3.4.2 Damage estimation method

Flood damage to rice crops is defined as a function of flood depth, flood duration, and growth stage of rice plants and can be estimated by using the calculation

vulnerability of each item by showing the correlation of the intensity of a hazard with damage quantified by a damage curve. Therefore, flood damage curves are important in flood damage estimation. To estimate flood damage to rice crops,

Specification Website link

https://lta.cr.usgs.gov/glcc/globdoc2\_0

https://globalmaps.github.io/

http://ref.data.fao.org/map? entryId=ba4526fd-cdbf-4028-a1bd-

https://lpdaac.usgs.gov/ dataset\_discovery

1–2 days 3–4 days 5–6 days 7 days Estimated yield loss (%)

https://www.esa-landcover-cci.org/

5a559c4bff38

Flood damage curves can be mainly derived from two approaches: (1) using actual damage data of past floods and (2) using synthetic data (expert estimation or questionnaire surveys) [8]. In the former approach, flood damage curves are developed based on data and information of past hazards and resulting actual flood damage. Therefore, accumulation of data on hazards (inundation records) and flood damage is essential. In the latter approach, damage curves are derived from hypothetical analysis, information obtained from questionnaire surveys, or land

Table 2 shows the flood damage matrix for rice-crop damage published by the Philippines Bureau of Agricultural Statistics [14]. Figure 5 shows the height of rice plants at each growth stage and its duration. Based on the flood damage matrix and the information on rice plant height at each growth stage, Shrestha et al. [16] proposed flood damage curves for rice crops as presented in Figure 6. Flood damage curves of rice crops vary with each rice growing stage. Based on the duration of each growth stage of rice plants and the information on a cropping calendar, the growth stage of rice plants during a flood event can be identified, and an appropriate damage curve corresponding to the growth stage of rice plants should be applied

Growth stage of rice plants Days of submergence

Vegetative stage 10–20 20–30 30–50 50–100 Reproductive stage (partially inundated) 10–20 30–50 40–85 50–100 Reproductive stage (completely inundated) 15–30 40–70 40–85 50–100 Maturity stage 15–30 40–70 50–90 60–100 Ripening stage 5 10–20 15–30 15–30

Flood damage matrix for rice crop published by the Philippines Bureau of Agricultural Statistics [14].

depth-duration-damage function curves are normally used.

cover and standardized typical property types.

Data description Data

Recent Advances in Flood Risk Management

MODIS Land Cover NASA/

List of globally available land-cover data.

Global Land Cover Characterization (GLCC)

Global Land Cover (GLCNMO)

Global Land Cover-SHARE (GLC-SHARE)

Climate Change Initiative Land Cover

(CCI-LC)

Table 1.

Table 2.

116

provider

European Space Agency

USGS

USGS Spatial resolution:

ISCGM Spatial resolution: 15

FAO Spatial resolution:

300 m

500 m

30 arc-seconds

and 30 arc-seconds

30 arc-seconds

Spatial resolution:

Spatial resolution:


Table 3.

Calculation method of flood damage to rice crop for each growth stage of rice plants.

method presented in Table 3. When flooding occurs during the early growth stage of rice plants, i.e., from the seedling to vegetative stages, at which no rice production is expected, farmers normally replant rice crops. In such a case, flood damage to rice crops can be estimated as losses of cost of input. On the other hand, when flooding occurs during the reproductive and maturity stages, at which rice production is usually expected, there is no time for replanting rice crops. In this case, flood damage to rice crops can be estimated as volume of production losses, i.e., yield loss, and then the value of production losses can be estimated based on farm gate price as calculation method presented in Table 3. The yield loss caused by flooding can be determined using a flood damage curve presented in Figure 6, according to flood depth and duration.

created using DEM in ArcGIS. Hourly rainfall and water-level data were collected from the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). The RRI model was calibrated and validated based on recent flood events by comparing the calculated and observed flood discharges at the San Isidro station in the basin. The flood event in September 2011 was the biggest flood in the basin in the last 30 years. The model parameters were thus calibrated to the September 2011 flood event. The calibrated parameters were validated with the 2015 flood event. Figure 8 shows the comparison of the calculated discharge with the observed discharge at San Isidro gauging station for the flood events in 2011 and 2015 and also the comparison of the calculated flood inundation depths with the recorded flood depth in the barangays (villages) of Calumpit Municipality. The calculated results reasonably agreed with the observed data. The flood event in 2011 was due to Typhoons Pedring and Quiel. Typhoon Pedring was directly followed by Typhoon Quiel, and rice crops were severely damaged by this flood event. The flood event in 2015 was due to Typhoon Lando, which also damaged rice-crop areas in the

To calculate the flood inundation depth and duration for a specific return period, flood frequency analysis was conducted by using 48-hour maximum annual rainfall data. Flood frequency analysis is essential for calculating expected damage. The main objective of flood frequency analysis is to relate the magnitude of extreme events to their frequency of occurrence through the use of probability distributions [17, 18]. The Gumbel distribution method was used for rainfall analysis. Since the rainfall volume of the September 2011 flood during Typhoon Pedring was the highest rainfall volume in the last 30 years, the rainfall pattern of the September 2011 flood (only for the Typhoon Pedring case) was selected for designing a rainfall pattern for a specific return period. Figure 9 shows the results of flood frequency analysis using the Gumbel method and the estimation of a design hyetograph for an event of a specific return period such as a 100-year flood. To calculate flood characteristics for a 100-year flood, a design hyetograph for a 100-year return period was estimated by multiplying the rainfall hyetograph of the September 2011 flood by a conversion factor. The conversion factor for a 100-year return period was

basin.

119

Figure 7.

Location of the Pampanga River basin in the Philippines.

Methodology for Agricultural Flood Damage Assessment DOI: http://dx.doi.org/10.5772/intechopen.81011
