A5037342105- Hydrology and Watershed Management Studies
- Flood Risk Assessment and Management
- Hydrology and Drought Analysis
- Climate variability and models
- Meteorological Phenomena and Simulations
- Hydrological Forecasting Using AI
- Tropical and Extratropical Cyclones Research
- Soil Moisture and Remote Sensing
- Precipitation Measurement and Analysis
- Cryospheric studies and observations
- Climate change impacts on agriculture
- Water resources management and optimization
- Disaster Management and Resilience
- Climate change and permafrost
- Plant Water Relations and Carbon Dynamics
- Coastal and Marine Dynamics
- Soil and Unsaturated Flow
- Water-Energy-Food Nexus Studies
- Geophysics and Gravity Measurements
- Agricultural risk and resilience
- Soil and Water Nutrient Dynamics
- Landslides and related hazards
- Groundwater flow and contamination studies
- Ocean Waves and Remote Sensing
- Public Relations and Crisis Communication
University of Alabama
2018-2025
Charles River Laboratories (Netherlands)
2024
ORCID
2021
Goddard Space Flight Center
2021
Marshall Space Flight Center
2021
NOAA Center for Satellite Applications and Research
2021
NOAA National Environmental Satellite Data and Information Service
2021
Hohai University
2021
John Wiley & Sons (United States)
2016-2020
Portland State University
2009-2018
Two elementary issues in contemporary Earth system science and engineering are (1) the specification of model parameter values which characterize a (2) estimation state variables express dynamic. This paper explores novel sequential hydrologic data assimilation approach for estimating parameters using particle filters (PFs). PFs have their origin Bayesian estimation. Methods batch calibration, despite major recent advances, appear to lack flexibility required treat uncertainties current as...
Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances DA have not been adequately or timely implemented operational forecast systems to improve the skill of forecasts better informed real-world decision making. This is due part a lack mechanisms properly quantify uncertainty observations and models real-time forecasting situations conduct merging data way that efficient transparent...
Particle filters (PFs) have become popular for assimilation of a wide range hydrologic variables in recent years. With this increased use, it has necessary to increase the applicability technique use complex hydrologic/land surface models and make these methods more viable operational probabilistic prediction. To PF suitable option scenarios, is improve reliability techniques. Improved achieved work through an improved parameter search, with variable variance multipliers Markov Chain Monte...
Abstract The uncertainties associated with atmosphere‐ocean General Circulation Models (GCMs) and hydrologic models are assessed by means of multi‐modelling using the statistically downscaled outputs from eight GCM simulations two emission scenarios. atmospheric forcing is used to drive four models, three lumped one distributed, differing complexity: Sacramento Soil Moisture Accounting (SAC‐SMA) model, Conceptual HYdrologic MODel (HYMOD), Thornthwaite‐Mather model (TM) Precipitation Runoff...
Abstract Soil moisture plays a critical role in improving the weather and climate forecast understanding terrestrial ecosystem processes. It is key hydrologic variable agricultural drought monitoring, flood forecasting, irrigation management as well. Satellite retrievals can provide unprecedented soil information at global scale; however, products are generally provided coarse resolutions (25–50 km 2 ). This often hampers their use regional or local studies. The National Aeronautics Space...
MXenes have significantly impacted materials science and nanotechnology since their discovery in 2011. Theoretical calculations predicted more than 100 possible compositions of lab-scale fabrication 40 MXene structures has been reported to date. The unique characteristics made them an ideal fit for a wide variety applications, including energy storage, environmental, electronics, communications, gas liquid separations adsorption, biomedical, optoelectronics. attracted many researchers, as...
Abstract Over the past decades, scientific community has made significant efforts to simulate flooding conditions using a variety of complex physically based models. Despite all advances, these models still fall short in accuracy and reliability are often considered computationally intensive be fully operational. This could attributed insufficient comprehension causative mechanisms flood processes, assumptions model development inadequate consideration uncertainties. We suggest adopting an...
Flooding is one of the most frequent and disastrous natural hazards triggered by extreme precipitation, high river runoff, hurricane storm surges, compounding effects various flood drivers. This study introduces a new multisource remote sensing approach that leverages both multispectral optical imagery weather- illumination-independent characteristics synthetic aperture radar (SAR) data to streamline, automate, map geographically reliable inundation extents. Utilizing near real-time cloud...
The aim of this paper is to foster the development an end‐to‐end uncertainty analysis framework that can quantify satellite‐based precipitation estimation error characteristics and assess influence propagation into hydrological simulation. First, associated with estimates assumed as a nonlinear function rainfall space‐time integration scale, rain intensity, sampling frequency. Parameters are determined by using high‐resolution gauge‐corrected radar data over southwestern United States....
In hydrologic modeling, state‐parameter estimation using data assimilation techniques is increasing in popularity. Several studies, both the ensemble Kalman filter (EnKF) and particle (PF) to estimate model states parameters have been published recent years. Though there interest a growing literature this area, relatively little research has presented examine effectiveness robustness of these methods uncertainty. This study suggests that studies need provide more rigorous testing than...
The joint behavior of drought characteristics under climate change is evaluated using the copula method, which has recently attained popularity in analysis complex hydrologic systems with correlated variables. Trivariate copulas are applied, this study, to analyze major variables, including duration, severity, and intensity, Oregon's Upper Klamath River Basin. Among results show that duration severity exhibits strongest correlation, whereas intensity least correlation. impact on future...
Multimodeling in hydrologic forecasting has proved to improve upon the systematic bias and general limitations of a single model. This is typically done by establishing new model as linear combination or weighted average several models with weights on basis individual performance previous time steps. The most commonly used multimodeling method, Bayesian averaging (BMA), assumes fixed probability distribution around models' forecast prior uses calibration period determine static for each More...