Drought monitoring in Cyprus via the integration of hydrological, meteorological, and satellite data
Date Issued
September 19, 2025
DOI
10.1117/12.3073014
Abstract
The prolonged drought in Cyprus has several impacted agricultural productivity, leading to water shortages an increased
economic stress on local communities. The study investigates the underlying drivers of drought using hydrological,
meteorological, and satellite data derived from field measurements and online database. Primary drought indicators,
particularly precipitation, temperature, evapotranspiration, dam water storage, and the Normalized Difference Vegetation
Index (NDVI), were analyzed from 2000 to 2024 according to seasonal and annual time periods. Pearson correlation
coefficient was used to identify linear relationships among the drought indicators, whereas long-term mean averages and
anomalies(deviationsfromthe long-termmeans)were estimated to identify temporal patterns and trends.Annual analyses
revealed the presence of significant positive correlations between air temperature and NDVI, as well as between
temperature and evapotranspiration, whereas all factorsshowed increasing temporal trendsin recent years—indicating an
increase in atmospheric moisture demand and vegetation activity.Conversely, a negative correlation is observed between
airtemperature and damwaterstorage,highlightingthe stress on water availabilityat a nationalsca le.Amoderate positive
correlation is observed between precipitation and water storage, including temporal delays due to infiltration and surface
runoff.Seasonal analysesreveal a distinctreduction of precipitation rates,especially during thewet period,combined with
increasing air temperatures. Temporal anomalies are identified across multiple variables, indicating the presence of
extreme drought periods,such as 2008, where precipitation rates were significantly below the long-term mean values, and
2019,which exhibited anomaliesincreasein precipitation.The findings ofthisstudyhighlighttheimportanceof combining
remote sensing and ground truth data to improve drought monitoring and water management in semi-arid regions
economic stress on local communities. The study investigates the underlying drivers of drought using hydrological,
meteorological, and satellite data derived from field measurements and online database. Primary drought indicators,
particularly precipitation, temperature, evapotranspiration, dam water storage, and the Normalized Difference Vegetation
Index (NDVI), were analyzed from 2000 to 2024 according to seasonal and annual time periods. Pearson correlation
coefficient was used to identify linear relationships among the drought indicators, whereas long-term mean averages and
anomalies(deviationsfromthe long-termmeans)were estimated to identify temporal patterns and trends.Annual analyses
revealed the presence of significant positive correlations between air temperature and NDVI, as well as between
temperature and evapotranspiration, whereas all factorsshowed increasing temporal trendsin recent years—indicating an
increase in atmospheric moisture demand and vegetation activity.Conversely, a negative correlation is observed between
airtemperature and damwaterstorage,highlightingthe stress on water availabilityat a nationalsca le.Amoderate positive
correlation is observed between precipitation and water storage, including temporal delays due to infiltration and surface
runoff.Seasonal analysesreveal a distinctreduction of precipitation rates,especially during thewet period,combined with
increasing air temperatures. Temporal anomalies are identified across multiple variables, indicating the presence of
extreme drought periods,such as 2008, where precipitation rates were significantly below the long-term mean values, and
2019,which exhibited anomaliesincreasein precipitation.The findings ofthisstudyhighlighttheimportanceof combining
remote sensing and ground truth data to improve drought monitoring and water management in semi-arid regions
File(s)![Thumbnail Image]()
Name
138161Y.pdf
Size
1.38 MB
Format
Adobe PDF
Checksum (MD5)
3e227ae2ae726e43f758b7776f16c997