scholarly journals Potential Dam Breach Analysis and Flood Wave Risk Assessment Using HEC-RAS and Remote Sensing Data: A Multicriteria Approach

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Emmanouil Psomiadis ◽  
Lefteris Tomanis ◽  
Antonis Kavvadias ◽  
Konstantinos X. Soulis ◽  
Nikos Charizopoulos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Volume ◽  
Surface Model ◽  
Flood Wave ◽  
Dam Breach ◽  
Sensing Data ◽  
Crete Island ◽  
Hydrogeological Characteristics ◽  
The Impact

Dam breach has disastrous consequences for the economy and human lives. Floods are one of the most damaging natural phenomena, and some of the most catastrophic flash floods are related to dam collapses. The goal of the present study is to analyse the impact of a possible failure–collapse on a potentially affected area downstream of the existing Bramianos dam on southern Crete Island. HEC-RAS hydraulic analysis software was used to study the dam breach, the flood wave propagation, and estimate the extent of floods. The analysis was performed using two different relief datasets of the same area: a digital elevation model (DEM) taken from very high-resolution orthophoto images (OPH) of the National Cadastre and Mapping Agency SA and a detailed digital surface model (DSM) extracted from aerial images taken by an unmanned aerial vehicle (UAV). Remote sensing data of the Sentinel-2 satellite and OPH were utilised to create the geographic information system (GIS) layers of a thorough land use/cover classification (LULC) for the potentially flooded area, which was used to assess the impact of the flood wave. Different dam breach and flood scenarios, where the water flows over man-made structures, settlements, and olive tree cultivations, were also examined. The study area is dominated mainly by three geological formations with different hydrogeological characteristics that dictated the positioning and structure of the dam and determine the processes that shape the geomorphology and surface roughness of the floodplain, affecting flow conditions. The results show that the impact of a potential dam break at Bramianos dam is serious, and appropriate management measures should be taken to reduce the risk. The water flow downstream of the collapsed dam depends on the water volume stored in the reservoir. Moreover, the comparison of DSM and DEM cases shows that the detailed DSM may indicate more accurately the surface relief and existing natural obstacles such as vegetation, buildings, and greenhouses, enabling more realistic hydraulic simulation results. Dam breach flood simulations and innovative remote sensing data can provide valuable outcomes for engineers and stakeholders for decision-making and planning in order to confront the consequences of similar incidents worldwide.

scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

scholarly journals ANALYSIS OF THE IMPACT OF OIL PRODUCT SPILLS ON NATURAL OBJECTS BASED ON SENTINEL-2 REMOTE SENSING DATA IN EASTERN SIBERIA

2021 ◽  
Vol 6 ◽  
pp. 24-31
Author(s):  
Dmitry A. Baikin
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Remote Sensing Data ◽  
Oil Products ◽  
Eastern Siberia ◽  
Sensing Data ◽  
Sensing System ◽  
Natural Objects ◽  
The Impact ◽  
Sentinel 2

The article analyzes the impact of oil spills on natural objects according to the remote sensing system Sentinel-2 in Eastern Siberia. Remote sensing data analysis is used to detect traces of oil products in the accident area. Conclusions about the usage of Sentinel-2 data for detecting traces of oil products were made.

scholarly journals An Event-Based Inventory Approach in Landslide Hazard Assessment: The Case of the Skolis Mountain, Northwest Peloponnese, Greece

2020 ◽  
Vol 9 (7) ◽  
pp. 457
Author(s):  
Aspasia Litoseliti ◽  
Ioannis K. Koukouvelas ◽  
Konstantinos G. Nikolakopoulos ◽  
Vasiliki Zygouri
Keyword(s):  
Remote Sensing ◽  
Aspect Ratio ◽  
Remote Sensing Data ◽  
Historical Seismicity ◽  
Landslide Hazard ◽  
Disaster Mitigation ◽  
Earthquake Activity ◽  
Sensing Data ◽  
Reach Angle ◽  
The Impact

Assessment of landslide hazard across mountains is imperative for public safety. Pre- and post-earthquake landslide mapping envisage that landslides show significant size changes during earthquake activity. One of the purposes of earthquake-induced landslide investigation is to determine the landslide state and geometry and draw conclusions on their mobility. This study was based on remote sensing data that covered 72 years, and focused on the west slopes of the Skolis Mountains, in the northwest Peloponnese. On 8 June 2008, during the strong Movri Mountain earthquake (Mw = 6.4), we mapped the extremely abundant landslide occurrence. Historical seismicity and remote sensing data indicate that the Skolis Mountain west slope is repeatedly affected by landslides. The impact of the earthquakes was based on the estimation of Arias intensity in the study area. We recognized that 89 landslides developed over the last 72 years. These landslides increased their width (W), called herein as inflation or their length (L), termed as enlargement. Length and width changes were used to describe their aspect ratio (L/W). Based on the aspect ratio, the 89 landslides were classified into three types: I, J, and Δ. Taluses, developed at the base of the slope and belonging to the J- and Δ-landslide types, are supplied by narrow or irregular channels. During the earthquakes, the landslide channels migrated upward and downward, outlining the mobility of the earthquake-induced landslides. Landslide mobility was defined by the reach angle. The reach angle is the arctangent of the landslide’s height to length ratio. Furthermore, we analyzed the present slope stability across the Skolis Mountain by using the landslide density (LD), landslide area percentage (LAP), and landslide frequency (LF). All these parameters were used to evaluate the spatial and temporal landslide distribution and evolution with the earthquake activity. These results can be considered as a powerful tool for earthquake-induced landslide disaster mitigation

Evapotranspiration and soil moisture indexes derived from remote sensing data to identify and investigate the mechanisms of the spatio-temporal patterns of drought in the Ebro-Basin (NE Spain).

2020 ◽  
Author(s):  
Jaime Gaona ◽  
Pere Quintana-Seguí ◽  
Maria José Escorihuela
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Temporal Patterns ◽  
Surface Model ◽  
Ebro Basin ◽  
Sensing Data ◽  
Spatio Temporal

<p>The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically.</p><p>The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce.</p><p>The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates.</p><p>Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km.  The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales.</p><p>Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought’s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.</p>

Moderate-Resolution Remote Sensing and Geospatial Analyses of Microclimates, Mounds, and Maize in the Northern Great Lakes

2014 ◽  
Vol 2 (3) ◽  
pp. 195-207 ◽  
Author(s):  
Meghan C.L. Howey ◽  
Michael Palace ◽  
Crystal H. McMichael ◽  
Bobby Braswell
Keyword(s):  
Remote Sensing ◽  
Great Lakes ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Inland Lakes ◽  
Maize Cultivation ◽  
Geospatial Analyses ◽  
Moderate Resolution ◽  
Burial Mounds ◽  
The Impact

AbstractRemote sensing applications are increasingly common in archaeology but they often focus on high resolution imagery and direct archaeological site detection. Moderate spatial resolution remote sensing instruments, which have (near) daily repeat intervals, but contain less detailed spectral and spatial information, have been employed much less frequently in archaeology. However, moderate remote sensing data offer distinct advantages for archaeological research as they can be used to relate archaeological, ecological, and climactic data at vast spatial scales. To show this potential, we use moderate remote sensing data to examine the impact of landscape heterogeneity on the spread of indigenous maize horticulture in the northern Great Lakes during Late Precontact (ca. AD 1200-1600). Analyzing National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, we identify differences in freeze/thaw cycles across inland lakes in Michigan, showing that some large inland lakes produce a microclimatic amelioration, possibly extending the growing season for prehistoric maize cultivation. Conducting geospatial analyses, we find that burial mounds and maize cultivation practices were associated preferentially with larger inland lakes with microclimates. We could not have found these dynamic interrelationships between microclimates, burial mounds, and maize cultivation if not for both the frequent temporal imaging and large spatial coverage provided by moderate resolution remote sensing imagery.

scholarly journals Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data

2012 ◽  
Vol 5 (4) ◽  
pp. 941-962 ◽  
Author(s):  
B. Ringeval ◽  
B. Decharme ◽  
S. L. Piao ◽  
P. Ciais ◽  
F. Papa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
River Flow ◽  
Spatial Scales ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Global Evaluation ◽  
Good Opportunity ◽  
Sensing Data ◽  
Basin Scale

Abstract. The quality of the global hydrological simulations performed by land surface models (LSMs) strongly depends on processes that occur at unresolved spatial scales. Approaches such as TOPMODEL have been developed, which allow soil moisture redistribution within each grid-cell, based upon sub-grid scale topography. Moreover, the coupling between TOPMODEL and a LSM appears as a potential way to simulate wetland extent dynamic and its sensitivity to climate, a recently identified research problem for biogeochemical modelling, including methane emissions. Global evaluation of the coupling between TOPMODEL and an LSM is difficult, and prior attempts have been indirect, based on the evaluation of the simulated river flow. This study presents a new way to evaluate this coupling, within the ORCHIDEE LSM, using remote sensing data of inundated areas. Because of differences in nature between the satellite derived information – inundation extent – and the variable diagnosed by TOPMODEL/ORCHIDEE – area at maximum soil water content, the evaluation focuses on the spatial distribution of these two quantities as well as on their temporal variation. Despite some difficulties in exactly matching observed localized inundated events, we obtain a rather good agreement in the distribution of these two quantities at a global scale. Floodplains are not accounted for in the model, and this is a major limitation. The difficulty of reproducing the year-to-year variability of the observed inundated area (for instance, the decreasing trend by the end of 90s) is also underlined. Classical indirect evaluation based on comparison between simulated and observed river flow is also performed and underlines difficulties to simulate river flow after coupling with TOPMODEL. The relationship between inundation and river flow at the basin scale in the model is analyzed, using both methods (evaluation against remote sensing data and river flow). Finally, we discuss the potential of the TOPMODEL/LSM coupling to simulate wetland areas. A major limitation of the coupling for this purpose is linked to its ability to simulate a global wetland coverage consistent with the commonly used datasets. However, it seems to be a good opportunity to account for the wetland areas sensitivity to the climate and thus to simulate its temporal variability.

scholarly journals Analyzing the Relationship between Animal Diversity and the Remote Sensing Vegetation Parameters: The Case of Xinjiang, China

2021 ◽  
Vol 13 (17) ◽  
pp. 9897
Author(s):  
Jinhui Wu ◽  
Haoxin Li ◽  
Huawei Wan ◽  
Yongcai Wang ◽  
Chenxi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Bird Diversity ◽  
Area Index ◽  
Sensing Data ◽  
Animal Diversity ◽  
The Mean ◽  
The Impact

An explicit analysis of the impact for the richness of species of the vegetation phenological characteristics calculated from various remote sensing data is critical and essential for biodiversity conversion and restoration. This study collected long-term the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and the Fractional Vegetation Cover (FVC), and calculated the six vegetation phenological characteristic parameters: the mean of the growing season, the mean of the mature season, the mean of the withered season, the annual difference value, the annual cumulative value, and the annual standard deviation in the Xinjiang Uygur Autonomous Region. The relationships between the vegetation phenological characteristics and the species richness of birds and mammals were analyzed in spatial distribution. The main findings include: (1) The correlation between bird diversity and vegetation factors is greater than that of mammals. (2) For remote sensing data, FAPAR is the most important vegetation parameter for both birds and mammals. (3) For vegetation phenological characteristics, the annual cumulative value of the LAI is the most crucial vegetation phenological parameter for influencing bird diversity distribution, and the annual difference value of the NDVI is the most significant driving factor for mammal diversity distribution.

scholarly journals Evaluating the quality of remote sensing products for agricultural index insurance

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258215
Author(s):  
Benson K. Kenduiywo ◽  
Michael R. Carter ◽  
Aniruddha Ghosh ◽  
Robert J. Hijmans
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Well Being ◽  
Insurance Contract ◽  
Index Insurance ◽  
Sensing Data ◽  
Insurance Contracts ◽  
The Impact ◽  
Economic Well Being

Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

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