Modelling Wetland Growing Season Rainfall Interception Losses Based on Maximum Canopy Storage Measurements

Ciężkowski, Wojciech; Berezowski, Tomasz; Kleniewska, Małgorzata; Szporak-Wasilewska, Sylwia; Chormański, Jarosław

doi:10.3390/w10010041

Cited by 13 publications

(5 citation statements)

References 53 publications

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“…At the global scale, wetlands accumulate a significant amount of organic carbon [4][5][6] and regulate the regional climate [7]. At the watershed scale, wetlands fulfil an important hydrological function by controlling different components of the water balance such as snow retention [8], flood retention [9][10][11], evapotranspiration [12], and interception [13,14]. At the local scale, wetlands provide food, remove water pollution, retain water (hence, controlling floods), stop sediments, and much more [15].…”

Section: Introductionmentioning

confidence: 99%

Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats

et al. 2020

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Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems.

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Section: Introductionmentioning

confidence: 99%

Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats

et al. 2020

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“…It was due to intensivevegetation development at the beginning of the growing season. Generally, in the north-eastern part of Poland, the growing season starts late; in the multiannual period , it began in the middle of April (Ciężkowski et al, 2018). Therefore, the results show that transpiration has a major share in the water vapour flux within the analysed area.…”

Section: Actual Evapotranspiration (Eta) In the Context Of Available ...mentioning

confidence: 74%

Actual and reference evapotranspiration for a natural, temperate zone fen wetland – Upper Biebrza case study

Kleniewska,

Berezowski,

Mitrowska

et al. 2024

Journal of Water and Land Development

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Evapotranspiration is the key and predominant component of the water balance in wetlands. Direct evapotranspiration measurements are challenging in wetlands due to their remoteness and high surface water level. This article describes the actual (ETa and reference evapotranspiration (ET0) from a cultivated wet meadow located in the Biebrza National Park – the largest national park in north-east Poland, Central Europe. The data were sourced from a micrometeorological station equipped with an eddy covariance system to measure heat and vapour fluxes and such meteorological elements as radiation balance components, air temperature and humidity. The values of directly measured ETa were presented daily in the context of available energy and ET0. Daily sums of ETa ranged from below 0.2 mm in winter to 6.5 mm in summer. The share of daily sums of ETa in the ET0 usually ranged from 50 to 60%, with extreme values from 10 to 170%. Aside from giving more insight into Biebrza wetlands’ functioning, the actual data produced in this study may be used instead of indirect methods, which were used the most in modelling wetlands areas.

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“…The time series of spectral reflectance were measured in the range of 400-1000 nm in about 0.2 nm intervals. Data collected by a set of two spectrometers USB2000+VIS-NIR (manufactured by Ocean Optics, Inc., Largo USA) looking forward and upwards with timestamp of 10 min were processed for obtaining one-per day reflectance curve, and finally narrow-band NDVI indices [24]. NDVI derived from RED and NIR bands of Proba-V are covering spectral ranges of 616-694 nm and 724-920 nm respectively thus to allow a proper comparison between ASD FieldSpec4, tower spectral data and satellite data, all measured spectra were aggregated to match exactly those ranges.…”

Section: Evaluation Data From the Fieldmentioning

confidence: 99%

Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

et al. 2019

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The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).

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Modelling Wetland Growing Season Rainfall Interception Losses Based on Maximum Canopy Storage Measurements

Cited by 13 publications

References 53 publications

Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats

Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats

Actual and reference evapotranspiration for a natural, temperate zone fen wetland – Upper Biebrza case study

Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

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