a region of global importance for the carbon and hydrological cycles, a biodiversity hotspot, and a potential centre for future economic development. The region is also a major source of water vapour recycled into continental precipitation through evapotranspiration processes. This review applies an ecohydrological approach to Amazonia's water cycle by looking at contributions of water resources in the context of future agricultural production. At present, agriculture in the region is primarily rain-fed and relies almost exclusively on green-water resources (soil moisture regenerated by precipitation). Future agricultural development, however, will likely follow pathways that include irrigation from blue-water sources (surface water and groundwater) as insurance from variability in precipitation. In this review, we first provide an updated summary of the green–blue ecohydrological framework before describing past trends in Amazonia's water resources within the context of land use and land cover change. We then describe green- and blue-water trade-offs in light of future agricultural production and potential irrigation to assess costs and benefits to terrestrial ecosystems, particularly land and biodiversity protection, and regional precipitation recycling. Management of green water is needed, particularly at the agricultural frontier located in the headwaters of major tributaries to the Amazon River, and home to key downstream blue-water users and ecosystem services, including domestic and industrial users, as well as aquatic ecosystems.

  • Open Access English
    Authors: 
    W. Dorigo; I. Himmelbauer; D. Aberer; L. Schremmer; I. Petrakovic; L. Zappa; W. Preimesberger; A. Xaver; F. Annor; F. Annor; +62 more
    Countries: Netherlands, France, Denmark, Belgium, Italy, France, Germany, Spain
    Project: EC | EARTH2OBSERVE (603608), EC | GROW (690199)

    In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

  • Open Access English
    Authors: 
    C. D. Smith; A. Ross; A. Ross; J. Kochendorfer; M. E. Earle; M. Wolff; S. Buisán; Y.-A. Roulet; T. Laine;
    Publisher: European Geosciences Union
    Country: Spain

    The World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) involved extensive field intercomparisons of automated instruments for measuring snow during the 2013/2014 and 2014/2015 winter seasons. A key outcome of SPICE was the development of transfer functions for the wind bias adjustment of solid precipitation measurements using various precipitation gauge and wind shield configurations. Due to the short intercomparison period, the data set was not sufficiently large to develop and evaluate transfer functions using independent precipitation measurements, although on average the adjustments were effective at reducing the bias in unshielded gauges from −33.4 % to 1.1 %. The present analysis uses data collected at eight SPICE sites over the 2015/2016 and 2016/2017 winter periods, comparing 30 min adjusted and unadjusted measurements from Geonor T-200B3 and OTT Pluvio2 precipitation gauges in different shield configurations to the WMO Double Fence Automated Reference (DFAR) for the evaluation of the transfer function. Performance is assessed in terms of relative total catch (RTC), root mean square error (RMSE), Pearson correlation (r), and percentage of events (PEs) within 0.1 mm of the DFAR. Metrics are reported for combined precipitation types and for snow only. The evaluation shows that the performance varies substantially by site. Adjusted RTC varies from 54 % to 123 %, RMSE from 0.07 to 0.38 mm, r from 0.28 to 0.94, and PEs from 37 % to 84 %, depending on precipitation phase, site, and gauge configuration (gauge and wind screen type). Generally, windier sites, such as Haukeliseter (Norway) and Bratt's Lake (Canada), exhibit a net under-adjustment (RTC of 54 % to 83 %), while the less windy sites, such as Sodankylä (Finland) and Caribou Creek (Canada), exhibit a net over-adjustment (RTC of 102 % to 123 %). Although the application of transfer functions is necessary to mitigate wind bias in solid precipitation measurements, especially at windy sites and for unshielded gauges, the variability in the performance metrics among sites suggests that the functions be applied with caution.

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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
88 Research products, page 1 of 9
  • Open Access English
    Authors: 
    J. Janapati; B. K. Seela; P.-L. Lin; P.-L. Lin; P.-L. Lin; M.-T. Lee; E. Joseph; E. Joseph;
    Publisher: Copernicus Publications

    Information about the raindrop size distribution (RSD) is vital for comprehending the precipitation microphysics, improving the rainfall estimation algorithms, and appraising the rainfall erosivity. Previous research has revealed that the RSD exhibits diversity with geographical location and weather type, which leads to the assessment of the region and weather-specific RSDs. Based on long-term (2004 to 2016) disdrometer measurements in northern Taiwan, this study attempts to demonstrate the RSD aspects of summer seasons that were bifurcated into two weather conditions, namely typhoon (TY) and non-typhoon (NTY) rainfall. The results show a higher concentration of small drops and a lower concentration of large-sized drops in TY compared to NTY rainfall, and this behavior persisted even after characterizing the RSDs into different rainfall rate classes. RSDs expressed in gamma parameters show higher mass-weighted mean diameter (Dm) and lower normalized intercept parameter (Nw) values in NTY than TY rainfall. Moreover, sorting these two weather conditions (TY and NTY rainfall) into stratiform and convective regimes revealed a larger Dm in NTY than in TY rainfall. The RSD empirical relations used in the valuation of rainfall rate (Z–R, Dm–R, and Nw–R) and rainfall kinetic energy (KE–R and KE–Dm) were enumerated for TY and NTY rainfall, and they exhibited profound diversity between these two weather conditions. Attributions of RSD variability between the TY and NTY rainfall to the thermodynamical and microphysical processes are elucidated with the aid of reanalysis, remote sensing, and ground-based data sets.

  • Open Access English
    Authors: 
    C. Scudeler; C. Scudeler; L. Pangle; D. Pasetto; G.-Y. Niu; G.-Y. Niu; T. Volkmann; C. Paniconi; M. Putti; P. Troch; +1 more
    Publisher: Copernicus Publications
    Countries: Italy, Switzerland
    Project: NSF | COLLABORATIVE RESEARCH: C... (1417097), NSF | Collaborative Research: H... (1344552)

    Abstract. This paper explores the challenges of model parameterization and process representation when simulating multiple hydrologic responses from a highly controlled unsaturated flow and transport experiment with a physically based model. The experiment, conducted at the Landscape Evolution Observatory (LEO), involved alternate injections of water and deuterium-enriched water into an initially very dry hillslope. The multivariate observations included point measures of water content and tracer concentration in the soil, total storage within the hillslope, and integrated fluxes of water and tracer through the seepage face. The simulations were performed with a three-dimensional finite element model that solves the Richards and advection–dispersion equations. Integrated flow, integrated transport, distributed flow, and distributed transport responses were successively analyzed, with parameterization choices at each step supported by standard model performance metrics. In the first steps of our analysis, where seepage face flow, water storage, and average concentration at the seepage face were the target responses, an adequate match between measured and simulated variables was obtained using a simple parameterization consistent with that from a prior flow-only experiment at LEO. When passing to the distributed responses, it was necessary to introduce complexity to additional soil hydraulic parameters to obtain an adequate match for the point-scale flow response. This also improved the match against point measures of tracer concentration, although model performance here was considerably poorer. This suggests that still greater complexity is needed in the model parameterization, or that there may be gaps in process representation for simulating solute transport phenomena in very dry soils.

  • Open Access
    Authors: 
    Chunwei Liu; Ge Sun; Steven G. McNulty; Asko Noormets; Yuan Fang;
    Publisher: Copernicus GmbH
    Project: NSERC

    The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient (Kc) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, Kc has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. This study aimed at deriving monthly Kc for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly Kc data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), Kc values had large seasonal variation across all land covers. The spatial variability of Kc was well explained by latitude, suggesting site factors are a major control on Kc. Seasonally, Kc increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly Kc in all land covers, except in EBF. During the peak growing season, forests had the highest Kc values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for Kc by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. The Kc models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.

  • Open Access English
    Authors: 
    Anaïs Barella-Ortiz; Jan Polcher; Patricia de Rosnay; Maria Piles; Emiliano Gelati;
    Publisher: Copernicus Publications
    Countries: Spain, France, Spain
    Project: EC | EARTH2OBSERVE (603608)

    L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies This work contributes to the FP7 Earth2Observe project under grant agreement no. 603608 19 pges, 10 figures, 6 tables Peer Reviewed

  • Open Access English
    Authors: 
    Bernd R. Schöne; Aliona E. Meret; Sven Baier; Jens Fiebig; Jan Esper; Jeffrey J. McDonnell; Laurent Pfister;

    The stable isotope composition of lacustrine sediments is routinely used to infer Late Holocene changes in precipitation over Scandinavia and, ultimately, atmospheric circulation dynamics in the North Atlantic realm. However, such archives only provide a low temporal resolution (ca. 15 years), precluding the ability to identify changes on inter-annual and quasi-decadal timescales. Here, we present a new, high-resolution reconstruction using shells of freshwater pearl mussels, Margaritifera margaritifera, from three streams in northern Sweden. We present seasonally to annually resolved, calendar-aligned stable oxygen and carbon isotope data from 10 specimens, covering the time interval from 1819 to 1998. The bivalves studied formed their shells near equilibrium with the oxygen isotope signature of ambient water and, thus, reflect hydrological processes in the catchment as well as changes, albeit damped, in the isotope signature of local atmospheric precipitation. The shell oxygen isotopes were significantly correlated with the North Atlantic Oscillation index (up to 56 % explained variability), suggesting that the moisture that winter precipitation formed from originated predominantly in the North Atlantic during NAO+ years but in the Arctic during NAO− years. The isotope signature of winter precipitation was attenuated in the stream water, and this damping effect was eventually recorded by the shells. Shell stable carbon isotope values did not show consistent ontogenetic trends, but rather oscillated around an average that ranged from ca. −12.00 to −13.00 ‰ among the streams studied. Results of this study contribute to an improved understanding of climate dynamics in Scandinavia and the North Atlantic sector and can help to constrain eco-hydrological changes in riverine ecosystems. Moreover, long isotope records of precipitation and streamflow are pivotal to improve our understanding and modeling of hydrological, ecological, biogeochemical and atmospheric processes. Our new approach offers a much higher temporal resolution and superior dating control than data from existing archives.

  • Open Access English
    Authors: 
    Shufen Pan; Naiqing Pan; Hanqin Tian; Pierre Friedlingstein; Stephen Sitch; Hao Shi; Vivek K. Arora; Vanessa Haverd; Atul K. Jain; Etsushi Kato; +7 more
    Publisher: HAL CCSD
    Countries: Switzerland, France
    Project: NSF | INFEWS: U.S.-China: Integ... (1903722), NSF | Collaborative Research: E... (1243232), SNSF | Die Entmündigung wegen ps... (20020), EC | 4C (821003)

    Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1(454 mm yr−1)to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p  0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

  • Open Access
    Authors: 
    Pengsen Sun; Zhen Yu; Shirong Liu; Xiaohua Wei; Jingxin Wang; Nicolas Zegre; Ning Liu;
    Publisher: Copernicus GmbH

    Considerable work has been done to examine the relationship between environmental constraints and vegetation activities represented by the remote sensing-based normalized difference vegetation index (NDVI). However, the relationships along either environmental or vegetational gradients are rarely examined. The aim of this paper was to identify the vegetation types that are potentially susceptible to climate change through examining their interactions between vegetation activity and evaporative water deficit. We selected 12 major vegetation types along the north–south transect of eastern China (NSTEC), and tested their time trends in climate change, vegetation activity and water deficit during the period 1982–2006. The result showed significant warming trends accompanied by general precipitation decline in the majority of vegetation types. Despite that the whole transect increased atmospheric evaporative demand (ET<sub>0</sub>) during the study period, the actual evapotranspiration (ET<sub>a</sub>) showed divergent trends with ET<sub>0</sub> in most vegetation types. Warming and water deficit exert counteracting controls on vegetation activity. Our study found insignificant greening trends in cold temperate coniferous forest (CTCF), temperate deciduous shrub (TDS), and three temperate herbaceous types including the meadow steppe (TMS), grass steppe (TGS) and grassland (TG), where warming exerted more effect on NDVI than offset by water deficit. The increasing growing season water deficit posed a limitation on the vegetation activity of temperate coniferous forest (TCF), mixed forest (TMF) and deciduous broad-leaved forest (TDBF). Differently, the growing season brownings in subtropical or tropical forests of coniferous (STCF), deciduous broad-leaved (SDBF), evergreen broad-leaved (SEBF) and subtropical grasslands (STG) were likely attributed to evaporative energy limitation. The growing season water deficit index (GWDI) has been formulated to assess ecohydrological equilibrium and thus indicating vegetation susceptibility to water deficit. The increasing GWDI trends in CTCF, TCF, TDS, TG, TGS and TMS indicated their rising susceptibility to future climate change.

  • Open Access
    Authors: 
    Michael J. Lathuillière; Michael T. Coe; Mark S. Johnson;
    Publisher: Copernicus GmbH
    Project: NSERC

    Abstract. The Amazon Basin is a region of global importance for the carbon and hydrological cycles, a biodiversity hotspot, and a potential centre for future economic development. The region is also a major source of water vapour recycled into continental precipitation through evapotranspiration processes. This review applies an ecohydrological approach to Amazonia's water cycle by looking at contributions of water resources in the context of future agricultural production. At present, agriculture in the region is primarily rain-fed and relies almost exclusively on green-water resources (soil moisture regenerated by precipitation). Future agricultural development, however, will likely follow pathways that include irrigation from blue-water sources (surface water and groundwater) as insurance from variability in precipitation. In this review, we first provide an updated summary of the green–blue ecohydrological framework before describing past trends in Amazonia's water resources within the context of land use and land cover change. We then describe green- and blue-water trade-offs in light of future agricultural production and potential irrigation to assess costs and benefits to terrestrial ecosystems, particularly land and biodiversity protection, and regional precipitation recycling. Management of green water is needed, particularly at the agricultural frontier located in the headwaters of major tributaries to the Amazon River, and home to key downstream blue-water users and ecosystem services, including domestic and industrial users, as well as aquatic ecosystems.

  • Open Access English
    Authors: 
    W. Dorigo; I. Himmelbauer; D. Aberer; L. Schremmer; I. Petrakovic; L. Zappa; W. Preimesberger; A. Xaver; F. Annor; F. Annor; +62 more
    Countries: Netherlands, France, Denmark, Belgium, Italy, France, Germany, Spain
    Project: EC | EARTH2OBSERVE (603608), EC | GROW (690199)

    In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

  • Open Access English
    Authors: 
    C. D. Smith; A. Ross; A. Ross; J. Kochendorfer; M. E. Earle; M. Wolff; S. Buisán; Y.-A. Roulet; T. Laine;
    Publisher: European Geosciences Union
    Country: Spain

    The World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) involved extensive field intercomparisons of automated instruments for measuring snow during the 2013/2014 and 2014/2015 winter seasons. A key outcome of SPICE was the development of transfer functions for the wind bias adjustment of solid precipitation measurements using various precipitation gauge and wind shield configurations. Due to the short intercomparison period, the data set was not sufficiently large to develop and evaluate transfer functions using independent precipitation measurements, although on average the adjustments were effective at reducing the bias in unshielded gauges from −33.4 % to 1.1 %. The present analysis uses data collected at eight SPICE sites over the 2015/2016 and 2016/2017 winter periods, comparing 30 min adjusted and unadjusted measurements from Geonor T-200B3 and OTT Pluvio2 precipitation gauges in different shield configurations to the WMO Double Fence Automated Reference (DFAR) for the evaluation of the transfer function. Performance is assessed in terms of relative total catch (RTC), root mean square error (RMSE), Pearson correlation (r), and percentage of events (PEs) within 0.1 mm of the DFAR. Metrics are reported for combined precipitation types and for snow only. The evaluation shows that the performance varies substantially by site. Adjusted RTC varies from 54 % to 123 %, RMSE from 0.07 to 0.38 mm, r from 0.28 to 0.94, and PEs from 37 % to 84 %, depending on precipitation phase, site, and gauge configuration (gauge and wind screen type). Generally, windier sites, such as Haukeliseter (Norway) and Bratt's Lake (Canada), exhibit a net under-adjustment (RTC of 54 % to 83 %), while the less windy sites, such as Sodankylä (Finland) and Caribou Creek (Canada), exhibit a net over-adjustment (RTC of 102 % to 123 %). Although the application of transfer functions is necessary to mitigate wind bias in solid precipitation measurements, especially at windy sites and for unshielded gauges, the variability in the performance metrics among sites suggests that the functions be applied with caution.