Frameworks for Drought Vulnerability Assessment
A general framework for assessing climate resilience – and, hence, for drought resilience - is the vulnerability framework. According to the Intergovernmental Panel on Climate Change (IPCC, 2007) [p.27] “Vulnerability is the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and extremes”. In order to assess vulnerability, there is a host of available vulnerability frameworks, each with its advantages and disadvantages. (e.g. Turner et al., 2003; Bohle et al., 1994; Birkmann et al., 2013). On top of that, many vulnerability assessment techniques have been proposed by scholars; Regmi at al. (2010); Hossain and Roy (2012) used a qualitative method (community risk assessment) whereas Alcamo et al. (2005) used fuzzy set theory to weight model factors and convert qualitative variables to quantitative indicators. Data driven techniques have also been applied; da Silva et al. (2021); Sharma & Patwardhan (2008) used cluster analysis and Li et al. (2021) random decision trees.
King et al. (2020) grouped the drought vulnerability methods used in eight developing countries (Brazil, Mexico, Colombia, India, Slovenia, Nigeria, Senegal and Kenya) into three overlapping categories: land-based, people-centered, and water-balanced paying attention to the most vulnerable communities. Fritzsche et al. (2014) created frameworks with exposure, sensitivity, and adaptive capacity as its main components, in line with their definitions in IPCC (2007), providing the basis for a risk-based approach to drought resilience.
Drought vulnerability of cities was studied particularly in China. Dong et al. (2020) used a quantitative indicator-based vulnerability assessment of urban water infrastructures to floods and droughts in 22 of provincial capital cities in China. 33 indicators were considered and categorized into exposure, sensitivity and adaptive capacity ones. For the latter two categories, indicators were clustered into the following dimensions: physical, social, economic, and environmental. The indicators weights were assigned as equal except the physical dimension which was larger for both sensitivity and adaptive capacity. Chang, Yu, and Zheng (2016) investigated the vulnerability of seven cities in China located on the northern slope of Tianshan Mountain. Wang et al. (2020) assessed the vulnerability of Beijing-Tianjin-Hebei (BTH) region which is formed by two municipalities, Beijing and Tianjin, and 11 cities of Hebei Province. In both studies, they used a variety of data regarding economy, society, ecology, and social resources and employed the Entropy method (Iyengar & Sudarshan, 1982) for estimating of their weights in the evaluation. Yuan et al. (2015) also studied the drought vulnerability of 65 cities in China, using variables based on exposure, sensitivity, and adaptive capacity. These authors estimated the weights using an Analytical Hierarchy Process (AHP) (Saaty, 1980). Another study worthwhile mentioning is by Corti et al. (2009, 2011). They investigated the damage of buildings due to subsidence and created vulnerability curves which converted the soil moisture deficit to monetary damage.
Urban Drought Categorization Framework
Urban drought is to be defined in the context of drought vulnerability. To that end we state: Urban drought occurs when at least one of the water-dependent urban functions and services is ‘disrupted’ due to the fact that a specific element of the urban water system – surface water, groundwater, soil moisture in the unsaturated zone, supplied water - has less water than the minimal expected, in terms of quantity and/or quality. The term ‘disrupted’ means there is a deviation for a water function from normal conditions to such a degree that the city starts facing losses regarding society, economy, and/or environment. Hence, urban droughts are region and climate dependent, since an urban drought in one city influences other water-dependent urban functions than in another city and since the minimal expected quantity /quality in an arid zone is different from an area with a wet sea climate.
A solid urban drought classification system can be made by linking the water-dependent urban functions and services to the specific elements of the urban water system (see Fig. 1). To operationalize this classification, policy-makers need to spatially define the water-dependent urban functions and services in their city, as these determine the (damage) sensitivity of being exposed to drought. Areas – including surface waters - can have multiple water-dependent functions and provide multiple services. Water-dependent urban functions can be categorized into the following classes: water landscape, water ecology, water resources, water security, water economy, and water culture (Yu et al., 2018).
The degree to which drought has an impact on an urban environment is quantified by the drought risk, that is the product of exposure to drought – including its frequency of exposure and intensity - and the (damage) sensitivity of the urban system. The hazard of being exposed to drought is to be assessed from the perspective of the specific elements of the urban water system that support these functions and services. The four relevant specific elements are local soil moisture, local groundwater, local surface water (canals\ponds\wetlands), and surface water and groundwater reservoirs from which the city receives its drinking and industrial water. These reservoirs are generally located beyond the boundaries of the built environment. According to the four affected elements of the water system, we can differentiate between four urban drought categories: Soil Moisture Drought (SMD), Groundwater Drought (GD), Open Water Drought (OWD), and Water Supply Drought (WSD). These four types of drought can be the result of a shortage of water (quantity), but also due to poor quality, so that the water can no longer be used for the functions it is supposed to serve. It is very well possible that in a specific area more than one of these urban drought types occur simultaneously.
Some examples of disrupted water functions are provided so that the framework becomes more tangible and easier to be applied. SMD will damage urban parks and urban agriculture and will increase ambient air temperatures due to the reduction of evapotranspiration. GD can lead to land subsidence and damage to buildings due to foundation problems due to shrinking clay layers in the subsurface. Wooden pole foundations of (historic) buildings are in particular sensitive to low groundwater levels, as they start rotting when exposed to air in an unsaturated zone. OWD leads to damage in lowland and delta cities with their canals, ponds and wetlands, as surface water functions such as recreation, shipping goods, and preservation of aqua flora and fauna are restricted by a lack of sufficient water of sufficient quality. Last but not least, the urban drought category which has the most significant impact on a city is WSD. Providing clean and sufficient water to its residents and for a smooth operation of water-dependent industries is essential for the continuity of the urban society.
Though their impacts are different, the different types of urban droughts are interrelated. SMD can cause GD since less percolation occurs due to lower soil moisture. In addition, GD can cause SMD, as less capillary rise takes place. A GD can cause an OWD in case there are canals or a pond in a city. Lower groundwater levels result in less water flows into the canals or pond, leading to an OWD. Additionally, GD can cause an WSD in case the supply comes from groundwater within a city’s boundaries.
Next step in the operationalization of this framework is to find out which hydrological variables are required for quantifying the exposure to each specific urban drought, and how to quantify the area’s sensitivity. This search for the relevant variables will be addressed in the case study.
The selected study area for this study was Leiden in the Netherlands for the two following reasons: (i) the city is well monitored, and (ii) it may face drought challenges in the future. Four pilot districts were focused on, to allow for a higher resolution vulnerability analysis: Binnenstad-Zuid, Binnenstad-Noord, Bos en Gasthuis, and Boerhaave (Fig. 2). These districts were selected because they were built in different periods and their soil type is different. In districts Binnenstad-Zuid, Binnenstad-Noord the main geological type is ‘trench/trench deposits’ whereas ‘tidal basin/tidal deposits’ dominates in Boerhaave. At district level there is homogeneity regrading hydrological conditions.
Our analysis will focus on GD; OWD is not relevant for this area, as all open water is connected to the regional system and is kept at a fixed level, even under extremely dry conditions. Drinking water is supplied from rich external sources, making WSD irrelevant as well. And SMD is less relevant to our case as the groundwater levels are normally around or even above 1 m below surface and during extreme droughts hardly ever more than 1.5 m below surface. Consequently, the unsaturated zone of these silty soils remains relatively wet and continues to provide water to the urban green even under conditions of drought. Groundwater levels however are important to minimize land subsidence, to keep buildings (foundations) stable and preserve wooden pile foundations.
Groundwater level observations were available for the hydrological year April 2018 - March 2019. The summer of 2018 was extremely dry all over the country. Three wells were selected in Binnenstad-Zuid, five ones in Binnenstad-Noord, seven ones in Bos- en Gasthuis, and five ones in Boerhaave.
Raw groundwater level data was collected on an hourly basis but was resampled to daily averages as tiny groundwater fluctuations within 24 hours are irrelevant for our analysis. Regarding missing values, linear interpolation was applied (see the overview of data gaps in the Supplementary Material).
Assessing exposure: Threshold methods
To quantify exposure to groundwater droughts, groundwater level deficit and duration were determined, including their cumulative probability distribution and percentile values. Two different methods were compared for their ability to express and quantify GD exposure: (i) Fixed threshold, (ii) Variable threshold, i.e. using moving average of monthly quantile values as threshold (Beyene et al.2014).
A Fixed threshold is a constant value groundwater level, based on a groundwater level percentile (e.g. the 30th percentile) considering the whole time series. Regarding the Variable threshold method, the following steps (see Fig. 3) were applied: (i) for each month of the year, the 30th percentile was determined using the cumulative distribution function (CDF) from all daily values in that month (ii) the value of the 30th percentile was assigned to all days of that month, and (iii) backwards moving average of 20 days was applied to the whole year to smooth the ‘staircase differences’ and extinguish the abrupt jumps in threshold function between consecutive months. For both threshold methods the study period was one year making sure that there are no inconsistencies in their comparison.
On beforehand, there is no reason to prefer a specific percentile as threshold value for assessing exposure. To investigate the differences, three percentiles (20th, 30th, and 40th ) were applied for each of the three aforementioned techniques and their results were evaluated. The most common percentiles in literature are 10th, 20th, and 30th (e.g. studies of Heudorfer and Stahl (2017) and Hisdal and Tallaksen (2000)). Tallaksen et al. (2009) have used only 20th percentile whereas Gurwin (2014) has employed percentiles based on the standard deviation (50% of standard deviation). In the current study, given the range of groundwater data in study area and after testing a threshold of the 10th, 20th, 30th and 40th percentile, it was decided to use the 30th percentile. A percentile as low as 10 or 20% would lead to a very limited number of drought events, impeding further analysis. .
Regarding groundwater level related drought indicators, deficit is defined as the maximum difference from the threshold during the drought event (Peters, 2003; Van Loon & Van Lanen, 2012), as shown in Fig. 4. A drought event starts when the level is lower than the threshold and ends when it is higher again. Their difference defines drought duration. In Fig. 4, a Fixed threshold was used, but the same definitions for drought duration and deficit hold for Variable threshold method.
Assessing sensitivity; Physical and Social Indicators
Sensitivity was split up into physical and social sensitivity.
Physical Sensitivity Indicators
Physical sensitivity indicators include physical attributes of the city which may be damaged by GD. Their malfunctioning can create serious disruption to the city. Seven variables were used to quantify this physical sensitivity: ‘public buildings’; ‘shops’; ‘ontwatering’; ‘buildings before 1960’; ‘monuments’; ‘land use’; and ‘soil’.
’shops’ are related to the economic state of the district whereas ‘monuments’ to its cultural identity. ‘public buildings’ were considered as a critical part of the city and any malfunction of them due to subsidence could create disruption. This indicator was also used to determine flood vulnerability of South-Western Ontario, Canada (Karmakar et al., 2010).
‘ontwatering’ was estimated as the difference of the lowest observed groundwater level and the ground surface. The larger the ‘ontwatering’, the more sensitive the area is for drought. The lowest groundwater level was selected for each monitoring well for the period April 2018 - March 2019 and then these values were interpolated via Inverse Weighted Interpolation (IWI) algorithm to cover the entire study area. Ground level data was derived from the Actueel Hoogtebestand Nederland (AHN2), the digital elevation map of the Netherlands) with a spatial resolution of 0.5 m. Related to ‘ontwatering’, annual groundwater drop was used by Alamdarloo et al. (2020) to determine drought vulnerability.
Due to the soft soil and subsurface, most buildings in Leiden are built on poles. Since around 1960 concrete poles are used. But ’buildings before 1960’ are considered to be built on wooden poles. And these will degrade when no longer immersed in – almost anaerobic – groundwater, with fatal effects for the stability of the building. This data came from a municipal database. Indicators such as building age were also used to determine vulnerability to floods (Rana & Routray, 2016, 2018; Jamshed et al., 2020), but are relevant to groundwater drought analysis too.
‘soil’ and ‘land use’ are two characteristics which determine how fast subsidence occurs. They are both derived from a municipal database. Henrique et al. (2021) and Alamdarloo et al. (2020) used ‘land use’ to identify vulnerable regions of Sao Paulo and Iran to droughts. ’land use’, and soil texture and depth were also used by Dayal et al. (2018) in the drought-prone region of south-east Queensland, Australia.
Social Sensitivity Indicators
Social indicators for groundwater droughts showcase minorities which may face hardships when a groundwater drought occurs. A variety of variables was available, including population, economy, and social security data. All social indicators data was collected at neighborhood level (Centraal Bureau voor de Statistiek, 2017). Many variables proved to be highly correlated, as shown by their Kendall Tau coefficient; hence, they were rejected as being redundant. The percentage of households belonging to the lowest 40% income nationwide was selected as the most relevant social sensitivity indicator. From here onwards, it is named as ‘low income’. Economic indicators regarding household income or per capita were used in previous studies by Abbas & Routray (2014); Rana & Routray (2016, 2018); Jamshed et al. (2020); Li et al. (2021) to determine vulnerability to floods but also to droughts (Henrique et al., 2021).
To determine vulnerability to groundwater droughts, the framework suggested by Fritzsche et al. (2014) was followed, though with some modifications. Changes were: (i) AHP was used to assign weights, (ii) coping capacity (called adaptive capacity by Fritzsche et al. (2014)) was not considered, as this component is equal in all districts. As all districts of the study area belong to the same municipality and are located close to each other, exposure and sensitivity were similar but not equal in all districts and are therefore relevant to the vulnerability estimation. Figure 5 illustrates the main concepts of this framework and the indicators we included.
The indicators used in this study include metric and categorical ones. Categorical indicators can be categorized into two categories; ordinal and nominal ones. The former are ranked classes whereas the latter are descriptive ones. In the current analysis, there are two nominal indicators (‘land use’ and ‘soil’); all other exposure and sensitivity indicators are metric. The min-max method is used for metric indicators’ normalization; maximum vulnerability is represented by one and lowest vulnerability by zero.
For normalizing nominal indicators these are first converted to ordinal classes and then normalized to a range between zero and one. For example, ‘soil’ is a nominal sensitivity indicator and was transformed to an ordinal one; peat soil was classified as ‘rather negative’, clay deposits as ‘negative’, and old dunes (consisted of loamy sand) as ‘positive’ regarding groundwater urban droughts. Then, values in the range [0–1] were assigned based on the classes. More information on the normalization of the nominal indicators can be found in the Supplementary Material.
AHP was used for assigning weights to vulnerability indicators and to aggregated components. Entropy evaluation and Garrett ranking were not feasible due to the very limited number of districts (spatial units) and available expert respondents. Eight drought experts, who worked either in public or private sector, compared indicators or vulnerability components in pairs for the AHP. For each expert, a comparison matrix was created and based on all experts’ matrices, a combined one was developed to assess the weights of the indicators.
After normalizing the indicators of exposure, and sensitivity, aggregation was applied by weighted multiplication of all indicators and mapping the results. The vector type of most indicators (i.e. ‘duration’, ‘deficit’, ‘buildings before 1960’, ‘monuments’, ‘public buildings’, and ‘shops’) is point. Raster interpolation was applied for them so that the entire area has values regarding these indicators. The vector type of the remaining indicators (‘soil’, ‘land use’, ‘ontwatering’, ‘low income’) is polygon but they converted to raster so that raster multiplication can be performed. Next, the aggregated components were combined to determine the vulnerability map for groundwater drought.
As a final step sensitivity analysis is performed to quantify the degree to which the removal of each indicator influences the GD vulnerability. For these sensitivity analyses, exposure indicators were determined using Variable threshold of 30th percentile. For each removed indicator the weights of the remaining indicators is estimated afresh by removing the corresponding row and column from the combined pair-wise comparison matrix in AHP.