A5058292832- Climate variability and models
- Meteorological Phenomena and Simulations
- Hydrology and Drought Analysis
- Precipitation Measurement and Analysis
- Hydrology and Watershed Management Studies
- Cryospheric studies and observations
- Urban Heat Island Mitigation
- Plant Water Relations and Carbon Dynamics
- Hydrological Forecasting Using AI
- Climate Change and Health Impacts
- Land Use and Ecosystem Services
- Water resources management and optimization
- Remote Sensing in Agriculture
- Computational Physics and Python Applications
- Climate change and permafrost
- Simulation Techniques and Applications
- Climate change impacts on agriculture
- Air Quality and Health Impacts
- Remote Sensing and Land Use
- Water-Energy-Food Nexus Studies
- Neural Networks and Applications
- Geophysics and Gravity Measurements
University of Manitoba
2023-2025
Global Institute for Water Security
2020-2022
University of Saskatchewan
2020-2022
Indian Institute of Science Bangalore
2015
Abstract Multi‐model climate experiments carried out as part of different phases the Coupled Model Intercomparison Project (CMIP) are crucial to evaluate past and future change. The reliability models' simulations is often gauged by their ability reproduce historical across many time scales. This study compares global mean surface air temperature from 29 CMIP6 models with observations three datasets. We examine (1) warming cooling rates in five subperiods 1880 2014, (2) autocorrelation...
Abstract Climate models are crucial for assessing climate variability and change. A reliable model future should reasonably simulate the historical climate. Here, we assess performance of CMIP6 in reproducing statistical properties observed annual maxima daily precipitation. We go beyond commonly used methods simulations on three scales by performing: (a) univariate comparison based L‐moments relative difference measures; (b) bivariate using Kernel densities mean L‐variation, L‐skewness...
Abstract Gridded precipitation datasets are used in many applications such as the analysis of climate variability/change and hydrological modelling. Regridding is common for model coupling (e.g., atmospheric models) or comparing different models datasets. However, regridding can considerably alter statistics. In this global analysis, effects a dataset emphasized using three methods (first order conservative, bilinear, distance weighted averaging). The differences between original regridded...
Abstract Bias correction methods are used to adjust simulations from global and regional climate models use them in informed decision‐making. Here we introduce a semi‐parametric quantile mapping (SPQM) method bias‐correct daily precipitation. This uses parametric probability distribution describe observations an empirical for simulations. Bias‐correction techniques typically the bias between observation historical correct projections. The SPQM however corrects based only on assuming...
Statistical tools are crucial for a variety of hydrological applications, whether to model processes and enhance understanding knowledge or design infrastructure systems. Given the rapid evolution statistical methods need solid theoretical foundation their correct application, multidisciplinary community (STAHY-WG) aggregated under IAHS umbrella contribute this research field. Now, after more than fifteen years since its inception, paper summarizes main achievements productive collaboration...
High-resolution precipitation and temperature projections are indispensable for informed decision-making, risk assessment, planning. Here, we have developed an extensive database (SPQM-CMIP6-CAN) of high-resolution (0.1°) extending till 2100 at a daily scale Canada. We employed novel Semi-Parametric Quantile Mapping (SPQM) methodology to bias-correct the Coupled Model Intercomparison Project, Phase-6 (CMIP6) four Shared Socio-economic Pathways. SPQM is simple, yet robust, in reproducing...
Abstract Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how varies across time space, specifically in regions where ground-based observations are scarce. While the application has become widespread, there is limited knowledge on well these represent magnitude frequency extreme precipitation—the key features triggering flood...
Globally, extreme temperatures have severe impacts on the economy, human health, food and water security, ecosystems. Mortality rates been increased due to heatwaves in several regions. Specifically, megacities high with increasing temperature ever-expanding urban areas; it is important understand changes terms of duration, magnitude, frequency for future risk management disaster mitigation. Here we framed a novel Semi-Parametric quantile mapping method bias-correct CMIP6 minimum maximum...
Abstract As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation mitigation strategies reduce drought vulnerability. Climate models are important in quantifying changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, reproduce duration severity three observational data sets using Standardized Precipitation Index (SPI). We used summary statistics beyond mean standard deviation, devised a novel probabilistic...
Quantitative precipitation estimates (QPE), the main input driving hydrological model simulations, are known to have different levels of uncertainty across spatial and temporal scales. These uncertainties propagate through simulations thus lead erroneous estimations variables extreme events. The role equifinality—where structures or parameter sets produce similarly acceptable results—needs further research in context error propagation. Additionally, while data assimilation (DA) is a...
Regional water resource modelling is important for evaluating system performance by analyzing the reliability, resilience and vulnerability criteria of system. In systems modelling, several uncertainties abound, including data inadequacy errors, modeling inaccuracy, lack knowledge, imprecision, inexactness, randomness natural phenomena, operational variability, in addition to challenges such as growing population, increasing demands, diminishing sources climate change. Recent advances...
Abstract We merge classical extreme value methods to extract high (high temperatures (HT)) and low (low (LT)) form time series having at least one per year. Observed daily maximum minimum temperature records are used from 4,797 quality‐controlled, global, surface stations over 1970–2019. assess changes in the magnitude frequency of by introducing applying novel that exploit definition stationarity. Analysis shows significant increasing (40.6% stations) decreasing (41.1%) trends LT,...
High-resolution precipitation and temperature projections are indispensable for informed decision-making, risk assessment, planning. Here, we have developed an extensive database of high-resolution (0.1°) precipitation, maximum, minimum extending till 2100 at a daily scale Canada. We employed novel Semi-Parametric Quantile Mapping (SPQM) methodology to bias-correct the Coupled Model Intercomparison Project, Phase-6 (CMIP6) four distinct Shared Socio-economic Pathways. SPQM is...
<p>Many physically based models aiming to quantify the vulnerability and risk of hydrologic geomorphic hazards need as input or forcing time series processes such precipitation, temperature, humidity, etc. The reliability their output depends on how realistic inputs are. CoSMoS is a multi-platform software that generates reliable from hydroclimatic variables (precipitation, wind, relative streamflow, etc.). It developed in R (version 2.0) well other platforms (Matlab,...
<p>Changes in the frequency and intensity of extreme precipitation resulting from climate change are responsible for natural disasters such as severe floods have been a major study focus during last decades. Previous studies mainly focused on trends annual maxima at global regional scales. However, little is known about how among different types. This offers analysis changes terms type by using over 8500 gauge-based records. We period 1964 to 2013 when warming was accelerating....
<p>Re-gridding considerably alters precipitation statistics. Despite this fact, regridding datasets is commonly performed for coupling or comparing different models/datasets. In general, several studies have highlighted the effects of at regional scale. study, re-gridding are emphasized a global scale using methods, size shifts and resolutions dataset. Substantial differences noted high quantiles dry (or wet-dry frequency) altered to great extent. Specifically, difference 46 mm...
<p>Climate models are the available tools to assess risks of extreme precipitation events due climate change. Models simulating historical successfully often reliable simulate future climate. Here, we performance CMIP6 in reproducing observed annual maxima daily (AMP) beyond commonly used methods. This assessment takes three scales: (1) univariate comparison based on L-moments and relative difference measures; (2) bivariate using Kernel densities mean L-variation, L-skewness...