55 research outputs found
Value of uncertain streamflow observations for hydrological modelling
Previous studies have shown that hydrological models can be parameterised using a
limited number of streamflow measurements. Citizen science projects can
collect such data for otherwise ungauged catchments but an important question
is whether these observations are informative given that these streamflow
estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a
simple bucket-type runoff model for six Swiss catchments. We pretended that
only a few observations were available and that these were affected by
different levels of inaccuracy. The level of inaccuracy was based on a
log-normal error distribution that was fitted to streamflow estimates of 136
citizens for medium-sized streams. Two additional levels of inaccuracy, for
which the standard deviation of the error distribution was divided by 2 and
4, were used as well. Based on these error distributions,
random errors were added to the measured hourly streamflow data. New time
series with different temporal resolutions were created from these synthetic
streamflow time series. These included scenarios with one observation each
week or month, as well as scenarios that are more realistic for crowdsourced
data that generally have an irregular distribution of data points throughout
the year, or focus on a particular season. The model was then calibrated for
the six catchments using the synthetic time series for a dry, an average and
a wet year. The performance of the calibrated models was evaluated based on
the measured hourly streamflow time series. The results indicate that
streamflow estimates from untrained citizens are not informative for model
calibration. However, if the errors can be reduced, the estimates are
informative and useful for model calibration. As expected, the model
performance increased when the number of observations used for calibration
increased. The model performance was also better when the observations were
more evenly distributed throughout the year. This study indicates that
uncertain streamflow estimates can be useful for model calibration but that
the estimates by citizen scientists need to be improved by training or more
advanced data filtering before they are useful for model calibration.</p
Climatic predictors of species distributions neglect biophysiologically meaningful variables
This is the final version. Available on open access from Wiley via the DOI in this record.Aim: Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. To generate reliable and realistic predictions from these models
requires the use of climate variables that adequately capture physiological responses of species to
climate and therefore provide a proximal link between climate and their distributions. Here, we
examine whether the climate variables used in plant SDMs are different from those known to
influence directly plant physiology.
Location: Global.
Methods: We carry out an extensive, systematic review of the climate variables used to model the
distributions of plant species and provide comparison to the climate variables identified as
important in the plant physiology literature. We calculate the top ten SDM and physiology
variables at 2.5 degree spatial resolution for the globe and use principal component analyses and
multiple regression to assess similarity between the climatic variation described by both
variable sets.
Results: We find that the most commonly used SDM variables do not reflect the most important
physiological variables and differ in two main ways: (i) SDM variables rely on seasonal or annual
rainfall as simple proxies of water available to plants and neglect more direct measures such as
soil water content; and (ii) SDM variables are typically averaged across seasons or years and
overlook the importance of climatic events within the critical growth period of plants. We
identify notable differences in their spatial gradients globally and show where distal variables
may be less reliable proxies for the variables to which species are known to respond.
Main conclusions: There is a growing need for the development of accessible, fine-resolution
global climate surfaces of physiological variables. This would provide a means to improve the
reliability of future range predictions from SDMs and support efforts to conserve biodiversity in a
changing climate
Global transpiration data from sap flow measurements: The SAPFLUXNET database
Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr"R package-designed to access, visualize, and process SAPFLUXNET data-is available from CRAN. © 2021 Rafael Poyatos et al.This research was supported by the Minis-terio de Economía y Competitividad (grant no. CGL2014-55883-JIN), the Ministerio de Ciencia e Innovación (grant no. RTI2018-095297-J-I00), the Ministerio de Ciencia e Innovación (grant no. CAS16/00207), the Agència de Gestió d’Ajuts Universitaris i de Recerca (grant no. SGR1001), the Alexander von Humboldt-Stiftung (Humboldt Research Fellowship for Experienced Researchers (RP)), and the Institució Catalana de Recerca i Estudis Avançats (Academia Award (JMV)). Víctor Flo was supported by the doctoral fellowship FPU15/03939 (MECD, Spain)
Global transpiration data from sap flow measurements : the SAPFLUXNET database
Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr" R package - designed to access, visualize, and process SAPFLUXNET data - is available from CRAN.Peer reviewe
Information content of stream level class data for hydrological model calibration
Citizen science can provide spatially distributed data over large
areas, including hydrological data. Stream levels are easier to
measure than streamflow and are likely also observed more easily by
citizen scientists than streamflow. However, the challenge with
crowd based stream level data is that observations are taken at
irregular time intervals and with a limited vertical resolution. The
latter is especially the case at sites where no staff gauge is
available and relative stream levels are observed based on
(in)visible features in the stream, such as rocks. In order to
assess the potential value of crowd based stream level observations
for model calibration, we pretended that stream level observations
were available at a limited vertical resolution by transferring
streamflow data to stream level classes. A bucket-type
hydrological model was calibrated with these hypothetical stream
level class data and subsequently evaluated on the observed
streamflow records. Our results indicate that stream level data can
result in good streamflow simulations, even with a reduced vertical
resolution of the observations. Time series of only two stream level
classes, e.g. above or below a rock in the stream, were already
informative, especially when the class boundary was chosen towards
the highest stream levels. There was some added value in using up to
five stream level classes, but there was hardly any improvement in
model performance when using more level classes. These results are
encouraging for citizen science projects and provide a basis for
designing observation systems that collect data that are as
informative as possible for deriving model based streamflow time
series for previously ungauged basins
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