Papers by Poulomi Ganguli
Earth's future, Feb 1, 2024
arXiv (Cornell University), Dec 23, 2015
Understanding droughts in a climate context remains a major challenge. Over the United States, di... more Understanding droughts in a climate context remains a major challenge. Over the United States, different choices of observations and metrics have often produced diametrically opposite insights. This paper focuses on understanding and characterizing meteorological droughts from station measurements of precipitation. The Standardized Precipitation Index is computed and analyzed to obtain drought severity, duration and frequency. Average drought severity trends are found to be uncertain and data-dependent. Furthermore, the mean and spatial variance do not show any discernible non-stationary behavior. However, the spatial coverage of extreme meteorological droughts in the United States exhibits an increasing trend over nearly all of the last century. Furthermore, the coverage over the last half decade exceeds that of the dust bowl era. Previous literature suggests that climate extremes do not necessarily follow the trends or uncertainties exhibited by the averages. While this possibility has been suggested for droughts, this paper for the first time clearly delineates and differentiates the trends in the mean, variability and extremes of meteorological droughts in the United States, and uncovers the trends in the spatial coverage of extremes. Multiple data sets, as well as years exhibiting large, and possibly anomalous, droughts are carefully examined to characterize trends and uncertainties. Nonlinear dependence among meteorological drought attributes necessitates the use of copula-based tools from probability theory. Severity-duration-frequency curves are generated to demonstrate how these insights may be translated to design and policy.
Climate Dynamics, Jun 20, 2022
Compound warm-dry spells over land, which is expected to occur more frequently and expected to co... more Compound warm-dry spells over land, which is expected to occur more frequently and expected to cover a much larger spatial extent in a warming climate, result from the simultaneous or successive occurrence of extreme heatwaves, low precipitation, and synoptic conditions, e.g., low surface wind speeds. While changing patterns of weather and climate extremes cannot be ameliorated, effective mitigation requires an understanding of the multivariate nature of interacting drivers that influence the occurrence frequency and predictability of these extremes. However, risk assessments are often focused on univariate statistics, incorporating either extreme temperature or low precipitation; or at the most bivariate statistics considering concurrence of temperature versus precipitation, without accounting for synoptic conditions influencing their joint dependency. Based on station-based daily meteorological records from 23 urban and peri-urban locations of India, covering the 1970-2018 period, this study identifies four distinct regions that show temporal clustering of the timing of heatwaves. Further, combining joint probability distributions of interacting drivers, this analysis explored compound warm-dry potentials that result from the co-occurrence of warmer temperature, scarcer precipitation, and synoptic wind patterns. The results reveal 50-year severe heat stress solely based on the temperature at each location tends to be more frequent and is expected to become 5 to 17-year compound warm-dry events considering interdependence between attributes. Notably, considering dependence among drivers, a median 6-fold amplification (ranging from 3 to 10-fold) in compound warm-dry spell frequency is apparent relative to the expected annual number of a local (univariate) 50-year severe heatwave episode, indicating warming-induced desiccation is already underway over most of the urbanized areas of the country.
Landslides are one of the natural hazards that are most prominent in tectonically active regions,... more Landslides are one of the natural hazards that are most prominent in tectonically active regions, such as mountainous terrains of the Himalayas. The Himalayan region is vulnerable to landslides due to its fragile lithology, steep slopes, geology, rainfall patterns, and high topographical roughness. Among several factors responsible for slope instability, rainfall is one of the significant drivers that cause a maximum number of landslides. However, very few studies have explored precipitation-induced landslide susceptibility of the Himalayan region due to observational constraints. This study attempts to fill the gaps in the literature by developing a power-law relationship between the maximum rainfall intensity that potentially triggers landslides and the corresponding event duration of selected 'hotspot' locations across the Western Himalayan Region (hereafter WHR) that are highly susceptible to landslides. We identified more than 500 landslide events between 2007 and 2016 based on the landslide inventory database, which suggests more than 70% of landslide events are clustered during the southwest monsoon season. Further, we show an increase in rainfall in recent decades (2007-16) over low elevated areas of the WHR compared to the long-term climatology (1988-2006), revealing intensification of rain events, which could amplify landslide occurrence. Our observational assessment suggests around 28% of landslides events are followed by within a week of occurrence of triggering rain events. The regionalization of the maximum intensity of triggering rain events versus the corresponding event duration shows that the tail of the triggering rain events tends to follow a power-law relationship with a robust positive exponent of more than one, suggesting synchronicity between two variables. The derived insights would aid in the predictability of shallow-to-deep rain-induced landslide events and inform climate adaptations in steepslope areas.
Landslides are one of the natural hazards that are most prominent in tectonically active regions,... more Landslides are one of the natural hazards that are most prominent in tectonically active regions, such as mountainous terrains of the Himalayas. The Himalayan region is vulnerable to landslides due to its fragile lithology, steep slopes, geology, rainfall patterns, and high topographical roughness. Among several factors responsible for slope instability, rainfall is one of the significant drivers that cause a maximum number of landslides. However, very few studies have explored precipitation-induced landslide susceptibility of the Himalayan region due to observational constraints. This study attempts to fill the gaps in the literature by developing a power-law relationship between the maximum rainfall intensity that potentially triggers landslides and the corresponding event duration of selected 'hotspot' locations across the Western Himalayan Region (hereafter WHR) that are highly susceptible to landslides. We identified more than 500 landslide events between 2007 and 2016 based on the landslide inventory database, which suggests more than 70% of landslide events are clustered during the southwest monsoon season. Further, we show an increase in rainfall in recent decades (2007-16) over low elevated areas of the WHR compared to the long-term climatology (1988-2006), revealing intensification of rain events, which could amplify landslide occurrence. Our observational assessment suggests around 28% of landslides events are followed by within a week of occurrence of triggering rain events. The regionalization of the maximum intensity of triggering rain events versus the corresponding event duration shows that the tail of the triggering rain events tends to follow a power-law relationship with a robust positive exponent of more than one, suggesting synchronicity between two variables. The derived insights would aid in the predictability of shallow-to-deep rain-induced landslide events and inform climate adaptations in steepslope areas.
This study contributes to the understanding of the timing of occurrence of floods and role of the... more This study contributes to the understanding of the timing of occurrence of floods and role of the catchment wetness in flood processes (i.e., magnitude and the timing of floods) over one of the largest tropical pluvial river basin system, Mahanadi, in India. Being located in the monsoon ‘core’ region (18° - 28° N latitude and 73° - 82° E longitude) and its proximity to Bay of Bengal, Mahanadi River Basin (MRB) system is vulnerable to tropical depression-induced severe storms and extreme precipitation-induced fluvial floods during southwest monsoon. Here we examine the incidence of flooding over MRB in recent decades (2007-2016) using monsoonal maxima peak discharge (MMPD) and peak over threshold (POT) events at 12 stream gauges, spatially distributed over the basin. We find the mean dates of flood occurrences are temporally clustered in the month of August for all gauges irrespective of the type of flood series. Our results reveal, sensitiveness of runoff responses (Flood Magnitude,...
Journal of Hydrology: Regional Studies
The evaluation of possible climate change consequence on extreme rainfall has significant implica... more The evaluation of possible climate change consequence on extreme rainfall has significant implications for the design of engineering structure and socioeconomic resources development. While many studies have assessed the impact of climate change on design rainfall using global and regional climate model (RCM) predictions, to date, there has been no comprehensive comparison or evaluation of intensity-duration-frequency (IDF) statistics at regional scale, considering both stationary versus nonstationary models for the future climate. To understand how extreme precipitation may respond to future IDF curves, we used an ensemble of three RCMs participating in the North-American (NA)-CORDEX domain over eight rainfall stations across Southern Ontario, one of the most densely populated and major economic region in Canada. The IDF relationships are derived from multi-model RCM simulations and compared with the stationbased observations. We modeled precipitation extremes, at different durations using extreme value distributions considering parameters that are either stationary or nonstationary, as a linear function of time. Our results showed that extreme precipitation intensity driven by future climate forcing shows a significant increase in intensity for 10-year events in 2050s (2030-2070) relative to 1970-2010 baseline period across most of the locations. However, for longer return periods, an opposite trend is noted. Surprisingly, in term of design storms, no significant differences were found when comparing stationary and nonstationary IDF estimation methods for the future (2050s) for the larger return periods. The findings, which are specific to regional precipitation extremes, suggest no immediate reason for alarm, but the need for progressive updating of the design standards in light of global warming.
Hydrology and Earth System Sciences
In Canada, risk of flooding due to heavy rainfall has risen in recent decades; the most notable r... more In Canada, risk of flooding due to heavy rainfall has risen in recent decades; the most notable recent examples include the July 2013 storm in the Greater Toronto region and the May 2017 flood of the Toronto Islands. We investigate nonstationarity and trends in the short-duration precipitation extremes in selected urbanized locations in Southern Ontario, Canada, and evaluate the potential of nonstationary intensity-duration-frequency (IDF) curves, which form an input to civil infrastructural design. Despite apparent signals of nonstationarity in precipitation extremes in all locations, the stationary vs. nonstationary models do not exhibit any significant differences in the design storm intensity, especially for short recurrence intervals (up to 10 years). The signatures of nonstationarity in rainfall extremes do not necessarily imply the use of nonstationary IDFs for design considerations. When comparing the proposed IDFs with current design standards, for return periods (10 years or less) typical for urban drainage design, current design standards require an update of up to 7 %, whereas for longer recurrence intervals (50-100 years), ideal for critical civil infrastructural design, updates ranging between ∼ 2 and 44 % are suggested. We further emphasize that the above findings need re-evaluation in the light of climate change projections since the intensity and frequency of extreme precipitation are expected to intensify due to global warming. 1 Introduction Short-duration extreme rainfall events can have devastating consequences, damage to crops and infrastructures, leading to severe societal and economic losses in Canada (CCF, 2013; TRCA, 2013). In a warming climate, extreme precipitation events are expected to intensify due to moistening of the atmosphere (
Encyclopedia of GIS, 2016
Earth's Future
Compound flooding in coastal regions, that is, the simultaneous or successive occurrence of high ... more Compound flooding in coastal regions, that is, the simultaneous or successive occurrence of high sea levels and high river flows, is expected to increase in a warmer world. To date, however, there is no robust evidence on projected changes in compound flooding for northwestern Europe. We combine projected storm surges and river floods with probabilistic, localized relative sea-level rise (SLR) scenarios to assess the future compound flood hazard over northwestern coastal Europe in the high (RCP8.5) emission scenario. We use high-resolution, dynamically downscaled regional climate models (RCM) to drive a storm surge model and a hydrological model, and analyze the joint occurrence of high coastal water levels and associated river peaks in a multivariate copula-based approach. The RCM-forced multimodel mean reasonably represents the observed spatial pattern of the dependence strength between annual maxima surge and peak river discharge, although substantial discrepancies exist between observed and simulated dependence strength. All models overestimate the dependence strength, possibly due to limitations in model parameterizations. This bias affects compound flood hazard estimates and requires further investigation. While our results suggest decreasing compound flood hazard over the majority of sites by 2050s (2040-2069) compared to the reference period (1985-2005), an increase in projected compound flood hazard is limited to around 34% of the sites. Further, we show the substantial role of SLR, a driver of compound floods, which has frequently been neglected. Our findings highlight the need to be aware of the limitations of the current generation of Earth system models in simulating coastal compound floods.
Analyzing of trends in flood magnitude and the timing of the dates of flood occurrences of large ... more Analyzing of trends in flood magnitude and the timing of the dates of flood occurrences of large river basins across the globe are essential for understanding changes in water availability (high or low flows) and assessing the fidelity of global hydrological models. Our research is motivated by the recent six major consecutive floods in Mahanadi (years: 2001, 2003, 2006, 2008, 2011 and 2013) River Basin (MRB), which is one of the largest peninsular Rivers in India with a catchment area of 14,1589 km2. We examine the altered risk of flooding focusing on changes in the flood regimes and a shift in the timing of floods over the past four decades (1970-2016) using hydrometric observations across the MRB. A framework for identification of flood regime changes is developed using monsoonal maxima peak discharge (MMPD) and peak over threshold (POT) events at 24 stream gauges over the basin. We find a mix of (insignificant) up/downward trends in flood magnitude at Upper MRB (Region I). On th...
This paper investigates the role of El Niño-Southern Oscillation (ENSO)-based climate variability... more This paper investigates the role of El Niño-Southern Oscillation (ENSO)-based climate variability in modulating multivariate drought risks in the drought-prone region of Western Rajasthan in India. Droughts are multivariate phenomenon, often characterized by severity, duration and peak. By using multivariate ENSO index, annual drought events are partitioned into three climatic states – El Niño, La Niña and neutral phases. For multivariate probabilistic representation of drought characteristics, trivariate copulas are employed, which have the ability to preserve the dependence structure of drought variables under uncertain environment. The first copula model is developed without accounting the climate state information to obtain joint and conditional return periods of drought characteristics. Then, copula-based models are developed for each climate state to estimate the joint and conditional probabilities of drought characteristics under each ENSO state. Results of the study suggest that the inclusion of ENSO-based climate variability is helpful in knowing the associated drought risks, and useful for management of water resources in the region.
Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoe... more Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.
This article presents evaluation of trends and multivariate frequency analysis of droughts in thr... more This article presents evaluation of trends and multivariate frequency analysis of droughts in three meteorological subdivisions of western India, namely, western Rajasthan, Saurashtra and Kutch and Marathwada regions. These regions are frequently affected by droughts and there is an urgent need for effective planning and management of droughts. Meteorological drought is modelled using Standardized Precipitation Index (SPI) at a time scale of 6 months over 110 years during 1896–2005. Trends in SPI time series are investigated by using nonparametric Mann–Kendall trend test for different time windows: entire study period of 1896–2005 and then splitting into three time windows of 1896–1931, 1932–1966 and 1967–2005. For total study period, the long-term trend in SPI time series is found to be in an upward direction for the three regions. However, statistically significant downward trend is observed during 1932–1966 for western Rajasthan region, and Saurashtra and Kutch region in the month of June, indicating increase in number of drought occurrences during this period. Further, drought is a multivariate natural calamity characterizing severity, duration and peak; hence, probabilistic assessment of drought characteristics is investigated using copula method. The joint distribution of drought properties is modelled using three fully nested forms of Archimedean copulas: Clayton, Gumbel–Hougaard and Frank and one elliptical class of Student's t copula. On performing various statistical tests as well as upper tail dependence test it is found that Student's t copula better represents trivariate drought properties when compared with other copula families. The joint distribution obtained from the copula is utilized for computation of conditional probabilities and joint return periods. The importance of trivariate frequency analysis of drought is elucidated over univariate and bivariate frequency analysis. Overall, the results of the study could provide valuable insight towards regional drought risk management under changing climate.
JAWRA Journal of the American Water Resources Association, 2015
We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorologic... more We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present-day climate over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis-based multi-model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis-based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought-affected areas over the Southwest and West-North Central regions. However, global climate model-driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine-tune their parameters to address impact-relevant metrics.
Computing in Science & Engineering, 2015
Ea rly Di sc ou nt Pr ic in g No w Av ai la bl e! c o m p u t e r .o r g / c y b e r 2 0 1 5 CH R... more Ea rly Di sc ou nt Pr ic in g No w Av ai la bl e! c o m p u t e r .o r g / c y b e r 2 0 1 5 CH RIS CA LVE RT Gl ob al Di rec tor , HP En ter pr ise So lut ion s Pr od uc ts M AR CU S H. SA CH S Se nio r Vic e Pr es ide nt, Ch ief Se cu rit y Of fi ce r (N ER C) DR . SP EN CER SO OH OO CS O/ Di rec tor , Sc ien tifi c Co mp uti ng Ce da rs-Sin ai Me dic al Ce nte r
21st Century Watershed Technology: Improving Water Quality and Environment Conference Proceedings, May 27-June 1, 2012, Bari, Italy, 2012
This study presents spatio-temporal analysis of droughts in one of the most drought prone region ... more This study presents spatio-temporal analysis of droughts in one of the most drought prone region in Indiawestern Rajasthan and develops drought intensity-areafrequency curves for the region. The meteorological drought conditions are analyzed using 6-month standardized precipitation index (SPI-6) estimated at spatial resolution of 0.5°9 0.5°. Spatio-temporal analysis of SPI-6 indicates increase in frequency of droughts at the central part of the region. The non-parametric Mann-Kendall test for seasonal trend analysis showed increase in number of grids under drought during the study period.
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Papers by Poulomi Ganguli