HS6.7

Remote sensing of interactions between vegetation and hydrology​

Remote sensing techniques are widely used to monitor the relationship between the water cycle and vegetation dynamics and its impact on the carbon and energy cycles. Measurements of vegetation water content, transpiration and water stress contribute to a better global understanding of the water movement in the soil-plant system. This is critical for the detection and monitoring of droughts and their impact on biomass, productivity and feedback on water, carbon and energy cycles. With the number of applications and (planned) missions increasing, this session aims to bring researchers together to discuss the current state and novel findings in the remote observation of the interactions between vegetation and hydrology. We aim to (1) discuss novel research and findings, (2) exchange views on what should be done to push the field forward, and (3) identify current major challenges.

We encourage authors to submit presentations on:
• Remote sensing data analyses,
• Modelling studies,
• New hypothesis,
• Enlightening opinions.

Convener: Brianna PagánECSECS | Co-conveners: Julia K. GreenECSECS, Isabella PfeilECSECS, Paul VermuntECSECS, Mariette VreugdenhilECSECS
Presentations
| Wed, 25 May, 13:20–14:40 (CEST)
 
Room 2.31

Presentations: Wed, 25 May | Room 2.31

Chairpersons: Isabella Pfeil, Paul Vermunt
13:20–13:30
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EGU22-11542
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solicited
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Virtual presentation
Susan Steele-Dunne

Many studies have demonstrated the value of microwave remote sensing for soil and vegetation applications in agricultural and natural systems. The sensitivity of microwave observables to internal water content makes them suitable for monitoring changes in above ground biomass associated with growth, seasonality and land cover change. In recent years, microwave observations have increasingly been used to monitor changes in vegetation water content associated with water status. Here, novel experimental data from field campaigns and analyses of satellite data records will be synthesized to provide a perspective on the current and future use of radar for monitoring vegetation water dynamics.

Ground-based radar and in-situ data will be used to illustrate the sensitivity of sub-daily radar data to detect the subtle response of the vegetation to variations in moisture supply and demand. These data will also be used to highlight the sensitivity of radar observables to surface canopy water (dew and/or interception). On the one hand, it will be shown that surface canopy water can have a confounding effect on vegetation parameter retrieval. On the other hand, microwaves can provide valuable information on this quantity of considerable interest in hydrology and land-atmosphere interactions.

 Analyses of existing satellite data records (ASCAT, Sentinel-1) will be used to show that the opportunities and challenges identifiable at field scale translate to the footprint scale. Furthermore, they will be used to outline a way forward. Future microwave missions offer unprecedented diversity in terms of sensors (in terms of frequency, polarization, viewing geometry, observation technique), as well as finer spatial and temporal resolution. To make optimal use of these new capabilities, we need to be willing to revisit our fundamental understanding of the factors affecting microwave interactions with vegetation. Finally, it will be argued that the use of machine learning can facilitate extracting the full information content of microwave observations by providing a means to reconcile microwave observables with land surface model states and parameters.

How to cite: Steele-Dunne, S.: A perspective on the current and future use of satellite radar observations for monitoring vegetation water dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11542, https://doi.org/10.5194/egusphere-egu22-11542, 2022.

13:30–13:35
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EGU22-482
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ECS
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On-site presentation
Paul Vermunt, Susan Steele-Dunne, Saeed Khabbazan, Jasmeet Judge, Nick van de Giesen, Vineet Kumar, Leila Guerriero, Alejandro Monsivais-Huertero, and Pang-Wei Liu

In recent years, radar remote sensing has been increasingly used for studies on interactions between vegetation and hydrology. New opportunities arise for more advanced studies, as unprecedented spatiotemporal monitoring capability will be offered by the next generation of spaceborne radar instruments. To avail of these opportunities, we need a better understanding of the water dynamics in a canopy which is captured in a radar signal.

Here, we present our latest findings from two experimental campaigns. In these campaigns, we used a ground-based prototype L-band radar instrument to obtain sub-daily observations, and extensive hydrometeorological measurements to monitor the water flow and storage in the soil-plant-atmosphere continuum of a corn field. Estimating 15-minute fluctuations of vegetation water content (VWC), surface canopy water (dew, interception), and surface soil moisture allowed us to quantify backscatter sensitivity to each of these moisture stores.

It will be shown that the nocturnal cycle of dew, and the diurnal cycle of VWC have a considerably higher effect on L-band backscatter than previously assumed. Both particularly affected the vertically polarized signals. Furthermore, we will demonstrate that the non-uniform vertical distribution of moisture in the canopy is dynamic, both on seasonal and diurnal timescales. A modelling study quantified the impact this has on backscatter. Our findings demonstrate the opportunities for spaceborne sub-daily radar observations to monitor rapid vegetation water dynamics. Moreover, they offer insights for future validation field campaigns.

 

References

Vermunt, P. C., Khabbazan, S., Steele-Dunne, S. C., Judge, J., Monsivais-Huertero, A., Guerriero, L., & Liu, P. W. (2020). Response of Subdaily L-Band Backscatter to Internal and Surface Canopy Water Dynamics. IEEE Transactions on Geoscience and Remote Sensing, 59, 7322-7337.

Vermunt, P. C., Steele-Dunne, S. C., Khabbazan, S., Judge, J., & van de Giesen, N. C. (2021). Reconstructing Continuous Vegetation Water Content To Understand Sub-daily Backscatter Variations. Hydrology and Earth System Sciences Discussions, 1-26.

How to cite: Vermunt, P., Steele-Dunne, S., Khabbazan, S., Judge, J., van de Giesen, N., Kumar, V., Guerriero, L., Monsivais-Huertero, A., and Liu, P.-W.: Hour-to-hour vegetation water dynamics captured in radar backscatter: lessons learned from experimental studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-482, https://doi.org/10.5194/egusphere-egu22-482, 2022.

13:35–13:40
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EGU22-5814
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ECS
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On-site presentation
Bethan L. Harris, Christopher Taylor, Graham P. Weedon, Joshua Talib, Wouter Dorigo, and Robin van der Schalie

The response of vegetation to changes in rainfall is a key factor in understanding terrestrial water availability, as well as land-atmosphere feedbacks that can occur as a result of the changes in evapotranspiration, albedo and surface roughness.

Studies of vegetation responses to rainfall have typically focused on variations at the seasonal timescale or longer. However, there is considerable rainfall predictability associated with atmospheric modes of intraseasonal (25 to 60 day) variability, for example the Madden-Julian Oscillation. An improved understanding of land surface predictability at the intraseasonal timescale could aid decision-making in areas such as water management or agriculture, as well as feeding back onto atmospheric predictability. Quantifying intraseasonal vegetation responses could also highlight required improvements in dynamic vegetation modelling for land surface models.

Here, we use satellite-based measurements of rainfall and Vegetation Optical Depth (VOD) to assess the relationships between the intraseasonal variability of rainfall and vegetation across the tropics and mid-latitudes. VOD is a proxy for vegetation water content and is also linked to biomass dynamics. Since it is derived from microwave observations, it can be retrieved under cloudy conditions, giving sufficient daily observations to permit the investigation of variations on the 25-60 day timescale in regions with frequent cloud cover such as the tropics. We use cross-spectral analysis to characterise the intraseasonal vegetation responses at a 0.25° pixel scale in each season.

Coherent intraseasonal relationships between rainfall and vegetation are typically found in arid or semi-arid regions, where vegetation is water-limited and hence sensitive to wet and dry spells. We also analyse the phase difference between rainfall and vegetation, i.e. by how many days one lags the other. Changes in vegetation are generally found to lag changes in rainfall, with increased rainfall followed by increased VOD. The results show that the observations capture distinct distributions of phase difference according to land cover type, with very fast (0-5 day) vegetation responses most likely in sparsely vegetated areas. Following strong intraseasonal wet events, the increase in VOD can persist for at least two months after the peak in rainfall.

How to cite: Harris, B. L., Taylor, C., Weedon, G. P., Talib, J., Dorigo, W., and van der Schalie, R.: Satellite-observed vegetation responses to intraseasonal rainfall variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5814, https://doi.org/10.5194/egusphere-egu22-5814, 2022.

13:40–13:45
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EGU22-12339
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ECS
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Virtual presentation
Malkin Gerchow, Kathrin Kühnhammer, Alberto Iraheta, and Matthias Beyer

Leaf and canopy temperature have long been recognized as an important indicators of plant water status. Recently, unmanned aerial vehicles (UAVs) became arguably the superior platform to acquire leaf temperatures due to their low cost and high spatiotemporal resolution and flexibility compared to satellite platforms. However, when interested in absolute leaf temperatures of individual leaves, the resolution of thermal cameras is often not sufficient and UAV overflight height needs to be adjusted. This causes heterogeneous forests to become inherently complex structures with great challenges for generating thermal orthomosaics. In addition, currently applied uncooled thermal sensors are affected by their ambient conditions causing temperature readings to drift during flight operation.
To address these issues, we employed a dual camera setup consisting of a visible and thermal sensor to aid the geometric calibration of the thermal sensor. To account for the temperature drift, we developed an alternative flight planning approach: During the UAV mapping mission ground temperature references are repeatedly captured from above forest clearings to estimate temperature drift and continuously adjust temperature calibration. We compare our temperature calibration approach to the default camera calibration and to a simple pre- and post-flight calibration method under different atmospheric conditions (temperature, wind and cloud coverage). The geometric accuracy of the forests thermal orthomosaics is validated against ground control points.
Accurate calibrated canopy temperatures will allow to compare canopy temperature differences while also providing uncertainty estimates of the temperature data. High resolution thermal maps at the forest leaf scale will open up the possibility to analyze plant water status during seasonal dry forest changes.

How to cite: Gerchow, M., Kühnhammer, K., Iraheta, A., and Beyer, M.: UAV based thermal imaging at the leaf scale – A case study in a tropical dry forest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12339, https://doi.org/10.5194/egusphere-egu22-12339, 2022.

13:45–13:50
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EGU22-5128
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ECS
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On-site presentation
Isabella Greimeister-Pfeil, Wolfgang Wagner, Raphael Quast, Sebastian Hahn, Susan Steele-Dunne, and Mariette Vreugdenhil

Microwave scatterometers provide global and frequent observations of the Earth’s surface. In particular, C-Band scatterometers are sensitive to the moisture content of the soil and vegetation in the sensor footprint, and can therefore be used for the retrieval of soil moisture (SM) and vegetation optical depth (VOD).

To model the vegetation component in the signal, and subsequently retrieve VOD, the slope (σ’) of the backscatter dependence on the incidence angle of the observation is exploited. This is possible because σ’ is related to the water content and structure of the canopy. Early studies moreover showed that SM effects on σ’ are weak and can, in a first approximation, be neglected. However, short-term dynamics in σ’ have raised questions about the validity of this assumption.

In this study, we investigate a potential SM effect on σ’ time series derived from the Advanced Scatterometer (ASCAT) by exploring relationships between σ’, SM, and leaf area index. We carry out the analysis over six study regions in Portugal, Austria, and Russia with different climate, land cover and vegetation cycles.

Spearman correlations between short-term anomalies of σ’ and SM are stronger than between σ’ and LAI, indicating that SM does have an effect on σ’. The analysis of daily σ’ values, as opposed to the smoothed σ’ that is used in retrieval algorithms, shows SM effects even more clearly: SM increases correspond to decreases of σ’, even during periods of vegetation growth, which are typically characterized by increasing σ’ values. Thus, we conclude that there is a SM signal in σ’ time series on top of the vegetation signal. Over sparse vegetation, the SM effect may be as large as 20% of the seasonal, vegetation-induced variation of σ’, whereas it is smaller over dense vegetation. Moreover, the short-term dynamics in σ’ time series might be caused by water on the canopy, i.e., interception or dew, to some extent. Further work is needed to confirm this hypothesis. For the retrieval of SM from ASCAT observations, these results confirm the use of a long-term average σ’ climatology instead of dynamic σ’ time series to correct for vegetation effects.

How to cite: Greimeister-Pfeil, I., Wagner, W., Quast, R., Hahn, S., Steele-Dunne, S., and Vreugdenhil, M.: Disentangling soil moisture and vegetation effects on the ASCAT backscatter-incidence angle relationship, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5128, https://doi.org/10.5194/egusphere-egu22-5128, 2022.

13:50–13:55
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EGU22-10934
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ECS
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Virtual presentation
Alexandra Konings, Krishna Rao, Meng Zhao, A. Park Williams, Noah Diffenbaugh, and Marta Yebra

Spatio-temporal patterns of plant water uptake, loss, and storage are a first-order control on photosynthesis, evapotranspiration dynamics, and thus, land-atmosphere interactions. These patterns depend on temporally variable hydrometeorological conditions but also on geographically varying characteristics. These include, but are not limited to, topographic and soil properties that influence rainfall infiltration and water distribution in the unsaturated zone and vegetation properties, such as rooting depth, stomatal and xylem properties, leaf area, and more. Understanding how these different factors interact to control the overall dynamics of plant water uptake is fundamental to understanding the response of vegetation to hydrologic variations, but has traditionally been hindered by data limitations. In situ measurements are too sparse to sufficiently span the range of possible variations across different geographic factors. Remote sensing estimates of plant water uptake either are not available or (in the case of ET estimates) are sufficiently indirect that they are unlikely to correctly account for all of the factors above. Here, we study the effects of different geographic factors on plant-water interactions by analyzing the dynamics of live fuel moisture content (LFMC, defined as the vegetation water content divided by dry biomass) determined from Sentinel-1 synthetic aperture radar and Landsat multispectral observations. LFMC directly reflects vegetation water content and therefore patterns of plant water uptake and evapotranspiration. We quantify the "plant-water sensitivity" by using an auto-regressive model comparing LFMC to climate and analyze the spatial patterns of plant water sensitivity at 4 km resolution across the Western United States. No individual factor explains a majority of the spatial patterns in plant-water sensitivity. With the exception of the maximum soil conductance, no soil, topographic, or vegetation traits exerts a dominant control on plant water sensitivity. However, when aggregated, soil characteristics explain about twice as much variability in plant water sensitivity as topographic or plant characteristics do, despite little previous recognition of the influence of soil hydraulic properties on plant-water interactions. 

How to cite: Konings, A., Rao, K., Zhao, M., Williams, A. P., Diffenbaugh, N., and Yebra, M.: The contribution of soil, topographic, and vegetation traits to plant-water sensitivity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10934, https://doi.org/10.5194/egusphere-egu22-10934, 2022.

13:55–14:00
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EGU22-10700
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ECS
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Virtual presentation
Felipe Fileni, Shunan Feng, Johannes Erikson, Rickard Pettersson, and Mattias Winterdahl

As future changes in climate announce an increase in the frequency of drier periods, it is important to understand how climatic variables can influence vegetation productivity. An analysis in the growing season is especially relevant, as it is the period when vegetation is most sensitive to climate change.  In this study, the NDVI and SPEI were used to represent vegetation productivity and climate variables, respectively, at a global scale, in different temporal scales. The growing season variable was defined as a function of vegetation productivity. Pearson correlation between both variables at different timescales was carried in Google Earth Engine, with a total of 72 scenarios: 3 different NDVI scales vs 24 different SPEI scales. An optimal scenario was defined for each pixel, representing the NDVI vs SPEI timescale where the correlation was higher. Aiming to understand the importance of different climatic variables on vegetation productivity a CART model was run. Temperature (T), precipitation (P) and solar radiation (Swd) were used as independent variables while optimal Pearson’s R was the dependent variable of the model.  Additionally, to further detail how the climatic variables were spatially distributed, a multiple linear regression between optimal values of vegetation health (NDVI) and optimal climatic variables (T, P, Swd) was run in each pixel of the map.

The optimal NDVI timescale found for most of the globe was of 5 months, with exceptions in northern latitudes (optimal NDVI: 1 month) and in some arid regions of the globe (optimal NDVI: 3 months). The optimal SPEI timescale exhibits little variation, with optimal timescales between 9 and 12 months for most pixels. CART results showed that locations of low precipitation (<800mm/years) and high solar radiation (net radiation>97 W/m2) were the locations with the best correlations between climate and vegetation productivity during the growth season, with branches of the model averaging a Pearson correlation above 0.5. The pixel-by-pixel multiple linear regression indicated that precipitation is the controlling factor of vegetation in arid regions, such as Australia, southern Africa and the Mojave and Sonoran deserts. Vegetation in northern latitudes and regions of temperate climate, i.e., Patagonia and temperate prairies in the US, tended to have radiation as its limiting climate factor. Whilst temperature was the driving factor in wetlands, such as the Pantanal in South America and parts of Southern China and Vietnam. Finally, vegetation in tropical forests and temperate forests showed none of the three climatic factors analysed controlling more than 20% of vegetation response, potentially indicating the dominance of secondary factors.

How to cite: Fileni, F., Feng, S., Erikson, J., Pettersson, R., and Winterdahl, M.: The influence of climatic variables on vegetation response during the growing season – Using Decision Trees (CART) and Multiple Linear Regression (MLR) to define how precipitation, temperature, and solar radiation shape vegetation response globally., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10700, https://doi.org/10.5194/egusphere-egu22-10700, 2022.

14:00–14:05
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EGU22-1737
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On-site presentation
Wenqin Huang, Tengfei Hu, Jingqiao Mao, Carsten Montzka, Roland Bol, Songxian Wan, and Jin Yue

Hydrological processes are known as driving forces in structuring wetland plant communities, but still specific relationships are not always well understood. The dynamic, seasonally inundated wetland at Poyang Lake (less than 1000  km2 in the dry season and more than 3000 km2 in the wet season), the largest freshwater lake in China, underpinnes critical regional ecosystem services (e.g. flood water retention, water supply and biodiversity conservation). However, recent drier conditions of Poyang Lake are having a profound impact on its wetland vegetation leading to degradation of the entire wetland ecosystem, thereby also threatening the winter habitat of migratory birds. We aim to develop an integrated framework to quantitatively investigate the spatial distribution of major herbaceous communities that provide habitat for the migratory birds in response to Poyang Lake flood inundation. First, ground references are obtained from a combination of drone imagery and field surveys as an input for the wetland herbaceous community classification model. Our classification model is based on a machine learning technique applied to Sentinel-2 satellite data. This new search strategy provides an accurate classification based on the more optimal input variables and model parameters gained simultaneously. Secondly, based on the dynamic changes in water levels since 2000, we statistically evaluate the key environmental drivers of the hydrological regime on the spatial distribution of the wetland vegetation communities. This showed that: 1) different plant communities exhibited varying tolerance to flood inundation and 2) two key factors, i.e., average water depth and average duration of the inundation events, were found to be able to characterize the communities’ tolerance independently. For example, Carex cinerascens Ass. which had the widest inundation stress tolerance, being adapted to an inundation duration of 120~230 d and depth of 1.5~1.7 m, accounted for the largest herbaceous community (>27% cover) within the entire study area. Different survival strategies to inundation stress, such as dormancy and morphological restructuring, can explain the varying tolerance of plant species/communities. Our work elucidated the linkages between hydrological processes and herbaceous plants’ distribution in wetlands, and the approach can be readily applied at small to large catchment scales and provides a straightforward practical tool to predict the possible responses of the lake wetland vegetations to potential hydrological changes.

How to cite: Huang, W., Hu, T., Mao, J., Montzka, C., Bol, R., Wan, S., and Yue, J.: Hydrological drivers of the spatial distribution of herbaceous wetland communities at Poyang Lake, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1737, https://doi.org/10.5194/egusphere-egu22-1737, 2022.

14:05–14:10
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EGU22-11165
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ECS
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Virtual presentation
Harsh Beria and James W. Kirchner

The hydrologic cycle in Switzerland relies heavily on snowmelt sustaining streamflows during spring and summer. Climate warming will shrink the regional coverage of seasonal snowpacks thereby leading to an earlier onset of snowmelt, which in turn will alter streamflow regimes. However, the effects of changes in snow regimes on Alpine vegetation are largely unknown. In this context, it is imperative to understand how much streamflow and vegetation water uptake depend on different precipitation phases (rainfall versus snowfall), and what factors control the relative proportion of rainfall and snowfall that are ultimately used by vegetation (versus that flow to streams).

In this presentation, we use stable water isotopes to assess seasonal origin of waters used by Alpine trees vs water flowing into the nearby stream across different sites in Switzerland. We then correlate remote sensing based plant water abundance indicators (NDVI, NDWI, VOD) against long term streamflow records to assess how strongly waters flowing into streams are decoupled from waters taken up by vegetation, and how this decoupling varies across space and time. Using these results, we propose a theoretical framework that explains the phenomenon of “drought paradox”, where precipitation deficits during periods of drought disproportionally impact streamflow generation over vegetation in the Swiss Alps.

How to cite: Beria, H. and Kirchner, J. W.: Partitioning of rainfall and snowmelt between trees and streams in the Swiss Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11165, https://doi.org/10.5194/egusphere-egu22-11165, 2022.

14:10–14:15
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EGU22-12559
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Highlight
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Virtual presentation
Mariette Vreugdenhil, Isabella PFeil, Susan Steele-Dunne, and Wouter Dorigo

With the Copernicus Sentinel-1 series, for the first time high temporal and spatial resolution backscatter time series have become available. Sentinel-1 backscatter observations are sensitive to changes in water content and structural changes in vegetation and soils and provide complementary information next to optical remote sensing datasets such as Leaf Area Index. However, most studies have looked at the sensitivity of Sentinel-1 backscatter to vegetation water dynamics at very local scale. Furthermore, no specific focus has yet been on monitoring drought impact on vegetation with Sentinel-1. Here we will present results of a study over Europe which assesses the potential of Sentinel-1 to monitor drought impact on vegetation.

In this study we use the record summer drought of 2018 as a case study. This drought led to decreased yields in northwestern Europe, and to decreased GPP in for example grasslands and forests (Fu et al., 2020). We have calculated anomalies of co-, and cross-polarized backscatter, and the ratio thereof, the so-called cross-ratio (CR) of 2018 with the reference year of 2016 which had normal conditions. These anomalies were compared to anomalies calculated with Copernicus Global Land Service LAI anomalies and ESA CCI soil moisture anomalies and analyzed per land cover type. The results show very strong negative anomalies in VV, VH backscatter and CR from June to November over central and northwestern Europe, similar to those observed in LAI. However, differences in patterns can be seen between LAI and CR, especially over forest areas. Although LAI showed stronger anomalies in the Netherlands and western Germany in July, CR shows stronger anomalies in eastern Germany and Czech Republic, and especially over forests. These differences in patterns may be related to the penetration depth of microwaves, and the sensitivity to vegetation water content of the above ground biomass.

How to cite: Vreugdenhil, M., PFeil, I., Steele-Dunne, S., and Dorigo, W.: Monitoring drought impact on vegetation with Sentinel-1, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12559, https://doi.org/10.5194/egusphere-egu22-12559, 2022.

14:15–14:20
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EGU22-8239
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ECS
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On-site presentation
Wantong Li, Mirco Migliavacca, Markus Reichstein, Matthias Forkel, Christian Frankenberg, and René Orth

The frequency and the intensity of drought events have increased during the past decades in some regions, yet the implications of drought for the terrestrial vegetation functioning are not fully understood. In particular, drought in related studies has often been characterized by meteorological conditions rather than the actual soil moisture deficit. Further, previous research focused predominantly on the structural vegetation response, such that the large-scale physiological response remains poorly understood.

Here, we analyze and compare the vegetation’s physiological and structural responses to drought across the globe using high-resolution daily TROPOMI sun-induced fluorescence (SIF) as a proxy for productivity, short-wavelength vegetation optical depth (VOD) as a proxy for canopy water conditions and biomass, and near-infrared reflectance of terrestrial vegetation (NIRv) during the period March 2018 - August 2021. Taking advantage of an extended soil moisture record (1979-2021, ERA5-Land reanalysis) we identify and focus on regions where severe soil moisture droughts occurred during our relatively short analysis period. Therein, we quantify and compare the amounts of SIF, VOD and NIRv changes during the early and late drought stages as well as for the recovery period. We also compute the vegetation response to short-term soil moisture changes, i.e. the vegetation sensitivity to short-term soil moisture and the respective changes during the course of droughts. The absolute changes of vegetation indices allow to disentangle physiological and structural responses, while the sensitivity analysis can quantify vegetation responses straightforward to water limitation by accounting for meteorological forcings. To infer vegetation sensitivity, we train random forest regression models at each grid cell, and apply the SHapley Additive exPlanations (SHAP) method to isolate the influence of soil moisture on vegetation from that of other meteorological variables such as temperature, solar radiation, precipitation and vapor pressure deficit. 

Analyzing the absolute value changes of NIRv, SIF and VOD during the 2018 European drought and the 2020 Russian drought events, we find asynchronous responses of vegetation productivity and vegetation water content. This indicates different hydraulic regulation strategies in response to drought. Moving beyond these case studies, we quantified and averaged the vegetation sensitivity to soil moisture across many severe droughts across the globe, which reveals systematic sensitivity increases during the course of the drought. From an ecological perspective, this indicates increases in ecosystem vulnerability to drought, and can induce feedback in climate and ecosystem services. Vegetation responses to drought differ depending on different vegetation types, climate and soil conditions. In addition, we conduct carbon fluxes from eddy covariance measurements to confirm the vegetation responses to drought derived from Earth observation satellites. In summary, this study provides insights into vegetation responses to droughts at large spatial scales which can help to accurately quantify respective anomalies in terms of land carbon uptake to consequently improve climate projections.

How to cite: Li, W., Migliavacca, M., Reichstein, M., Forkel, M., Frankenberg, C., and Orth, R.: Revealing the drought response of large-scale vegetation physiology from multiple satellite-based observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8239, https://doi.org/10.5194/egusphere-egu22-8239, 2022.

14:20–14:25
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EGU22-510
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ECS
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Virtual presentation
Triparna Sett, Bhaskar R Nikam, Hukum Singh, and Saurabh Purohit

Forest evapotranspiration (ET) is one of the most important processes regulating the terrestrial hydrological cycle, and it is increasingly affected by drought episodes. This emphasizes the need of comprehending the relationship between forest ET and forest drought stress, we chose two forested regions for our investigation, a Deciduous Broadleaf Forest (DBF) and an Evergreen Needle leaf forest (ENF) from India. ET was reduced in most forests around the world during severe and extreme droughts that lasted for lengthy periods, yet their susceptibility to forest drought stress is the crucial component in ensuring their long-term viability. Rainfall data from CHIRPS was used to estimate the monthly Standardized Precipitation Index (SPI) and identify wet and dry spells during the period 1981 to 2020. Vegetation drought indexes viz. Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) were estimated using MODIS EVI and LST data from 2002 to 2020.

According to our findings, the ENF experienced a lengthier dry spell from 1998 to 2007. In 2002, the lowest (-2.28) SPI was recorded. There is a substantial increase in the frequency of dry spells for DBF. In 2002, the most negative SPI of -2.03 was recorded in DBF. As a result, 2002 is considered a drought year for the type of forests. 2015 was selected as a wet year due based on SPI values 3.94 and 3.46 for ENF and DBF, respectively. The ET of these two regions was estimated using an auto-calibrated METRIC model. During the drought period (2002) the ET of the DBF region decreased to 0.17-2.19 mm/day from 0.66-4.89 mm/day during the normal/wet period (2015). Similarly, ET of the ENF region was also decreased to 2.81-4.51 mm/day during the dry period in comparison to 2.92-6.65 mm/day in the year 2015. The ET rate is not changed as much by ENF as it is by DBF.

There are three possible explanations for why these distinct plant species react to drought stress. The first is the pattern of precipitation. Because ENF's overall precipitation is always higher than DBF's, the ET rate is naturally higher, and there is very little change in ENF's ET between drought and non-drought years. The next factor to consider is temperature variation; during droughts, the temperature in DBF is higher than in ENF, hence the ET is higher. The final cause is due to physiological and anatomical differences between DBF and ENF. The governing variables of evapotranspiration are leaf water content, stomatal conductance, the relative water content in leaves, absolute and relative transpiration rates, variation in species-wise water usage efficiency, and deep plant root systems. Drought conditions impair plant development and productivity by reducing stomatal conductance, reducing leaf area, stem extension, and root growth, and disrupting plant osmotic relations and water-use efficiency, among other things. As a result, DBF has a higher intensity of drought stress than ENF. In this sense, ENF outperforms DBF in terms of plant resistance and strategic adaptation to drought stress.

Key Words: Forest drought stress, forest evapotranspiration, SPI, VHI, METRIC

How to cite: Sett, T., Nikam, B. R., Singh, H., and Purohit, S.: Effect of Drought Stress on Forest Evapotranspiration- A case study on Indian forests, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-510, https://doi.org/10.5194/egusphere-egu22-510, 2022.

14:25–14:30
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EGU22-10048
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ECS
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Virtual presentation
Andrea Gaspa, Roberto Corona, and Nicola Montaldo

In the presence of uncertain initial conditions and model parameters coupled land surface model (LSM)- vegetation dynamic model (VDM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the soil moisture and normalized difference vegetation index (NDVI) as observed from satellite remote platforms.

The possibility to merge grass and tree NDVI observations and radar data with the model optimally for providing robust predictions of soil moisture and grass and tree leaf area index (LAI) in heterogenous ecosystems is demonstrated. We propose an assimilation approach that assimilates backscatter data from radar and NDVI from optical sensors through the Ensemble Kalman filter (EnKF) and provides a physics-based update of soil moisture and grass and tree LAI predicted by VDM. We used Sentinel 1 radar data for soil moisture, and Landsat 8 and Sentinel 2 optical data for NDVI. Soil moisture is predicted by the LSM, while the VDM predicts the LAI, which is strictly related to NDVI, through a field-estimated empirical relationship.

This approach, as with other common assimilation approaches, may fail when key model parameters, e.g. the saturated hydraulic conductivity of LSM and the maintenance respiration coefficient (ma) of VDM, are estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach was developed, which accepts this violation in the early model run-times and dynamically calibrates all the components of the model parameter ensembles as a function of the persistent bias in soil moisture and LAI predictions, allowing to remove the model bias, restore the fidelity to the EnKF requirements and reduce the model uncertainty.

The proposed multiscale assimilation approach was tested in a Sardinian field site, a typical Mediterranean ecosystem characterized by strong heterogeneity of the vegetation and water limited conditions. The site is also a case study of the SWATCH European Research Project, and in this field a micrometeorological eddy-covariance based tower is operating from 2003.

The positive impact of the proposed assimilation approach on the soil water budget, evapotranspiration and CO2 uptake predictions in the heterogenous ecosystem is demonstrated finally. 

How to cite: Gaspa, A., Corona, R., and Montaldo, N.: Multi Scale Assimilation of NDVI and radar data for soil moisture and Leaf Area Index Predictions in an Heterogeneous Mediterranean Ecosystem , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10048, https://doi.org/10.5194/egusphere-egu22-10048, 2022.

14:30–14:35
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EGU22-1775
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ECS
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On-site presentation
Rodolfo Nóbrega and Iain Colin Prentice

Increases in streamflow are often attributed to land-cover clearing (LCC) on the basis that it reduces soil infiltration capacity and increases surface runoff. Nonetheless, these changes can result from different hydrological mechanisms depending on the vegetation, and temporal and spatial scales. LCC triggers a series of changes in hydrological fluxes that have non-linear responses to precipitation and that were established upon the long-term balance with regional climatological, edaphic, and geological characteristics. We analysed streamflow and root zone water capacity (RZSC) to identify underlying relationships between stream dynamics and water consumption by plants. We used a time-series segmentation and residual trend analysis on streamflow and precipitation of high-order tributaries of the Tapajós River whose catchments underwent intense land-use changes over the past decades. We estimated the RZSC using the "Earth observation-based" mass-curve balance method by considering the annual land-cover changes over a >30-year period. We show that the reduction in the RZWC caused by changes in the water consumption by plants over the dry season is tightly associated with the increased baseflow contribution to rivers. Finally, we analysed gross primary productivity (GPP) and ET estimates generated by a model based on eco-evolutionary optimality that integrates the water and carbon cycles at the canopy level. We found that trends in ET from croplands are not as pronounced as trends in GPP. Although RZWC is quantified using the water deficit driven by ET, changes in RZWC are more correlated to changes in GPP. We show that the potential effects of vegetation responses to increasing atmospheric CO2 concentrations on streamflow are still outweighed by impacts of land-use change on low flows in Amazon rivers. However, this might not be the case for all water cycle components, and, therefore, we highlight the importance of considering the carbon cycle in hydrological assessment studies.

How to cite: Nóbrega, R. and Prentice, I. C.: Rapid and temporary increases in low flows in the Amazon explained by changes in root-zone water storage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1775, https://doi.org/10.5194/egusphere-egu22-1775, 2022.

14:35–14:40
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EGU22-13063
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ECS
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On-site presentation
Luca Salerno, Alvaro Moreno-Martínez, Emma Izquierdo-Verdiguier, Nicholas Clinton, Annunziato Siviglia, and Carlo Camporeale

The regular flood pulse of large tropical rivers is the main driver of ecological and biogeochemical process in large Amazonian floodplains. Endemic vegetation species developed adaptation to survive in seasonal flood environments and tune their vital process with periodic flood events, water levels, and sedimentary processes. The construction of hydroelectric dam causes alterations of natural hydrological regime and sediment supply, threatening downstream floodplain forest.

An assessment of impact of river regulation on floodplain vegetation is crucial to develop a modern approach to the regulated rivers management in the Neotropics and to mitigate the impact of damming on floodplain environment. Nevertheless, floodplain forest monitoring requires high resolution mapping as vegetation dynamics are in the narrow area at the interface terrestrial and aquatic systems. Most of the existing satellite images that afford land observations have severe limitations due to their coarse resolution or missing  data caused by the extreme cloudiness conditions in of tropical regions.

In the present work, we propose an innovative approach based on high-resolution mapping for the monitoring long-term evolution of vegetation in a highly impacted environment (Uatama river) due to Balbina dam regulation.  We combine Landsat (30m spatial resolution and 16 days revisit cycle) and the MODIS missions (500m spatial resolution and daily revisit cycle), using HISTARFM algorithm, to reduce noise and produce monthly gap-free high-resolution (30 m) observations over land. Areas characterized by vegetation changes are identified through the analysis of of vegetation index products derived from the high-resolution reflectance data.  Furthermore, hydrological modification within these areas are assessed by using a global water surface dataset.

We found a deep redistribution of floodplain forest concentrated in areas that experienced a hydrologic regime transition after dam construction. The vegetation changes comprise not only vegetation degradation of areas with greater hydrological stress but also with large floodplain areas not flooded afterwards, which were invaded by upland forest. Although the dam was built more than 30 years ago, its effects on the vegetation continue and the situation seems far from reaching a new environmental equilibrium.

The framework proposed offers a practical and novel tool to accurately monitor riparian vegetation dynamics over time even for very remote and poorly accessible areas such as tropical floodplains. Furthermore, the assessment of the impact that the human footprint has on tropical floodplain allows a more careful management of the watersheds.

How to cite: Salerno, L., Moreno-Martínez, A., Izquierdo-Verdiguier, E., Clinton, N., Siviglia, A., and Camporeale, C.: The long-term floodplain forest modifications of a regulated tropical river, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13063, https://doi.org/10.5194/egusphere-egu22-13063, 2022.