Geostatistical analysis of precipitation in the island of Crete (Greece) based on a sparse monitoring network

Agou, Vasiliki D.; Varouchakis, Emmanouil A.; Hristopulos, Dionissios T.

doi:10.1007/s10661-019-7462-8

Cited by 28 publications

(30 citation statements)

References 68 publications

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“…The direction maps ( Figures 6 and 7) created from the majority of combinations (except for TCG-TCB) indicated that during the period of 1999-2009 the study area mostly experienced land improvement expressed by vegetation regrowth. Due to the spatial concentration of this regrowth mainly in the western (Chania prefecture) and central (Rethymno prefecture) parts of the island, the high annual precipitation of the period [37] with the relative increasing amount of rainfall water from east to west (as a result of its spatial variability) can be characterized as the main natural driving force. On the other hand, the intensification of the agricultural sector had also an anthropogenic influence on the dominant type of land cover change.…”

Section: Discussion and Interpretationmentioning

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: Discussion and Interpretationmentioning

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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“…To estimate the RSOD predictability, the leave‐one‐out cross‐validation method (e.g. Shao et al ., ; Hur and Ahn, ; Agou et al ., ) was applied to evaluate the performance of fitted models after selecting the best prediction equation by stepwise regression. The model performance was evaluated using mean error (ME), mean absolute error (MAE) and its skill score (SS MAE ), which are defined as:

ME = \frac{1}{n} \sum_{i = 1}^{n} ((), F_{i} - O_{i})

MAE = \frac{1}{n} \sum_{i = 1}^{n} (||, F_{i} - O_{i})

{SS}_{MAE} = 1 - \frac{MAE}{{MAE}_{Obs}}

{MAE}_{Obs} = \frac{1}{n} \sum_{i = 1}^{n} (||, O_{i} - \overset{O}{true})

\overset{O}{true} = \frac{1}{n} \sum_{i = 1}^{n} O_{i}

where n is number of years, F i and O i are modelled and observed RSODs for the i th year,

\overset{O}{true}

is the mean of the RSOD based on observations and MAE Obs is the mean absolute error of climatological forecasts (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…To estimate the RSOD predictability, the leave-one-out cross-validation method (e.g. Shao et al, 2010;Hur and Ahn, 2015;Agou et al, 2019) was applied to evaluate the performance of fitted models after selecting the best prediction equation by stepwise regression. The model performance was evaluated using mean error (ME), mean absolute error (MAE) and its skill score (SS MAE ), which are defined as:…”

Section: Predictability Of the Rsodmentioning

confidence: 99%

Predictability of the rainy season onset date in Central Highlands of Vietnam

Pham-Thanh

Linden

Ngo‐Duc

et al. 2019

Intl Journal of Climatology

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In this study, different criteria to determine the rainy season onset date (RSOD) and its predictability in the Central Highlands (CH) of Vietnam are investigated. Using daily rainfall amounts from 10 meteorological stations for the period 1981-2015, four criteria to determine the RSOD were tested in order to select the one that most reasonably depicts the climatology of the RSOD over the CH. Results show that the RSOD varies strongly from year to year. In the long-term mean, the onset starts first in southern parts of the CH and then progresses to the northern parts. The earliest onset date is around the beginning of April, and the latest in the second half of May. The average RSOD is on 28 April with a standard deviation of 14 days. Thus, the RSOD is distinct from the summer monsoon onset, namely, leading the mean summer monsoon onset by about 3 weeks in some years. In terms of remote influences, the RSOD in the CH has a high correlation with the El Niño-Southern Oscillation phenomenon, with most RSODs being later during El Niño years, while being earlier during La Niña years. The RSOD in the CH also shows high correlations with sea surface temperatures (SSTs), 850-hPa zonal winds (U850) and mean sea level pressures (PMSL) over certain regions of the Pacific and Indian Oceans. Building on these relationships, the RSOD can be successfully predicted using large-scale fields of SST, U850 and PMSL as predictors in two different approaches, namely the field mean and principal component analysis. This suggests that the overall approach can also be applied in predicting the RSOD in the CH on sub-seasonal to seasonal timescales. K E Y W O R D SCentral Highlands, predictability, rainy season onset, seasonal prediction, Vietnam

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“…P represents a life-critical energy and hydrologic exchange between the Earth's atmosphere and its surface and that knowledge of where, when and how much rain falls is essential for scientific research and societal applications (Skofronick-Jackson et al 2018). However, a better understanding of the spatial and temporal P patterns is still necessary in order to quantify risks and design suitable mitigation measures in a context of P pattern change (Agou et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Geostatistical interpolation (kriging) has become very important to environmental studies (Matheron, 1969), since it has been defined as the best unbiased linear estimator (Cressie, 1990;Hengl, 2007). These techniques have been successfully explored to generate more representative P spatial layers from point measurements (Holawe & Dutter 1999, Goovaerts, 2000, Keblouti et al 2012, Agou et al 2019). However, its application in countries such as Mexico presents problems in capturing spatial variability due to the aforementioned problem regarding the quantity and location of field stations.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

Tapia-Silva¹

Kalpa Publications in Computing

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Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

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Geostatistical analysis of precipitation in the island of Crete (Greece) based on a sparse monitoring network

Cited by 28 publications

References 68 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

Predictability of the rainy season onset date in Central Highlands of Vietnam

Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

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