# Evaluation of Green and Grey Flood Mitigation Measures in Rural Watersheds
## 4 Discussion  
The presented methodology for finding an optimal flood mitigation setup for small rural watersheds is based on a comprehensive economic evaluation of flood losses under different flood mitigation scenarios. This was a challenging task because the results and experiences from other studies, especially on flood damage estimation, cannot be readily transferred and applied to other regions [38]. Most of the existing research on flood damages is focused on urban areas, and less so on rural agricultural areas. For example, methodologies for estimating damages in buildings are much  
Figure 12: Expected annual benefits EAB, expressed relative to the expected annual damages EAD under the baseline scenario, from implementing different flood mitigation measures in different river sections and in the entire Tannava watershed. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & \multicolumn{3}{c}{**50-Year Planning Horizon**} \\ \cline{4-7}
**Sector** & **General** & **Grey** & **NPV, Baseline** & \multicolumn{3}{c}{**Benefits [\% of NPV, Baseline]**} \\ \cline{3-7}
**Sector** & **Description** & **Measures** & **Scenario** & **Green** & **Grey** & **Green-Grey** \\  & & & **[\(\ell\) Million]** & **Scenario** & **Scenario** & **Scenario** \\ \hline
7 & Rural & No & 0.589 & 79\% & 0\% & 79\% \\
7 & Urban & Yes & 0.593 & 94\% & 81\% & 94\% \\
8 & Rural & No & 19.50 & 80\% & 0\% & 80\% \\
9 & Rural & Yes & 6.77 & 91\% & 59\% & 91\% \\
11 & Rural & No & 8.57 & 82\% & 0\% & 82\% \\
11\({}^{\prime}\) & Urban & Yes & 0.684 & 93\% & 41\% & 93\% \\
11\({}^{\prime}\) & Urban & No & 1.27 & 83\% & 0\% & 83\% \\
12 & Rural & Yes & 4.90 & 95\% & 77\% & 95\% \\
13 & Rural & No & 2.05 & 81\% & 0\% & 81\% \\
13\({}^{\prime}\) & Semi-urban & Yes & 10.49 & 93\% & 67\% & 96\% \\ \hline \multicolumn{7}{c}{The Tannava watershed} & 55.41 & 86\% & 28\% & 86.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Benefits due to implementing flood protection measures under different scenarios, relative to the baseline one (existing flood protection measures) over 50-year planning horizon in each river section and in the entire watershed. more developed than for other assets (e.g., [39]). Considering that floods cause the greatest losses to crops globally compared to other natural hazards [40], a need emerges for extensive research on flood damage and risk assessment in rural, agricultural watersheds. Flood hazard assessment under current conditions in the Tamnava watershed shows that the existing levee system already provides some level of flood protection, primarily in the populated areas where the highest-value assets are located. The proposed mitigation measures under green and green-grey scenarios provide the greatest reduction in flood hazard and, consequently, in flood losses in the agricultural areas that presently have no flood protection, such as in sectors 7, 8, and 13. Although the share of agricultural damages in total losses depends on local conditions in the watershed [34] and prevalence of higher-value crops [41], the results presented here clearly indicate that the agricultural damages are not marginal and have a critical impact on the selection of flood protection development scenarios in predominately agricultural watershed. Estimation of flood losses in this research is accompanied by inherent uncertainties in both flood hazard and damage assessments, as also demonstrated in many previous studies [18; 38; 42]. Specifically, flood hazard assessment under grey and green-grey scenario is estimated assuming that grey systems (levees and the bypass canal) fulfill their design role, and that there is no flooding of the interior areas. To accurately estimate the flood hazard, a more robust, potentially 2D hydrodynamic model capable of simulating backwater or some interior area flooding could be utilized.