Characterising the response of vegetation cover to water limitation in Africa using geostationary satellites

Küçük, Çağlar; Koirala, Sujan; Miralles, Diego G.; Reichstein, Markus; Jung, Martin

doi:10.1002/essoar.10504964.1

Cited by 2 publications

(2 citation statements)

References 89 publications

(59 reference statements)

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“…We note here that the soil water capacity data are favoured over the rooting depth data. This agrees with Küçük et al (2020), who suggest that estimating plant storage capacity based on Earth-Observation data may be more suitable than those using optimality principles. Besides, wSoilmax(2) is defined as maximum plant accessible soil storage, but also used to describe the maximum soil water storage.…”

Section: Calibrated Parameterssupporting

confidence: 90%

The importance of vegetation to understand terrestrial water storage variations

Trautmann

Koirala

Güntner

et al. 2021

Preprint

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Abstract. So far, various studies aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way how vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including vegetation on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment with vegetation parameters varying in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but including vegetation data led to even better performance and more physically plausible parameter values. Largest improvements regarding TWS and ET were seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of snow and different soil water storage components to the TWS variations, with the dominance of an intermediate water pool that interacts with the fast plant accessible soil moisture and the delayed water storage. The findings indicate the important role of deeper moisture storages as well as groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.

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Section: Calibrated Parameterssupporting

confidence: 90%

The importance of vegetation to understand terrestrial water storage variations

Trautmann

Koirala

Güntner

et al. 2021

Preprint

Self Cite

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“…Furthermore, the very high temporal resolution of geostationary satellites can capture diurnal changes in photosynthetic capacity in response to short-term climatic events, which are likely unobserved by polar-orbiting satellites. There is, however, indication of a revived interest in geostationary satellites, for example, for studying vegetation seasonality [202] and vegetation-water interactions at the continental scale [203].…”

Section: Incorporating Higher Resolutions and Multisourcementioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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Characterising the response of vegetation cover to water limitation in Africa using geostationary satellites

Abstract: Africa hosts the largest share of undernourished population, and the livelihood of the majority of its population relies on ecosystem services and water availability (Müller et al., 2014). Moreover, African ecosystems contribute strongly to fluctuations of the global carbon cycle (

Cited by 2 publications

References 89 publications

The importance of vegetation to understand terrestrial water storage variations

The importance of vegetation to understand terrestrial water storage variations

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

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