Abstract

1. Introduction

2. Data and methods

3. Results and discussion

4. Conclusions

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

References

Abstract

     Droughts affect the environment, the economy, and society causing socioeconomic problems derived from water scarcity, and actions are therefore required to mitigate them. To efficiently manage water resources, and prevent and mitigate the consequences of water scarcity, it is important to identify and characterize drought events. A study of the evolution of drought episodes over Galicia (NW Iberian Peninsula) from 1960 to 2020 was conducted based on data from several rain gauges in the region. The standardized precipitation index (SPI) at different time scales was used to characterize drought conditions. The results revealed an increase in the number of periods under drought conditions and the intensity of drought events towards the end of the period analyzed. The events tended to become longer over time, with a clear increase in the worst drought conditions.

Introduction

     Droughts are natural weather events that can potentially have a devastating effect on millions of people worldwide. Drought events can cause damage to agriculture, forestry, and land use by increasing the water consumption of crops and reducing their production. An increase in the occurrence of these episodes could modify the soil conditions producing changes in the geographical distribution of crops or a decrease in agricultural production in the affected areas. This will have important impacts on economic activities, ecosystems, and human health (Alary et al., 2014, Lesk et al., 2016, Stanke et al., 2013).

     Despite their recurrence, droughts are among the most complex and poorly-understood extreme hydrological events (Wilhite, 2000). Drought is rarely predicted accurately and, unlike other natural weather disasters, it begins imperceptibly and goes unnoticed by the population until the damage becomes visible, at which point it is too late to mitigate the negative effects (Wilhite et al., 2007, Below et al., 2007, Bond et al., 2008, Funk, 2011, Wilhite and Pulwarty, 2018). In recent decades, there has been an increase in drought episodes in many regions of the world that has affected various aspects of society (e.g., economic output and demography…), and caused significant losses in agricultural production and health (Barnett, 2011, Briffa et al., 2009, Masih et al., 2014, McGree et al., 2016, Sobral et al., 2018, Xu et al., 2015). Therefore, it is important to understand the dynamics of this type of natural hazard, and to characterize and study these events. This will improve the human capacity to adapt to the problem and mitigate its effects.

Conclusions

     Several studies have shown that the IP experiences recurrent droughts with a predominant trend towards drier conditions in recent decades. Nevertheless, different behaviors have been found among several sub-regions, indicating that the spatial distribution of drought episodes can be very diverse. Opposing signals have been identified in many regions indicating that local studies can contribute to a better characterization of drought events throughout the IP.

     This study investigated drought evolution in Galicia over a 60 year period (1960–2020) using data from a dense network of 23 stations located throughout the area in an attempt to improve the understanding of regional drought. Galicia is located in a transition zone between different types of air masses, and therefore the conclusions of studies of climatic variations, such as drought events, carried out for other areas of the IP, such as the Mediterranean, cannot be directly extrapolated to this area. An individualized study is required for this region.

Abstract

1. Introduction

2. Methodology

3. Results

4. Discussions

5. Conclusions

Author’s contributions

Author’s statements

Data availability statement

References

Abstract

     Under the severe impacts of climate change, drought has become one of the most undesirable and complex natural phenomena with critical consequences for the environment, economy and society. The orthodox drought monitoring approaches use observations of meteorological stations, which are typically restricted in time and space. Remote sensing, conversely, provides continuous global coverage of a variety of hydro-meteorological variables that are influential in drought, and data extracted from remote sensing and modeling missions are now considered more practical and alluring for researchers. In this study, we applied a combination of field data, remotely sensed data and modeled data to detect and quantitatively analyze drought phenomena. To achieve this objective, we utilized Terrestrial Water Storage Anomalies (TWSA) estimations from GRACE mission, Normalized Difference Vegetation Index (NDVI) from MODIS mission, Surface Runoff (R) and Evapotranspiration from ERA5 reanalysis datasets and Soil Moisture (SM) from GLDAS data model to evaluate their feasibility in detecting recent droughts over Turkey. We validated the accuracy of several remote sensing-based indices (GRACE Drought Severity Index, Water Storage Deficit Index [WSDI], Soil Moisture Index, Standardized Runoff Index and NDVI) with the traditional indices (SPI and SPEI) calculated from in situ observations of precipitation.

Methodology

     Turkey is located between the latitudes 36
N and 42
N and longitudes 26
E and 45
E. The country is situated at mid-latitudes that define its climatic characteristics together with its highly variable topography. Despite its Mediterranean geographic location, where mild climatic conditions are dominant (Sensoy et al. 2008), the diversity of its topography as well as proximity to the Black Sea, Aegean Sea and the Mediterranean in the north, west and south, respectively, lead to significant variations in climatic conditions in seven different regions of the country (Figure 1). Thus, coastal zones demonstrate different characteristics from inland plateaus of central, eastern and southeastern Anatolia.

     Regardless of the climatic variability of different regions of Turkey, the general climatic pattern of Turkey follows the characteristics of a generic semi-arid climate, which defines its water resources potential that in turn shapes its agriculture and industry (Selek and Aksu 2020). The variability of hydro-climatic parameters in Turkey introduces some challenges regarding water accessibility at proper times and space all over the country.

Conclusions

     Drought studies are traditionally based on field observations of hydro-meteorological parameters. These are typically limited in time and geographic space, which lowers the spatial and temporal accuracy of the studies. In this study, we used the remotely sensed and simulated data in an integrated manner to evaluate the recent droughts over the semi-arid climatic region of Turkey, for which there is no comprehensive comparative study from the viewpoint of the effectiveness of different drought indices available for the scientific community use

     The results indicated that among different RS indices, notwithstanding its coarse resolution estimates, the GRACE-driven WSDI index had the best performance in detecting the monthly dry periods while SRI relatively outperformed WSDI in the detection of yearly droughts in Turkey from 2003 to 2016. The correlations achieved between used indices indicated that RS and model-based indicators had, in general, better agreement with SPI rather than SPEI even though the differences between the correlation values achieved for these two indices were not so high. This can be ascribed to the fact that SPI was more dependent on precipitation than SPEI and the fluctuations of precipitation were more influential on the climatic condition of Turkey, which made SPI perform more effectively

Abstract

     Remote sensing provides crucial support for building damage assessment in the wake of hurricanes. This article proposes a coupled deep learning-based model for damage assessment that leverages a large very high-resolution satellite images dataset and a flexibility of building footprint source. Convolutional Neural Networks were used to generate building footprints from pre-hurricane satellite imagery and conduct a classification of incurred damage. We emphasize the advantages of multiclass classification in comparison with traditional binary classification of damage and propose resolving dataset imbalances due to unequal damage impact distribution with a focal loss function. We also investigate differences between relying on learned features using a deep learning approach for damage classification versus a commonly used shallow machine learning classifier, Support Vector Machines, that requires manual feature engineering. The proposed model leads to an 86.3% overall accuracy of damage classification for a case event of Hurricane Michael and an 11% overall accuracy improvement from the Support Vector Machines classifier, suggesting better applicability of such an open-source deep learning-based workflow in disaster management and recovery. Furthermore, the findings can be integrated into emergency response frameworks for automated damage assessment and prioritization of relief efforts.

Introduction

     Hurricanes have been the most expensive type of disaster in the USA (CRED 2019) and historically deathliest, continuing to drive the importance of improving techniques to assess post-hurricane urban damage. The process of such damage assessment involves detection, classification, and evaluation of disaster damage to an economy and society on local, county, state, or tribal levels (FEMA 2016). Remote sensing analysis has proved to be indispensable in aiding these assessment efforts (Adams et al. 2009; Waharte and Trigoni 2010; Stow et al. 2015). In particular, the Federal Emergency Management Agency (FEMA) relies on remote sensing analysis to rapidly assess a large-scale impact and monitor areas that cannot be effectively accessed on the ground. The timeliness, accuracy, and semantic information of such remote sensing analysis results are of key interest (Stow et al. 2015).

Conclusions

     In this study, we proposed a damage assessment workflow from VHR big data imagery, xBD, consisting of two CNNs that delineate building footprints and classify hurricane-incurred damage into four categories: un-damaged, lightly damaged, severely damaged, and destroyed buildings. The coupled model allows users to utilize an existing building footprint, unlike unified models. Another key contribution of this study was addressing a class imbalance problem in the xBD dataset with a focal loss function. We examined a case study of Hurricane Michael in 2018 around the Panama City metropolitan area, where our proposed models achieved an overall accuracy of 84.6% for building footprint segmentation and 86.3% for damage classification tasks. The model successfully identified undamaged buildings with an F1-score of 95.4% and predicts three damage classes (minor, major damage, and destroyed) with 59.3%, 68.7%, and 74.2% F1-score, respectively. An output of this model presents a probability vector of each building belonging to damage classes, thus, creating an opportunity for emergency management and first responders to set a higher or lower threshold for alerting about a hurricane damage presence.

Abstract

Declaration

1. INTRODUCTION

2. STUDY REGION

3. METHODOLOGY

4. RESULTS AND DISCUSSION

5. CONCLUSION

ACKNOWLEDGMENT

REFERENCES

Abstract

     Sustainable water resource management is the immediate priority as the rapid depletion of water resources is aggravating drought conditions in many regions worldwide. One of the possible approaches to recover from such a condition is through water resource balancing. The water balance studies require knowledge of water availability for various sectors’ present and future requirements to balance the water resource. The present study uses the Palmer Drought Severity Index (PDSI) for water balancing. It evaluates the soil moisture deficit or surplus with spatial and temporal changes for the Marathwada region consisting of eight districts for 1957–2017. The analyzis shows water surplus from July to September and water deficit from November to May. The study observes that the Marathwada is drought-free during the Kharif season and water deficit in Rabi season. This study analyzes the need for crop-shift to avoid crop damage during drought. Based on the analyzis, all the urban system sectors need to augment short-term and long-term policies to overcome the water crisis for a balanced water resource. The Self-Calibrated PDSI (ScPDSI) is calculated based on PDSI, which gives more freedom in analyzing the drought period.

Introduction

     Drought is a long-term phenomenon lasting from months to years, causing significant ecological and economic damage (UNISDR, 2015; WMO, 2000). It manifests the concern of depletion in surface and groundwater resources and crop failure (NAAS, 2011). There is an expected increase in drought severity and frequency with climate change (Vasiliades et al., 2011; Wilhite et al., 2014). The future projection of available water signifies the annually increasing gap between water demand and supply (C. Sharma & Sharma, 2017). Sustainable water resources management is one of the key aspects of effective water management (Van & Marques, 2012).

     Water balance studies are becoming vital due to the increasing demand for freshwater in the domestic, industrial, and agricultural sectors (Aquastat, 2011; Arjun, 2017). Water balance is considered one of the important components while planning irrigational schemes in droughtprone areas (Arjun, 2017). It requires knowledge of water availability for current and impending conditions for various sectors to balance the water resource (Sokolov & Chapman, 1974). It is calculated based on three components, which are potential evapotranspiration (PET), monthly rainfall, water surplus or deficit (moisture departure) (Arjun, 2017; Mintz & Serafini, 1992). The net change in the supply and demand of water define7s moisture departure (Mckee et al., 1993; Vasiliades & Dalezios, 2002). Supply is precipitation and stored soil moisture (Mckee et al., 1993), whereas demand is potential evapotranspiration (PET) and runoff. PET is the amount needed to recharge the soil, and runoff keeps the rivers, lakes, and reservoirs at a normal level.

CONCLUSION

     The Marathwada region is undergoing drought for past years, resulting in crop failure and water scarcity for other sectors. The approach used to analyze the water balance of the region defines the water surplus and water deficit of a region. The analysis shows the increasing trend of drought years shortly. The PDSI analysis shows the drought months for the period 1990-2017 have increased in number than the base year, i.e. 1957-1990. The soil moisture departure from April to September is high for 1957-1990, which is also affected due to the cropping pattern. The forest type seen in the Marathwada is thorn forest, where scrubs develop in dry areas with low rainfall. Hence, this study analyses the need for crop-shift to avoid crop damage during drought. Also, strategically changing the cropping pattern as per available soil moisture will reduce water resource scarcity in domestic and industrial sectors. Based on this analysis, preparedness and policy formation for the coming year can be considered since variation in Indian precipitation levels affects the agricultural sector.

Abstract

1 Introduction

2 Data and methods

3 Characteristics of national drought awareness

4 Discussion

5 Conclusions

Appendix A

References

Abstract

     Drought awareness promotes people to be more sensitive to water shortage and more likely to support water-saving policies. Studying the spatiotemporal patterns and determinants of drought awareness is necessary for developing drought-resistant measures that can adapt to regional social responses. This paper employs the Baidu Index and sc-PDSI, in conjunction with the Principal Component Analysis, to explore the spatiotemporal patterns and influencing factors of drought awareness in China. The results indicate that the first two principal component modes can explain 75% of the total variance of drought awareness in China. The drought awareness of the people is more sensitive to summer droughts and less to winter droughts. The population and education level are the two most important influencing factors at the current stage. It is found that people are most concerned about the drought in Northeast China, while people in central and eastern China are concerned about the drought events in local and other regions. The responses of drought awareness to actual meteorological drought events can be synchronous or asynchronous for different regions. The results demonstrate the usability of big data in drought awareness research and provide crucial insights for the formulation of drought mitigation policies in China.

Introduction

     Drought is one of the most serious natural disasters, as it causes enormous economic and social losses every year due to its complex evolutionary mechanisms. Numerous studies have attempted to assess or predict the evolution and severity of droughts using hydrological models and climate data (Zhang et al., 2011,2017b; Niu et al., 2015; Ma et al., 2016; Sun et al., 2019; Valiya Veettil and Mishra 2020; Xing et al., 2020). Such studies have been able to understand the spatiotemporal dynamics of droughts and provide reasonably good predictions of droughts, thus allowing early warnings. However, tens of millions of people and livestock still suffer from the lack of water availability during drought periods, and droughts continue to cost huge economic losses. This situation may be due to the fact that drought research and relief policies are not sufficiently adapted to bring awareness to individuals on droughts, and so drought mitigation strategies cannot be effectively put into practice (Switzer and Vedlitz, 2017). Drought awareness can reflect the public concern on perception about water shortage and support for water-saving policies. A better understanding of drought awareness will promote drought monitoring and prediction and make drought relief measures to play a better social role. Thus, it is necessary to explore the determinants and effects of the public’s drought awareness.

Conclusions

     This study used the Baidu index search data to quantify drought awareness in China. The spatiotemporal patterns of drought awareness were investigated, including the distribution characteristics of the correlations between the drought awareness and meteorological drought in space or time, the effect of drought severity, and socioeconomic factors on drought awareness. The major findings from this study are as follows.

    Drought awareness in China is sensitive to summer droughts, and people generally pay less attention to drought in winter.

     People in the eastern or central regions (e.g., Fujian, Hebei, Zhejiang, and Hubei) pay attention to the droughts not only in their own regions, but also in others (especially in Northeast China). Therefore, one can start from the eastern and central regions to publicize the drought relief plans and take the northeast as a pilot to implement anti-drought or drought mitigation measures, which can quickly make the whole country be aware of and understand the drought relief work.

Abstract

Introduction

Materials and methods

Characterization and interconnections between drought and

Management responses

Governance and policy

Outlook for drought and groundwater management

Conclusions

Declaration of competing interest

Acknowledgments

References

Abstract

     Droughts have severe impacts on the economy, society, and environment. They also have impacts on groundwater and vice versa. While most analyses consider drought and groundwater as disconnected, we argue that drought and groundwater management should be conjunctively considered. This article presents some key interconnections, identifies challenges, and discusses illustrative policy responses. We highlight several advancements found in international scientific research and describe future directions for drought and groundwater management. While many technological innovations have improved our understanding of drought and groundwater’s complex nature, policy and governance advances have not kept pace.

Introduction

     Though droughts are a normal part of the hydrologic cycle, their frequency and severity are increasing. Droughts can cause severe socioeconomic, environmental, and political impacts, especially for arid/semi-arid regions that are highly vulnerable to consequences of groundwater overdraft [1, 2, 3∗, 4, 5∗]. Conversely, groundwater management affects droughts in many ways, with emergency strategies often deployed (e.g., drilling more wells) that can exacerbate groundwater depletion [6]. Aridification is likely to increase, and droughts are likely to be worsened, due to climate change [7]. Therefore, users need a greater understanding of how to appropriately manage drought and groundwater [3,8]. As the 2022 United Nations report states, groundwater has vast potential and attention must be paid to its careful management [9].

     Certainly, decision-makers, water users, and scientists have discovered new innovations for understanding and managing groundwater in drought conditions [10]. For instance, the European Groundwater Directive (2006/118/EC) has been designed to complement the more comprehensive 2000 Water Framework Directive (WFD), which was established as an integrated water management approach [11].

Conclusions

     Our review of the recent literature confirms the crucial and timely relevance of focusing on drought and groundwater management. The review highlighted the need for both a greater understanding of their interconnected impacts and a long-term, systemic perspective. The latter considers varying and complex spatial–temporal characterizations with multiple, overlapping jurisdictions. These characterizations include using remote sensing data to understand changes in storage, linking data for understanding drought, and modelling to link water, food, and policy decisions. While these technologies have helped scientists make great progress on understanding relationships between drought and groundwater management, much is left to be discovered.

     Groundwater quality and management techniques have seen advancements. Poorer groundwater quality resulting from droughts and impacts to human health has been further explored. New adaptation measures have been developed and tested, particularly in the areas of MAR and conjunctive management.

Abstract

Potential future increases in flooding due to climate change need to be taken into consideration when designing flood defences or planning new infrastructure or housing developments. Existing guidance on climate change allowances in Great Britain was based on research that developed a sensitivity-based approach to estimating the impacts of climate change on flood peaks, which was applied with catchment-based hydrological models. Here, the sensitivity-based approach is applied with a national-scale grid-based hydrological model, producing modelled flood response surfaces for every river cell on a 1 km grid. This provides a nationally consistent assessment of the sensitivity of flood peaks across Britain to climatic changes. The flood response surfaces are then combined with the most recent climate change projections, UK Climate Projections 2018 (UKCP18), to provide location-specific information on the potential range of impacts on floods across the country, for three flood return periods, three future time-slices and four emissions scenarios. An accompanying web-tool provides a convenient way to explore the large amount of data produced. Consideration is now being given to how to use the latest work to update guidance on climate change and flood peaks, including a workshop held to gather stakeholder views.

1. Introduction

One of the ways in which climate change may particularly impact on society and the natural environment is through changes in river flows, especially extreme high or low flows. Floods are a significant risk globally, and the potential for climate change to increase the magnitude and frequency of flood events is a major concern (Klijn et al., 2015). The few global studies available suggest that flood hazard will increase over about half the globe, but with significant spatial variability (Jim´enez Cisneros et al., 2014). In Britain, the general conclusion is that flooding is likely to increase in future (Bell et al., 2012, 2016; Kay et al., 2015; Collet et al., 2018b). Indeed, there is some evidence that recent flood events have already been influenced by climate change (Kay et al., 2011, 2018). Thus in Britain there is specific guidance available to both flood management authorities and local planners, which aims to help them allow for the potential impacts of climate change when managing flood risk (Reynard et al., 2017).

1- Introduction

2- Method

3- Case study area and data

4- Results

5- Conclusions

References

Abstract

Flash floods evolve rapidly in time, which poses particular challenges to emergency managers. One way to support decision-making is to complement models that estimate the flash flood hazard (e.g. discharge or return period) with tools that directly translate the hazard into the expected socio-economic impacts. This paper presents a method named ReAFFIRM that uses gridded rainfall estimates to assess in real time the flash flood hazard and translate it into the corresponding impacts. In contrast to other studies that mainly focus on individual river catchments, the approach allows for monitoring entire regions at high resolution. The method consists of the following three components: (i) an already existing hazard module that processes the rainfall into values of exceeded return period in the drainage network, (ii) a flood map module that employs the flood maps created within the EU Floods Directive to convert the return periods into the expected flooded areas and flood depths, and (iii) an impact assessment module that combines the flood depths with several layers of socio-economic exposure and vulnerability. Impacts are estimated in three quantitative categories: population in the flooded area, economic losses, and affected critical infrastructures. The performance of ReAFFIRM is shown by applying it in the region of Catalonia (NE Spain) for three significant flash flood events. The results show that the method is capable of identifying areas where the flash floods caused the highest impacts, while some locations affected by less significant impacts were missed. In the locations where the flood extent corresponded to flood observations, the assessments of the population in the flooded area and affected critical infrastructures seemed to perform reasonably well, whereas the economic losses were systematically overestimated. The effects of different sources of uncertainty have been discussed: from the estimation of the hazard to its translation into impacts, which highly depends on the quality of the employed datasets, and in particular on the quality of the rainfall inputs and the comprehensiveness of the flood maps.

Introduction

Numerous flash floods (FF hereafter) occur across the globe every year. In Europe, a yearly average of around 50 FF-related fatalities is recorded (Sene, 2013). Apart from posing a high danger to humans, economic losses in the range of several billion euros can be caused by individual events (CRED, 2016; EEA, 2010; Gaume et al., 2009). Typically, FFs occur in small to medium-sized catchments with steep slopes and prone to convective storms. This setting results in very short lag times between the causative rainfall and the discharge response in the stream channel – usually not more than a few hours (Georgakakos, 1986; Hapuarachchi et al., 2011). This extremely sudden onset is one of the main drivers for the devastating potential of FFs (EEA, 2010; Sene, 2013; Spitalar et al., 2014) since it leaves only little time to coordinate flood response measures (e.g. evacuations or installations of nonpermanent flood protection systems). Early warning systems (EWS) can help to extend the anticipation horizon of FFs; the European Environmental Agency (EEA, 2010) argues that the improvement of EWSs is the most effective measure to reduce FF impacts. One way to improve EWSs is to increase the forecast skill (e.g. by improved rainfall estimation and forecasting). Another way is to include additional information into the EWS to enhance the decision support for the emergency responders (e.g. by simulating the response to the rainfall in the stream network). The latter was the motivation of several methods that forecast the FF hazard (e.g. the return period or discharge at predefined river sections; see Alfieri et al., 2019, 2012; Corral et al., 2019; Gourley et al., 2014; Hapuarachchi et al., 2011 for reviews).

1- Introduction

2- Materials and methods

3- Results and discussion

4- Conclusions

References

Abstract

Accelerated changes in land cover cause changes in environmental dynamics and may cause land degradation. The goals of the present paper were to analyze changes in land cover and to estimate a future scenario for 2035 using an artificial neural network for the Taperoá River basin, located in northeastern Brazil. The classification of land cover was carried out for years t1 (1990), t2 (1999) and t3 (2002), with the latter being used to validate the land cover prediction to obtain an estimate for year t4 (2035). The land cover classes identified in the basin were (a) water bodies, (b) tree-shrub vegetation, (c) shrub vegetation, (d) herbaceous-shrub vegetation, and (e) herbaceous vegetation. The results of the classifications and of the land cover prediction were analyzed using the kappa coefficient, total operating characteristic (TOC), and area under the curve (AUC). The dynamic modeling of the land cover was based on a multilayer perceptron (MLP) neural network, which presented very good results, with an accuracy = 89.69% after 10,000 iterations, kappa = 0.61 and AUC = 0.67. The results of the land cover change analysis showed a decrease in the tree-shrub class and an increase in the shrub vegetation class between the years analyzed. The scenario predicted for 2035 showed an increase in the herbaceous-shrub vegetation class and a decrease in the area occupied by tree-shrub vegetation.

Introduction

Changes in land cover generated by anthropogenic actions are responsible for changing landscape characteristics and modifying the dynamics of natural processes in river basins (Mendoza et al., 2011) and are of great interest to researchers due to their impact on the local and global environment (Abuelaish and Olmedo, 2016). The main causes of land cover changes vary according to the nature and extent of the area but include deforestation, changing to pasture, agricultural intensification, and overexploitation (Bezak et al., 2015). Spatial changes in land cover over time also affect the future provision and localization of ecosystem services in the landscape and the fragmentation of green areas (Hoyer and Chang, 2014; Bai et al., 2019). Changes in land cover pose threats to landscapes, for example, by removing characteristic features of the landscape by scale enlargement or deterioration due to lack of management (Schulp et al., 2019). The risk of these problems is even higher in the semiarid region of Brazil due to the lack of studies regarding the phenological behavior of the vegetation and the recurrence of droughts and rainfall variability, together with the geology of the region and the saline soil types (Silva et al., 2018a). The characteristics of the Caatinga biome make it difficult to produce forecasting maps of the vegetation cover due to the prolonged dry season in the region, which influences the spectral response of the vegetation and, consequently, the estimates of land cover changes (Santos et al., 2017).

1. Introduction

2. Methodology

3. Results and discussion

4. Conclusions

Declaration of Competing Interest

Acknowledgements

References

Abstract

Urban flash flood poses significant hazards on urbanised area, in particular to buildings and infrastructure due to its fast occurrence and high magnitude in financial loss. Risk assessment of the flash flood identify the critically flood-prone areas and provide assistance in improving the resiliency of mitigation plans. In this study, we developed an assessment of flood susceptibility, vulnerability, the impact of socio-economic, and integrated flash flood index based on the historical data of flood events recorded in Kuala Lumpur. Data of rainfall characteristics, inundated location, and areas are extracted from the reports of flood events from the year 2005-2015. Each event is then segregated according to the place of incidence, providing point-based recurrence of flood at each identified location. Indicators of assessment include frequency and month of occurrence, rainfall characteristics (of intensity, duration, and depth), and land use categories. A total of 137 (point) locations have been identified, where each location is colour-coded based on a 5-point rating scale. The point-based flood-prone locations are validated with the watershed based of 50-ARI rainfall modelling, providing comprehensive hotspot maps. Developed interactive colour-coded flood prone maps facilitate relevant agencies for improved coordination in flash flood mitigation, response and early warning.

Introduction

Urban flooding occurs when the capacity of both natural and drainage systems could not cater to the volume of precipitation and runoff discharge within an urbanised area. High surface runoff discharge from heavy rainfall due to impervious surfaces and high building densities escalate the urban flooding (Gaitan et al., 2016; Yao et al., 2016). Although urban flooding is commonly associated with the short duration-high intensity precipitation, such flooding also has been recorded due to prolonged moderate rainfall (Coulthard et al., 2007; Abdullah and Julien, 2014a; Abdullah et al., 2018).

Flash flood is defined as the flood events resulted from high precipitation in short duration, usually less than 6 hours (Suparta et al., 2014). Localised convective storms in a small catchment within short time results in fast-rising of water level, usually with no advance or little time of warning. As such, the flood occurrence in the urbanised area due to insufficient drainage capacity from short duration-high intensity rainfall-induced the urban flash flooding (UFF). Considering the short duration of rainfall, the limb of the hydrograph rose fast to the peak flow whereby the UFF can even be visible just only after 30 minutes of rainfall. The non-alerted and unpredicted flood events caused devastating impacts to the road infrastructures, particularly at the low-lying areas (Doocy et al., 2013). Risk assessment of UFF on the intra-urban transportation network by evaluating the impact of flood depth (obtained through the 2D hydrodynamic modelling) and traffic or probability analysis provide a quantitative impact of UFF (Yin et al., 2016; Li et al., 2018).

In the context of global climate change and frequent meteorological hazards, farmers’ adaptive behavior has attracted attention of many scholars. Meanwhile, the perceived adaption efficacy is related to the farmer’s ability by which they can accurately and effectively cope with influences of climate change and meteorological hazards. Based on the survey data from interviews with cherry growers of 9 villages in Shaanxi Province of China, this study employs the Theory of Planned Behavior and path analysis to identify the factors affecting the degree of perceived adaption efficacy, and then utilizes the ordinal logistic regression model to explore the influence mechanism of cherry growers’ attributes, perception of climate change and perception of meteorological hazards at different stages of cherry growth on the weaker perceived adaption efficacy. The results show that attitude toward the behavior, subjective norm and perceived behavioral control of cherry growers all have significant positive influences on the perceived adaption efficacy. The relatively weaker perceived adaption efficacy is mainly affected by cherry growers’ age, household agricultural income, perception of local climate warming in the past 30 years and perception of frost. Finally, targeted recommendations are put forward on the basis of above conclusions.

Introduction

The Fifth Assessment Report (AR5) of IPCC points out that climate change and meteorological hazards pose a serious threat to agriculture, villages and farmers’ livelihoods [1]. In particular, the sensitivity of developing countries are more pronounced because of their relatively weaker economy and inadequate social infrastructure, where the majority of population make a living by doing agricultural work [2]. In this context, adaptation has become the only way to deal with climate change and meteorological hazards [1]. Scholars have carried out much on the issue of farmers’ adaptation to climate change, and have made rich research achievements in theoretical construction [3–6], adaptive behavior conforming local conditions [5,7–13] and other aspects. In order to have an in-depth understanding of the motivation and the orientation of farmers’ adaptive behavior, it is necessary to study not only what kind of adaptive behavior they choose in the face of climate change and meteorological hazards, but also how they evaluate their choices, that is, farmers’ perceived adaption efficacy, which will directly affect the motivation and the orientation of farmers’ adaptive behavior [14]. Nevertheless, current studies have focused more on the specific adaptive strategies of farmers to climate change and less on the perceived adaption efficacy [15]. Therefore, this study can attract more attention to farmers’ perceived adaption efficacy and help the local government deepen understanding of it so as to formulate more favorable policies to aid farmers in adapting to climate change and meteorological hazards. Farmers are particularly susceptible to climate change [16], for highly depending on natural resources [17] which are extremely vulnerable to climate change. So farmers are reckoned as the main body of the study on adapting to climate change and meteorological hazards. According to the focus of agricultural activities, farmers are divided into different categories. Some studies were carried out on farmers who grow crops [18–20], some focused on pastoralists [21,22], and others were concerned with fishermen [8].

Introduction

Urban flood modelling within the DCs

Calibration of flood models

Conclusion – the challenges now for flood modelling?

Acknowledgements

References

Abstract

The prevalence of flooding events and the associated risk in the urban areas is an increasingly important issue of global significance, although it is more critical for the developing countries (DCs), such as Nigeria, where the hazard is often poorly understood and understudied. With current predictions of worsening future scenarios, it is important to pursue integrated flood risk management approaches which incorporate flood modelling. This paper is part of a research programme which is assessing and modelling urban flood risks in the DCs and data poor areas. It focuses on the latest science and philosophy in relation to urban flood risk management in the DCs. It reviews the literature around current flood modelling techniques and provides a comprehensive table of the different approaches alongside the strengths and weaknesses of the different models. Indeed, research in the vicinity of flood modelling has been extensive, and over the years has resulted in the development of a wide variety of schema, datasets and methodologies for simulating flood hydrodynamics. However, the actual potential of these developments has not been demonstrated in the management of flood risk within the DCs. To date, a perfect model or generic technique which can capture every aspect of flood hydrodynamics in an optimal fashion within the diversity of study locations is still unrealistic. Thus, to bypass the present flood modelling challenges within the context of the DCs, extensive calibration of state-of-the-art flood models is of significance. Additionally, researchers within the DCs should be fascinated by the prospect of developing bespoke flood models based on simple mathematical formulations which are easy to parameterise using global, open source and freely- accessible datasets.

Introduction

he rate at which flooding occurs in recent times has been unprecedented, with the implication that only few coastal, rural, and urban environments still have their natural states unaltered [67,133,151]. This situation is of global significance, although it seems that the perception of flooding in the developing countries (DCs) for example Nigeria, is being nuanced by obvious limitations in research, economy and policy framework [111]. During flooding, water largely covers land areas not usually covered by water, destroying farmland and critical infrastructure, displacing human populations, disrupting economic activities, and in the worst cases, leading to epidemic and death ([135], pg.309). These incidences, especially those relating to fluvial and coastal flooding often were due to sea-level rise, ice melt, overtopping or destruction of water defences and coastal tsunamis [35,93]. However, pluvial flooding, the focus of this review, appears to be widespread in recent times especially within the urban areas where its impacts are increasingly a major source of concern for urban residents and policy makers [38,75,90,152].

Pluvial flooding is basically due to increased frequency and intensity of rainfall, although there are a number of other potential causative factors which have been identified. In Dawson et al. [39] and Adeloye and Rustum [5] pluvial flooding was problematised on the basis of land use change, geomorphology, failure of urban drainage facilities and poor urban planning. Mark et al. [95] perceived pluvial flooding with regards to the scale of its impacts which in fact seem to correlate positively with the large number of human population and development assets within the urban areas. The study demonstrates that urban areas are the hotspot of large-scale flooding impacts to the same degree that they are typically fundamental to any nations’ sustainable development. Along with the much-discussed global climate change, which triggers heavy storms in recent times, current knowledge of rapid urbanisation and demographic pressures which characterise the DCs underscores the inclusive nature of urban pluvial flooding, and the primacy of galvanizing discussions for the management of its threats within the DCs [58,156,164,168].

1. Introduction

2. Background

3. Methods

4. Results

5. Discussion

6. Conclusions

Declaration of competing interest

Acknowledgements

References

Abstract

The lower 800 km of the Missouri River has been incising for decades. However, the 2011 flood scoured more sediment than the previous 12 yr combined. The river rebounded in the following two years, re-depositing over 70% of the sediment scoured during the 2011 flood. Historic evidence suggests that past Missouri River floods may have followed similar scour-rebound patterns. This paper analyzes 16 channel surveys from 1987 to 2014, including 50,723 total cross sections to document the morphological history of the lower 800 km of the Missouri River over the last thirty years, including flood disturbance and long-term trends. The last thirty years on the Missouri River included two basic morphologic regimes: steady incision during low-to-moderate flow periods and rapid scour-rebound responses to floods. Surveys collected during the 2011 flood demonstrate that the river scoured throughout the flood (including the falling limb) and did not begin a rebound phase until after the flood. The analysis also identifies sediment sinks associated with each of these scour regimes (flood scour and incision between floods) that exceed the total volume eroded from the bed during those periods. Results suggest that floodplain deposition may induce the supply limitation that drives flood scour on this large sand-bed river. Additionally, aggregate mining removes substantially more sediment than the total river incision between floods and the reaches with maximum scour during the 1993 and 2011 floods correspond to the zones of aggregate mining

Introduction

Flood disturbance and recovery are critical components of river morphology (Schumm, 1976; Wolman and Gerson, 1978; Lewin, 1989; Knighton, 1998; Simon and Rinaldi, 2006; Phillips and Dyke, 2016) and ecology (Resh 1982; Ward and Stanford, 1983; Lake, 2000). Fluvial form and lotic communities reflect the influence of periodic, powerful events separated by extended periods of low-to-moderate flows. Rivers respond to disturbance events in a variety of ways, on multiple time scales. Additionally, river responses to disturbance-recovery cycles can be complicated by a variety of natural and anthropogenic influences driving long-term incision (e.g., Downs et al., 2013). Parsing transient disturbance-recovery impacts from long-term morphological trends can be particularly challenging on large sand-bed rivers, where noise from rapidly-migrating bedforms can drown out the signal of actual morphological change at individual cross sections. Moreover, remote sensing technologies such as LiDAR, photogrammetry, and structure from motion cannot detect morphological change under meters of turbid water. Currently, only underwater sonar can measure bed shape and change in these deep, turbid systems, which is time consuming and costly over large spatial domains.

Because of the difficulty of large scale morphological studies on large sand-bed rivers, these systems are underrepresented in morphological literature (Hudson, 2002), including the disturbance-recovery literature, despite growing suspicion that their morphologic behaviors differ from those documented in smaller rivers (Latrubesse, 2008). Repeated surveys on the Missouri River present an opportunity to investigate the relative importance of disturbance-recovery cycles and long-term incision on a large sand-bed river.