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30 Research products, page 1 of 3

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  • Open Access English
    Authors: 
    Hartmut Holländer; Helge Bormann; Theresa Blume; Wouter Buytaert; Giovanni Battista Chirico; Jean-François Exbrayat; David Gustafsson; H. Hölzel; T. Krauße; Philipp Kraft; +3 more
    Publisher: Copernicus Publications
    Countries: Switzerland, Germany

    In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers – using the model of their choice – for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of added information. In this qualitative analysis of a statistically small number of predictions we learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements. Hydrology and Earth System Sciences, 18 (6) ISSN:1027-5606 ISSN:1607-7938

  • 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 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 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.

  • Open Access English
    Authors: 
    N. Orlowski; N. Orlowski; N. Orlowski; L. Breuer; L. Breuer; N. Angeli; P. Boeckx; C. Brumbt; C. S. Cook; M. Dubbert; +23 more
    Publisher: Copernicus Gesellschaft Mbh
    Countries: Belgium, France, Germany
    Project: NSERC

    For more than two decades, research groups in hydrology, ecology, soil science, and biogeochemistry have performed cryogenic water extractions (CWEs) for the analysis of δ2H and δ18O of soil water. Recent studies have shown that extraction conditions (time, temperature, and vacuum) along with physicochemical soil properties may affect extracted soil water isotope composition. Here we present results from the first worldwide round robin laboratory intercomparison. We test the null hypothesis that, with identical soils, standards, extraction protocols, and isotope analyses, cryogenic extractions across all laboratories are identical. Two standard soils with different physicochemical characteristics along with deionized (DI) reference water of known isotopic composition were shipped to 16 participating laboratories. Participants oven-dried and rewetted the soils to 8 and 20 % gravimetric water content (WC), using the deionized reference water. One batch of soil samples was extracted via predefined extraction conditions (time, temperature, and vacuum) identical to all laboratories; the second batch was extracted via conditions considered routine in the respective laboratory. All extracted water samples were analyzed for δ18O and δ2H by the lead laboratory (Global Institute for Water Security, GIWS, Saskatoon, Canada) using both a laser and an isotope ratio mass spectrometer (OA-ICOS and IRMS, respectively). We rejected the null hypothesis. Our results showed large differences in retrieved isotopic signatures among participating laboratories linked to soil type and soil water content with mean differences compared to the reference water ranging from +18.1 to −108.4 ‰ for δ2H and +11.8 to −14.9 ‰ for δ18O across all laboratories. In addition, differences were observed between OA-ICOS and IRMS isotope data. These were related to spectral interferences during OA-ICOS analysis that are especially problematic for the clayey loam soils used. While the types of cryogenic extraction lab construction varied from manifold systems to single chambers, no clear trends between system construction, applied extraction conditions, and extraction results were found. Rather, observed differences in the isotope data were influenced by interactions between multiple factors (soil type and properties, soil water content, system setup, extraction efficiency, extraction system leaks, and each lab's internal accuracy). Our results question the usefulness of cryogenic extraction as a standard for water extraction since results are not comparable across laboratories. This suggests that defining any sort of standard extraction procedure applicable across laboratories is challenging. Laboratories might have to establish calibration functions for their specific extraction system for each natural soil type, individually.

  • Open Access
    Authors: 
    L. Quéno; L. Quéno; F. Karbou; V. Vionnet; V. Vionnet; I. Dombrowski-Etchevers;
    Publisher: Copernicus GmbH

    Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. In this study, a thorough evaluation of the solar and longwave downwelling irradiance products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite-derived products were compared with forecast fields from the meteorological model AROME and with analysis fields from the SAFRAN system. A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, we were not able to select the best product due to contrasted results falling in the range of uncertainty of the sensors. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived irradiance products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

  • Publication . Other literature type . Preprint . Article . 2006
    Open Access
    Authors: 
    Peter Lehmann; Christoph Hinz; Gavan McGrath; H. J. Tromp-van Meerveld; Jeffrey J. McDonnell;
    Publisher: Copernicus GmbH
    Countries: Netherlands, Switzerland, France
    Project: NSF | Hillslope-Riparian Zone R... (0196381)

    Nonlinear relations between rain input and hillslope outflow are common observations in hillslope hydrology field studies. In this paper we use percolation theory to model the threshold relationship between rainfall amount and outflow and show that this nonlinear relationship may arise from simple linear processes at the smaller scale. When the rainfall amount exceeds a threshold value, the underlying elements become connected and water flows out of the base of the hillslope. The percolation approach shows how random variations in storage capacity and connectivity at the small spatial scale cause a threshold relationship between rainstorm amount and hillslope outflow. As a test case, we applied percolation theory to the well characterized experimental hillslope at the Panola Mountain Research Watershed. Analysing the measured rainstorm events and the subsurface stormflow with percolation theory, we could determine the effect of bedrock permeability, spatial distribution of soil properties and initial water content within the hillslope. The measured variation in the relationship between rainstorm amount and subsurface flow could be reproduced by modelling the initial moisture deficit, the loss of free water to the bedrock, the limited size of the system and the connectivity that is a function of bedrock topography and existence of macropores. The values of the model parameters were in agreement with measured values of soil depth distribution and water saturation. Hydrology and Earth System Sciences, 11 (2) ISSN:1027-5606 ISSN:1607-7938

  • Publication . Preprint . Other literature type . 2019
    Open Access English
    Authors: 
    Magali F. Nehemy; Paolo Benettin; Mitra Asadollahi; Dyan Pratt; Andrea Rinaldo; Jeffrey J. McDonnell;

    The stable isotopes of oxygen and hydrogen (δ2H and δ18O) have been widely used to investigate plant water source partitioning. These tracers have shed new light on patterns of plant water use in time and space. However, this black box approach has limited our source water interpretations and mechanistic understanding. Here, we combine measurements of stable isotope composition in xylem and soil water pools with measurements of plant hydraulics, fine root distribution and soil matric potential to investigate mechanism(s) driving tree water source partitioning. We used a 2 m3 lysimeter planted with a small willow tree (Salix viminalis) to conduct a high spatial-temporal resolution experiment. We found that tree water source partitioning was driven mainly by tree water status and not by patterns of fine root distribution. Source water partitioning was regulated by plant hydraulic response to changing atmospheric demand and soil matric potential. The depth distribution of soil matric potential appeared to be the largest control on the patterns of soil water partitioning during periods of tree water deficit. Contrary to the common steady state assumption in ecohydrological source water investigations, our results show that tree water use is a dynamic process, driven by tree water deficit. Overall, our findings suggest new research foci for future plant water isotopic investigations, highlighting the importance of hydrometric measurements from the plant perspective.

  • Open Access

    Abstract. Remotely sensed soil moisture products are influenced by vegetation and how it is accounted for in the retrieval, which is a potential source of time-variable biases. To estimate such complex, time-variable error structures from noisy data, we introduce a Bayesian extension to triple collocation in which the systematic errors and noise terms are not constant but vary with explanatory variables. We apply the technique to the Soil Moisture Active Passive (SMAP) soil moisture product over croplands, hypothesizing that errors in the vegetation correction during the retrieval leave a characteristic fingerprint in the soil moisture time series. We find that time-variable offsets and sensitivities are commonly associated with an imperfect vegetation correction. Especially the changes in sensitivity can be large, with seasonal variations of up to 40 %. Variations of this size impede the seasonal comparison of soil moisture dynamics and the detection of extreme events. Also, estimates of vegetation–hydrology coupling can be distorted, as the SMAP soil moisture has larger R2 values with a biomass proxy than the in situ data, whereas noise alone would induce the opposite effect. This observation highlights that time-variable biases can easily give rise to distorted results and misleading interpretations. They should hence be accounted for in observational and modelling studies.

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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
30 Research products, page 1 of 3
  • Open Access English
    Authors: 
    Hartmut Holländer; Helge Bormann; Theresa Blume; Wouter Buytaert; Giovanni Battista Chirico; Jean-François Exbrayat; David Gustafsson; H. Hölzel; T. Krauße; Philipp Kraft; +3 more
    Publisher: Copernicus Publications
    Countries: Switzerland, Germany

    In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers – using the model of their choice – for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of added information. In this qualitative analysis of a statistically small number of predictions we learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements. Hydrology and Earth System Sciences, 18 (6) ISSN:1027-5606 ISSN:1607-7938

  • 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 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 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.

  • Open Access English
    Authors: 
    N. Orlowski; N. Orlowski; N. Orlowski; L. Breuer; L. Breuer; N. Angeli; P. Boeckx; C. Brumbt; C. S. Cook; M. Dubbert; +23 more
    Publisher: Copernicus Gesellschaft Mbh
    Countries: Belgium, France, Germany
    Project: NSERC

    For more than two decades, research groups in hydrology, ecology, soil science, and biogeochemistry have performed cryogenic water extractions (CWEs) for the analysis of δ2H and δ18O of soil water. Recent studies have shown that extraction conditions (time, temperature, and vacuum) along with physicochemical soil properties may affect extracted soil water isotope composition. Here we present results from the first worldwide round robin laboratory intercomparison. We test the null hypothesis that, with identical soils, standards, extraction protocols, and isotope analyses, cryogenic extractions across all laboratories are identical. Two standard soils with different physicochemical characteristics along with deionized (DI) reference water of known isotopic composition were shipped to 16 participating laboratories. Participants oven-dried and rewetted the soils to 8 and 20 % gravimetric water content (WC), using the deionized reference water. One batch of soil samples was extracted via predefined extraction conditions (time, temperature, and vacuum) identical to all laboratories; the second batch was extracted via conditions considered routine in the respective laboratory. All extracted water samples were analyzed for δ18O and δ2H by the lead laboratory (Global Institute for Water Security, GIWS, Saskatoon, Canada) using both a laser and an isotope ratio mass spectrometer (OA-ICOS and IRMS, respectively). We rejected the null hypothesis. Our results showed large differences in retrieved isotopic signatures among participating laboratories linked to soil type and soil water content with mean differences compared to the reference water ranging from +18.1 to −108.4 ‰ for δ2H and +11.8 to −14.9 ‰ for δ18O across all laboratories. In addition, differences were observed between OA-ICOS and IRMS isotope data. These were related to spectral interferences during OA-ICOS analysis that are especially problematic for the clayey loam soils used. While the types of cryogenic extraction lab construction varied from manifold systems to single chambers, no clear trends between system construction, applied extraction conditions, and extraction results were found. Rather, observed differences in the isotope data were influenced by interactions between multiple factors (soil type and properties, soil water content, system setup, extraction efficiency, extraction system leaks, and each lab's internal accuracy). Our results question the usefulness of cryogenic extraction as a standard for water extraction since results are not comparable across laboratories. This suggests that defining any sort of standard extraction procedure applicable across laboratories is challenging. Laboratories might have to establish calibration functions for their specific extraction system for each natural soil type, individually.

  • Open Access
    Authors: 
    L. Quéno; L. Quéno; F. Karbou; V. Vionnet; V. Vionnet; I. Dombrowski-Etchevers;
    Publisher: Copernicus GmbH

    Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. In this study, a thorough evaluation of the solar and longwave downwelling irradiance products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite-derived products were compared with forecast fields from the meteorological model AROME and with analysis fields from the SAFRAN system. A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, we were not able to select the best product due to contrasted results falling in the range of uncertainty of the sensors. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived irradiance products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

  • Publication . Other literature type . Preprint . Article . 2006
    Open Access
    Authors: 
    Peter Lehmann; Christoph Hinz; Gavan McGrath; H. J. Tromp-van Meerveld; Jeffrey J. McDonnell;
    Publisher: Copernicus GmbH
    Countries: Netherlands, Switzerland, France
    Project: NSF | Hillslope-Riparian Zone R... (0196381)

    Nonlinear relations between rain input and hillslope outflow are common observations in hillslope hydrology field studies. In this paper we use percolation theory to model the threshold relationship between rainfall amount and outflow and show that this nonlinear relationship may arise from simple linear processes at the smaller scale. When the rainfall amount exceeds a threshold value, the underlying elements become connected and water flows out of the base of the hillslope. The percolation approach shows how random variations in storage capacity and connectivity at the small spatial scale cause a threshold relationship between rainstorm amount and hillslope outflow. As a test case, we applied percolation theory to the well characterized experimental hillslope at the Panola Mountain Research Watershed. Analysing the measured rainstorm events and the subsurface stormflow with percolation theory, we could determine the effect of bedrock permeability, spatial distribution of soil properties and initial water content within the hillslope. The measured variation in the relationship between rainstorm amount and subsurface flow could be reproduced by modelling the initial moisture deficit, the loss of free water to the bedrock, the limited size of the system and the connectivity that is a function of bedrock topography and existence of macropores. The values of the model parameters were in agreement with measured values of soil depth distribution and water saturation. Hydrology and Earth System Sciences, 11 (2) ISSN:1027-5606 ISSN:1607-7938

  • Publication . Preprint . Other literature type . 2019
    Open Access English
    Authors: 
    Magali F. Nehemy; Paolo Benettin; Mitra Asadollahi; Dyan Pratt; Andrea Rinaldo; Jeffrey J. McDonnell;

    The stable isotopes of oxygen and hydrogen (δ2H and δ18O) have been widely used to investigate plant water source partitioning. These tracers have shed new light on patterns of plant water use in time and space. However, this black box approach has limited our source water interpretations and mechanistic understanding. Here, we combine measurements of stable isotope composition in xylem and soil water pools with measurements of plant hydraulics, fine root distribution and soil matric potential to investigate mechanism(s) driving tree water source partitioning. We used a 2 m3 lysimeter planted with a small willow tree (Salix viminalis) to conduct a high spatial-temporal resolution experiment. We found that tree water source partitioning was driven mainly by tree water status and not by patterns of fine root distribution. Source water partitioning was regulated by plant hydraulic response to changing atmospheric demand and soil matric potential. The depth distribution of soil matric potential appeared to be the largest control on the patterns of soil water partitioning during periods of tree water deficit. Contrary to the common steady state assumption in ecohydrological source water investigations, our results show that tree water use is a dynamic process, driven by tree water deficit. Overall, our findings suggest new research foci for future plant water isotopic investigations, highlighting the importance of hydrometric measurements from the plant perspective.

  • Open Access

    Abstract. Remotely sensed soil moisture products are influenced by vegetation and how it is accounted for in the retrieval, which is a potential source of time-variable biases. To estimate such complex, time-variable error structures from noisy data, we introduce a Bayesian extension to triple collocation in which the systematic errors and noise terms are not constant but vary with explanatory variables. We apply the technique to the Soil Moisture Active Passive (SMAP) soil moisture product over croplands, hypothesizing that errors in the vegetation correction during the retrieval leave a characteristic fingerprint in the soil moisture time series. We find that time-variable offsets and sensitivities are commonly associated with an imperfect vegetation correction. Especially the changes in sensitivity can be large, with seasonal variations of up to 40 %. Variations of this size impede the seasonal comparison of soil moisture dynamics and the detection of extreme events. Also, estimates of vegetation–hydrology coupling can be distorted, as the SMAP soil moisture has larger R2 values with a biomass proxy than the in situ data, whereas noise alone would induce the opposite effect. This observation highlights that time-variable biases can easily give rise to distorted results and misleading interpretations. They should hence be accounted for in observational and modelling studies.