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Most suitable GCM/scenario at each grid point for 3-month time scale SPEI after uncertainty analysis
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Understanding the devastating nature of drought, this work has assessed the variability in the Severity-Area-Frequency (SAF) curve using Standardised Precipitation Evapotranspiration Index (SPEI) as meteorological drought indicator over Maharashtra, India. The future meteorological outputs from 19 Global Circulation Models (GCMs) of the NASA Earth...
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The hydrological cycle has been massively impacted by climate change and human activities. Thus it is of the highest concern to examine the effect of climate change on water management, especially at the regional level, to understand possible future shifts in water supply and water-related crises, and to provide support for regional water managemen...
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... This allows for the characterization of spatial variability in drought events and the determination of the extent of historical droughts by plotting a drought descriptor against the corresponding percentage of the affected area [17]. Taking this approach further, more advanced insights can be gained by establishing a relationship between a drought index, its probability of occurrence, and the corresponding areal extent percentage [18][19][20]. Thus, by quantifying the frequency of both severity and areal extent, drought Severity-Area-Frequency curves can be derived [15]. ...
... Indeed, these curves provide valuable information regarding the probabilities of the drought's areal extent at various severity levels across a geographical area, turning out to be a useful tool in drought planning and management [21,22]. According to several studies, the identification of SAF curves can be generally summarized as follows [18,[23][24][25][26][27]: (i) estimating drought severity as a function of associated different areal extents and for different threshold values; (ii) selecting the best-fitting probability distribution for the severity data and performing frequency analysis in order to relate its probability of occurrence; (iii) computing the spatial extent of drought occurrence in terms of percentage of area below the considered threshold value of drought severity; (iv) constructing SAF curves linking the values of severity, areal extent, and frequency. Another approach to probabilistically characterizing regional droughts involves analytical methods or approximations of probability distributions related to regional drought characteristics. ...
Assessing and monitoring the spatial extent of drought is of key importance to forecasting the future evolution of drought conditions and taking timely preventive and mitigation measures. A commonly used approach in regional drought analysis involves spatially interpolating meteorological variables (e.g., rainfall depth during specific time intervals, deviation from long-term average rainfall) or drought indices (e.g., Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index) computed at specific locations. While plotting a drought descriptor against the corresponding percentage of affected areas helps visualize the historical extent of a drought, this approach falls short of providing a probabilistic characterization of the severity of spatial drought conditions. That can be overcome by identifying drought Severity-Area-Frequency (SAF) curves over a region, which establishes a link between drought features with a chosen probability of recurrence (or return period) and the corresponding proportion of the area experiencing those drought conditions. While inferential analyses can be used to estimate these curves, analytical approaches offer a better understanding of the main statistical features that drive the spatial evolution of droughts. In this research, a technique is introduced to mathematically describe the Severity-Area-Frequency (SAF) curves, aiming to probabilistically understand the correlation between drought severity, measured through the SPEI index, and the proportion of the affected region. This approach enables the determination of the area’s extent where SPEI values fall below a specific threshold, thus calculating the likelihood of observing SAF curves that exceed the observed one. The methodology is tested using data from the ERA5-Land reanalysis project, specifically studying the drought occurrences on Sicily Island, Italy, from 1950 to the present. Overall, findings highlight the improvements of incorporating the spatial interdependence of the assessed drought severity variable, offering a significant enhancement compared to the traditional approach for SAF curve derivation. Moreover, they validate the suitability of reanalysis data for regional drought analysis.
... In the Indian subcontinent, it has been reported that climate extremes including droughts, floods, and heatwaves are becoming more often and severe over time Sharma & Mujumdar, 2017). Studies have also shown that the likelihood of these disasters is projected to increase in the future as well (Das et al., 2022b(Das et al., , 2023Mukherjee et al., 2018). All of the studies related to CDHEs have been conducted based on percentile thresholds. ...
Compound dry hot events (CDHEs), where hot events and droughts coexist, have received a lot of attention lately due to their catastrophic effects on the economy, environment and human health. In this study, we use two CDHE indices, the Standardized Compound Event Indicator (SCEI) and the Standardized Dry and Hot Index (SDHI), to assess changes in CDHE characteristics (severity, frequency, spatial extent) over the historical past and future CMIP6 simulations across the Indian subcontinent. To understand the role of the drought index selected on CDHE characterization two drought indices namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are employed in the calculation of the CDHE indices. Further, the role of climatic oscillations such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Equatorial Indian Ocean Monsoon Oscillation and Indian Ocean Dipole in modulating CDHE characteristics have also been explored. Results show that SPI and SPEI based CDHE indices exhibit contrasting trends in northern India, while similar trends are observed in southern India in the historical past. Also, more frequent, extended, and severe CDHEs are reported by CDHE indices calculated using SPEI than by SPI. Temperature is found to be the dominant factor contributing to increases in CDHEs in the recent past and ENSO phases significantly modulate the severity and frequency of CDHE events in India. CMIP6 simulations generally report an increase in CDHE events for a 3°C global warming scenario. Overall, our findings show that the choice of the drought index has a greater impact on CDHE characterization than the choice of the CDHE index itself. Results from this study provide useful information towards understanding the risk of CDHEs in India under global warming and urge for the development and implementation of adaptation and mitigation measures.
... Urban risks and their associated impacts intensify due to climate change correlated hazards and Urban development-induced vulnerability and exposure. The cascading effects intensify urban population vulnerability, and exposure such as the impact of drought on agriculture production in rural regions will cause food security for urban areas (S. Das et al., 2022bDas et al., , 2022aIPCC, 2022). It is estimated that the extent of urban land development in high-frequency flood land zones and drylands will increase by 658.07% ...
In 2015 the beginning of the Indian Smart Cities’ mission was one of the significant steps taken by the Indian government to make the urban environment resilient to climate change impact and extreme weather events like drought, floods, heatwaves, etc. This study computes the urban drought risk for Indian smart cities before the beginning of the Indian smart cities mission. This study considers three decadal variability (1982–2013) in meteorological, hydrological, vegetation, and soil moisture parameters for inducing water scarcity and drought conditions in urban regions. Hazards associated with urban drought-inducing parameters variability, vulnerability, and exposure of Indian smart cities were used to compute the Urban drought risk. The research investigations revealed the maximum urban drought risk for Bangalore, Chennai, and Surat cities. Northwest, West Central, and South Peninsular urban regions have higher risk among all the urban regions of India. Indian smart cities mission can be used to make Indian cities resilient to urban drought risk and increase their sustainability. The present research aligned with several national and international agreements by providing an urban drought risk rank for Indian cities, making them less vulnerable to extreme weather events and improving their resilience to climate change.
Global climate models (GCMs) play a crucial role in studying climate change and forecasting future climate events. The reliability and independence of GCM simulations are key factors in selecting models for research and decision-making. In this study, the performance of 22 GCMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) is evaluated to identify the ideal model for daily maximum temperature. The models are evaluated against daily gridded data provided by the India Meteorological Department for the period 1951–2014. This study evaluates the performance of different GCMs representing maximum temperature over time. It provides a comprehensive analysis by integrating Goodness of Fit (GoF) metrics to reveal the strengths and weaknesses of individual models across various temporal scales. The compromise rating index (CRI) approach is employed to identify the most suitable subset of GCMs for the East India region. The findings indicate that the methodology applied consistently identifies MIROC-ES2L, GISS-E2, and ACCESS-ESM1-5 as the top-performing models across different temporal durations. While climate change is expected to affect temperature variability, we recommend conducting a regional, multi-temporal, and comprehensive assessment of an ensemble of GCMs to better understand and address these changes.
This study investigates the spatio-temporal consistency of different MMDI formulations and their role in meteorological drought characterization uncertainty under historic and future climates using ERA5 reanalysis, and outputs from eight Coupled Model Intercomparison Project Phase 6 models, respectively, across different climate zones and shared socioeconomic pathways (SSP) in the Indian subcontinent. Six MMDI formulations namely the Standardized Precipitation Evaporation Index (SPEI), Reconnaissance Drought Index (RDI), and self-calibrated Palmer Drought Severity Index (scPDSI), Standardized Palmer Drought Index (SPDI), Standardized Moisture Anomaly Index (SZI) and Supply Demand Drought Index (SDDI) are used. A suite of analysis including agreement mapping, category difference analysis and uncertainty contribution analysis using global sensitivity analysis (GSA) are employed to quantify the consistency of MMDIs and uncertainty in drought characterization due to the MMDI formulation. The variation in MMDI consistency due to different reference evapotranspiration (ETo) methods is also studied. Results demonstrate strong agreement among the MMDIs under historic climate. Under climate change scenarios our findings demonstrate broad agreement among majority of the MMDIs across the study domain, but in substantial areas where MMDI not agree, especially for higher emission scenarios and arid zones. Increased uncertainty under climate change is due to SDDI and SPEI projecting dryer conditions while scPDSI projecting wetter conditions in the far future period owing to varying degrees of sensitivity of MMDIs to its constituent variables (Precipitation and ETo). Results also show that the uncertainty due to MMDIs varied considerably based on ETo methods as well. Finally, based on GSA analysis, the most significant sources of uncertainty in drought projections under climate change are attributed to MMDI-GCM interactions and MMDIs for the Penman-Monteith method. Discrepancies in drought estimates caused by the MMDI selection highlight the need for careful evaluation of drought indices before adopting for climate change impact assessment.
This chapter describes the applicability of copula-based probabilistic methodology to model the dependence structure among drought characteristics for meteorological drought in India. The Plackett, Frank, and Gumbel copulas were used across 1162 pixels across 24 Indian river basins. We then analyzed the joint dependence of drought characteristics to extract significant features such as return periods and exceedance probability, which could be beneficial for the effective management and planning of water resource systems. Our findings suggest that drought events across Central and Western part of the country are severe and longer, whereas river basins in Southern part experience droughts more frequently but with low severity. The outcomes of this research offer crucial insights into the drought hotspots with longer and severe drought events across the study area and thus provides useful insights for policymakers to formulate comprehensive national-level drought mitigation and prevention strategies to safeguard the sustainable ecosystem.
In this study, drought-affected zones were modeled using satellite data and geographical information system (GIS) techniques in Thoubal district, Manipur (north eastern part of India), from 2013 to 2021. Different drought indices, that is, standard precipitation index (SPI), temperature condition index (TCI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), NDVI deviation (DevNDVI), and vegetation health index (VHI), were used in the modeling. From the results, the study area has been classified into five classes (severely dry, moderately dry, near normal, mildly wet, and moderately wet), and mostly the study area witnesses two drought conditions, that is, moderate drought and near normal. Thus, drought-like conditions occurred in the years 2015, 2016, 2018, 2019, 2020, and 2021 while in the years 2013 and 2014, the study area experienced both moderate drought and near normal condition in different parts of the district, and in 2017, the whole district received a sufficient amount of precipitation and experienced a near normal condition. A comparison of the predicted results with the collected data was done, and it was observed that the crop yield is high when the near normal condition is predicted for the year 2017. In support of the validation of predicted results, a community opinion-based survey was also conducted by interacting with the local people at various parts of the study area. Their opinions of crop production affected either by drought or flood are found to be relevant with the predicted results of the present study.
