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| Titel |
Prediction of soil stability and erosion in semiarid regions using numerical hydrological model (MCAT) and airborne hyperspectral imagery |
| VerfasserIn |
Anna Brook, Lea Wittenberg |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250101554
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| Publikation (Nr.) |
 EGU/EGU2015-713.pdf |
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| Zusammenfassung |
| Long-term environmental monitoring is addressed to identify physical and biological changes
and progresses taking place in the ecosystem. This basic action of landscape monitoring is an
essential part of the systematic long-term surveillance, aiming to evaluate, assess and predict
the spatial change and progresses. Indeed, it provides a context for wide range of diverse
studies and research frameworks from regional or global scale. Spatial-temporal trends and
changes at various scales (massive to less certain) require establishing consistent baseline
data over time. One of the spatial cases of landscape monitoring is dedicated to soil
formation and pedological progresses. It is previously acknowledged that changes in
soil affect the functionality of the environment, so monitoring changes recently
become important cause considerable resources in areas such as environmental
management, sustainability services, and protecting the environment healthy. Given
the above, it can be concluded that monitoring changes in the base for sustainable
development.
The hydrological response of bare soils and watersheds in semiarid regions to intense
rainfall events is known to be complex due to multiply physical and structural impacts and
feedbacks. As a result, the comprehensive evaluations of mathematical models including
detailed consideration of uncertainties in the modeling of hydrological and environmental
systems are of increasing importance. The presented method incorporates means of remote
sensing data, hydrological and climate data and implementing dedicated and integrative
Monte Carlo Analysis Toolbox (MCAT) model for semiarid region. Complexity of practical
models to represent spatial systems requires an extensive understanding of the spatial
phenomena, while providing realistic balance of sensitivity and corresponding uncertainty
levels. Nowadays a large number of dedicated mathematical models applied to
assess environmental hydrological process. Among the most promising models is the
MCAT, which is a MATLAB library of visual and numerical analysis tools for the
evaluation of hydrological and environmental models. The model applied in this paper
presents an innovative infrastructural system for predicting soil stability and erosion
impacts. This integrated model is applicable to mixed areas with spatially varying soil
properties, landscape, and land-cover characteristics. Data from a semiarid site in
southern Israel was used to evaluate the model and analyze fundamental erosion
mechanisms. The findings estimate the sensitivity of the suggested model to the
physical parameters and encourage the use of hyperspectral remote sensing imagery
(HSI).
The proposed model is integrated according to the following stages: 1. The soil texture,
aggregation, soil moisture estimated via airborne HSI data, including soil surface clay and
calcium carbonate erosions; 2. The mechanical stability of soil assessed via pedo-transfer
function corresponding to load dependent changes in soil physical properties due to
pre-compression stress (set of equations study shear strength parameters take into
account soil texture, aggregation, soil moisture and ecological soil variables); 3. The
precipitation-related runoff model program (RMP) satisfactorily reproduces the observed
seasonal mean and variation of surface runoff for the current climate simulation; 4. The
Monte Carlo Analysis Toolbox (MCAT), a library of visual and numerical analysis
tools for the evaluation of hydrological and environmental models, is proposed as a
tool for integrate all the approaches to an applicable model. The presented model
overcomes the limitations of existing modeling methods by integrating physical data
produced via HSI and yet stays generic in terms of space and time independency. |
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