Projecting floods and droughts characteristics under climate change is important to formulate an integrative management plan and enhance resiliency of society. However, studies that provide the integration of floods-drought hazards are scarce within literature. This study assessed flood and drought hazards separately and together for future climate in the Mun River basin, a tributary of the Mekong river. A non-modelling and multi-variate approach was used to assess flood and drought hazard respectively. Climate model ensemble suggests that the area under ‘high’ and ‘very high’ drought hazard level will increase from 27% and 4% during baseline period (1981–2010) to 43% and 37%, respectively, during near-future period (2021–2050). Similarly, an increase in ‘high’ and ‘very high’ flood hazard from 11% and 22% during baseline period to 16% and 24% during near-future period is projected. When both hazards are considered together, the total hazard is projected to increase by 155% in the near-future period. 76% of the catchment during the near future period will have combined hazard level from ‘medium’ to ‘very high’ compared to the 30% during the baseline period. The research presents a grim outlook on for the basin, with the area at risk from both hydro-meteorological hazards.

  • Individual and combined flood and drought hazard assessment for the near-future period.

  • Non-modelling approach for flood and a multi-variate approach for drought hazards used.

  • Area under high and very high drought hazards up from 30 to 80% in the near future.

  • Area under high and very high flood hazards up from 33 to 40% in the near future.

  • Area under the combined hazard projected to increase by 155% across the catchment.

Hydro-meteorological extremes, either an acute lack of water or excess of it, are characterized by spatial–temporal variability and manifest as droughts or floods , respectively. They have the potential to cause severe damage to the environment (Masud et al. 2015), have a significant effect on the hydrology and water resources of watersheds (Aalijahan et al. 2021), and cause socio-economic impacts (Marcos-Garcia et al. 2017). Between 2005 and 2014, 83% of the recorded disasters, 39% of the recorded human deaths and 70% of the documented damages were linked to weather, water and climate (WMO 2018), respectively. With climate change, hydro-meteorological hazards are likely to be more frequent and severe, and influence a greater area. A warmer climate will intensify the hydrological cycle, resulting in the spatial and temporal redistribution of global water resources (Chen & Sun 2017).

With almost one billion people living in flood-prone areas (Kittipongvises et al. 2020), flooding is one of the most destructive natural hazards in the world, affecting 45% of the global population (Hammond et al. 2014; Kittipongvises et al. 2020) and causing billions of dollars of damage each year (Davenport et al. 2021). Several studies have shown that precipitation extremes will increase in the frequency and magnitude in the future (Zhu 2013; Rudra et al. 2015; Burke & Stott 2017; Ohba & Sugimoto 2019). An increase in the extreme precipitation will be manifested as an increase in the flood risk.

Problems caused by flooding are increasingly exacerbated by increased water vapour in the atmosphere (Morita 2011; Aalijahan et al. 2023), a higher frequency of intense rainfall events (Chitwatkulsiri et al. 2021) and changes in land use, increasing surface runoff, resulting in greater flood extents and depths (Khan et al. 2018; Penny et al. 2023). In particular, South Asia is one of the most flood-prone regions in the world due to its natural geography (Shah et al. 2020).

On par with flooding, between the 2008 and 2018 period, global drought resulted in economic losses of about 8.42 billion USD (CRED 2018). A study by Lesk et al. (2016) showed that between 1964 and 2007, drought resulted in an estimated loss of 1,820 million metric tons of cereal globally. There are several types of droughts: meteorological, agricultural, hydrological, groundwater, ecological, socio-economical, etc. (Mishra & Singh 2010; Zargar et al. 2011; Crausbay et al. 2018). Under climate change, global agricultural droughts are projected to increase (Zhao & Dai 2017), several studies in Asian river basins have similar conclusions (Nam et al. 2015; Lu et al. 2016; Li et al. 2017; Kwon & Sung 2019).

Floods and droughts are common natural hazards with dire consequences in Thailand. The probability of drought in any given year is 0.45 for Thailand, the highest among Asia–Pacific countries (Pandey et al. 2007). While the droughts of 2004–2005 caused an estimated damage of about 220 million USD (Wichitarapongsakun et al. 2016), the devastating drought of 2015–2017 resulted in a damage of 3,300 million USD (EM-DAT 2019). On the other hand, flooding is also a serious issue in Thailand, experiencing 69 major floods since 1985 (Singkran 2017). The historic flooding of 2011–2012 affected 16 million people in 64 provinces out of 77 and caused a damage of about 45.7 billion USD (DDPM 2015). The World Bank estimated it to be the fourth most costly natural disaster in the world from 1995 to 2011 (Kittipongvises et al. 2020). Other studies in Thailand have shown that extreme precipitation is likely to increase in the future (Singhrattna et al. 2012; Komori et al. 2018; Igarashi et al. 2019), consequently increasing the flood hazard.

There have been many methods that examine flood and drought hazards: hydrologic–hydraulic modelling approach (Vojinovic et al. 2016; Jacob et al. 2020), hydrographs (Samu & Akıntuğ 2020), frequency ratio (Kongmuang et al. 2020), Flood and Drought Disasters (FDD) index (Guan et al. 2021), Normalized Difference Vegetation Index (NDVI) and LSWI (Chandrasekar et al. 2010; Navarathinam et al. 2015) and the modified Mann–Kendall (MMK) (Gemmer et al. 2008; Zhang et al. 2015). However, lately Geographical Information Systems (GIS) techniques are increasingly used in flood hazard, with the Standardized Precipitation Index (SPI) and Standardized Precipitation Index (SPEI) used in drought assessments (Nawai et al. 2015; Prabnakorn 2020; Prabnakorn et al. 2016, 2021; Shao & Kam 2020). MCDA-GIS is a combination of GIS with multi-criteria decision analysis (MCDA). GIS-based MCDA is a method increasingly used over the past two decades for flood risk and hazard assessment (Tang et al. 2018; Shadmehri Toosi et al. 2019, 2020; Kittipongvises et al. 2020) that provides a simple, effective and accurate means to investigate spatial distributions and characteristics using basic GIS analysis and mapping (Paquette & Lowry 2012; Samanta et al. 2016; Shadmehri Toosi et al. 2020). It is an especially useful method in areas where there are limited data (Shadmehri Toosi et al. 2020). MCDA-GIS provides more flexibility for decision makers to evaluate factors that cause flooding (Nigusse & Adhanom 2019), providing significant advantages over different methods in overcoming decision-making difficulties (Vavatsikos et al. 2019).

Modelling multiple climate hazards together is rarely done. However, Ming et al. (2015) provided a quantitative approach of multi-hazard risk assessment to assess crop losses caused by high winds and flooding. Typically, droughts and flooding are treated independently due to challenges, associated with their subjective nature and their coupled dynamics. Recently, Brunner et al. (2021) have suggested that droughts and floods should be studied in a joint framework to help learn about fast event transitions. For example, Forzieri et al. (2016) modelled multiple hazards (flooding, drought, heatwaves) over Europe using the Overall Exposure Index (OEI), which works under the assumption that hazards are mutually non-exclusive and the Change Exposure Index (CEI), which expresses the number of hazards at a given baseline period. Tabari et al. (2021) agrees with Forzieri et al. (2016), stating that few studies and combined and projected flood and drought risk owing to large uncertainties, non-linerities and complex spatial-temporal dynamics. Tabari et al. (2021) also develops a global framework to encapsulate changes in flood and drought hazards using Extreme Value Distribution (GEV), SPEI and SPI. Nevertheless, both the above studies failed to combine the hazards within one map/entity. More recently, Yang et al. (2023) have combined the drought and flood status with crop yield through a correlation approach using SPEI to identify the risk from each hazard through both linear and probabilistic analyses. The study concluded that there were uncertainties within the models used and the level of risk classification.

To conclude, floods and droughts are significant disasters with severe implications to the society. It is important to understand and quantify them, especially with the ever disheartening climate change outlook, so that measures can be designed to mitigate them. Consequently, this study considers the joint spatial patterns of the flood and drought hazards in the Mun River basin, Thailand, for present and future climate scenarios. While previous studies have looked at either flood or drought hazard separately, this study presents its novelty by providing a non-modelling approach to joint hazard analysis considering both flood and drought. The methodology, using MCDA-GIS, is applicable to other studies as it is not limited by detailed data needs as found with hydrological/hydraulic modelling. The study will be useful to identify the areas where both single flood and drought hazards are located, as well as areas at risk from combined hazards, which could be a basis for implementing measures to improve resilience and reduce the associated risk in the future against both hydro-meteorological extremes commonly occurring in basins across the globe.

Study area

The Mun River basin (as shown in Figure 1) is the largest basin in northeast Thailand and has a catchment area of about 53,800 km2. It is one of the major tributaries of the Mekong river. As part of the Korat plateau, the elevation varies between 64 m in the central region to 1,351 m in the south-west area (Khadka et al. 2022). The basin comprises four provinces, namely, Nakhon Ratchasima, Buriram, Surin and Si Sa Ket, and partially another four (Roi Et, Maha Sarakham, Khon Khen and Ubon Ratchathani). Even though the basin is one of the most drought prone regions in the country (Khadka et al. 2021), it is also an important agricultural region (Penny et al. 2021) of the country with rice cultivation occupying approximately 75% of the agricultural land and 55% of the basin's total area (Penny et al. 2021; Prabnakorn et al. 2021). The climate of the basin is primarily governed by the south-west monsoonal system, which brings heavy rainfall during mid-May to mid-October (providing about 80% of the annual rainfall). Consequently, the basin is prone to flooding especially within these months (Prabnakorn et al. 2019). However, the winter monsoonal system (December to February) is relatively dry, with the summer months extending from March to May. As the basin regularly experiences considerable problems caused by floods in the wet season and droughts in the dry season (Prabnakorn 2020; Prabnakorn et al. 2021), it makes an excellent case study.
Figure 1

The study area of the Mun River basin, located in northeast Thailand, hydrological and provincial boundaries.

Figure 1

The study area of the Mun River basin, located in northeast Thailand, hydrological and provincial boundaries.

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Topographic and land use (LU) dataset

Data sources and GIS tools used to create the driving map can be observed in Table 1. Land use classes were identified according to the land use maps supplied by the Land Development Department (LDD) of Thailand and were the ones identified by Penny et al. (2021) paddy fields, field crops, perennials and orchards, other agriculture, forest, water bodies, marsh and swamp, urban and miscellaneous. From land use data using the ‘Distance tool’, drainage density could also be calculated. Soil data were classified according to the Harmonized World Soil Database, a global soil database framed within a GIS, which contains up-to-date information on world soil resources, classification range from combinations of clay, loam and sand. A slope map was created using the surface toolbox within ArcMap 10.6.1 (ESRI) and an elevation map provided by ALOS-JAXA.

Table 1

Details of the data type and sources used within flood hazard analysis

Data typeObservation periodSpatial resolutionOrganization sourceGIS tools used
Land use of northeast Thailand 2016 100 m Land Development Department (LDD) Reclass – Reclassify 
Drainage density (distance from river)  100 m  Data Management Tool – mosaic to new raster distance – Euclidean Distance 
Soil data – lithology type 2008 100 m Mekong river Commission (MRC) Reclass – Reclassify 
Topographic map – elevation and slope 2019 30 m ALOS – JAXA Reclass – Reclassify 
The Surface Tool – Slope 
Data typeObservation periodSpatial resolutionOrganization sourceGIS tools used
Land use of northeast Thailand 2016 100 m Land Development Department (LDD) Reclass – Reclassify 
Drainage density (distance from river)  100 m  Data Management Tool – mosaic to new raster distance – Euclidean Distance 
Soil data – lithology type 2008 100 m Mekong river Commission (MRC) Reclass – Reclassify 
Topographic map – elevation and slope 2019 30 m ALOS – JAXA Reclass – Reclassify 
The Surface Tool – Slope 

Observed climatic data

The study utilizes the gridded rainfall and temperature dataset prepared by Khadka et al. (2022). The observed rainfall data from 43 stations (acquired from Thai Meteorological Department) were spatially interpolated to 0.25-degree grids using the inverse distance weighting (IDW) method. The maximum and minimum temperature datasets from the Climate Prediction Center (CPC) Global land surface air temperature analysis (Fan & van den Dool 2008) were used after comparison with other data products to ensure accuracy and consistency.

Spatially, both maximum and minimum temperatures show very small variations within the study basin (<1.0 °C); however, considerable spatial variation in the rainfall is observed as it gradually increases from the west towards the east (Figure 2). The Nakhon Ratchasima province in the west receives the least rainfall (about 800 mm/year), while the Si Sa Ket province in the east receives the most rainfall (about 1,600 mm/year).
Figure 2

Observed annual (a) maximum temperature, (b) minimum temperature, and (c) rainfall in the study area for 1981–2010 period.

Figure 2

Observed annual (a) maximum temperature, (b) minimum temperature, and (c) rainfall in the study area for 1981–2010 period.

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Future climate projection dataset

Near-future (2021–2050) climate, projected by Khadka et al. (2022), was used in this study. The climate projection (rainfall and temperatures) is based on eight climate models participating in HighResMIPs (Haarsma et al. 2016) of CMIP6 (Table 2). The study has assessed the changes in the climate for the near-future (2021–2050) with respect to the baseline period of 1981–2010. The future projections are available for shared socio-economic pathway (SSP) 5–8.5, which represents the high emission scenario (O'Neill et al. 2017). Bias correction for raw temperature and rainfall data from climate model is carried out using the quantile mapping method (Ines & Hansen 2006).

Table 2

Details of climate models used for the near-future climate in the Mun River basin (Khadka et al. 2022)

S.N.Model designationModelling groupAtmospheric resolution (lat × lon)Number of vertical levelsEnsemble member
1. CNRM-CM6-1 Centre National de Recherches Meteorologiques (CNRM)/ Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique 1.4° × 1.4° 91 r1i1p1f2 
2. CNRM-CM6-1-HR Centre National de Recherches Meteorologiques (CNRM)/Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique 0.5° × 0.5° 91 r1i1p1f2 
3. EC-Earth3P EC-EARTH Consortium 0.7° × 0.7° 91 r1i1p2f1 
4. EC-Earth3P-HR EC-EARTH Consortium 0.35° × 0.35° 91 r1i1p2f1 
5. HadGEM3-GC31-HH UK Met Office Hadley Centre (MOHC) 0.23° × 0.35° 85 r1i1p1f1 
6. HadGEM3-GC31-HM UK Met Office Hadley Centre (MOHC) 0.23° × 0.35° 85 r1i1p1f1 
7. HadGEM3-GC31-MM UK Met Office Hadley Centre (MOHC) 0.55° × 0.83° 85 r1i1p1f1 
8. HadGEM3-GC31-LL UK Met Office Hadley Centre (MOHC) 1.25° × 1.875° 85 r1i1p1f1 
S.N.Model designationModelling groupAtmospheric resolution (lat × lon)Number of vertical levelsEnsemble member
1. CNRM-CM6-1 Centre National de Recherches Meteorologiques (CNRM)/ Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique 1.4° × 1.4° 91 r1i1p1f2 
2. CNRM-CM6-1-HR Centre National de Recherches Meteorologiques (CNRM)/Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique 0.5° × 0.5° 91 r1i1p1f2 
3. EC-Earth3P EC-EARTH Consortium 0.7° × 0.7° 91 r1i1p2f1 
4. EC-Earth3P-HR EC-EARTH Consortium 0.35° × 0.35° 91 r1i1p2f1 
5. HadGEM3-GC31-HH UK Met Office Hadley Centre (MOHC) 0.23° × 0.35° 85 r1i1p1f1 
6. HadGEM3-GC31-HM UK Met Office Hadley Centre (MOHC) 0.23° × 0.35° 85 r1i1p1f1 
7. HadGEM3-GC31-MM UK Met Office Hadley Centre (MOHC) 0.55° × 0.83° 85 r1i1p1f1 
8. HadGEM3-GC31-LL UK Met Office Hadley Centre (MOHC) 1.25° × 1.875° 85 r1i1p1f1 

Results from Khadka et al. (2022) suggest that the maximum and minimum temperature in the study basin will increase by 1.45 °C (0.8–1.9 °C) and 1.54 °C (1.1–1.9 °C) in the near-future compared to the baseline. While there will be no significant changes (0.5%, between −5 and +10%) in the annual rainfall, temporal variations will increase in the future with projected increase by 6–11% and decrease in the summer by 2–8%. Similarly, the ensemble of the selected climate models indicated that the extreme rainfall events, computed as 10-year return values of 1-day maximum and 5-day consecutive maximum rainfall, will increase by 23% (9–40%) in the near-future period. The results are suggestive of significant impacts of climate change on the rainfall extremes.

Methodology

Figure 3 presents the overall methodology used in the study. SPEI is used to characterize droughts in terms of duration, severity and frequency. Multi-variate analysis is then carried out to quantify the drought hazard. Similarly, six physiographic and climatic factors have been utilized to produce the flood hazard maps. Joint flood and drought hazard map is prepared by combining the individual hazard maps in ArcMap 10.6.1 (ESRI). Climate change projections (including the extreme rainfall) are taken from the study by Khadka et al. (2022). The detailed methodology is explained in the following sub-sections.
Figure 3

Overall methodology for preparing the joint hazard maps for observed and climate change scenarios.

Figure 3

Overall methodology for preparing the joint hazard maps for observed and climate change scenarios.

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Current and future flood hazard

The first phase of the methodology was a literature review of papers (2004–2021) covering articles using MCDA-GIS for flood hazard and risk (Figure 4 and Supplementary material, Table S1). This methodology was chosen as it is frequently used within the literature (Tanavud et al. 2004; Duan et al. 2009; Ozturk & Batuk 2011; Paquette & Lowry 2012; Elsheikh et al. 2015; Rahmati et al. 2016; Samanta et al. 2016; Alves et al. 2018; Ghosh & Kar 2018; Tang et al. 2018; Liu et al. 2019; Nigusse & Adhanom 2019; Shadmehri Toosi et al. 2019, 2020; Vavatsikos et al. 2019; Ajjur & Mogheir 2020; Kittipongvises et al. 2020; Kongmuang et al. 2020; Feizizadeh et al. 2021) and provides the ability to weight and combine multiple inputs to create an integrated analysis. Using the driving factors that appeared repeatedly within the literature, the top six were selected: slope, elevation, distance from water bodies (drainage density), land use, rainfall and lithology (Figure 4). Other factors that appeared in the literature included distance from urban area, urban drainage network, population and distance from coastal zone; these factors were disregarded from the study as the catchment is primarily agricultural and land locked. Others such as groundwater depth, Topographical Wetness index (TWI), Potential maximum Retention (PMR) and SPI could not be used due to the limited data pool available to the study.
Figure 4

Factors used within the MCDA-GIS literature for flood-related risk or hazard analysis. The first six driving factors used were slope, elevation, distance from water bodies (drainage density), land use, rainfall, and lithology.

Figure 4

Factors used within the MCDA-GIS literature for flood-related risk or hazard analysis. The first six driving factors used were slope, elevation, distance from water bodies (drainage density), land use, rainfall, and lithology.

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The weighting for driving factors can be decided via questioning from a series of experts or stakeholders (Alves et al. 2021); however, for this case study, the six chosen driving factors were considered equal weights (Januadi & Nabila 2020).

The ArcMap spatial analysis weighted sum tool was used to overlay several raster layers of factors presented in Figure 3. By multiplying each by a given weight and summing them together so the sum of all weights equalled 100%, a single flood hazard map was created. Each raster map within the analysis was reassigned a reclassified to identify the preference for the class relative to the criterion in the overlay analysis (Table 3). Topographic maps were reclassified from 1 to 5 according to the Jenks natural break method (Vojinovic et al. 2016; Shadmehri Toosi et al. 2019, 2020; Kongmuang et al. 2020). Rainfall extremes are the main driver of flooding events. In this study, 10-year return values of annual maximum consecutive 5-day rainfall (RX5day) are used as a climatic factor for assessing future flood hazards. It is one of the extreme rainfall indices recommended by ‘The Expert Team on Climate Change Detection and Indices’ (ETCCDI) (Peterson 2005). As shown in Figure 5, the extreme rainfall represented by RX5day is projected to increase under climate change scenarios. CNRM-CM6-1 projects a least increase in RX5day (6%), while the highest increases are projected by HadGEM3-GC31-HH (51%) and HadGEM3-GC31-LL (34%) in the near-future. Rainfall maps were created using Empirical Bayesian kriging (EBK). A study by Ali et al. (2021) assessed the optimal spatial distribution and rainfall characterization of 27 interpolation techniques from geo-statistical and deterministic categories and concluded that EBK was a robust and straightforward interpolation technique that requires minimal interactive modelling. Subsequently, EBK was used as the interpolation technique for the rainfall maps.
Table 3

Classification of flood hazard levels based on normalized values for topographic factors – reclassified via the Jenks natural break method

Thematic layerClassified valueReclassified valueLevel of hazard
Elevation 64–140 Very high hazard 
140–160 High hazard 
160–186 Medium 
186–241 Low hazard 
241–1,356 Very low hazard 
Slope 0–0.0755 Very low hazard 
0.0755–0.2266 Low hazard 
0.2266–0.4533 Medium 
0.4533–1.0577 High hazard 
1.0577–19.264 Very high hazard 
Drainage density (distance from river) 0–2,105 Very high hazard 
2,105–4,975 High hazard 
4,975–8,803 Medium 
8,803–14,352 Low hazard 
14,352–48,800 Very low hazard 
Land use Forest/Miscellaneous Very low hazard 
Perennial and orchard Low hazard 
Field crops Medium 
Paddy field High hazard 
Water bodies, Marshland, urban Very high hazard 
Soil types Sand Very low hazard 
Sandy loam Low hazard 
Sandy loam/clay loam Medium 
Loam Medium 
Clay loam/loam High hazard 
Clay loam High hazard 
Clay Very high hazard 
Thematic layerClassified valueReclassified valueLevel of hazard
Elevation 64–140 Very high hazard 
140–160 High hazard 
160–186 Medium 
186–241 Low hazard 
241–1,356 Very low hazard 
Slope 0–0.0755 Very low hazard 
0.0755–0.2266 Low hazard 
0.2266–0.4533 Medium 
0.4533–1.0577 High hazard 
1.0577–19.264 Very high hazard 
Drainage density (distance from river) 0–2,105 Very high hazard 
2,105–4,975 High hazard 
4,975–8,803 Medium 
8,803–14,352 Low hazard 
14,352–48,800 Very low hazard 
Land use Forest/Miscellaneous Very low hazard 
Perennial and orchard Low hazard 
Field crops Medium 
Paddy field High hazard 
Water bodies, Marshland, urban Very high hazard 
Soil types Sand Very low hazard 
Sandy loam Low hazard 
Sandy loam/clay loam Medium 
Loam Medium 
Clay loam/loam High hazard 
Clay loam High hazard 
Clay Very high hazard 
Figure 5

The 10-year return values (RP) of annual maximum consecutive 5-day rainfall (RX5day) for the baseline period and for the near-future period using eight climate models and the ensemble average.

Figure 5

The 10-year return values (RP) of annual maximum consecutive 5-day rainfall (RX5day) for the baseline period and for the near-future period using eight climate models and the ensemble average.

Close modal

Current and future drought hazard

To characterize the drought events in the basin, SPEI (Vicente-Serrano et al. 2010) has been used. It is a multi-scalar meteorological drought index, which considers climatic water balance [i.e., difference between precipitation (P) and potential evapotranspiration (PET)]. Since it considers both temperature and precipitation, it is suitable for climate change analysis. PET is estimated using the Hargreaves–Samani (Hargreaves & Samani 1982) method. Although the SPEI can be calculated at different timescales, a 12-month timescale is more relevant for climate change studies (Ahmadalipour et al. 2017; Lee et al. 2019) so that short-term rainfall variabilities can be avoided. SPEI is calculated by fitting the data in GEV distribution (Stagge et al. 2015; Khadka et al. 2021).

Droughts are characterized in terms of duration, severity and frequency. In order to avoid the minor drought events, a SPEI threshold method suggested by Khadka et al. (2021) has been used. This method uses two criteria to define droughts: (a) a certain length of consecutive period has SPEI less than zero, and (b) the maximum intensity during that period is ≤− 0.5. The method will omit minor events while preserving the duration and severity characteristics. Univariate analysis of drought duration and severity is widely practiced (Liu et al. 2011; Chen et al. 2013; Sun et al. 2019), but since duration and severity are highly correlated (Masud et al. 2015; Khadka et al. 2021), it is often prudent to consider them together. In this study, a multi-variate approach using the copula function (Sklar 1959) is used to determine the drought hazard. The joint return period of drought events with severity (S) exceeding ‘s’ and duration (D) exceeding ‘d’ is given by the following equation (Shiau 2006):
formula
(1)
The denominator represents the joint occurrence probability P (D > d and S > s), with chosen thresholds of duration (d) and severity (s) taken as the median values of univariate frequency analysis; F(s) and F(d) are the cumulative probability of the drought event exceeding severity of ‘s’ and duration of ‘d’; E(L) is the average or expected drought interval. Following Nelson 2006, the empirical copula function [C(s,d)] of two random variables, x and y, is derived as:
formula
(2)
where xi and yj are the order statistics of the samples of size n and m is the number of observations satisfying xxi and yyj, with 1 i, jn. The joint probability of the 10-year joint return drought event is considered to define the drought hazard.

Normalization and categorization

Computed factors for the flood hazard and the joint probability for the drought hazard are normalized between 0 and 1 before the hazards are classified into five equal-spaced categories (Table 4),
formula
(3)
where Xi is the factor/joint probability for grid i, and Xmin and Xmax are the minimum and maximum values among all grids, respectively. Normalization for the climate change scenario is carried out with respect to the baseline period.
Table 4

Classification of flood/drought hazard levels based on normalized values

Normalized valueClassLevel of hazard
<0.2 Very low hazard 
0.2–0.4 Low hazard 
0.41–0.6 Medium hazard 
0.61–0.8 High hazard 
>0.8 Very high hazard 
Normalized valueClassLevel of hazard
<0.2 Very low hazard 
0.2–0.4 Low hazard 
0.41–0.6 Medium hazard 
0.61–0.8 High hazard 
>0.8 Very high hazard 

Combined flood and drought hazards

The hazard score was calculated by combining the previous normalized flood and drought hazard maps using the ArcMap 10.6.1 mosaic to the new raster tool. As illustrated in Figure 6, each square represents a different level of hazard whether this be a single hazard (flood or drought) or a combined hazard (flood and drought), where the drought hazard was on the y-axis and the flood hazard was on the x-axis. The hazard level was determined based on these categories and given a colour code. Combined hazard maps were prepared for the baseline period, and for the future period, using the eight CMIP6 models (Table 2). A multi-model ensemble defined as the average was also created.
Figure 6

Combined hazard matrix for flood and drought.

Figure 6

Combined hazard matrix for flood and drought.

Close modal

Current and future flood hazard

There is a limited spatial variation in the locations of the flood hazard within the basin between the baseline period and future hazard (Figure 7) with CNRM-CM6-1, EC-Earth3P and HadGEM3-GC31-MM having the greatest resemblance to the multi-model ensemble. Overall, there is a low/very low flood hazard seen in the west (Nakhon Ratchasima) of the catchment and in the very south locations associated with increased elevations and degree of the slope level. Main areas of ‘very high’ to high flood hazards are seen in the east (Si Sa Ket) and north-eastern areas (Roi Et and Maha Sarakham), locations where elevations are the lowest, the slope is the steepest, and the distance from the river tributaries is smaller. It is well documented that an increase in the elevation reduces flooding propensity (Khalequzzaman 1994), whereas the slope influences the direction of and amount of surface runoff or subsurface drainage, having a dominant effect on the contribution of rainfall to stream flow. Consequently, steeper slopes are more susceptible to surface runoff, while flat terrains are susceptible to water logging (Ouma & Tateishi 2014).
Figure 7

Flood hazard map of the Mun River basin for the baseline period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble. Hazards are classified into five classes: very low (1), low (2), medium (3), high (4), and very high (5).

Figure 7

Flood hazard map of the Mun River basin for the baseline period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble. Hazards are classified into five classes: very low (1), low (2), medium (3), high (4), and very high (5).

Close modal
For the near-future period, the majority of climate models suggest the increased ‘very high’ flood hazard area compared to the observed period with 11% classified as ‘high’ flood hazard (Figures 7 and 8). In particular, climate models HadGEM3-GC31-LL (19%) and HadGEM3-GC31-HH (17%) predicted the greatest increase in the ‘very high’ flood hazard area, whereas HadGEM3-GC31-HM (9%) predicted the least and less than the observed period. Areas of ‘high’ and ‘medium’ hazards have also increased, but this is less extreme varying from 22 to 24% and 26 to 28%, respectively, for the near-future period compared to the 22 and 27% for the observed period (Figure 8). Flood hazard increases are observed in the east of the basin especially in Rio Et and Si Sa Ket, correlating to areas where increasing rainfall was observed and along Mun tributaries within Nakhon Ratchasima and Buri Ram. This concurs with findings from Khadka et al. (2021) that the magnitude of extreme rainfall will increase in the future within all models except HadGEM3-GC31-HM. In addition, areas of very low to low flood hazard have decreased in all future scenarios compared to the observed period (18%), especially within HadGEM3-GC31-LL (11%) and HadGEM3-GC31-HH (12%). Locations for this decrease in the flood hazard are observed in the south catchments Buri Ram and Surin (Figures 7 and 8).
Figure 8

Percentage area (%) under different hazard levels for the observed period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble.

Figure 8

Percentage area (%) under different hazard levels for the observed period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble.

Close modal

While the higher resolution version of CNRM-CM6-1-HR shows a reduced flood hazard (Figure 8) compared to the multi-model ensemble, both models agreed that the flood hazard will increase in the basin. This corresponds to where the projected increase in the rainfall is 10%. The results from the two models from EC-Earth Consortium are dissimilar, with Earth3P-HR finding an increase in the ‘very high’ flood hazard area in Nakhon Ratchasima compared to EC-Earth3P. Nevertheless, percentage-wise (Figure 8) the two models have similar results, although the high-resolution version of the model does find a slight increase in the % area covered by ‘very high’, ‘medium’ and ‘low’ flood hazards (Figure 8).

Current and future drought hazard

Drought hazard maps of the study area are created for the baseline period (1981–2010) as well as for the near-future period (2021–2050) using the multi-variate approach are shown in (Figure 9). While the results on drought hazard only are presented here, the detailed analyses of the observed droughts are discussed in a study by Khadka et al. (2021).
Figure 9

Drought hazard map of the Mun River basin for the baseline period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble. Hazards are classified into five classes: very low (1), low (2), medium (3), high (4), and very high (5).

Figure 9

Drought hazard map of the Mun River basin for the baseline period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble. Hazards are classified into five classes: very low (1), low (2), medium (3), high (4), and very high (5).

Close modal

There is a significant variation in the drought hazard level within the basin during the baseline period [Figure 9(a)]. Nakhon Ratchasima (western part) has the higher area under the ‘very high’ hazard category, while Nakhon Ratchasima, Si Sa Ket (eastern part) and Roi Et (north-eastern part) have a significant area under the ‘high’ drought hazard category. Buriram, Surin and Maha Sarakham provinces have a relatively ‘low’ drought hazard in the basin. Overall, it can be observed that the drought hazard in the basin is irrespective of the annual rainfall received by the area. For example, Si Sa Ket receives the highest annual rainfall, but a significant area is under ‘high’ and ‘very high’ drought hazard levels. Drought characteristics and, hence, drought hazards are more affected by the temporal variability of the climatic parameters.

For the near-future period, most of the climate models (except CNRM-CM6-1-HR and HadGEM3-GC31-LL) have consensus that the ‘high’ and ‘very high’ hazards area will significantly increase and mostly concentrate on the western part of the basin. The multi-model ensemble also indicates the dire scenario where hazard levels have increased significantly throughout the basin. Table 5 shows that the area under high and ‘very high’ drought hazard levels will increase from 27 and 4% during the baseline period to 43 and 37% during the near-future period (multi-model ensemble average), respectively. All of the climate models (except CNRM-CM6-1-HR) suggest that the area with ‘low’ and ‘very low’ drought hazards will decrease in the near-future. High-resolution version of climate models from Met Office Hadley Centre (MOHC) and EC-Earth Consortium projected a significant increase in the area with a ‘very high’ drought hazard level, while the model from CNRM projected least increase. Spatially, the high-resolution version of the model from CNRM shows a reduced hazard in the eastern part of the basin (where the projected increase in the annual rainfall is 10%); both versions agree that the drought hazard will increase in the western part. The results from two models from EC-Earth Consortium are similar, even though the low-resolution version shows a low hazard in the south-east part. Except the low-resolution version of the model from the MOHC, three models project a considerable increase in the drought hazard in the basin.

Table 5

Projected area of the Mun River basin (in %) under different drought hazard levels during observed and near-future period

Hazard levelVery lowLowMediumHighVery high
Area under different hazard levels (%) Observed period 26 36 27 
Near-future period (2021–2050) CNRM-CM6-1-HR 23 15 17 11 34 
CNRM-CM6-1 17 17 57 
EC-Earth3P-HR 11 18 70 
EC-Earth3P 12 18 55 
HadGEM3-GC31-HH 20 68 
HadGEM3-GC31-HM 17 79 
HadGEM3-GC31-LL 35 41 22 
HadGEM3-GC31-MM 14 32 30 21 
Ensemble average 18 43 37 
Hazard levelVery lowLowMediumHighVery high
Area under different hazard levels (%) Observed period 26 36 27 
Near-future period (2021–2050) CNRM-CM6-1-HR 23 15 17 11 34 
CNRM-CM6-1 17 17 57 
EC-Earth3P-HR 11 18 70 
EC-Earth3P 12 18 55 
HadGEM3-GC31-HH 20 68 
HadGEM3-GC31-HM 17 79 
HadGEM3-GC31-LL 35 41 22 
HadGEM3-GC31-MM 14 32 30 21 
Ensemble average 18 43 37 

Under a climate change scenario, future droughts are projected to be longer (increase of 22%) and severe (increase of 63%), while the joint occurrence probability of the drought event, which exceeds the 10-year return period threshold of duration and severity, is also projected to increase by 37% (Khadka et al. 2022). These are mainly driven by an increase in evapotranspiration (by approximately 5%) and higher temporal variability of the rainfall (increase by approximately 35%). Consequently, areas under ‘high’ and very high drought hazards are projected to increase in the basin. The results concur with the findings by Khadka et al. (2022), which shows that the increase in the magnitude of the extreme events will be much higher than the increase in the average climate.

Current and future combined hazards

Floods and droughts are the two major natural hazards that create significant impacts on people lives and their well-being; it is already well established that the Mun River basin suffers from both at different periods (Prabnakorn 2020). Single hazard areas from the observed period to the near-future decrease dramatically and increase depending on the climate model (Figure 10), decreasing from 40% for the observed to 4% for the multi-model ensemble. Furthermore, areas classified as a low combined hazard have also decreased from 30% of the catchment to 20% of the catchment for the observed and ensemble mean, respectively. However, more harrowing findings are that areas of the combined hazard of medium and above have increased significantly, especially for ‘medium’ and ‘high’ rising from 20 and 9% to 51 and 20% for the observed period and multi-model ensemble, respectively. Overall, this means that over 75% (76%) of that catchment will be covered by combined hazards from ‘medium’ to ‘very high’ hazard levels compared to the 30% in the observed period. This is a percentage increase in the area of 155%.
Figure 10

Combined flood and drought hazard maps of the Mun River basin for the baseline period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble. Hazard maps also display locations of the critical infrastructure identified as cities, communications, utilities, and industrial and institutional lands by the Land Development Department of Thailand for the years 2016–2017.

Figure 10

Combined flood and drought hazard maps of the Mun River basin for the baseline period (1981–2010) and near-future period (2021–2050) using eight climate models and their ensemble. Hazard maps also display locations of the critical infrastructure identified as cities, communications, utilities, and industrial and institutional lands by the Land Development Department of Thailand for the years 2016–2017.

Close modal
Results found that in the near-future period, ‘very high’ combined hazards will be located in Nakhon Ratchasima and in the north provinces of Rio Et, Khon Kean and Maha Sarakham. The greatest ‘very high’ and ‘high’ combined hazard is seen in HadGEM3-GC31-HH (13% + 20%) and the two models from EC-Earth Consortium: Earth3P-HR (10 and 21%) and Earth3P (8 and 18%) (Figures 10 and 11). Generally, the combined hazards will be worst in the west (Nakhon Ratchasima), and this is the area where drought hazards increase (Figure 9); however, HadGem3-GC31-HM and HadGem3-GC31-MM exclude this reasoning finding hazard reduced in the west compared to the observed period. The models HadGem3-GC31-HM and CNRM-CM6-1-HR display the least increase in the combined hazard area and actually found a decrease in the ‘very high’ combined hazard (1.2 and 1.3% areas, respectively) compared to that observed at 1.4% (Figure 11). Areas of ‘very high’ combined hazards are in locations that follow the river route or are in close proximity to the river, where flood hazards were observed (Figures 1 and 10). Compared to the baseline period, the near-future multi-model ensemble finds an increase in the overall hazard across the catchment of at least one level (Figure 11); this is most prominent in the west – Nakhon Ratchasima and Buri Ram, and north-eastern provinces – Khon Kean, Roi Et and Maha Sarakham (Figure 10). On the other hand, Si Sa Ket finds a similar combined hazard to the baseline period.
Figure 11

Percentage combined hazard cover for flood and drought hazard maps of the Mun River basin for the observed period (1981–2010) and near-future period (2021–2050) using eight climate models and the multi-ensemble mean.

Figure 11

Percentage combined hazard cover for flood and drought hazard maps of the Mun River basin for the observed period (1981–2010) and near-future period (2021–2050) using eight climate models and the multi-ensemble mean.

Close modal

Although we present how agriculture faces multiple challenges in terms of floods and droughts, we also want to point challenges for the critical infrastructure (CI) (Kumar et al. 2021a). The IPCC states that climate change unequivocally impacts various aspects of the built environment: transport, energy, water/wastewater and communications (Hawchar et al. 2020). Therefore, we argue that it is also essential to gain an understanding of CI vulnerability to climate-related threats (current and future), in order to develop effective strategies to enhance the resilience of CI (Hawchar et al. 2020; Schipper 2020). Floods and droughts can cause both direct (direct damage to physical infrastructure) and indirect effects (hindering supply chains and raw material production) (Kumar et al. 2021a). The current CI within the Mun River basin for the baseline period is situated in ‘high’ multi-hazardous areas, for example, Nakhon Ratchasima, Surin and Si Sa Ket (Figure 10). It is likely in the future with land use changes, CI development (cities, communications, utilities, and industrial and institutional lands) will increase across the catchment (Penny et al. 2021). Steps towards mitigating hydro-meteorological hazards could include nature-based solutions (NBS) (Majidi et al. 2019; Kumar et al. 2021b; Vojinovic et al. 2021). A study by Penny et al. (2023) provides an extension of this research, which looks into NBS for the Mun River basin. Nevertheless, caution must be taken as though adaptation to climate change is necessary, if adaptation strategies fail, this can worsen situations, leading to maladaptation (Schipper 2020).

We recommend that a future study may also consider (i) using additional indices, (ii) different weights’ allocation for the hazard indexes, and (iii) other methods such as NDVI and LSWI, or (iv) investigating the role of antecedent rainfall and soil moisture conditions (Ávila 2015; Navarathinam et al. 2015) in flood hazards. Currently, the present work only considers physiographic variables at a certain point, focusing more on local flooding. However, we also see that the work could be improved by including variables that affect the flood hazard originating from upstream, localized flooding, i.e., flood waters that occur upstream of the catchment but, in fact, affect the flood hazard within the catchment area, for example, stream flow. For this, we recommend to consider studies that have included flow accumulation within their MCDA-GIS methodologies for assessing flood hazard and/or risk, including Nandi et al. 2016; Ozkan & Tarhan 2016; Kabenge et al. 2017; Dash & Sar 2020; Dung et al. 2022; and Baykal et al. 2023. In addition to the above, the work could be taken further by using a hydrologic-hydrodynamic model and or ‘Bluespot’ analysis (Balstrøm 2022; Penny et al. 2023).

We acknowledge that this study could be enhanced by using methodologies such as Saaty AHP and ANP (de Brito et al. 2018), and rather than using equal weights (used in this study), individual weighting or proportional scoring for MCDA-GIS variables should be used. Nevertheless, when compared to outcomes from other researchers, Khadka et al. (2021, 2022) and Prabnakorn (2020), both flood and drought are in agreement with similar studies/forecasts carried out in the Mun River basin. This consolidates/validates the methodology used here.

Formulation and implementation of necessary drought and flood adaptation plans in the basin has paramount importance in making the society more resilient. Provision of high efficiency irrigation systems by the integrative use of surface and groundwater resources could be a major step to combat the negative impacts of droughts in the near-future. Combining temporary storage solutions for excess flood waters in the open fields and increasing swamp/wetland areas could help store flood waters and recharge groundwater aquifers, in turn, combating drought.

The study has assessed individual and joint hazard levels of floods and droughts in the Mun River basin, Thailand, for baseline (1981–2010) and the near-future period (2021–2050). The flood hazard is computed using six physiographic and climatic factors. The method used for the flood hazard is useful for data-scarce regions, particularly when hydrologic and hydraulic modelling is not available and when rapid assessment is required. Similarly, the spatial drought hazard was assessed using the multi-variate approach that combines three drought characteristics (duration, severity and frequency). A joint hazard map is created by combining the information of drought and flood hazards.

Overall, there is a limited variation in the locations of flood hazard; however, between the baseline and near-future period, the majority of climate models suggest an increase in the ‘high’ and ‘very high’ flood areas. The area under ‘high’ and ‘very high’ flood hazard levels increases from 11 and 22% during the baseline period (1981–2010) to 16 and 24% during the near-future period (2021–2050), respectively. Flood hazard increases are observed in lower elevations in the east of the basin especially in Roi Et and Si Sa Ket, correlating to areas where increased extreme rainfall is predicted and along Mun River and its tributaries within Nakhon Ratchasima and Buri Ram.

Drought hazard during the baseline period shows an uneven spatial distribution within the basin. While majority of Nakhon Ratchasima and Si Sa Ket have the ‘high’ drought hazard, Buriram, Surin and Maha Sarakham have low levels, not following the average rainfall trends, which decrease from east towards west. This shows that the drought hazards are more defined by the temporal variability of the climatic parameter (particularly rainfall) than the spatial variability. Results clearly show that the drought hazard levels as well as their spatial extents will increase in the near-future period. The ensemble of climate models suggests that area under ‘high’ and ‘very high’ drought hazard levels will increase from 27 and 4% during the baseline period (1981–2010) to 43 and 37% during the near-future period (2021–2050), respectively.

When combining flood and drought hazards together, compared to the baseline period, the multi-model ensemble finds an increased area of 155% in the overall hazard across the catchment, this is most prominent in the west – Nakhon Ratchasima and Buri Ram, and north-eastern provinces – Khon Kean, Roi Et and Maha Sarakham. In addition, the CI in the basin, particularly situated in Nakhon Ratchasima, Surin and Si Sa Ket, is also at risk from hydro-meteorological hazards. Thus, adaptation for multi-hazard within multiland uses, potentially in the form of NBS, is needed across the catchment not just for the primarily agricultural regions but for areas where the CI is located. Findings present a grim outlook on future floods and droughts in the basin, and this is particularly important as the majority of the population and land area are tied to agriculture, which at present are mostly rainfed cultivation systems. Thus, the results show that the agriculture sector in the basin is highly vulnerable.

To summarize, our research delivers an integrated assessment of flood and drought hazards within the Mun River basin, providing an important step in mitigating risks in multi-hazard environments, the results of which can be employed to test scenarios of measures to reduce the impacts of drought and flooding, and the purpose of which can not only improve predictions but also help inform decision-making. We have reaffirmed that multi-criteria analysis in the GIS environment can be extremely useful in real-world water-related applications.

The presented work is conducted under the project titled ‘Integrated Management of Flood and Drought in the Mun River basin, Thailand’. The authors would like to acknowledge the funding agency the Natural Environment Research Council (NERC) under NERC COP26 Adaptation and Resilience Project Scoping Call. Some of the processed climatic data were accessed from the research project ‘Enhancing Resilience to Future Hydro-meteorological Extremes in the Mun River basin in north-eastern Thailand (ENRICH)’, funded by the National Research Council of Thailand (NRCT) and NERC.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Aalijahan
M.
,
Salahi
B.
&
Hatami
D.
2021
Investigating the relationship between changes in atmospheric greenhouse gases and discharge fluctuations in the Basin of Aras River
.
Int. J. Geogr. Geogr. Educ. (IGGE)
44
,
461
474
.
Aalijahan
M.
,
Karataş
A.
,
Lupo
A. R.
,
Efe
B.
&
Khosravichenar
A.
2023
Analyzing and modeling the spatial-temporal changes and the impact of GLOTI index on precipitation in the Marmara Region of Türkiye
.
Atmosphere (Basel)
14
.
https://doi.org/10.3390/atmos14030489
.
Ahmadalipour
A.
,
Moradkhani
H.
&
Demirel
M. C.
2017
A comparative assessment of projected meteorological and hydrological droughts: Elucidating the role of temperature
.
J. Hydrol.
553
,
785
797
.
https://doi.org/10.1016/j.jhydrol.2017.08.047
.
Ajjur
S. B.
&
Mogheir
Y. K.
2020
Flood hazard mapping using a multi-criteria decision analysis and GIS (case study Gaza Governorate, Palestine)
.
Arab. J. Geosci.
13
.
https://doi.org/10.1007/s12517-019-5024-6
.
Ali
G.
,
Sajjad
M.
,
Kanwal
S.
,
Xiao
T.
,
Khalid
S.
,
Shoaib
F.
&
Gul
H. N.
2021
Spatial–temporal characterization of rainfall in Pakistan during the past half-century (1961–2020)
.
Sci. Rep.
11
,
1
16
.
https://doi.org/10.1038/s41598-021-86412-x
.
Alves
P. B. R.
,
de Melo Filho
H.
,
Tsuyuguchi
B. B.
,
Rufino
I. A. A.
&
Feitosa
P. H. C.
2018
Mapping of flood susceptibility in Campina Grande County – PB: A spatial multicriteria approach
.
Bol. Ciencias Geod.
24
,
28
43
.
https://doi.org/10.1590/S1982-21702018000100003
.
Alves
P. B. R.
,
Djordjević
S.
&
Javadi
A. A.
2021
An integrated socio-environmental framework for mapping hazard-specific vulnerability and exposure in urban areas
.
Urban Water J.
18
(
7
),
530
543
.
doi:10.1080/1573062X.2021.1913505
.
Ávila
A. D.
2015
Representative rainfall thresholds for flash floods in the Cali river watershed, Colombia
.
Nat. Hazards Earth Syst. Sci. Discuss.
3
,
4095
4119
.
https://doi.org/10.5194/nhessd-3-4095-2015
.
Balstrøm
T.
2022
Model Bluespots to map Flood Risk [WWW Document]
.
esri
. https://learn.arcgis.com/en/projects/model-bluespots-to-map-flood-risk/?fbclid=IwAR2Nfune0XsWiUwxNdKKEWn2UP3CWvwGJt-I1S9LeyFlZ7Mf3Hug1auYDmE
(accessed 11 January 2023)
.
Baykal
T.
,
Şener
E.
&
Terzi
Ö
.
2023
Application of analytical hierarchy process for flood risk analysis: A case study in Küçük Aksu River Basin (Antalya, Turkey)
.
Iran. J. Sci. Technol. - Trans. Civ. Eng.
https://doi.org/10.1007/s40996-023-01055-4
.
Brunner
M. I.
,
Slater
L.
,
Tallaksen
L. M.
&
Clark
M.
2021
Challenges in modeling and predicting floods and droughts: A review
.
Wiley Interdiscip. Rev. Water
8
,
1
32
.
https://doi.org/10.1002/wat2.1520
.
Burke
C.
&
Stott
P.
2017
Impact of anthropogenic climate change on the east Asian summer monsoon
.
J. Clim.
30
,
5205
5220
.
https://doi.org/10.1175/JCLI-D-16-0892.1
.
Chandrasekar
K.
,
Sesha Sai
M. V. R.
,
Roy
P. S.
&
Dwevedi
R. S.
2010
Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS vegetation index product
.
Int. J. Remote Sens.
31
,
3987
4005
.
https://doi.org/10.1080/01431160802575653
.
Chen
H.
&
Sun
J.
2017
Anthropogenic warming has caused hot droughts more frequently in China
.
J. Hydrol.
544
,
306
318
.
https://doi.org/10.1016/j.jhydrol.2016.11.044
.
Chen
L.
,
Singh
V. P.
,
Guo
S.
,
Mishra
A. K.
&
Guo
J.
2013
Drought analysis using copulas
.
J. Hydrol. Eng.
18
,
797
808
.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000697
.
Crausbay
S. D.
,
Ramirez
A. R.
,
Carter
S. L.
,
Cross
M. S.
,
Hall
K. R.
,
Bathke
D. J.
,
Betancourt
J. L.
,
Colt
S.
,
Cravens
A. E.
,
Dalton
M. S.
,
Dunham
J. B.
,
Hay
L. E.
,
Hayes
M. J.
,
McEvoy
J.
,
McNutt
C. A.
,
Moritz
M. A.
,
Nislow
K. H.
,
Raheem
N.
&
Sanford
T.
2018
Defining ecological drought for the twenty-first century
.
Bull. Am. Meteorol. Soc.
98
,
2543
2550
.
https://doi.org/10.1175/BAMS-D-16-0292.1
.
CRED
.
2018
Natural Disaster 2018
.
Centre for Research on the Epidemiology of Disasters (CRED)
,
Brussels
.
Davenport
F. V.
,
Burke
M.
&
Diffenbaugh
N. S.
2021
Contribution of historical precipitation change to US flood damages
.
Proc. Natl. Acad. Sci. USA
118
,
1
7
.
https://doi.org/10.1073/pnas.2017524118
.
DDPM
.
2015
National Disaster Risk Management Plan
.
Department of Disaster Prevention and Mitigation (DDPM)
.
de Brito
M. M.
,
Evers
M.
&
Almoradie
A. D. S.
2018
Participatory flood vulnerability assessment: A multi-criteria approach
.
Hydrol. Earth Syst. Sci.
22
,
373
390
.
https://doi.org/10.5194/hess-22-373-2018
.
Duan
M.
,
Zhang
J.
,
Liu
Z.
&
Aekakkararungroj
A.
2009
Use of remote sensing and GIS for flood hazard mapping in Chiang Mai Province, northern Thailand
. In
Int. Conf. Geospatial Solut. Emerg. Manag. 50th Anniv. Chinese Acad. Surv. Mapp.
pp.
14
16
.
Dung
N. B.
,
Long
N. Q.
,
Goyal
R.
,
An
D. T.
&
Minh
D. T.
2022
The role of factors affecting flood hazard zoning using analytical hierarchy process: A review
.
Earth Syst. Environ.
6
,
697
713
.
https://doi.org/10.1007/s41748-021-00235-4
.
Elsheikh
R. F. A.
,
Ouerghi
S.
&
Elhag
A. R.
2015
Flood risk map based on GIS, and multi criteria techniques (Case study Terengganu Malaysia)
.
J. Geogr. Inf. Syst.
07
,
348
357
.
https://doi.org/10.4236/jgis.2015.74027
.
EM-DAT
.
2019
M-DAT: The OFDA/CRED International Disaster Database
.
(accessed 2019) [WWW Document]
.
Fan
Y.
&
van den Dool
H.
2008
A global monthly land surface air temperature analysis for 1948–present
.
J. Geophys. Res.
113
,
D01103
.
https://doi.org/10.1029/2007JD008470
.
Feizizadeh
B.
,
Gheshlaghi
H. A.
&
Bui
D. T.
2021
An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling
.
J. Environ. Plan. Manage.
64
,
485
516
.
https://doi.org/10.1080/09640568.2020.1775561
.
Forzieri
G.
,
Feyen
L.
,
Russo
S.
,
Vousdoukas
M.
,
Alfieri
L.
,
Outten
S.
,
Migliavacca
M.
,
Bianchi
A.
,
Rojas
R.
&
Cid
A.
2016
Multi-hazard assessment in Europe under climate change
.
Clim. Change
137
,
105
119
.
https://doi.org/10.1007/s10584-016-1661-x
.
Gemmer
M.
,
Jiang
T.
,
Su
B.
&
Kundzewicz
Z. W.
2008
Seasonal precipitation changes in the wet season and their influence on flood/drought hazards in the Yangtze River Basin, China
.
Quat. Int.
186
,
12
21
.
https://doi.org/10.1016/j.quaint.2007.10.001
.
Guan
X.
,
Zang
Y.
,
Meng
Y.
,
Liu
Y.
,
Lv
H.
&
Yan
D.
2021
Study on spatiotemporal distribution characteristics of flood and drought disaster impacts on agriculture in China
.
Int. J. Disaster Risk Reduct.
64
,
102504
.
https://doi.org/10.1016/j.ijdrr.2021.102504
.
Haarsma
R. J.
,
Roberts
M. J.
,
Vidale
P. L.
,
Senior
C. A.
,
Bellucci
A.
,
Bao
Q.
,
Chang
P.
,
Corti
S.
,
Fučkar
N. S.
,
Guemas
V.
,
von Hardenberg
J.
,
Hazeleger
W.
,
Kodama
C.
,
Koenigk
T.
,
Leung
L. R.
,
Lu
J.
,
Luo
J.-J.
,
Mao
J.
,
Mizielinski
M. S.
,
Mizuta
R.
,
Nobre
P.
,
Satoh
M.
,
Scoccimarro
E.
,
Semmler
T.
,
Small
J.
&
von Storch
J.-S.
2016
High resolution model intercomparison project (HighResMIP ∼ v1.0) for CMIP6
.
Geosci. Model Dev.
9
,
4185
4208
.
https://doi.org/10.5194/gmd-9-4185-2016
.
Hammond
M.
,
Chen
A.
,
Djordjević
S.
&
Butler
D.
2014
Making cities more resilent to flooding
. In:
Global Water Forum
“Collaborative Research on Flood Resilience in Urban Areas” (CORFU), Exeter
, pp.
1
5
.
Hargreaves
G. H.
&
Samani
Z. A.
1982
Estimating potential evapotranspiration. Technical note
.
J. Irrig. Drain. Eng.
108
,
225
230
.
Hawchar
L.
,
Naughton
O.
,
Nolan
P.
,
Stewart
M. G.
&
Ryan
P. C.
2020
A GIS-based framework for high-level climate change risk assessment of critical infrastructure
.
Clim. Risk Manage.
29
,
100235
.
https://doi.org/10.1016/j.crm.2020.100235
.
Igarashi
K.
,
Koichiro
K.
,
Tanaka
N.
&
Aranyabhaga
N.
2019
Prediction of the impact of climate change and land use change on flood discharge in the song Khwae District, Nan Province, Thailand
.
J. Clim. Change
5
,
1
8
.
https://doi.org/10.3233/JCC190001
.
Ines
A. V. M.
&
Hansen
J. W.
2006
Bias correction of daily GCM rainfall for crop simulation studies
.
Agric. For. Meteorol.
138
,
44
53
.
https://doi.org/10.1016/j.agrformet.2006.03.009
.
Jacob
X. K.
,
Bisht
D. S.
,
Chatterjee
C.
&
Raghuwanshi
N. S.
2020
Hydrodynamic modeling for flood hazard assessment in a data scarce region: A case study of Bharathapuzha River Basin
.
Environ. Model. Assess.
25
,
97
114
.
https://doi.org/10.1007/s10666-019-09664-y
.
Januadi
M. I.
&
Nabila
D. N. U.
2020
Routing the highway development by using SuperMap Least Cost Path Analysis (LCPA) and multi-criteria decision analysis (MCDA) and its assessment toward spatial planning
.
IOP Conf. Ser. Earth Environ. Sci.
561
.
https://doi.org/10.1088/1755-1315/561/1/012019
.
Kabenge
M.
,
Elaru
J.
,
Wang
H.
&
Li
F.
2017
Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index
.
Nat. Hazards
89
,
1369
1387
.
https://doi.org/10.1007/s11069-017-3024-y
.
Khadka
D.
,
Babel
M. S.
,
Shrestha
S.
,
Virdis
S. G. P.
&
Collins
M.
2021
Multivariate and multi-temporal analysis of meteorological drought in the northeast of Thailand
.
Weather Clim. Extrem.
34
,
100399
.
https://doi.org/10.1016/j.wace.2021.100399
.
Khadka
D.
,
Babel
M. S.
,
Collins
M.
,
Shrestha
S.
,
Virdis
S. G. P.
&
Chen
A. S.
2022
Projected changes in the near-future mean climate and extreme climate events in northeast Thailand
.
Int. J. Climatol.
42
,
2470
2492
.
https://doi.org/10.1002/joc.7377
.
Khalequzzaman
M. D.
1994
Recent floods in Bangladesh: Possible causes and solutions
.
Nat. Hazards
9
,
65
80
.
https://doi.org/10.1007/978-94-011-0976-5_4
.
Khan
D. M.
,
Veerbeek
W.
,
Chen
A. S.
,
Hammond
M. J.
,
Islam
F.
,
Pervin
I.
,
Djordjević
S.
&
Butler
D.
2018
Back to the future: Assessing the damage of 2004 Dhaka flood in the 2050 urban environment
.
J. Flood Risk Manage.
11
,
S43
S54
.
https://doi.org/10.1111/jfr3.12220
.
Kittipongvises
S.
,
Phetrak
A.
,
Rattanapun
P.
,
Brundiers
K.
,
Buizer
J. L.
&
Melnick
R.
2020
AHP-GIS analysis for flood hazard assessment of the communities nearby the world heritage site on Ayutthaya Island, Thailand
.
Int. J. Disaster Risk Reduct.
48
,
101612
.
https://doi.org/10.1016/j.ijdrr.2020.101612
.
Komori
D.
,
Rangsiwanichpong
P.
,
Inoue
N.
,
Ono
K.
,
Watanabe
S.
&
Kazama
S.
2018
Distributed probability of slope failure in Thailand under climate change
.
Clim. Risk Manage.
20
,
126
137
.
https://doi.org/10.1016/j.crm.2018.03.002
.
Kongmuang
C.
,
Tantanee
S.
&
Seejata
K.
2020
Urban flood hazard map using GIS of Muang Sukhothai District, Thailand Article
.
Geogr. Tech.
15
,
143
152
.
https://doi.org/10.21163/GT
.
Kumar
N.
,
Poonia
V.
,
Gupta
B. B.
&
Goyal
M. K.
2021a
A novel framework for risk assessment and resilience of critical infrastructure towards climate change
.
Technol. Forecast. Soc. Change
165
,
120532
.
https://doi.org/10.1016/j.techfore.2020.120532
.
Kumar
P.
,
Debele
S. E.
,
Sahani
J.
,
Rawat
N.
,
Marti-Cardona
B.
,
Alfieri
S. M.
,
Basu
B.
,
Basu
A. S.
,
Bowyer
P.
,
Charizopoulos
N.
,
Jaakko
J.
,
Loupis
M.
,
Menenti
M.
,
Mickovski
S. B.
,
Pfeiffer
J.
,
Pilla
F.
,
Pröll
J.
,
Pulvirenti
B.
,
Rutzinger
M.
,
Sannigrahi
S.
,
Spyrou
C.
,
Tuomenvirta
H.
,
Vojinovic
Z.
&
Zieher
T.
2021b
An overview of monitoring methods for assessing the performance of nature-based solutions against natural hazards
.
Earth-Science Rev.
217
.
https://doi.org/10.1016/j.earscirev.2021.103603
.
Kwon
M.
&
Sung
J. H.
2019
Changes in future drought with HadGEM2-AO projections
.
Water
.
https://doi.org/10.3390/w11020312
.
Lesk
C.
,
Rowhani
P.
&
Ramankutty
N.
2016
Influence of extreme weather disasters on global crop production
.
Nature
529
,
84
87
.
https://doi.org/10.1038/nature16467
.
Li
X.
,
Ju
H.
,
Sarah
G.
,
Yan
C.
,
Batchelor
W. D.
&
Liu
Q.
2017
Spatiotemporal variation of drought characteristics in the Huang-Huai-Hai Plain, China under the climate change scenario
.
J. Integr. Agric.
16
,
2308
2322
.
https://doi.org/10.1016/S2095-3119(16)61545-9
.
Liu
C. L.
,
Zhang
Q.
,
Singh
V. P.
&
Cui
Y.
2011
Copula-based evaluations of drought variations in Guangdong, South China
.
Nat. Hazards
59
,
1533
1546
.
https://doi.org/10.1007/s11069-011-9850-4
.
Liu
J.
,
Xu
Z.
,
Chen
F.
,
Chen
F.
&
Zhang
L.
2019
Flood hazard mapping and assessment on the Angkor World Heritage Site, Cambodia
.
Remote Sens.
11
,
1
19
.
https://doi.org/10.3390/rs11010098
.
Lu
G.
,
Wu
H.
,
Xiao
H.
,
He
H.
&
Wu
Z.
2016
Impact of climate change on drought in the Upstream Yangtze River Region
.
Water
.
https://doi.org/10.3390/w8120576
.
Majidi
A. N.
,
Vojinovic
Z.
,
Alves
A.
,
Weesakul
S.
,
Sanchez
A.
,
Boogaard
F.
&
Kluck
J.
2019
Planning nature-based solutions for urban flood reduction and thermal comfort enhancement
.
Sustainability
11
.
https://doi.org/10.3390/su11226361
.
Marcos-Garcia
P.
,
Lopez-Nicolas
A.
&
Pulido-Velazquez
M.
2017
Combined use of relative drought indices to analyze climate change impact on meteorological and hydrological droughts in a Mediterranean basin
.
J. Hydrol.
554
,
292
305
.
https://doi.org/10.1016/j.jhydrol.2017.09.028
.
Masud
M. B.
,
Khaliq
M. N.
&
Wheater
H. S.
2015
Analysis of meteorological droughts for the Saskatchewan River Basin using univariate and bivariate approaches
.
J. Hydrol.
522
,
452
466
.
https://doi.org/10.1016/j.jhydrol.2014.12.058
.
Ming
X.
,
Xu
W.
,
Li
Y.
,
Du
J.
,
Liu
B.
&
Shi
P.
2015
Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period
.
Stoch. Environ. Res. Risk Assess.
29
,
35
44
.
https://doi.org/10.1007/s00477-014-0935-y
.
Mishra
A. K.
&
Singh
V. P.
2010
A review of drought concepts
.
J. Hydrol.
391
,
202
216
.
https://doi.org/10.1016/j.jhydrol.2010.07.012
.
Morita
M.
2011
Quantification of increased flood risk due to global climate change for urban river management planning
.
Water Sci. Technol.
63
,
2967
2974
.
https://doi.org/10.2166/wst.2011.172
.
Nam
W.-H.
,
Hayes
M. J.
,
Svoboda
M. D.
,
Tadesse
T.
&
Wilhite
D. A.
2015
Drought hazard assessment in the context of climate change for South Korea
.
Agric. Water Manage.
160
,
106
117
.
https://doi.org/10.1016/j.agwat.2015.06.029
.
Nandi
A.
,
Mandal
A.
,
Wilson
M.
&
Smith
D.
2016
Flood hazard mapping in Jamaica using principal component analysis and logistic regression
.
Environ. Earth Sci.
75
,
1
16
.
https://doi.org/10.1007/s12665-016-5323-0
.
Navarathinam
K.
,
Gusyev
M. A.
,
Hasegawa
A.
,
Magome
J.
&
Takeuchi
K.
2015
Agricultural flood and drought risk reduction by a proposed multi-purpose dam: A case study of the Malwathoya River Basin, Sri Lanka
. In
Proc. - 21st Int. Congr. Model. Simulation, MODSIM 2015
. pp.
1600
1606
.
https://doi.org/10.13140/RG.2.1.3620.7126
.
Nawai
J.
,
Gusyev
M. A.
,
Hasegawa
A.
&
Takeuchi
K.
2015
Flood and drought assessment with dam infrastructure: A case study of the Ba River basin, Fiji
. In
Proc. - 21st Int. Congr. Model. Simulation, MODSIM 2015
. pp.
1607
1613
.
https://doi.org/10.13140/RG.2.1.2572.1361
.
Nelson
R. B.
2006
An Introduction to Copulas
.
Springer
,
New York
.
Nigusse
A. G.
&
Adhanom
O. G.
2019
Flood hazard and flood risk vulnerability mapping using Geo-Spatial and MCDA around Adigrat, Tigray Region, Northern Ethiopia
.
Momona Ethiopian J. Sci.
11
,
90
.
https://doi.org/10.4314/mejs.v11i1.6
.
O'Neill
B. C.
,
Kriegler
E.
,
Ebi
K. L.
,
Kemp-Benedict
E.
,
Riahi
K.
,
Rothman
D. S.
,
van Ruijven
B. J.
,
van Vuuren
D. P.
,
Birkmann
J.
,
Kok
K.
,
Levy
M.
&
Solecki
W.
2017
The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century
.
Glob. Environ. Change
42
,
169
180
.
https://doi.org/10.1016/j.gloenvcha.2015.01.004
.
Ouma
Y. O.
&
Tateishi
R.
2014
Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: Methodological overview and case study assessment
.
Water (Switzerland)
6
,
1515
1545
.
https://doi.org/10.3390/w6061515
.
Ozkan
S. P.
&
Tarhan
C.
2016
Detection of flood hazard in urban areas using GIS: Izmir case
.
Procedia Technol.
22
,
373
381
.
https://doi.org/10.1016/j.protcy.2016.01.026
.
Ozturk
D.
&
Batuk
F.
2011
Implementation of GIS-based multicriteria decision analysis with VB in ArcGIS
.
Int. J. Inf. Technol. Decis. Making
10
,
1023
1042
.
https://doi.org/10.1142/S0219622011004695
.
Pandey
S.
,
Bhandari
H.
&
Hardy
B.
2007
Economic Costs of Drought and Rice Farmers’ Coping Mechanisms: A Cross-Country Comparative Analysis
.
International Rice Research Institute
,
Los Baños
,
Philippines
, p.
203
.
Paquette
J.
&
Lowry
J.
2012
Flood hazard modelling and risk assessment in the Nadi River Basin, Fiji, using GIS and MCDA
.
South Pacific J. Nat. Appl. Sci.
30
,
33
.
https://doi.org/10.1071/sp12003
.
Penny
J.
,
Djordjević
S.
&
Chen
A. S.
2021
Using public participation within land use change scenarios for analysing environmental and socioeconomic drivers
.
Environ. Res. Lett.
17
.
https://doi.org/10.1088/1748-9326/ac4764
.
Penny
J.
,
Alves
P. B. R.
,
De-silva
Y.
,
Chen
A. S.
,
Djordjevic
S.
,
Shrestha
S.
&
Babel
M.
2023
Uncorrected proof analysis of potential nature-based solutions for the Mun River Basin, Thailand
.
Uncorrected Proof
,
1
19
.
https://doi.org/10.2166/wst.2023.050
.
Peterson
T. C.
2005
Climate change indices
.
WMO Bull.
54
(
2
),
83
86
.
Prabnakorn
S.
2020
Integrated Flood and Drought Mitigation Measures and Strategies
.
https://doi.org/10.1201/9781003024033
.
Prabnakorn
S.
,
Maskey
S.
,
Suryadi
F. X.
&
de Fraiture
C.
2016
Climate and Drought Trends and their relationships with rice production in the Mun river basin, Thailand
. In:
2nd World Irrigation Forum
.
6–8 November 2016, Chiang Mai, Thailand
.
Prabnakorn
S.
,
Suryadi
F. X.
,
Chongwilaikasem
J.
&
de Fraiture
C.
2019
Development of an integrated flood hazard assessment model for a complex river system: A case study of the Mun River Basin, Thailand
.
Model. Earth Syst. Environ.
5
,
1265
1281
.
https://doi.org/10.1007/s40808-019-00634-7
.
Prabnakorn
S.
,
Ruangpan
L.
,
Tangdamrongsub
N.
,
Suryadi
F. X.
&
de Fraiture
C.
2021
Improving flood and drought management in agricultural river basins: An application to the Mun River Basin in Thailand
.
Water Policy
23
,
1153
1169
.
https://doi.org/10.2166/wp.2021.011
.
Rahmati
O.
,
Zeinivand
H.
&
Besharat
M.
2016
Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis
.
Geomatics Nat. Hazards Risk
7
,
1000
1017
.
https://doi.org/10.1080/19475705.2015.1045043
.
Rudra
R. P.
,
Dickinson
W. T.
,
Ahmed
S. I.
,
Patel
P.
,
Zhou
J.
,
Gharabaghi
B.
&
Khan
A. A.
2015
Changes in rainfall extremes in Ontario
.
Int. J. Environ. Res.
9
,
1117
1126
.
https://doi.org/10.22059/ijer.2015.1000
.
Samanta
S.
,
Koloa
C.
,
Pal
D. K.
&
Palsamanta
B.
2016
Flood risk analysis in lower part of Markham river based on multi-criteria decision approach (MCDA)
.
Hydrology
3
,
1
13
.
https://doi.org/10.3390/hydrology3030029
.
Samu
R.
&
Akıntuğ
B.
2020
Pre-disaster planning and preparedness: Drought and flood forecasting and analysis in Zimbabwe
.
Water SA
46
,
448
457
.
https://doi.org/10.17159/wsa/2020.v46.i3.8655
.
Schipper
E. L. F.
2020
Maladaptation: When adaptation to climate change goes very wrong
.
One Earth
3
,
409
414
.
https://doi.org/10.1016/j.oneear.2020.09.014
.
Shadmehri Toosi
A.
,
Calbimonte
G. H.
,
Nouri
H.
&
Alaghmand
S.
2019
River basin-scale flood hazard assessment using modified multi-criteria decision analysis approach: A case study
.
J. Hydrol.
574
,
660
671
.
Shadmehri Toosi
A.
,
Doulabian
S.
,
Ghasemi Tousi
E.
,
Calbimonte
G. H.
&
Alaghmand
S.
2020
Large-scale flood hazard assessment under climate change: A case study
.
Ecol. Eng.
147
,
105765
.
https://doi.org/10.1016/j.ecoleng.2020.105765
.
Shah
S. M. H.
,
Mustaffa
Z.
,
Teo
F. Y.
,
Imam
M. A. H.
,
Yusof
K. W.
&
Al-Qadami
E. H. H.
2020
A review of the flood hazard and risk management in the South Asian Region, particularly Pakistan
.
Sci. Afr.
10
,
e00651
.
https://doi.org/10.1016/j.sciaf.2020.e00651
.
Shao
W.
&
Kam
J.
2020
Retrospective and prospective evaluations of drought and flood
.
Sci. Total Environ.
748
.
https://doi.org/10.1016/j.scitotenv.2020.141155
.
Shiau
J. T.
2006
Fitting drought duration and severity with two-dimensional copulas
.
Water Resour. Manage.
20
,
795
815
.
https://doi.org/10.1007/s11269-005-9008-9
.
Singhrattna
N.
,
Babel
M. S.
&
Perret
S. R.
2012
Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables
.
Hydrol. Sci. J.
57
,
26
41
.
https://doi.org/10.1080/02626667.2011.633916
.
Singkran
N.
2017
Flood risk management in Thailand: Shifting from a passive to a progressive paradigm
.
Int. J. Disaster Risk Reduct.
25
,
92
100
.
https://doi.org/10.1016/j.ijdrr.2017.08.003
.
Sklar
A.
1959
Fonctions de répartition à n dimensions et leurs marges
.
Publication of the Institute of Statistics, University Paris
,
Paris
, Vol.
8
, pp.
229
231
.
Stagge
J. H.
,
Tallaksen
L. M.
,
Gudmundsson
L.
,
Van Loon
A. F.
&
Stahl
K.
2015
Candidate distributions for climatological drought indices (SPI and SPEI)
.
Int. J. Climatol.
35
,
4027
4040
.
https://doi.org/10.1002/joc.4267
.
Sun
P.
,
Zhang
Q.
,
Yao
R.
&
Wen
Q.
2019
Hydrological drought regimes of the Huai River
.
Water
11
,
2390
.
https://doi.org/10.3390/w11112390
.
Tabari
H.
,
Hosseinzadehtalaei
P.
,
Thiery
W.
&
Willems
P.
2021
Amplified drought and flood risk under future socioeconomic and climatic change
.
Earth's Futur.
9
,
1
24
.
https://doi.org/10.1029/2021EF002295
.
Tanavud
C.
,
Yongchalermchai
C.
,
Bennui
A.
&
Densreeserekul
O.
2004
Assessment of flood risk in Hat Yai Municipality, Southern Thailand, using GIS
.
J. Nat. Disaster Sci.
26
,
1
14
.
https://doi.org/10.2328/jnds.26.1
.
Tang
Z.
,
Zhang
H.
,
Yi
S.
&
Xiao
Y.
2018
Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis
.
J. Hydrol.
558
,
144
158
.
https://doi.org/10.1016/j.jhydrol.2018.01.033
.
Vavatsikos
A. P.
,
Demesouka
O. E.
&
Anagnostopoulos
K. P.
2019
GIS-based suitability analysis using fuzzy PROMETHEE
.
J. Environ. Plan. Manage.
63
,
604
628
.
https://doi.org/10.1080/09640568.2019.1599830
.
Vicente-Serrano
S. M.
,
Beguería
S.
&
López-Moreno
J. I.
2010
A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index
.
J. Clim.
23
,
1696
1718
.
https://doi.org/10.1175/2009JCLI2909.1
.
Vojinovic
Z.
,
Hammond
M.
,
Golub
D.
,
Hirunsalee
S.
,
Weesakul
S.
,
Meesuk
V.
,
Medina
N.
,
Sanchez
A.
,
Kumara
S.
&
Abbott
M.
2016
Holistic approach to flood risk assessment in areas with cultural heritage: A practical application in Ayutthaya, Thailand
.
Nat. Hazards
81
,
589
616
.
https://doi.org/10.1007/s11069-015-2098-7
.
Vojinovic
Z.
,
Alves
A.
,
Gómez
J. P.
,
Weesakul
S.
,
Keerakamolchai
W.
,
Meesuk
V.
&
Sanchez
A.
2021
Effectiveness of small- and large-scale nature-based solutions for flood mitigation: The case of Ayutthaya, Thailand
.
Sci. Total Environ.
789
,
147725
.
https://doi.org/10.1016/j.scitotenv.2021.147725
.
Wichitarapongsakun
P.
,
Sarin
C.
,
Klomjek
P.
&
Chuenchooklin
S.
2016
Rainfall prediction and meteorological drought analysis in the Sakae Krang River basin of Thailand
.
Agric. Nat. Resour.
50
,
490
498
.
https://doi.org/10.1016/j.anres.2016.05.003
.
WMO
.
2018
WMO Statement on the Status of the Global Climate in 2017
.
World Meteorological Organization, 2017. WMO_1108_EN_web_000.pdf.WMO statement on the status of the global climate in 2017
.
World Meteorological Organization
.
Yang
W.
,
Zhang
L.
&
Gao
Y.
2023
Drought and flood risk assessment for rainfed agriculture based on Copula-Bayesian conditional probabilities
.
Ecol. Indic.
146
,
109812
.
https://doi.org/10.1016/j.ecolind.2022.109812
.
Zargar
A.
,
Sadiq
R.
,
Naser
B.
&
Khan
F. I.
2011
A review of drought indices
.
Environ. Rev.
19
,
333
349
.
https://doi.org/10.1139/a11-013
.
Zhang
Q.
,
Gu
X.
,
Singh
V. P.
,
Kong
D.
&
Chen
X.
2015
Spatiotemporal behavior of floods and droughts and their impacts on agriculture in China
.
Glob. Planet. Change
131
,
63
72
.
https://doi.org/10.1016/j.gloplacha.2015.05.007
.
Zhu
J. T.
2013
Impact of climate change on extreme rainfall across the United States
.
J. Hydrol. Eng.
18
,
1301
1309
.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000725
.
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