A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R2 values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies.
River flooding causes significant losses to crops and negatively affects local agriculture economies, particularly in rural riverine areas. In this work, a techno-economic methodology for the monetary estimation of crop losses due to flash flooding is presented. The methodology takes into account flood depth and flow velocity, as provided by MIKE FLOOD, as well as the season of flood occurrence, and provides monetary estimates of crop damage based on synthetic logistic flow velocity-flood depth-crop damage surfaces. The development of the flood damage surfaces involved a questionnaire survey targeting practicing and research agronomists. Subsequently, a weighted Monte Carlo simulation was performed in order to enhance the questionnaire-based loss estimate information. Finally, synthetic flow velocity-flood depth-crop damage surfaces were developed for every crop under study and for every month using logistic regression analysis. The damage surfaces are an essential component of the developed model which was implemented in Python, enabling the GIS visualization of the estimated agricultural damage. The aforementioned methodology was applied for estimating the damage caused by a flash flood that took place in the Koiliaris River Basin in Crete for which no historical data exist. The novelty of the proposed methodology is the development of local synthetic flow velocityflood depth-crop damage surfaces. Furthermore, the velocity parameter, which is taken into account, makes the methodology suitable for flash flood events, where significant discharges and high velocities dominate, or for flood event cases which are characterized by high flow velocities. The methodology identifies rural areas and agricultural land uses that are most prone to flooding and serious crop damages and thus require greater attention. Furthermore, the methodology aptitude for developing local damage surfaces could be modulated in order to confront different flood scenarios on various crops distributions and be used to address agricultural planning activities.
Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.
In the framework of a water resources management class in the Technical University of Crete, a narrative-driven role-playing game (RPG) was planned and tested in the classroom, with the intent to raise awareness among the students on how floods can have an impact on the everyday lives of different citizens. During this game, the students had the opportunity to act as different stakeholders. In order to assess the impact of this game on participants’ thoughts of who might be affected by a flood event, two questionnaires were used, one before and one after the game. The results show that there was very positive feedback from the participants on how this RPG helped them realize the different implications a flood event might have on citizens and decision makers. The community-based aspect that was chosen for this RPG implementation showed the difficulties the specific roles would face as single individuals and as a community in general. Using a similar approach can help any stakeholder understand the challenges in a more direct way than with traditional lecturing and presentations.
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