University of Twente Student Theses


A comprehensive country-scale drought monitoring, The Netherlands

Guardamino Soto, Lucía Yesabell (2021) A comprehensive country-scale drought monitoring, The Netherlands.

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Abstract:In drought monitoring, some misinterpretations arise due to drought propagation over the water cycle involving one or more of its three dimensions: meteorological, soil moisture (SM) or agricultural, and/or hydrological. For instance, after a drought, increasing precipitation (meteorological dimension) usually does not have an immediate effect on the groundwater (hydrological dimension). Furthermore, while dry and wet anomalies have a strong effect over the meteorological dimension, evidenced by a higher variability and extreme changes, SM and groundwater (GW) show milder variations. Therefore, due to the lag effect and the smoother variations that mainly characterize the GW domain, drought over the SM and, primarily the GW, might persist even when the atmosphere switched from dry to wet anomalies. This research applies a unified framework for drought monitoring in the Netherlands by analyzing the three drought components and their interactions using drought indices. Accordingly, a set of five indices were calculated, corresponding to precipitation (SPI, Standardized Precipitation Index), evapotranspiration (SPAEI, Standardized Precipitation and Actual Evapotranspiration Index, SPEI, Standardized Precipitation Evapotranspiration Index), SM (SSMI, Standardized Soil Moisture Index), and terrestrial water storage anomalies (STWSI, Standardized Terrestrial Water Storage Index). A climatology dataset generated with the Terrestrial System Modeling Platform (TSMP) was used in the drought analysis. This dataset is the first high-resolution (12.5 km), long-term (1989-2019) terrestrial system climatology that includes an integrated simulation of the atmosphere, land surface, and subsurface over Europe. The dataset over the Netherlands was first validated from a qualitative and quantitative (Pearson correlation coefficient-r and RMSE) approach, followed by a spatio-temporal country-scale drought assessment from January 1989 to August 2019. The validation consisted of comparing TSMP variables (precipitation-PR, actual evapotranspiration-ETa, SM, and water table depth-WTD) with their in-situ counterparts. On the other hand, the spatio-temporal drought assessment was carried out at different time scales 1, 3, 6, 9, 12, and 24-month average. Consequently, three extreme droughts (1996, 2003, and 2018) were selected, and the patterns and physical processes were further analyzed. Critical areas to droughts were then determined based on the frequency and probability of extreme droughts (events where drought indices drop below -2). We concluded that TSMP can provide a first approach to a comprehensive country-scale drought assessment in the Netherlands. The analysis indicates that TSMP is a robust dataset despite the different levels of agreement obtained for the WTD. Additionally, drought propagation is visible in the TSMP time series and shows a higher variability and intensity for PR and PR-ETa anomalies compared to SM and TWS anomalies. The time lag effect over SM and TWS is also visible in the temporal and spatial domains. On the other hand, based on the variables with the most extensive in-situ datasets (PR and reference evapotranspiration-ET0), we found that medium (9) to longer (24-month average) time scales have a higher r and lower RMSE when compared with the in-situ reference. This could be considered for future drought monitoring. Finally, we found that the TSMP performs better at defining the most susceptible areas to extreme droughts based on the long-term time series. Conversely, the performance is not so accurate at describing the development and intensities of separate extreme droughts such as 1996, 2003, and 2018.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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