3.2.4. Step 3: Calculation of Indicator Values
The following section describes how the data was collected and analysed and how the equations given in Supplementary Materials
(1) Water related indicators
The nine water related indicators in Table 4
were analysed for the Rangsit case study areas and the details are provided below.
MIKE HYDRO River one-dimensional hydrodynamic modelling software, developed by the Danish Hydraulic Institute (DHI), was used for flood analysis in the Area A canals using October 2016 flood data. It used unsteady, nonuniform flow to simulate flows from different sub-catchment areas; which resulted in changes in water levels and discharges at five cross-sections along Klong 10 and five along Klong 1 (Klong is the Thai word for canal). The NBS storage was represented as artificial storage within the network and a weir with storage was introduced to represent the furrows.
shows the MIKE HYDRO River model network of the Rangsit area. Area A (marked with orange colour) is located in the upper right-hand corner. October 2016 flood data was first simulated in the case study area without storage and then again with the NBS storage; refer to Figure 5
for the storage location on Klong 10 (K10). The model was used to determine values for indicators W1 and W2; W1 measured Area A flooding at K10 cross-sections and W2 measured downstream flooding at Klong one cross-section.
W1: Local flood mitigation
The furrow storage was estimated at one million cubic meters; flood water levels (heights above the canal bank elevations) were recorded at five cross sections upstream and downstream of the storage location at K10 with and without the storage (see Table 5
for water height levels). Figure 6
depicts a typical cross section of the canal (black line) where high and low water levels (red and green dashed lines), current water level (solid blue), and canal dimensions (faint red dashed lines) are shown for the October 2016 flood event; the left bank shows flooding in this particular cross-section.
To understand the significance of the furrow storage, the flood level reduction was compared to the water level at which maximum damage occurs in typical Asian agriculture (obtained from a depth-damage curve). Figure S1
in Supplementary Materials
shows the agricultural depth–damage graph for Asia [26
As shown in Figure S1
, the maximum damage occurred in agriculture at 4.8 m of flood water; mid-range damage occurred at 1.4 m; the steepest slope, or highest rate of damage occurred between 0.5 and 1.0 m of flood water. Agricultural damage is the lost output when crops are destroyed by flooding. The maximum average agricultural damage in Asia was 0.022 USD/m2
of land (2010 prices) [26
]. Using a flood depth of 0.5 m (above which most damage occurs), the ability of the furrows to reduce this value was assessed. This indicator provided an estimate of how furrows affected local flooding in the rural area around K10 in Area A. Using Equation (3) the result for W1 was 43.
W2: Downstream flood mitigation
Indicator W2 measured the potential flood mitigation potential of the furrow storage in Area A at a downstream commercial location, Klong 1 (K1).
The same model and method were used for indicators W1 and W2; for W2 the flood water levels were recorded at five cross-sections along K1; indicated in Figure 5
; simulation data and the equation are shown in Table 6
To understand the significance of the furrow storage, the flood level reduction was also compared to the height at which maximum damage occurred in commercial areas in Asia; Figure S2
in Supplementary Materials
shows the Commercial depth–damage graph for Asia [26
]. Using Equation (3) the result for W2 was 0.
W3: Historical flood mitigation
This indicator compared flooded areas in Area A and Area B for the 2011 flood event. Figure 7
shows a 2011 flood map that was used to estimate the areas of flooding. Purple indicates flooding and light blue indicates dry land; Area A and Area B are shown in the northeast corner. Since the 2011 flood map shows only a portion of Area B, the same sized area in Area A was used for comparison. Approximately 35.6% of Area A and 63.8% of Area B were flooded during the 2011 flood event. The resulting value for W3, using Equation (2), was 44.
W4: Water storage and reuse
The water storage and reuse potential of the furrows in Area A were evaluated based on the percentage of time that the farmers had adequate irrigation water. Information was gathered during interviews with farmers in the NP sub-district (see Figure 2
). Area B was not used for comparison since there were no furrow water storage and reuse potentials in this sub-district. Farmers in Area A were able to use furrow water for irrigation 85% of the year; the resulting value for W4, using Equation (3) was 85.
W5: Irrigation cost
The cost (Baht/year/Rai) for all sources of irrigation (furrows, canals, and groundwater) was compared in Area A and Area B. During interviews, the farmers provided the total yearly irrigation cost for their farm; this included electricity, equipment, fuel, labour, and all operation costs; refer to Table 7
for details of irrigation costs (2016). Groundwater use was rare. Using Equation (2) the result for W5 was −75.
W6: Resilience to drought
This indicator compared lost farm income between a non-drought year (2016) and drought year (2015). During interviews, farmers provided their annual incomes for 2015 and 2016. Thailand experienced a drought in 2015, when dam levels dropped below 10%, and 30% of the country was on water restrictions. The rainy season, usually beginning in May did not start until August; refer to Table 8
for details of farm incomes. Using Equation (2) the result for W6 was −150.
The lengths of water channels (canals and furrows) in Area A and Area B were compared and lengths were estimated using Google Earth; refer to Table 9
for details. This indicator showed how furrows may have contributed to the distribution of sediment, organisms, and nutrients in the water systems. The higher the water connectivity was, the easier it would have been for these elements to move in the environment [27
]. Using Equation (1) the result for W7 was 72.
According to the literature, Area A had an estimated rate of GWR of between 5% [28
] and 14% [29
] of annual rainfall. The average annual rainfall (2001 to 2017) for the Thai province of Pathum Thani was 1497.8 mm/year; therefore, the anticipated GWR was between 75 mm/year (5%) and 210 mm/year (14%).
Both the Water Table Fluctuation (WTF) method and groundwater monitoring well records could not be used to estimate GWR due to the presence of confined aquifers below the case study area; water that infiltrated did not necessarily recharge aquifers directly below.
However, infiltration will be higher in the areas with furrows; infiltration is directly related to GWR, even if the GWR is occurring in other sub-districts. This indicator compared the surface area of water in Area A and Area B. The areas were estimated using Google Earth; the area was 22.2% for Area A and 4.7% for Area B. The resulting value for W8, using Equation (1), was 79.
W9: Water quality
Primary treatment of water removes the larger solid particles such as grit, sediment, and floating debris [30
]. Water that entered the NBS carried sediment and pollutants from other water bodies or from runoff. Without this process, the pollutants would have remained in the sub-canals and canals; therefore, the water quality in the canals was improved.
If the Area A furrows provided some primary water treatment for the main canal, the sediment in the furrows would have been higher than in the canals. Sediment was represented by measuring levels of total dissolved solids (TDS) and turbidity; total suspended solids (TSS) may also be used, but this test was unavailable. Furrow water samples from Area A farms (A) were tested onsite with a portable TDS probe and a portable turbidity meter, as well as Klong 12 (K) where the water originated; refer to Table 10
for test results. Using Equation (3) the result for W9 was 45.
(2) Nature related indicators
The five nature related indicators in Table 4
were analysed for the Rangsit case study areas; the details are provided below.
Infiltration is an indication of healthy soil. A soil that is porous, drains well, and helps prevent runoff and erosion is considered healthy [31
]. The locations in Area A with furrows would have experienced infiltration. How the furrows contributed to infiltration was of interest; since there are no furrows in Area B, they were not used for comparison. Instead, infiltration rates, measured beside the furrows were compared to the literature rates for the same soil type. Infiltration rates were measured at farms A-3 and A-4 using double stainless-steel infiltration rings (30 cm inner ring and 60 cm outer ring).
In-situ measurements of infiltration measured in the field (A) were compared to infiltration calculated using the Green Ampt method and through the literature infiltration rates (L); refer to Table 11
for infiltration rates and Table S7
in Supplementary Materials
for parameters [32
]. Using Equation (3) the result for N1 was 69.
Biodiversity, in terms of variety of plant and animal species, in Area A and Area B was compared by determining the number of different crops; this information was collected during interviews with farmers and municipal staff. High biodiversity is an indication of a healthy environment [35
]. Area A had 20 species and Area B had 5. The resulting value for N2, using Equation (1), was 75.
N3: Soil quality
This indicator was added at the request of stakeholders; it was specific to Area A. Farmers dredged sediment from the furrows once or twice per year and applied it to their land. Many farmers felt that the sediment was rich in nutrients, since it originated from the canals that contained agricultural runoff. This indicator compared the nutrients (nitrogen (N), phosphorus (P), and potassium(K)) of the furrow sediment (S) to nutrients in the native soil (N) at farms in Area A; samples were collected from two farms in Area A and analysed at Central Laboratory Co. in Bangkok; refer to Table 12
for test results. Central Laboratory used an in-house method TE-CH-211 based on AOAC (2012) 993.13 for total nitrogen analysis, in-house method TE-CH-183 based on AOAC (2012) 958.01 for total phosphorus, and manual on fertilizer analysis, APSRDO.DOA; 4/2551 for total potassium analysis. Using Equation (3) the result for N3 was 17.
N4: Fertilizer reduction
Soil quality can also be estimated based on the quantity of fertilizer that was applied; the less fertilizer required, the better the quality of the soil. This indicator was added at the request of stakeholders, and was specific to Area A. Many farmers believed that by spreading sediment from the furrows onto the land, they required less fertilizer. This indicator compared the mass of fertilizer used in Area A to Area B in 2016, and information was collected from farmers during interviews; refer to Table 13
for fertilizer usage details. Using Equation (2) the result for N4 was 5.
N5: Air quality
Lal (2004) conducted a review of research on the conversion of energy used by farm operations into its carbon equivalent (CE). It was estimated that for every kilogram of fertilizer used, 1.70 kg of CE were produced [36
]. Since the difference in fertilizer use in Area A and Area B was insignificant (see N4) this method was not used.
Air pollution may also be quantified by measuring emissions such as carbon and nitrogen dioxide in the air. This indicator evaluated air quality using a remote sensing database for nitrogen dioxide levels between 10 July 2018 and 28 January 2019 [37
]. Figure 8
shows the differences in emissions in Area A and Area B; the NO2
concentrations were 0.054 mmole/m2
for Area A and 0.059 mmole/m2
for Area B. The resulting value for N5, using Equation (2), was 8.5.
(3) People related indicators
The four people related indicators in Table 4
were analysed for the Rangsit case study areas; the details are provided below.
P1: Cultural and spiritual
This indicator compared the number of cultural and spiritual events in Area A and Area B in the same year. During interviews, farmers and municipal staff were unable to identify any cultural or spiritual events that took place in Area A or Area B in 2017, as a result, the value for P1, using Equation (1) is 0.
P2: Education and research
In Malmo, Sweden the Western Harbour has become an international model of green infrastructure; it brings more than 800 people every year to study the benefits and performance of the project [38
]. This case study is similar to Rangsit in that they are both innovative examples of NBS projects that attract recognition and offer valuable research opportunities. The Malmo example was used as a baseline value for indicator P2; NBS with 800 people attending events was assigned a high value of 80:800 ÷ 10.
The number of people that attended education and research events in Area A were identified through interviews with municipal staff. Over 900 people visited Area A in 2016 to study the furrows (students, communities, and government officials); the resulting value for P2, using Equation (3) was 90. This indicator was specific to NBS; therefore, Area B was not included.
The incomes (Baht/year/Rai) of farmers in Area A and Area B were compared for this indicator. During interviews, farmers from farms A-3, A-4, and B-1 to B-4 provided annual farm incomes for 2016; refer to Table 14
for details. Using Equation (1) the result for P3 was 77.
This indicator compared the productivity in Area A to Area B. The productivity was calculated as agriculture outputs divided by inputs ($
); the higher the productivity, the more profitable the farm was. Farm output and input for 2016 were collected during interviews with farmers. Agriculture outputs included profits made through the sale of crops (Baht/year); agriculture inputs included costs of seeds, pesticides, fertilizers, packaging, tools, equipment, gas and oil, and labour (Baht/year); investment costs were not included; refer to Table 15
for productivity details. Using Equation (1) the result for P4 was 70.