A Framework for Assessing Benefits of Implemented Nature-Based Solutions

Nature-based solutions (NBS) are solutions that can protect, sustainably manage, and restore natural or modified ecosystems in urban and rural areas, while providing many benefits and co-benefits including stormwater mitigation, biodiversity enhancement, and human well-being. As such, NBS have the potential to alleviate many of the environmental, social, and economic issues that we face today. Grey infrastructure, such as lined trenches and catch basins, pipes, and concrete dikes are frequently used for stormwater management and flood protection, but they do not provide many of the co-benefits that are common with NBS. Grey infrastructure is designed to quickly collect and remove rainwater, whereas NBS keep rainwater where it falls, and where it can be used by the environment. Many stakeholders lack knowledge of the capabilities and benefits of NBS, and as a result, they continue to rely on grey infrastructure in their projects. When information is made available on the benefits and how they can be quantitatively measured, it is hoped that NBS will be promoted to a mainstream infrastructure choice. A valuable way to quantify and highlight the benefits of NBS is by using an evaluation framework. There are several evaluation frameworks that qualitatively assess the potential benefits of possible NBS, however there is a need for quantitative frameworks that can assess the actual benefits (or performance) of implemented (or existing) NBS. This article presents an evaluation framework that aims to quantify the benefits and co-benefits of implemented NBS. The framework involves five main steps: (1) selection of NBS benefit categories, (2) selection of NBS indicators, (3) calculation of indicator values, (4) calculation of NBS grade, and (5) recommendations. The outcome of the framework is a single numerical grade that reflects the benefit functioning for an NBS site and values for each performance indicator. This information may be used by decision makers to determine their budget allocations to expand or construct a new NBS site, to update maintenance plans that will improve the benefits of that site, to set up programs to monitor the NBS benefits and co-benefits over time, and to schedule labour and resources for other NBS projects. The framework was tested and validated on a case study of NBS in Thailand. Through conversations with stakeholders and knowledge of the case study area, relevant categories and indicators were chosen. Using data and information obtained through various means, values for each indicator and the overall NBS grade were calculated. The values revealed which benefits were pronounced, those that were weak, and where improvements were required.

Many stakeholders are uncertain about the performance and reliability of NBS [18], the present paper fills some of these gaps in the NBS knowledge base. The framework can be applied to urban and rural, large and small scale, hybrid, and catchment scale NBS, and it proposes a combination of several methods to assess both qualitative and quantitative benefits while integrating stakeholder's preferences.
The River Health Index (RHI) [19], which assesses the health of rivers and their ecosystems in Thailand, represents an important aspect in the proposed framework. The present framework assigns a grade to an existing NBS by assessing each individual benefit through various methods and stakeholder input. It aims to provide a systematic evaluation of the benefits and their relative effectiveness in comparison to the same situation without NBS. The output shows where improvements are possible and can help farmers to improve their resilience to climate change, their livelihoods, and the quality of life for their communities. The framework output also provides valuable information about NBS benefits and co-benefits as well as their advantages to support academics, water managers, and planners when studying, promoting, and implementing NBS technologies. The framework was applied to an NBS case study in the Rangsit canal area of Thailand.

Proposed Framework Overview
The framework proposed here was developed from other relevant frameworks, the current knowledge base of different NBS technologies, their benefits and co-benefits, performance indicators, and practical experiences. It also reflects on some important discussions with the key stakeholders in the case study area. The following sections describe the framework steps and its application.

Framework Steps
The framework proposed here takes the approach of the RECONECT [20] project which builds from the challenges of the EC-funded EKLIPSE [17] project; these are combined into three categories, namely water, nature, and people, and form the foundation for evaluating and comparing sites with and without NBS. It is important to note that the two sites compared should be alike in most aspects except for the presence of the NBS so that a meaningful comparison can be made. For example, the sites should have the same water, nature, and people related features such as climate, rainfall, water supply, rivers, land use type, culture, etc. If the sites are alike then the differences in the benefits are assumed to be the result of the NBS performance. The schematic layout of the proposed framework is shown in Figure 1. After determining the requirements of the stakeholders and becoming familiar with the case study area, the five steps in the framework can be followed to determine the performance and benefits of NBS. Many stakeholders are uncertain about the performance and reliability of NBS [18], the present paper fills some of these gaps in the NBS knowledge base. The framework can be applied to urban and rural, large and small scale, hybrid, and catchment scale NBS, and it proposes a combination of several methods to assess both qualitative and quantitative benefits while integrating stakeholder's preferences.
The River Health Index (RHI) [19], which assesses the health of rivers and their ecosystems in Thailand, represents an important aspect in the proposed framework. The present framework assigns a grade to an existing NBS by assessing each individual benefit through various methods and stakeholder input. It aims to provide a systematic evaluation of the benefits and their relative effectiveness in comparison to the same situation without NBS. The output shows where improvements are possible and can help farmers to improve their resilience to climate change, their livelihoods, and the quality of life for their communities. The framework output also provides valuable information about NBS benefits and co-benefits as well as their advantages to support academics, water managers, and planners when studying, promoting, and implementing NBS technologies. The framework was applied to an NBS case study in the Rangsit canal area of Thailand.

Proposed Framework Overview
The framework proposed here was developed from other relevant frameworks, the current knowledge base of different NBS technologies, their benefits and co-benefits, performance indicators, and practical experiences. It also reflects on some important discussions with the key stakeholders in the case study area. The following sections describe the framework steps and its application.

Framework Steps
The framework proposed here takes the approach of the RECONECT [20] project which builds from the challenges of the EC-funded EKLIPSE [17] project; these are combined into three categories, namely water, nature, and people, and form the foundation for evaluating and comparing sites with and without NBS. It is important to note that the two sites compared should be alike in most aspects except for the presence of the NBS so that a meaningful comparison can be made. For example, the sites should have the same water, nature, and people related features such as climate, rainfall, water supply, rivers, land use type, culture, etc. If the sites are alike then the differences in the benefits are assumed to be the result of the NBS performance. The schematic layout of the proposed framework is shown in Figure 1. After determining the requirements of the stakeholders and becoming familiar with the case study area, the five steps in the framework can be followed to determine the performance and benefits of NBS.  The benefits of NBS are categorized as water (W), nature (N), or people (P) related. This classification was adopted from the RECONECT project, where the main focus is on hydro-meteorological, or weatherrelated water benefits [20]. The water related benefits are directly associated with water, and include flood mitigation, drought and flood resilience, water storage and reuse, and groundwater and surface water quality. The nature related benefits are associated with the environmental features of soil, air, and vegetation, and include infiltration, biodiversity, and soil quality. The people related benefits include cultural, education, recreation, and economics.
Depending on the needs of stakeholders, and the relevance to the case study NBS, one, two, or all three of the categories may be selected for further analysis.

Step 2: Selection of Indicators
Selection of indicators is accomplished through stakeholder conversations and for each benefit category, select the indicators of interest and relevance from Tables S1-S3 in Supplementary Materials. Every benefit in different categories is represented by an indicator. Certainly, not all indicators can be applicable to each case.
Tables S1-S3 provide a description of each indicator and guidance on how to select the appropriate ones for a specific case study NBS. The list of NBS referred to in these tables is not exhaustive and the framework can be equally applied to other NBS.

Step 3: Calculation of Indicator Values
For each indicator selected in Step 2, a numerical value will be calculated to determine its performance. Equations can be found for indicators in Supplementary Materials; every equation requires data specific to each indicator. The data may be collected through interviews, literature searches, field investigation and measurements, numerical modelling, remote sensing, etc. The benefits can be either qualitative or quantitative and the final result is expressed in a numerical value.
The equations provided in Supplementary Materials aim to compare the variables of Area A (the case study area with the NBS) with those of Area B (the comparison area without NBS or the case study area before implementation of the NBS). For example, the variable for the indicator biodiversity is the number of species in the area.
Indicators may have a positive effect on the environment, such as biodiversity (number of species), or a negative effect, like water quality (or increased level of pollution). For the biodiversity indicator to have a high value, the number of species in Area A must be much higher than in Area B, and this is referred to as a positive effect (Equation (1)). For the water quality indicator to have a high value, the level of pollution must be much higher in Area B than in Area A and this is referred to as a negative effect (Equation (2)). Indicator values that are close or the same for Area A and Area B imply that there are no differences in indicators, or NBS benefits.
Equations should be tested with hypothetical numbers to see if they result in the appropriate values. The difference between Area A and Area B parameters must be large to produce a high value and small to produce a low value. The higher the value of each indicator, the more pronounced the benefit will be in that area.
The equations used to determine the value for each indicator may be of the following types: 1. Percent change equation: the difference between Area A and Area B for indicator X.
For positive effect, indicator: For negative effect, indicator: As the difference between A and B increases, the value of indicator X approaches 100, which is a high score, meaning that the benefit is very pronounced. Conversely, as the difference between A and Sustainability 2019, 11, 6788 5 of 25 B approaches 0, which is a low score, the value of indicator X approaches 0, meaning that there is little or no difference in the benefit between the two areas. 2 Some indicator equations must be developed on an individual basis, these are in the category of Equation 3. These indicators may be evaluated by comparison between the NBS variable (A) and expected values, literature, other case study areas, and other methods.
More details for calculating each indicator's value are listed in Supplementary Materials. Equations may be altered to suit the specific benefits of NBS. The data collected for each indicator is used as input for its corresponding equation; the output is a value for each indicator that quantifies how the benefit is causing an impact on the area with the NBS. Table 1 shows the types of equations that may be used to calculate the values of indicators. Next, each indicator value from step 3 is converted to a score using Table 2. Indicator values show the percentage difference. For example, a value of 75 with a corresponding score of 4 implies that the performance of the benefit in Area A is 75% more pronounced than in Area B. Scores less than 2 indicate that there is little or no difference between the benefit in Area A and Area B. Furthermore, they may imply that the data was not collected or analysed correctly or that the benefit is not relevant to the case study. A negative value indicates that the benefit in Area A may be inferior to Area B. When the indicator score is less than 2, the relevance, data collection, and analysis method should be re-evaluated before including it in the grade calculation. The grade for the NBS is determined by taking the average of all the indicator scores. An optional step may be to assign weights to the indicator scores if the stakeholders find some to be more important than the others. Weighted criteria should be defined by stakeholders depending on the level of importance for each indicator's benefit to the community. After all the scores have been weighted, they will be Sustainability 2019, 11, 6788 6 of 25 averaged, added, and will result in a single number; this is the NBS grade that incorporates all the benefits assessed.
The NBS grade, specific to the case study, will be the outcome of the assessment and the grades range from 1, indicating no benefits of NBS, to 5, indicating numerous benefits (for details of grades refer to Table 3) [19]. Table 3. Nature-based solutions (NBS) grades.

NBS Grade Description Grade Number
Very poor The NBS do not provide any benefits; re-evaluation is necessary. 0-1

Poor
The NBS are providing very few benefits; improvements may be required; re-evaluation may be necessary. The final step in the framework is to make recommendations for all indicators, or only those with low scores. Recommendations can include guidance on how to better involve stakeholders in every step of the framework, how to better measure, collect, and analyse data, and how to maintain the NBS to maximize benefits. Furthermore, this step may provide advice on how to monitor the benefits of NBS to ensure they remain positive into the future, as well as how to plan for better balance between biodiversity, habitat and agricultural output, how to reduce expenses through efficient irrigation, solar powered pumping, alternate fuel and fertilizer sources, and upscaling of the particular NBS site.

Case Study Areas
The case study areas used in the present work are located in the Rangsit area, in the eastern part of the Chao Phraya valley in central Thailand. Rangsit is located between the Western Raphiphat and the Rangsit canals. The case study includes two areas-Area A in Pathum Thani province (Bueng Cham O (BCO) and Noppharat (NP) sub-districts), and Area B in Saraburi province (Nong Rong (NR) sub-district), shown in the upper right-hand corner of Figure 2. The type of NBS addressed in the case study work are furrows which are located in Area A only.
Furrows represent a unique rural NBS that exist in the Rangsit area of Thailand. These are small canals in agriculture fields connected to the sub-canals through locks with gates; they are similar to the RFR concept used for high-water channel. Furrows were first built to store water for irrigation purposes, but they can also provide several other benefits including flood protection by controlling and channelling flows. Figure 3 shows typical furrows in the Rangsit area.
Most of the farmland in BCO and NP sub-districts (Area A, Figure 2) were converted from rice paddies to orange orchards in 1984, but in 1991 a citrus disease destroyed most of the trees. The Ministry of Agriculture approached farmers to consider growing palm oil trees, which were only grown in southern Thailand at the time. As a result of that, many farmers turned to palm oil production, which became profitable. However, the farmers were faced with water shortages, poorly maintained shallow canals, and flooding. The 2011 floods caused extensive bank erosion and other damages. Hence, the farmers decided to expand and deepen the network of furrows on their land. The use of furrows for water storage boosted palm production as well as many other crops, such as bananas and vegetables during dry seasons. of the Chao Phraya valley in central Thailand. Rangsit is located between the Western Raphiphat and the Rangsit canals. The case study includes two areas-Area A in Pathum Thani province (Bueng Cham O (BCO) and Noppharat (NP) sub-districts), and Area B in Saraburi province (Nong Rong (NR) sub-district), shown in the upper right-hand corner of Figure 2. The type of NBS addressed in the case study work are furrows which are located in Area A only.  Furrows represent a unique rural NBS that exist in the Rangsit area of Thailand. These are small canals in agriculture fields connected to the sub-canals through locks with gates; they are similar to the RFR concept used for high-water channel. Furrows were first built to store water for irrigation purposes, but they can also provide several other benefits including flood protection by controlling and channelling flows. Figure 3 shows typical furrows in the Rangsit area. Most of the farmland in BCO and NP sub-districts (Area A, Figure 2) were converted from rice paddies to orange orchards in 1984, but in 1991 a citrus disease destroyed most of the trees. The Ministry of Agriculture approached farmers to consider growing palm oil trees, which were only grown in southern Thailand at the time. As a result of that, many farmers turned to palm oil production, which became profitable. However, the farmers were faced with water shortages, poorly maintained shallow canals, and flooding. The 2011 floods caused extensive bank erosion and other damages. Hence, the farmers decided to expand and deepen the network of furrows on their land. The use of furrows for water storage boosted palm production as well as many other crops, such as bananas and vegetables during dry seasons.
Due to water availability throughout the year, Rangsit farmers harvested almost double of the palm oil yield from southern Thai farmers. Approximately 13,000 palm oil trees were planted along 72.8 km of canal banks to prevent erosion and also to stop illegal construction. Palm oil production resulted in higher yields than the farms in southern Thailand which did not have furrows [23].
Although it is not necessary to irrigate palm oil trees, a study in Thailand found that during the dry season, by providing up to 450 L/tree/day there will be an increase in fruit yield of up to 50% [23]. Figure 4 shows the average October rainfall from 1991 to 2016 in Thailand; two of the worst, most recent floods occurred in 2011 and 2016 (198.4 mm and 195.9 mm respectively), shown in red were used to assess the values of W1, W2, and W3 [24]. Dry season in Rangsit usually occurs from 1 November to 30 April and the monsoon season is from 1 May to 31 October each year.  Due to water availability throughout the year, Rangsit farmers harvested almost double of the palm oil yield from southern Thai farmers. Approximately 13,000 palm oil trees were planted along 72.8 km of canal banks to prevent erosion and also to stop illegal construction. Palm oil production resulted in higher yields than the farms in southern Thailand which did not have furrows [23].
Although it is not necessary to irrigate palm oil trees, a study in Thailand found that during the dry season, by providing up to 450 L/tree/day there will be an increase in fruit yield of up to 50% [23]. Figure 4 shows the average October rainfall from 1991 to 2016 in Thailand; two of the worst, most recent floods occurred in 2011 and 2016 (198.4 mm and 195.9 mm respectively), shown in red were used to assess the values of W1, W2, and W3 [24]. Dry season in Rangsit usually occurs from 1 November to 30 April and the monsoon season is from 1 May to 31 October each year.
It is estimated that the Rangsit area can store up to 137 million cubic meters of water (including canals, sub-canals, and furrows), which is sufficient to supply farmers throughout the year [25].
Furrows are approximately 2 m in depth, 2.5 m wide, and the network of 129 km provides 4600 m 3 /ha of water storage. There were four Area A farms included in the analysis: A-1, A-2, A-3, and A-4. All farms were used for water quality measurements, and A-3 and A-4 farmers were interviewed; A-1 and A-2 farmers were not available for interviews. The average size of farms in Area A and Area B is 18 Rai (2.88 ha); approximately 20%-25% of the land surface in Area A farms is occupied by furrows. dry season, by providing up to 450 L/tree/day there will be an increase in fruit yield of up to 50% [23]. Figure 4 shows the average October rainfall from 1991 to 2016 in Thailand; two of the worst, most recent floods occurred in 2011 and 2016 (198.4 mm and 195.9 mm respectively), shown in red were used to assess the values of W1, W2, and W3 [24]. Dry season in Rangsit usually occurs from 1 November to 30 April and the monsoon season is from 1 May to 31 October each year.  In the neighbouring sub-district of NR (Area B), the farmers did not convert their rice paddies to furrows. They were reluctant to change from rice to other crops due to the high initial investment costs involved in creating furrows. Presently, the most common crop in NR is still rice. There were four Area B farms used in the analysis: B-1, B-2, B-3, and B-4. Farmers in all four farms were interviewed.

Involvement of Stakeholders
Ten stakeholders were involved in the development and testing of the framework. Six farmers were interviewed to collect information about expenses, incomes, irrigation use, floods, droughts, crops, key indicators, and fertilizer use. A Thai translator was present during all the interviews; the questions were written ahead of time to ensure the correct information was gathered (farmer interview questions and responses are available in Supplementary Materials).
Two local municipality personnel were separately interviewed to gather knowledge about water use, crop, flood, drought, recreation, education, key indicators, area history, and culture information. Two government experts provided information concerning total area irrigation volume, groundwater usage, and groundwater well level data for the case study areas.
Time and budget resources were limited in the application and testing of the framework. For future application of the framework, it is recommended that larger sample sizes are used, and that focus group discussions with stakeholders are included.

Step 1: Selection of Benefit Categories
Rangsit stakeholders were interested in all three benefit categories (water, nature, and people) and they wanted to see how the benefits of flooding and drought resilience, water storage, GWR, biodiversity of crops, water quality, farmer incomes, and farm productivity were performing in their respective communities.

Step 2: Selection of Indicators
Through discussions with stakeholders on what indicators were applicable to the case study areas and important to the community, the list in Table 4 was produced. The selection process involved choosing relevant indicators and eliminating those that were irrelevant. For each selected indicator, the reason for selection and data source are shown in Table 4. The following section describes how the data was collected and analysed and how the equations given in Supplementary Materials were applied.

(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.
The model used upstream and downstream flow regulators and downstream Q/h relationships for the boundary conditions; see Figure S3 in Supplementary Materials [22]. Figure 5 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).  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 Raphiphat Canal Klong 10 R a n g si t C a n a l Storage W e s te r n R a p h ip h a t C a n a l Klong 1

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  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/m 2 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.

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).  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 R a n g si t C a n a l Klong 1 Figure 6. MIKE HYDRO River model cross-section on K10.

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)

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 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.

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.  (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. Table 9. Total length of canals and furrows in case study areas.

W8: GWR
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.

N1: Infiltration
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.

N2: Biodiversity
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 NO 2 concentrations were 0.054 mmole/m 2 for Area A and 0.059 mmole/m 2 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 is 0.

(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.

P3: Economic
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. Weights were applied to the indicator scores. Stakeholders ranked the benefits in order of importance using four categories: safety, income, environmental improvement and pastime; weights were assigned accordingly as shown in Table 16. The next step converted the indicator values into scores using Table 2. If the score for any indicator was less than two, that indicator may not have been relevant to the NBS or a different method of assessment may have been required. Indicators that required further assessment are shown in brackets in Table 17. Weights were applied to the average score in each weight category by multiplying the average score by the weight (see the last column in Table 17), the sum of the weighted average scores became the furrow grade; refer to Table 17 for grade calculation details. The furrow grade was 3.65, referring to Table 3, this grade corresponds to very good: the furrows are providing added benefits; minor improvements may be required. The next step involved making recommendations for improving the performance of each indicator.

Step 5: Recommendations
The final step in the framework was to provide recommendations on how to improve or better quantify the benefits of the furrows; this information may be helpful in project management, budget, maintenance, and labour resource planning for decision makers. Recommendations for each indicator are shown in Table 18 for the case study area; decision makers may choose to follow all, or only those that are important to the community and within their budget.

Discussion
A framework for assessing implemented NBS was developed and tested on the Rangsit case study. The work to date suggests that the framework may be used to gain better understanding of benefits and co-benefits of NBS and to promote their implementation. The five-step quantitative post-implementation assessment framework can be seen as a valuable tool that may be used by stakeholders to evaluate the performance and potential advantages of their NBS.
Many of the Rangsit indicators provided an appropriate assessment of the NBS benefits. The calculated values of the 18 case study indicators are presented in Figure 9.
• optimize conditions for planting, watering, fertilizing, and harvesting P4 Agricultural productivity Improve productivity • study cultivation and rainfall patterns to optimize crop growth • plant more drought resistant crops in dry season and crops that consume less water • reduce expenses

Discussion
A framework for assessing implemented NBS was developed and tested on the Rangsit case study. The work to date suggests that the framework may be used to gain better understanding of benefits and co-benefits of NBS and to promote their implementation. The five-step quantitative postimplementation assessment framework can be seen as a valuable tool that may be used by stakeholders to evaluate the performance and potential advantages of their NBS.
Many of the Rangsit indicators provided an appropriate assessment of the NBS benefits. The calculated values of the 18 case study indicators are presented in Figure 9. The indicators with the best performance were water storage and reuse (W4) and education and research (P2). This is due to the extensive network of furrows in the area and the widespread communication with other communities about the benefits of furrows. The indicators with low performance were local flood mitigation (W1), historical flood mitigation (W3), and water quality (W9). These indicators show that if improvements are made, such as more frequent dredging of canals and furrows, their scores will likely improve.
Seven indicators with scores less than 2 were excluded from the final score, these were W2, W5, W6, N3, N4, N5, and P1. To understand downstream flood mitigation (W2) capabilities of the NBS more accurately, the total furrow storage volume of the entire district should be used and not just from Area A. To get a more accurate value for irrigation costs (W5), resiliency to drought (W6), and  The indicators with the best performance were water storage and reuse (W4) and education and research (P2). This is due to the extensive network of furrows in the area and the widespread communication with other communities about the benefits of furrows. The indicators with low performance were local flood mitigation (W1), historical flood mitigation (W3), and water quality (W9). These indicators show that if improvements are made, such as more frequent dredging of canals and furrows, their scores will likely improve.
Seven indicators with scores less than 2 were excluded from the final score, these were W2, W5, W6, N3, N4, N5, and P1. To understand downstream flood mitigation (W2) capabilities of the NBS more accurately, the total furrow storage volume of the entire district should be used and not just from Area A. To get a more accurate value for irrigation costs (W5), resiliency to drought (W6), and soil quality (N3), larger sample sizes are required. The indicators of fertilizer use (N4) and air quality (N5) are relevant to the case study but a different way to measure them is required; comparing fertilizer use in Area A to Area B, where the crops had completely different needs, was an incorrect way to assess these indicators; a better method may be to determine the carbon sequestration capabilities of the plant species. The cultural (P1) indicator was not applicable in this agricultural setting.
The final grade for the Rangsit furrows was 3.65 which is assessed to be very good. This grade indicates that the benefits due to the NBS in Area A are greater than in Area B which does not have an NBS. The sample sizes in the research were small due to limited time and budget. The Rangsit municipality contacted farmers in the areas to set up interviews; unfortunately, only two farmers in Area A and four in Area B were available for interviews. The average of the data collected from the Area A and Area B farms was used to calculate indicator values. For a more in-depth analysis of the benefits, it is recommended to increase the samples sizes, gather more data, and to hold more workshops and interviews with the stakeholders.
The outcome of the framework application demonstrates the extent of advantages of NBS, how each benefit is performing, and where improvements can be made. The framework can be repeated numerous times over a span of several years to ensure the performance of the NBS is maintained. Furthermore, a monitoring program may provide insights into how NBS and their benefits change over time.
The framework can be found valuable for: • Researchers who want to study the impacts from NBS on climate change; • Water managers and planners who wish to promote, upscale, and implement NBS; • Decision makers who may want to allocate budget for NBS construction, expansion, maintenance, and monitoring; • Farmers who may want to improve, maintain, and expand their NBS to optimize the economic benefits; • All stakeholders who would like to understand the full benefits of NBS.
Incorporating NBS in communities may improve their resilience to climate change; NBS can diminish the effects of drought, landslides, pollution, illness, poverty, and flooding. Figure 7 depicts the impacts that furrows had on the Rangsit area during the 2011 flood event. NBS are becoming more important as the climate worsens, the knowledge of their capabilities should be spread to all stakeholders and the framework presented here offers a method to accomplish such objective.
The framework fills several gaps in the existing knowledge base related to NBS evaluations. The framework can be applied to urban and rural, large and small scale, hybrid, and catchment scale NBS, and it suggests several methods to assess both qualitative and quantitative benefits while integrating stakeholder's preferences.

Conclusions
There are many examples of NBS around the world that have proven their potential in providing benefits to water management, nature, and people. Hence, it is important to quantify and document the performance of their benefits so that the others can gain better understanding of their potential and significance. There are several frameworks that are proposed to date, but none of them can be used to assess the full potential of implemented NBS. The present paper proposes a framework that can be applied to any implemented NBS and it was tested on and adapted to a case study area in Thailand. The framework addresses both qualitative and quantitative benefits while integrating stakeholder's preferences.
The framework presented here evaluates how implemented NBS are performing and provides information of how they may be improved or sustained. The framework consists of five main steps: selection of NBS benefit categories, selection of NBS indicators, calculation of indicator values, calculation of NBS grade, and making recommendations for each indicator. Most importantly, the framework offers a tangible way for decision makers to understand the benefits, giving NBS more credibility, and hopefully elevating them to a mainstream infrastructure choice.
Application of the framework involved ten stakeholders who provided the necessary information for each step; it was revealed that the NBS were providing a wide variety of benefits, some were performing well, and others required improvements. The work undertaken in Thailand demonstrated that NBS such as furrows in agricultural land are beneficial for flood mitigation as well as for several other co-benefits. This information can be used by the farmers to improve their livelihoods, resilience to climate change, and their communities. The framework output also provides valuable information to support academics, water managers, and planners when studying, promoting, and implementing NBS.
The framework presented here did not include the calculation of benefits in monetary terms. By translating the indicator values into economic benefits, stakeholders are more likely to see the value and incorporate NBS in their projects. Therefore, in our future work we will attempt to further develop the framework to include a methodology for assigning monetary values to a variety of benefits and co-benefits.