Estimating externalities

Externalities of land use and water management systems are often abundant and multifaceted (Peng et al., 2017). In this study we focused on externalities already observed on the VMD or deemed highly likely in recent impact assessments. In particular, for each scenario the following externalities were considered: changes in flood damage downstream; changes in salinity intrusion in the coastal zone; changes in suspended sediment carried by floodwaters; and changes in riverbank erosion.

Estimating direct monetary costs of externalities under the various scenarios was complicated by the fact that these externalities are not caused only by reduced floodwater retention capacity due to dike construction. Other factors are also at work, such as hydropower development and climate change (Table 5.2). After quantifying the externalities based on data from our studies and the literature, using simplified estimation methods, we assessed the association between these externalities and floodplain water retention capacity (Figure 5.3). For this we drew a proportional relationship between the dike construction area on the VMD floodplains and floodplain water retention capacity (Dung et al., 2018c). The methods used to quantify the externalities are presented below.

Table 5.2Methodology to quantify externalities from dike–agricultural system scenarios using

cost-benefit analysis

Externalities Indicator Source Factors

Flood damage downstream – Floodwater depth in rivers – Extent of flood >1m Wijayanti et al. (2017) Dung et al. (2018c) – Hydropower and irrigation system development upstream – Climate change and sea level rise

– Land subsidence Salinity

intrusion

– Areas of increased coastal salinity under the three scenarios, converted into monetary losses from rice production and freshwater aquaculture Kuiter (2014) Nhan et al. (2012) MARD (2016) Berg et al. (2017) – Hydropower and irrigation development upstream

– Climate change and sea level rise

– Land subsidence Sediment reduction – Decrease in sediment deposition (intensity) – Increase in fertilizer use for rice production (cost)

Chapman and Darby (2016) Chapman et al. (2016) Hung et al. (2014a, 2014b) Manh et al. (2015) Dung et al. (2018a)

– Hydropower and irrigation development upstream Riverbank erosion – Increase in river velocity and discharge

Dung et al. (2018c) – Hydropower and irrigation development upstream

– Climate change and sea level rise

– Sediment exploitation and sand mining

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Figure 5.3 Relationship between dike construction area and floodwater retention capacity (left) and between external effects (flood damage downstream, sediment reduction, salinity intrusion, and river bank erosion) and floodwater retention capacity (right) under the three scenarios: ED2011 = baseline, or dike system as in 2011; HD2030 = full high-dike development in 2030; and LD2030 = full low-dike development in 2030.

Flood damage

Complex computational methods are typically used for flood damage assessment (Winsemius et al., 2013). We simplified these, following Wijayanti et al. (2017). Downstream flood damage was thus based on the spatial change in flooded area at various flood depths estimated under the three scenarios. A geographic information system (GIS) was used to interpolate the flooded area under each scenario, with maximum water level data as simulated by a one dimensional–quasi two dimensional (1D-quasi2D) hydraulic model. For model setup and method see Dung et al. (2018c). Results were presented in both tabular and map form.

Flood damage under the HD2030 and LD2030 scenarios was evaluated in relation to the baseline scenario, ED2011. As such, we assessed the impact of dike construction on the downstream flood damage by comparing the sizes of the downstream area flooded at different flood depths. We defined areas as exposed to flood damage only if a flood depth greater than 1 m was registered, as this is the depth at which aquaculture and flood-based agriculture are disrupted (Balica et al., 2014).

Economic costs of flooding were calculated using national statistics on losses in flooded areas in four years of extreme flooding: 2000, 2001, 2002 and 2011. We then estimated economic losses from flooding for the HD2030 and LD2030 scenarios based on the

percentage increase or decrease in flooded area compared to available data for the baseline year of 2011.

Salinity intrusion

The VMD’s two main rivers, the Tien and Hau, empty into the South China Sea. These rivers deliver large amounts floodwater to the floodplains via branches that can be diverted for irrigation and to flush away saltwater intrusion downstream in the dry season. Reduced flood retention capacity of the floodplains due to high-dike construction could diminish this flushing capacity, worsening salinity intrusion.

We estimated the cost of salinity intrusion using rice yield reductions in a number of production areas affected in the extreme drought year of 2016. For the affected rice production area, we referenced data from MARD (2016). For yield reductions in rice, which is a crop very sensitive to salinity, we referenced Nhan et al. (2012). We calculated the economic losses by multiplying the economic loss for 1 ha by the affected rice production area (ha). Of which, the 1-ha loss was computed by multiplying the average rate of rice yield reduction (%) with the net profit from 1 ha rice. The 1-ha rice profit was provided by Berg et al. (2017) in their study in Tien Giang, a coastal province of the VMD (presented in Table 5.5 in the Result section).

The reduction in rice yield is proportional to the salinity concentration (see Figure D1 in Supplementary D). The higher the salt concentration is, the lower yields will be. In the VMD, areas affected by salinity intrusion and salinity concentrations vary over the years. We mapped salinity intrusion using contour lines provided by the Southern Institute for Water Resources Research (SIWRR) for the year 2008 and for 1998, 2010, 2015 and 2016, and the means for these years, as provided by the Southern Institute for Water Resources Planning (SIWRP). The salinity data for 2008 were calibrated and verified by the SIWRR using hydraulic modeling with an advection-dispersion (AD) module, while those of SIWRP were drawn using observed data.

Sediment loss

Various studies have examined sediment load on the floodplains as well as across the VMD. For example, Hung et al. (2014a) measured fluvial sediment inside and outside dike compartments in Dong Thap Province. Manh et al. (2015) used hydraulic modeling to simulate sedimentation, attributing reduced sediment load to hydropower dams and hydraulic works upstream. Dung et al. (2018a) found that fertilizer use had to be increased in rice farming systems under high-dike protection compared to those in low-dike areas. To estimate the cost of reduced sedimentation, we referred to Chapman et al. (2016). These authors conducted a household survey, interviewing 195 farmers in An Giang Province.

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99 They computed the value of sediment using a set of components including the amount of fertilizer applied, the average cost of fertilizer, the cost efficiency gain per centimeter of sediment, the average depth of sediment, number of crops per year and area in production. The annual value loss was then found by subtracting the annual value of sediment in low- dike farming systems from that in high-dike farming systems. Using the annual value loss, we computed economic losses due to reduced sediment load in our three scenarios by multiplying the average 1 ha sediment loss (US$.year-1_{) by the high-dike area (ha).}

Riverbank erosion

Riverbank erosion is caused by human activities such as construction of upstream hydropower dams which change the flow of rivers and sand mining (Anthony et al., 2015). Riverbanks can also erode due to increased river discharges attributed to dike construction. We sought to quantify the impact of dike construction by analyzing changes in river discharge on the floodplains under the three scenarios. For this we used the same 1D- quasi2D hydraulic modeling simulations as applied in our flood damage estimates.

5.4. Results