Assessment and Monitoring of Droughts in Southeastern Europe: A Review - page 02

Drought Monitoring

There are numerous natural drought indicators that should be monitored routinely to determine the onset and end of drought and its spatial characteristics. Effective drought early warning systems must integrate precipitation and other climatic parameters with water information such as stream flow, snow pack, groundwater levels, reservoir and lake levels, and soil moisture into a comprehensive assessment of current and future drought and water supply conditions (WMO, 2006).

The Drought Management Centre for Southeastern Europe (DMCSEE) produces drought monitoring based on SPI. SPI is based on statistical techniques, which can quantify the degree of wetness or dryness on multiple time scales. For example, the 1-month SPI reflects mainly short-term conditions, and its application can be related closely to soil moisture. It can be potentially related to drought stress in certain development stages of crops. The 3-month SPI provides a seasonal estimation of precipitation, typically related to overall crop yield and streamflow conditions of small rivers. The 6- and 9-month SPI indicates medium term trends in precipitation patterns; and the 12-month SPI reflects the long-term precipitation patterns, usually tied to larger stream flows, reservoir levels, and even groundwater levels.

National hydrometorological services of every country in the Southeastern European region also perform their own instance of drought monitoring. For example, the Republic Hydrometeorological Service of Serbia (RHMSS) perform drought monitoring and early warning for the territory of Serbia, including calculating Standardized precipitation index for various time periods, forecasting SPI for the next 10 and 30 days, calculating Palmer's Z index as a measure of monthly moisture anomaly, monitoring soil moisture in the top one meter of the soil layer, daily updates of calculated and forecasted reference potential evapotranspiration, etc. The Department of Agrometeorology at RHMSS also issues weekly bulletins with results of monitoring, alarms, thresholds and forecasts.


Remote Sensing for Droughts

Traditional methods for identifying water stress rely on punctual observations (like leaf water potential or stomatal conductance) that are time consuming and have the limitation of the number of observations, and often do not adequately represent the spatial distribution.

Remote sensing can be used to collect spatial data over large areas on routine basis, providing a capability to make frequent and spatially comprehensive measurements of the near surface soil moisture and vegetation cover. Remote sensing methods differ from most existing methods because they are not precipitation driven, but rather monitor vegetation stress or soil moisture status using diagnostic observations of key land-surface states.

The most commonly used vegetation index (VI) is the normalized difference vegetation index (NDVI), which is based on the difference between the maximum absorption of radiation in R as a result of chlorophyll pigments and the maximum reflectance in near-infrared (NIR) spectral region as a result of leaf cellular structure (Tucker 1979). Tucker and Choudhury (1987) found that NDVI could be used as a response variable to identify and quantify drought disturbance in semiarid and arid lands, with low values corresponding to stressed vegetation. Many studies have shown the NDVI to be related to leaf area index (LAI), green biomass, percent green cover, and fraction of absorbed photo synthetically active radiation (fAPAR).

Using soil water content as the most sensitive variable, soil moisture satellites such as SMOS (Soil Moisture and Ocean Salinity, ESA) provide a unique opportunity to incorporate remote sensing tools into agricultural drought monitoring. In one study, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture series, and showed very good agreement with other drought indices (Martinez-Fernandez et al, 2016), proving that SMOS SWDI reproduces well the soil water balance dynamics and is able to appropriately track agricultural drought.

Fraction of vegetation cover (FVC) is a vegetation index, based on multi-channel remote sensing measurements (e.g. data from Eumetsat's LSA SAF data base). The FVC shows the fraction of the total pixel area that is covered by green vegetation, which is relevant for applications in agriculture, forestry, environmental management and land use, and has also proved to be useful for drought monitoring. Values vary according to the vegetation stage and of course to the damages of possible natural disasters (including drought). FVC values are lower at the beginning of the growth season, the highest at the full vegetation development and then FVC slowly drops with vegetation senescence. LSA SAF's LAI and FVC are important indicators of the state and evolution of the vegetation cover and offer an opportunity to monitor drought conditions.


Figure 3: The value of FVC index over Southeastern Europe in early July 2011 (Muri et al, 2013).


Figure 4: Anomaly of accumulated monthly fraction of vegetation cover (FVC) from mid April to early May, for the time period 2007-2014  (Muri et al, 2013).


Drought Assessment

One popular method to analyze different characteristics of hydrologic and meteorological droughts is the Run Method (Yevjevich, 1972). This method allows for the analysis of continuous data series such as runoff hydrographs, as well as discrete series auch as daily rainfall data. The Run Method yields results in the form of time series with characteristic indicators of drought, which can be further analyzed using methods for time series analyses (e.g. trends, periodicity, stochasticity, etc.), and also using theoretical distribution functions to evaluate the probabilistic characteristics of drought.

The run method is based on the relationship between drought and negative runs in rainfall time series considering a hydrological variable and a critical threshold level. Figure 5 shows the runoff hydrograph (X1) and the threshold (X0) used to define the volume of water sufficit (S) and deficit (D), the duration of sufficit (τ5) and deficit (τ6), as well as the average intensity of sufficit (s) and deficit (d). The hydrologic characteristics above the threshold (X0) are associated with periods of excessive water in the basin, while periods below the threshold define drought periods over the course of the hydrological year.


Figure 5: Run Method: Runoff hydrograph (Xt) with constant threshold (X0) (Radić and Mihajlović, 2008).