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

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.

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. 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).

Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.

Introduction

PM2.5(atmospheric particles with aerodynamic diameters less than or equal to 2.5 μm) has become one major airborne pollutant across China and attracted widespread public interest, when a severe and persistent pollution episode occurred in Beijing in December 2012. Since then, PM2.5 dominated haze episodes have been frequently reported across China (Huang et al., 2014; He et al., 2017; etc.). Due to its negative influence on human health (Lanzinger et al., 2016; Li et al., 2015b; etc.), studies concerning the characteristics (Zhang et al., 2013; Wang et al., 2016; etc.) and sources (Gu et al., 2014; Liu et al., 2014; etc.) of PM2.5 have been massively conducted in recent years. One key step for comprehensively estimating and managing PM2.5 pollution is to quantify its major influencing factors. Anthropogenic emissions have been widely accepted as the dominant driver for PM2.5 concentrations, whilst meteorological conditions also exert a strong influence on long-term PM2.5 variations (He et al., 2017). Yang et al. (2016) quantified that meteorological conditions alone contributed 1.8 out of 15 µg m−3 decade−1 PM2.5 variation in eastern China from 1985 to 2005. Gui et al., (2019) calculated that meteorological conditions accounted for 48% of PM2.5 variations in Eastern China from 1998 to 2016. Zhang et al. (2018) indicated that the relative contribution of meteorological conditions to heavy pollution episodes within BeijingTianjin-Hebei region was larger than 50% from 2013 to 2017. Chen et al., (2019) indicated that meteorological variations contributed about 20% of PM2.5 reduction in Beijing from 2013 to 2017. To further examine the strong, yet inconsistent meteorological influences on PM2.5 concentrations, many studies have been conducted and suggest that multiple meteorological factors, including temperature (Li et al., 2015a; You et al., 2017; etc.), wind(Yin et al., 2017; etc.), humidity (Liao et al., 2017; Cheng et al., 2017; etc.), precipitation (Li et al., 2015; Guo et al., 2016; etc.), radiation (Chen et al., 2017), atmospheric pressure (Zhang et al., 2015; You et al., 2017; etc.) and planetary boundary layer height (Du et al., 2013; Zheng et al., 2017; etc.), are closely related to PM2.5 concentrations.

1- Introduction

2- Methods

3- Results

4- Discussion

5- Summary and conclusions

References

Abstract

The spatiotemporal monitoring of droughts is a complex task. In the past decades, drought monitoring has been increasingly developed, while the consideration of its spatio-temporal dynamics is still a challenge. This study proposes a method to build the spatial tracks and paths of drought, which can enhance its monitoring. The steps for the drought tracks calculation are (1) identification of spatial units (areas), (2) centroids localisation, and (3) centroids linkage. The spatio-temporal analysis performed here to extract the areas and centroids builds upon the Contiguous Drought Area (CDA) analysis. The potential of the proposed methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. The method to calculate the drought tracks allows for identification of drought paths delineated by an onset and an end in space and time. Tracks, severity and duration of the drought are identified, as well as localisation (onset and end position), and rotation. The response of the drought tracking method to different combinations of parameters is also analysed. Further research is in progress to set up a model to predict the drought tracks for particular regions across the world, including India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).

Introduction

Drought is a regional phenomenon that often covers large territorial extensions (World Meteorological Organization WMO, 2006). It can occur anywhere in the world with severe consequences (impacts) in water resources and socioeconomic activities (Below et al., 2007; Sheffield and Wood, 2011; Tallaksen and Van Lanen, 2004; Wilhite, 2000). WMO stresses that to improve drought impacts mitigation, it is necessary to develop and implement national policies based on the best description and characterisation of drought (World Meteorological Organization WMO, 2006). There is no unique definition of drought. However, there is an agreement that it is an anomaly in precipitation and temperature that when extended over a region causes a lack of soil moisture, runoff and groundwater (Mishra and Singh, 2010; Van Loon, 2015). This lack of water is expressed by a drought indicator, which transforms the hydrometeorological variable into a value that is related to such a water anomaly (Mishra and Singh, 2011; Wanders et al., 2010). In drought monitoring, the drought indicators are generally used to identify the lack of water.

1- Introduction

2- Methodology

3- Results

4- Discussion

5- Conclusion

References

Abstract

Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable.
This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution.
The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.

Background

Estimating spatially distributed precipitation grids is a prerequisite to flood management and flood forecasting (Merz et al. (2014)). Hydrological models need basin-scale, spatially explicit precipitation data, among other inputs, to construct accurate flood forecasts (Todini et al. (2005)) for surface runoff management. Rain radar can produce such spatial precipitation distributions, however the challenges in calibrating and correcting for the various sources of error (detailed in Villarini et al. (2008)) create spatial and temporal uncertainty in the precipitation distribution (Cecinati et al. (2017), Krajewski and Smith, 2002). Nevertheless, the underlying motivation for research in improving precipitation maps rests in the needs of hydrological modeling and flood forecasting. Since weather radar became an accepted source of spatially distributed rainfall (Krajewski and Smith (2002), Morin et al. (2003)), extensive research has examined adjustment procedures to merge rain gauge observations with weather radar. Gauge data are accepted as reference observations (see for example Colli et al. (2013)), but represent point locations. Such point data can adjust weather radar grids through several geostatistic methods, reviewed and evaluated by Goovaerts (2000), Berndt et al. (2014) and McKee and Binns (2016). Kriging based methods have been examined by Kebaili Bargaoui and Chebbi (2009), Adhikary et al. (2017), and Ly et al. (2013). A comparison of various kriging methods where elevation was the secondary variable was done by Carrera-Hernández and Gaskin (2007). Another unique algorithm known as Conditional Merging, developed and evaluated by Sinclair and Pegram (2005), applies multiple kriging steps to achieve successful adjustment (Kim et al. (2007)) of weather radar grids.

1- Introduction

2- Data and analysis

3- Discussion and results

4- Conclusions

References

Abstract

The enhancement of the atmospheric greenhouse effect due to the increase in the atmospheric greenhouse gases is often considered as responsible for global warming (known as greenhouse hypothesis of global warming). In this context, the temperature field of global troposphere and lower stratosphere over the period 12/1978–07/2018 is explored using the recent Version 6 of the UAH MSU/AMSU global satellite temperature dataset. Our analysis did not show a consistent warming with gradual increase from low to high latitudes in both hemispheres, as it should be from the global warming theory. In addition, in the lower stratosphere the temperature cooling over both poles is lower than that over tropics and extratropics. To study further the thermal field variability we investigated the long-range correlations throughout the global lower troposphere-lower stratosphere region. The results show that the temperature field displays power-law behaviour that becomes stronger by going from the lower troposphere to the tropopause.This power-law behaviour suggests that the fluctuations in global tropospheric temperature at short intervals are positively correlated with those at longer intervals in a power-law manner. The latter, however, does not apply to global temperature in the lower stratosphere. This suggests that the investigated intrinsic properties of the lower stratospheric temperature are not related to those of the troposphere, as is expected by the global warming theory.

Introduction

Over the last decades, the rise in surface air temperature in regions of our planet has led to a debate in the scientific community about the causes and impacts of this temperature rise, especially if it comes from anthropogenic activities or is of natural origin. We must bear in mind that by definition the climate system is part of the wider global system. In particular, it is composed of five subsystems the atmosphere, the cryosphere, the hydrosphere, the biosphere and the lithosphere, which interact with each other with mostly non-linear processes in space and time (e.g., IPCC, 2014; Lovejoy and Varotsos, 2016). Therefore, a change in a parameter of a climatic subsystem (e.g., atmospheric temperature) does not predict a climate change, as all other parameters of the atmosphere but also of other subsystems (known and measurable or not) are not necessarily known and stable. Also, by definition, the climate is a complicated (displaying many degrees of freedom) and a complex (non-linear, dynamical, sensitive) system (e.g. Lucarini, 2011). Therefore, it is a truism that climate has always been changing, and it will always be changing. Which subsystem dominates the climate change depends, for instance, on the time window, namely: For t < 10yrs the atmospheric degrees of freedom are active and the other sub-systems are frozen. For 100 < t < 1000yrs the ocean dominates, and for t > 5000yrs cryosphere dominates. Several analyses have been made on the key issues of scientific understanding of contemporary global climate change (e.g. Christy et al., 2007). The focus of most of these analyses is to discuss the uncertainties associated with existing observation data and the results of numerical modelling. These emphasize the need to analyze the ability of current models to simulate real climate change. As mentioned above, real climate change results from the non-linear interactions between numerous components of the climatic system. In these should also be taken into consideration and possible contributions by external forcings e.g., cosmic factors, such as solar activity. Despite the projected exponential growth in computer power, these processes will not be adequately resolved in numerical climate models in the near future (Franzke et al., 2015). Stochastic methods for numerical climate prediction may allow for an adequate representation of uncertainties, the reduction of systematic biases and improved representation of longterm climate variability (e.g., Droegemeier, 2009).

1- Introduction

2- Data and Methodology

3- General Conditions

4- Drizzle Shaft Properties

5- Turbulence Properties

6- Summary, Discussion, and Conclusion

References

Abstract

Drizzle is ubiquitous in marine boundary layer stratocumulus clouds with much of it evaporating before reaching the surface. Ten days of observations made at the Atmospheric Radiation Measurement’s Eastern North Atlantic site during closed cellular stratocumulus cloud conditions are used to characterize drizzle below the cloud base and its impact on the boundary layer turbulence. Cloud and drizzle microphysical and macrophysical properties were retrieved by combining the data from vertically pointing Doppler cloud radar, ceilometer, and microwave radiometer. On average, the drizzle shafts were 28.14 km wide, with cloud base rain rate and modal diameter of 0.98 mm/day and 138.62 μm, respectively. The rain rate at the surface was negligible yielding an average diabatic cooling of −28.68 W/m2 in the subcloud layer. The liquid water path and turbulence within the boundary layer increased with an increase in the cloud top radiative cooling; however, none of these variables exhibited any relationship with cloud base rain rate. For a similar amount of radiative cooling at the cloud top, the average variance of vertical velocity in the subcloud layer was about 16% lower during strongly precipitating conditions as compared to lightly precipitating conditions. The reduction in the variance of vertical velocity due to drizzle evaporation was primarily confined to the upper half of the subcloud layer and was due to reduction in the strengths of the downdrafts. Collectively, our results show substantial impact of drizzle evaporation on turbulence below stratocumulus clouds, necessitating its accurate representation in the Earth system models.

Introduction

Marine boundary layer stratocumulus clouds cover vast areas of Eastern subtropical oceans and persist for long timescales (Klein & Hartmann, 1993). These clouds reflect greater amount of solar radiation back to space compared to the ocean surface, causing net cooling of the Earth’s surface. Hence, these clouds are an important component of the Earth’s radiation budget and need to be accurately represented in the Earth system models (ESMs) aimed at predicting the future climate. Stratocumulus clouds are intimately coupled to the turbulence in the boundary layer that is primarily maintained by radiative cooling at the cloud top and is modulated by entrainment, surface turbulent fluxes, wind shear, and precipitation (Wood, 2012). Precipitation is known to be ubiquitous in these clouds with much of it evaporating before reaching the surface (Wood et al., 2015; Zhou et al., 2015 etc.). Marine boundary layer stratocumulus clouds occur in distinct mesoscale organizations named closed cellular (completely overcast) and open cellular (Sharon et al., 2006; Stevens et al., 2005), with precipitation being one of the key factors causing boundary layer decoupling and the transition between these organizations (Feingold et al., 2015; Rapp, 2016; Yamaguchi et al., 2017). Several parameterizations have been proposed to represent stratocumulus clouds in ESM as they occur at spatial scales smaller than the model grid spacing. The most sophisticated parameterizations use higher‐ order moments of the joint probability distribution functions of total water mixing ratio, liquid water potential temperature, and vertical air motion (e.g., Bogenschutz et al., 2013; Golaz et al., 2002). Recently, there have been attempts to represent these clouds in the ESM in a unified way by coupling these higher‐order cloud schemes to bin microphysics schemes (Gettelman & Morrison, 2015; Morrison & Gettelman, 2008). The model simulations made in such a setup showed a stable cold moist layer near the surface caused by evaporation of the drizzle in the subsaturated subcloud layer. This stable layer inhibited replenishing of cloud water by shutting off boundary layer turbulence thereby causing spurious oscillations in cloud cover (Zheng et al., 2016, 2017). These and other studies have called for observational studies that quantify and characterize drizzle and its evaporation in these systems and assess the impact of drizzle evaporation‐ induced cooling on the boundary layer turbulence (Ahlgrimm & Forbes, 2014; Wood et al., 2016).