Highlights

Abstract

Keywords

1. Introduction

2. Materials and methods

3. Results

4. Discussion

5. Conclusion

CRediT

Declaration of competing interest

Acknowledgements

Appendix A. Supplementary data

References

Abstract

Amongst different climatic and anthropogenic drivers, water resources management can cause massive changes to the natural regime of a lake after its regulation, thereby affecting the quantity and quality of water intended for satisfying the multiple basin water requirements. Here, we investigate the multi-decadal variation of the water levels and outflows of Lake Garda, the largest in Italy, where the dam operational rules and the related basin water needs heavily altered the annual and seasonal trend of the lake regime since its regulation in 1951. Daily lake levels and outflows were first collected and digitized for the period 1888–2020, thus providing a unique database of 133 years that allowed a consistent comparison between natural and regulated periods. Statistical analyses highlighted a significant change of the inter-annual trend of the lake outflows, which passed from upward to downward after regulation, against a constant increasing trend of the water levels. Conversely, water levels showed a more remarkable shifts on a seasonal scale if compared to the outflows, revealing the influence of summer and winter basin water needs. Additional analyses on the inter-annual variation of the main downstream water demands regulated by the dam, i.e. the irrigation, hydropower and fluvial ecosystem requirements, outlined their relevance in changing the lake regime, influencing dam operational policies, which progressively limited the share of water released for ecosystem integrity. A comparison between the lake levels and outflows recorded for the pre-regulation and post-regulation periods of some selected European perialpine lakes finally highlighted different effects on the lake regime, drawing attention to the importance of defining the role of the dam operational policies within the current scenario of climate change and changing water demands.

1. Introduction

Lakes are one of the major global sources of renewable freshwater (Gleick, 1993), and they have been massively turned into regulated systems since the second half of the XX century (World Commission on Dams, 2000; Gleick, 2003). Despite the potentially high amount of water that can be allocated through lake regulation, efficient management of lake levels and outflows is an increasingly critical issue. Major water demands, like hydroelectricity production (Zarfl et al., 2015) and crop irrigation (Oki and Kanae, 2006), long-standing water interests, e.g. navigation and touristic activities, together with climate change and the need to ensure decent environmental standards for freshwater bodies (European Commission, 2000), challenge the way we manage key lake regulation systems, such as dams, to optimally benefit the human society and the natural environment (Moore et al., 2010). Dam construction, even on previously existing natural lakes, indeed determines relevant alterations both on the downstream riverine system and on the lake itself, affecting a variety of hydrological, geochemical and biological aspects (Ward et al., 1983; Nilsson et al., 2005; Magilligan and Nislow, 2005; Poikane et al., 2020).

Abstract

1 Introduction

2 Literature survey

3 Dataset details and investigation protocol

4 Proposed method

5 Experimental results and analysis

6 Conclusion and future work

References

Abstract

     Forest fire poses a serious threat to wildlife, environment, and all mankind. This threat has prompted the development of various intelligent and computer vision based systems to detect forest fire. This article proposes a novel hybrid deep learning model to detect forest fire. This model uses a combination of convolutional neural network (CNN) and recurrent neural network (RNN) for feature extraction and two fully connected layers for final classification. The final feature map obtained from the CNN has been flattened and then fed as an input to the RNN. CNN extracts various low level as well as high level features, whereas RNN extracts various dependent and sequential features. The use of both CNN and RNN for feature extraction is proposed in this article for the first time in the literature of forest fire detection. The performance of the proposed system has been evaluated on two publicly available fire datasets—Mivia lab dataset and Kaggle fire dataset. Experimental results demonstrate that the proposed model is able to achieve very high classification accuracy and outperforms the existing state-of-the-art results in this regard.

Introduction

     Forest fire can potentially result in a large number of environmental disasters, causing vast economical and ecological losses apart from jeopardising human lives. These fires pose a serious threat to people, wildlife, and the environment. To preserve the natural resources and protect the properties and human lives, forest fire detection has become very crucial. It has lead to increasing number of research explorations in this area around the world. Early and accurate detection of forest fires is essential for mitigating the effect of the fire as once a forest fire spreads to a large area it becomes very difficult to control it and might result in a catastrophe. In its early stage any forest fire is relatively small and easy to control. Fire and smoke detector sensors can easily be installed in indoor environments, it is generally not the case for forest areas. Sensors also require the fire to burn for a while before they can be detected. On the contrary, vision based devices can be used to detect fire in real life and they can be deployed in any area by using different means. These systems are also cheap and easy to install.

Conclusion and future work

     This article proposes a combination of CNN and RNN based deep learning method for forest fire detection. The evaluation of the performance of the present system has been done on two different public datasets. The proposed forest fire detection system outperforms the existing studies in this regard. The present work also overcomes various drawbacks of the existing systems. It is evident from the high classification accuracy of the present system that the present system can be employed to detect forest fires in the real world scenarios. The present work shall provide fresh insight to the researchers in carrying out the new researches on fire detection using computer vision based techniques.

     In future, the attempt will be made to carry out the research work in this problem area by employing other sophisticated deep learning techniques. The plan is also there to develop a fire detection system in non-forest areas, especially fires that occur in residential areas and industrial areas. Other possible future directions of this research work include the exploration of the possibility of employing the proposed model for low resolution satellite images covering large geographical areas.

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

     Since the UN Guiding Principles on Business and Human Rights (UNGPs) were adopted by the UN Human Rights Council in 2011, they have diffused into policy frameworks, laws, and regulations across the globe. This special issue seeks to advance the interdisciplinary field of human rights research by examining key elements of the emerging transnational regime for the regulation of business and human rights. In seven original contributions, scholars from political science, law, accounting, and philosophy critically reflect on the theoretical foundations of the UNGPs, they analyze the effectiveness of implementation mechanisms and current regulatory practice, and they advance proposals for the future development of the business and human rights regime. In this introduction, we prepare the ground for these analyses, proceeding in three steps. Firstly, we argue that the adoption of the UNGPs has triggered a norm cascade which requires a distinctive, empirically oriented research agenda focusing on the scope, governance, and effectiveness of corporate human rights accountability norms and instruments. Secondly, we explain how the articles in this special issue contribute to that research agenda by addressing these themes. Thirdly, we provide an overview of the individual contributions and point out avenues for future research.

Introduction

     Following the endorsement of the Guiding Principles on Business and Human Rights (UNGPs) by the UN Human Rights Council (UNHRC) in 2011, they have become widely regarded as “the most authoritative statement of the human rights duties or responsibilities of states and corporations adopted at the UN level” (De Schutter 2013: xvii).Footnote1 Developed in a worldwide consultation process by the Special Representative of the UN Secretary-General (SRSG), John Ruggie, the UNGPs were aimed at tackling and preventing business-related human rights abuses by closing governance gaps in the largely unrestrained global market economy (Ruggie 2013: xxiii).

     Although many corporations contribute positively to realizing human rights, e.g., through job creation, the production and distribution of goods, services, and infrastructure, business activities also impact negatively on human rights. Some of the most severe and widespread forms of corporate human rights abuse include the dislocation of indigenous communities without compensation or consultation, the impairment of people’s health and safety due to unfit working conditions and destruction of the environment, the leaking of individuals’ data to government agents, the denial of freedom of expression and of association, discrimination and sexual harassment at the workplace, and sweatshops, bonded labor, and child labor in the transnational supply chains of global brands (see Bernaz 2017: 1–2; Ruggie 2013: xv-xvi; ILO 2017). However, it is rather difficult to hold business enterprises to account for human rights abuses, for various reasons.

Conclusion and Avenues for Future Research

     Taken together, the contributions to this special issue offer a rich multi-dimensional and transdisciplinary analysis of key elements of the business and human rights regime. On the one hand, they enhance our understanding of different conceptions of corporate human rights accountability, of the role and functions, the impact but also the limitations of important actors and governance instruments of that regime. On the other hand, the articles engage with these limitations in a critical yet constructive fashion, making proposals for resolving misalignments in the governance architecture and improving the effectiveness of specific instruments. In so doing, all contributions are driven by a normative commitment to strengthen corporate human rights accountability, help prevent business-related human rights abuses, and improve access to effective remedies for victims.

     Building on the insights provided by the analyses in this special issue, as well as on the research agenda set out above, we identify three main tasks for future research in the business and human rights field. Firstly, scholarship needs to further investigate the scope and substance of changes in corporate human rights conduct in response to regulatory initiatives. This is not only to differentiate good corporate practice from rhetorical adaptation or strategic attempts to co-opt the human rights discourse but also to identify the underlying conditions for the effective socialization of business actors into norm acceptance and compliant behavior. Secondly, researchers need to keep pace with the evolution of, and changing trends in, the business and human rights regime. This implies the close monitoring and critical assessment of the construction, contestation, and transformation of human rights and accountability norms, as well as of the emergence and performance of actors and governance instruments, such as National Contact Points, National Action Plans, corporate reporting standards, and due diligence laws. Here, it is important to move beyond dominant perspectives on the Global North and extend the research focus to regulatory frameworks and corporate conduct at the starting point of transnational supply chains, especially in countries of the Global South. Finally, business and human rights scholars should continue to feed their academic expertise into the policy process and public debate at the local, national, and transnational levels. With their privileged access to the insights of multidisciplinary human rights scholarship, business and human rights scholars are in an ideal position to create awareness for the complexity of the issues at stake, help building bridges between opposing actors, and contribute to the enhancement of corporate human rights accountability.

Abstract

     Although the extractive industry has contributed to the socio-economic development of many African countries, it has also led to incidences of human rights violations in many rural communities. However, the use of an evidence-based approach to search, locate, explore and synthesize the literature systematically in order to understand the nature and pattern of human rights violations within the extractive industry remains limited. Consequently, this study employs the systematic review method to determine the nature and drivers of human rights abuses within the extractive industry in Africa. Of the 791 articles retrieved from the search of the databases, 58 articles met the inclusion criteria and were included in evidence synthesis. Based on the thematic analysis conducted on the articles that met the inclusion criteria, we find that human rights abuses tend to be associated with the violation of economic, social, and cultural rights, tensions over land ownership, the loss of livelihood, and community marginalization. We conclude the study with some policy implications and suggest avenues for future research.

Introduction

     The relationship between resource extraction and sustainable development in Africa has largely remained contested. Indeed, while the World Bank and African governments/elites are often quick to point out that resource extraction contributes to the economic development of African countries, others have emphasized the negative socio-economic impacts of the extractive industry on the poor and marginalized communities (Akabzaa and Darimani, 2001; 1demudia, 2007; Tuokuu et al., 2018). As primarily an exporter of primary commodities, African governments pursuit of economic development through resource extraction with the adoption of neoliberal economic policies like the structural adjustment programs (SAPs) in the 1980s resulted in the liberalization of codes and laws that also allowed for the influx of foreign multinational corporations (MNCs) into rural communities where resource extraction tends to take place (Campbell, 2003, 2010; Tuokuu et al., 2019). As a result, in contrast to the 1960s that was dominated by African governments’ pursuit of nationalization of economic policies, the domain of opposition to MNC activities in the 1990s invariably shifted from host governments to local communities (Harvey, 2006). These contestations over the activities of MNCs in rural communities often resulted in a widespread incidence of corporate-community conflict and associated human rights violations. For example, local protests over incidences of environmental degradation associated with the operation of Shell in the Niger Delta area of Nigeria led to the displacement of the Ogoni people and the violations of their human rights (Obi, 2009). These kinds of incidences resulted in reputational damage, increased social risks, and associated costs for MNCs.

Conclusion and future directions

     The aim of this systematic review was to synthesize available evidence in the literature on the drivers and patterns of human rights abuses in the extractive industry as well as identify the common suggestions on how to address these abuses. Our analysis showed that the pattern of abuse is often in terms of the violation of economic, social, and cultural rights and the core drivers of human rights abuses are contestations over land ownership, the associated loss of livelihood, and the marginalization of communities in decision-making. Much of the suggestions on efforts to address human rights abuses in the sector focused on CSR programs, formalization of ASM, and active community engagement. The implication is that the literature on business and human rights within the extractive industry in Africa tend to focus on document incidence of human rights abuses as opposed to analyzing how MNCs seek to address their human rights obligations. Indeed, while the extant works are critical for better understanding of the nature of the human rights problems in the sector, there is a need to move towards examining the effectiveness of the different processes and practices that MNCs are putting in place to address their human rights obligations. This is particularly important given that several MNCs are increasingly signing up to global initiatives such as the UN global compact and the voluntary principles of security and human rights as means to demonstrate their commitments to become corporate human rights defenders. Put differently, future research can usefully examine the extent to which corporate commitment to such global initiatives have necessarily yielded positive human rights dividend for local communities on the ground. There is the need for future research to probe how corporate commitments to global initiatives have yielded human rights outcomes within the extractive industry in Africa. Similarly, a crucial assumption underpins the idea that it is in the best interest of MNCs to respect human rights and rely on the notion that they require social licenses to operate. While the concept of social licenses to operate has been a subject of various critiques (see Owen and Kemp, 2013), we still do not know whether and how poor communities can enforce social license to operate or what other effective alternative corporate accountability strategies that local communities might be deploying to ensure MNCs respect their human rights.

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.