Climate Change Impact on the Frequency of Hydrometeorological Extremes in the Island of Crete

Tapoglou, Evdokia; Vozinaki, Anthi Eirini; Tsanis, Ioannis K.

doi:10.3390/w11030587

Cited by 21 publications

(20 citation statements)

References 35 publications

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“…The mean annual precipitation is estimated to be 750 mm, presenting strong spatial and temporal differences. Specifically, more than 40% occurs in the winter months ranging from 440 mm in the east to 2188 mm in the west [17].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece

2020

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The main objective of this study was to explore the impact of various spectral indices on the performance of change vector analysis (CVA) for detecting the land cover changes on the island of Crete, Greece, between the last two decades (1999–2009 and 2009–2019). A set of such indices, namely, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), albedo, bare soil index (BSI), tasseled cap greenness (TCG), and tasseled cap brightness (TCB), representing both the vegetation and soil conditions of the study area, were estimated on Landsat satellite images captured in 1999, 2009, and 2019. Change vector analysis was then applied for five different index combinations resulting to the relative change outputs. The evaluation of these outputs was performed towards detailed land cover maps produced by supervised classification of the aforementioned images. The results from the two examined periods revealed that the five index combinations provided promising performance results in terms of kappa index (with a range of 0.60–0.69) and overall accuracy (with a range of 0.86–0.96). Moreover, among the different combinations, the use of NDVI and albedo were found to provide superior results against the other combinations.

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Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece

2020

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“…In order to better approach the impacts of severe flood events in the future, decision makers should take into account the size of the particular area and the time step used in the simulation when analyzing the flood event. Tapoglou et al [27] already mentioned the need for a lower than daily time step of analysis, in order to investigate the possible risks of hydro-meteorological extremes in Crete's basins. Vozinaki et al [13] have also stated the urgency for sub-daily data for more precise climate change projections on Mediterranean catchments.…”

Section: Discussionmentioning

confidence: 99%

“…The bias-corrected data are projected to 2100 and follow the procedure of the climatic data produced from the Climate Service Center REMO (CSC-REMO) regional climate model (RCM) simulations, being influenced by the Max Planck Institute Earth System Model, low resolution, reanalysis 1 (MPI-ESM-LR-r1) driving global climate model (GCM). Full climate change impact analysis was performed in the island of Crete by Nerantzaki et al [26] and Tapoglou et al [27] for the 1981-2100 period. Climate change impact on precipitation was limited to daily data, with the maximum daily precipitation derived by the GCM-RCM for the RCP4.5 emissions' scenario being equal to 340 mm, which is not appropriate to be used in flash floods in a small basin.…”

Section: Climate Model Datamentioning

confidence: 99%

Analysis of a Flash Flood in a Small Basin in Crete

Sarchani

Tsanis

2019

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Climate change will have a greater impact on the severity of flash floods, since precipitation intensity is expected to increase, even in areas where a reduction of precipitation is possible. This change in climate is expected to increase flood wave speed and its flood wave area extent. A case study of a small basin in the island of Crete was conducted to examine this effect, following the calibration and validation of the flow hydrograph of a flash flood event, in order to achieve model verification with the post-flood data. It was found that the most important parameters that affect the timing and magnitude of the peak discharge are the storage coefficient, the impervious rate and the curve number, as well as the time of concentration. Rainfall distribution was examined in different time intervals in order to study the effect of the intensity of precipitation on the peak hydrograph. From the precipitation records and according to the size of the watershed, the time step of the precipitation in the simulation model is recommended to be less than an hour. In other areas around the basin of interest, severe storms known as Medicanes that pass over Crete can produce higher precipitation in shorter time intervals. The impact of climate change scenarios results in an increase on the peak discharge by creating precipitation of higher intensity. Furthermore, the intensification of precipitation due to climate change results in higher flood depths and flooded area extent, as well as wave velocities.

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“…It can be generally characterized as mild with warm and dry summers, and slightly cold and humid winters. The average temperature is 10 • C in the winter season and 30 • C in the summer season, while the mean annual precipitation is about 750 mm, presenting strong spatial and temporal variations [41].…”

Section: Study Areamentioning

confidence: 99%

Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

et al. 2020

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Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha−1) with October (about 61 t ha−1) in 2016, and winter (about 96 t ha−1) with February (146 t ha−1) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha−1), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion.

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Climate Change Impact on the Frequency of Hydrometeorological Extremes in the Island of Crete

Cited by 21 publications

References 35 publications

Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece

Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece

Analysis of a Flash Flood in a Small Basin in Crete

Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

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