## 4 Results
### Prediction of Flooded Area and Volume  
After examining all the available data, it was observed that there had been a progressive decline in surface water in the research area due to an ongoing dry spell. This was similar to the GIS study in [50], which correlated the DEM of a natural area with the region's meteorological features. This analysis aimed to predict the average flooded surface volume in cubic hectometers, following the implementation of multiple linear regression [49], as shown in Equation (1) below:  
\[Vol\left(\text{hm}^{3}\right)=\frac{Depth}{5.61\times 10^{-4}}-Area\ \ \ (r=0.985;\ R^{2}=0.93;p\leq 0.001), \tag{1}\]  
where \"\(r\)\" is the Pearson correlation coefficient, \"\(R^{2}\)\" is the determination coefficient, \"\(Vol\)\" is the prediction of the total salt marshes' volume in \(\text{hm}^{3}\), \"\(Area\)\" is the equivalent area in \(\text{km}^{2}\) occupied by the water mass, and \"\(Depth\)\" is the mean value of the water depth, in \(\text{m}\), corresponding to the water-occupied area. The depth values were obtained through iterative processes using DEMs or satellite images. Figure 5: Land cover changes between 2016 and 2020. Coordinates in km. Source: own elaboration from [49]. Table 1 shows the depth of the water flooding the Dofana marsh in each of the 17 selected control points. Next, to confirm the reduction in the flooded surface area in the study region, Figures 6-10 display the state of the water surface for the years corresponding to the end of the 2008/2009, 2009/2010, 2010/2011, 2011/2012, and 2012/2013 periods, respectively. Similarly, satellite images for the periods between 2013/2014 and 2019/2020 are also shown to visualize the effect of climate change (see Figures 11-17). These images were taken from [51] and processed using Index Stack [52]. This index is a fusion image of Green, Red, Near-Infrared, Red Edge 8, and Shortwave Infrared 1 bands, whereby the vegetation is displayed in green, soil, rocks, and barren land are displayed in blue, and, most importantly, water is represented in magenta. Based on the above, a study was conducted to explore the potential relationship between precipitation and the presence of water mass in the Donana salt marshes. Figure 18 illustrates the polynomial relationship between the average monthly value of the normalized difference water index (NDWI), according to Equation (2), and the monthly precipitation from January 2010 to December 2020. \[NDWI=\frac{Green-NIR}{Green+NIR}, \tag{2}\] \begin{table}
\begin{tabular}{c c c c} \hline
**Control Point** & **Water Depth (m)** & **Water Depth (m)** & **Water Depth (m)** \\  & **(2009)** & **(2013)** & **(2020)** \\ \hline  
Mean value (m) & 0.155 & 0.152 & 0.132 \\ \hline \end{tabular}
\end{table} 
Table 1: Flooded depth by control point in 2009, 2013, and 2020. Figure 18: Relationship between the monthly average NDWI and the monthly precipitation from 2010 to 2020. Similarly, to complement Figure 18, the monthly interconnection between the precipitation and the average NDWI value during the period 2010-2020 was analyzed (Figure 19). This figure shows how, throughout the time series, an increase in precipitation coincides with an increase in the average NDWI value. It can be observed that there is generally a lag between the rise in precipitation and the increase in the average NDWI values, indicating that the flooded area (Figure 20) depends not solely on precipitation. As a result of the aforementioned observations and to better understand the filling and emptying phenomena in the Donana marshes, the authors developed a new index, the _LAR index_ (**Leiva**, **A**maro**, **R**amirez) (see Equation (3) and Figure 21). Values greater than 1 correspond to time periods with no precipitation, coinciding with the onset of the  
Figure 19: Time series (2010–2020) of monthly average NDWI and monthly precipitation. Figure 20: Time series (2010–2020) of monthly flooded area and monthly precipitation. increase in the marsh's water surface area.