Moradkhani Hamid

University of Alabama | UA · Department of Civil, Construction and Environmental Engineering

Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spat...

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely o...

Ten precipitation indices recommended by the Expert Team on Climate Change Detection and Indices were used to analyse spatial distribution and temporal changes in annual amount, intensity, frequency and duration of precipitation statistics based on daily data with a focus on extremes in Finland during 1961–2011. Also, the connections between these...

Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transf...

Application of remote sensing is emerging for operational drought monitoring and early warning as it offers opportunities for assessing drought from different perspectives. This chapter provides an overview of the advances in monitoring different types of drought using satellite remote sensing observations with an example on agricultural drought as...

Plain Language Summary Many regions have suffered greatly from recent occurrences of abrupt shift from drought to downpour, suggesting that the emerging threat is a global challenge. Such drought‐to‐downpour events pose challenges to water infrastructures in developed countries, let alone those poor countries with limited adaptation capacity and re...

Plain Language Summary In recent decades, there has been a notable increase in climate‐related disasters, highlighting the need for proactive measures. One specific concern is the potential for tropical cyclones (TCs) to create compound flood risks by triggering multiple drivers that lead to flooding. Typically, we rely on synthetic scenarios to as...

The emergence of large language models (LLMs), such as ChatGPT, has garnered significant attention, particularly in academic and scientific circles. Researchers, scientists, and instructors hold varying perspectives on the advantages and disadvantages of using ChatGPT for research and teaching purposes. This commentary offers a brief explanation of...

The southeastern United States has been identified as a region with high physical and social vulnerability to climate change and coastal hazards such as hurricanes and floods (Emrich and Cutter, 2011). The effects of these hazards are only expected to increase over time under climate change, so it is vital that the risk information is adequately co...

The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, a...

Design of coastal defense structures like seawalls and breakwaters can no longer be based on stationarity assumption. In many parts of the world, an anticipated sea‐level rise (SLR) due to climate change will constitute present‐day extreme sea levels inappropriate for future coastal flood risk assessments since it will significantly increase their...

To enhance flood modeling, it is imperative to gain a comprehensive understanding of the causative mechanisms and cutting-edge models and tools, while also acknowledging their uncertainties.

formation about the event. Here, we investigate the public response to large and destructive hur-ricane Ian in late September 2022 by examining the textual content of tweets shared on Twitteracross the contiguous United States (CONUS). We mined and processed over twenty milliontweets for discovering the main topics of discussion and relationship be...

Knowledge of the frequency of extreme sea levels informs construction of coastal defenses, flood insurance policies, and strategic coastal adaptation plans. In the midst of climate change, and particularly of intensifying and/or more frequent storms around the globe, accurate information about the frequency and magnitude of extreme storm surge beco...

Over the past decades, the 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 and are often considered computationally intensive to be fully operational. This could be attributed to insuf...

Coastal regions such as the U.S. Atlantic and Gulf Coasts are highly vulnerable to extreme coastal hazards such as tropical cyclones and major hurricanes. The effects of these hazards pose a threat now and are expected to increase in the future, which highlights the need for coastal communities to receive and understand information regarding risks...

Flooding is one of the most notorious natural disasters that adversely impact people's lives, and damages their properties. To properly simulate the flood waves, two-dimensional hydrodynamic models herein referred to as flood inundation models are widely used. These models apply conservation of mass and momentum principles to the riverine systems a...

The agricultural water market (or water trade) is an effective water resource management tool to redistribute water from low-value to high-value agricultural farms. Water markets can play an important role in reducing the economic losses attributed to droughts by increasing flexibility in responding to water scarcity. Climate change has increased t...

During natural disasters, there is a noticeably increased use of social media sites such as Twitter. Substantial research on social media data use during disasters has been conducted in the past decade since various social media platforms have emerged and gained popularity. This research highlights a thorough examination of the textual content of u...

Flooding is one of the most frequent and disastrous natural hazards triggered by extreme precipitation, high river runoff, hurricane storm surges, and compounding effects of various flood drivers. This study introduces a new multi-source remote sensing approach that leverages both multi-spectral optical imagery and the weather- and illumination-ind...

Precipitation intensity–duration–frequency (IDF) curves play a crucial role in the design and planning of urban infrastructure to reduce the risk of urban flooding and rainfall‐triggered landslides. However, changing rainfall characteristics in a warming climate render conventional IDF curves inappropriate due to the statistical assumption of stati...

The current study proposes a new method to downscale the monthly GRACE-derived Terrestrial Water Storage Anomaly (TWSA) to 10 km spatial resolution over Iran using deep learning methods. First, the growing neural gas (GNG) method is utilized to cluster the TWSA data to find similar patterns. The Silhouette Chou Davies (SCD) validity measure, a comb...

This perspective discusses the importance of characterizing, quantifying, and accounting for various sources of uncertainties involved in different layers of Hydrometeorological and Hydrodynamic model simulations as well as their complex interactions and cascading effects (e.g., uncertainty propagation) in forecasting Compound Flooding (CF). Over t...

Maritime transportation is crucial to national economic development as it offers a low-cost, safe, and efficient alternative for movement of freight compared to its land or air counterparts. River and channel dredging protocols are often adopted in many ports and harbors of the world to meet the increasing demand for freight and ensure safe passage...

Soil erosion is a significant threat to the environment and long-term land management around the world. Accelerated soil erosion by human activities inflicts extreme changes in terrestrial and aquatic ecosystems, which is not fully surveyed/predicted for the present and probable future at field-scales (30-m). Here, we estimate/predict soil erosion...

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously...

This study seeks to use machine learning to investigate the role of meteorological and climate variables on wildfire occurrence in the Arctic and the global tropical forests biomes. Using monthly fire counts observed by the MODIS satellites in combination with temperature and precipitation data, indices of large-scale atmospheric and oceanic anomal...

Rates of poverty and economic inequality in rural Alabama are among the nation's highest and increasing agricultural productivity can provide a needed boost to these communities. The transition from rain-fed to irrigation-fed (RFtoIF) agriculture has significantly increased farm productivity and profitability elsewhere in the United States. Despite...

Estimation of actual evapotranspiration (ET) is key to irrigation water application and basin-scale agricultural water demand assessment. While modelers and water managers rely on stand-alone ET estimation model application in their planning and management, several uncertainties including model structure, parameter set, and initial condition exist,...

The current Tropical Cyclones (TCs) scaling system, Saffir‐Simpson Hurricane Wind Scale (SSHWS), characterizes the hazardousness of these events solely based on wind speed. This is despite the fact that TCs are classic examples of compound hazards during which multiple hazard drivers that are wind, storm surge, and intense rainfall interact and yie...

On behalf of the editorial board of Water Resources Research (WRR) and the entire water science community, we want to express our most heartfelt gratitude to all who reviewed manuscripts for the journal in 2021. Your great efforts have ensured and improved the high quality and impact of the WRR papers and generally of research in our field. In 2021...

In the last decade, DEM-based classifiers based on height above nearest drainage (HAND) have been widely used for rapid flood hazard assessment, demonstrating satisfactory performance for inland floods. The main limitation is the high sensitivity of HAND to the topography, which degrades the accuracy of these methods in flat coastal regions. In add...

We are delighted to publish the names of the 613 peer reviewers who submitted 904 reviews in 2021. We express our deep appreciation for your thoughtful comments and suggestions on manuscripts submitted to Earth's Future.

Flood simulation with two-dimensional hydrodynamic models is subject to different sources of uncertainties in model configuration, boundary conditions, and model parametrization. HEC-RAS 2D is a widely used hydrodynamic model. Here, we assess the sensitivity of the HEC-RAS 2D to its configuration factors and parameters. We evaluate the impacts of d...

An imperative aspect of agricultural planning is accurate yield prediction. Artificial Intelligence (AI) techniques, such as Deep Learning (DL), have been recognized as effective means for achieving practical solutions to this problem. However, these approaches most often provide deterministic estimates and do not account for the uncertainties invo...

MXenes have significantly impacted materials science and nanotechnology since their discovery in 2011. Theoretical calculations have predicted more than 100 possible compositions of MXenes and lab-scale fabrication of more than 40 MXene structures has been reported to date. The unique characteristics of MXenes have made them an ideal fit for a wide...

Irrigation is one of the major adaptation strategies to combat the negative impacts of drought on crop yields. However, during droughts, the water resources are limited and determining the exact irrigation amount and its response to crop yields are crucial. Existing crop yield simulation models are data-intensive and a majority of these are point-b...

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread, affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought condition...

Drought and tropical storm (TS) are associated with water deficit and surplus, respectively. Soil moisture is a key component in the hydrological cycle that plays an important role in monitoring drought and reflects the infiltrated or stored water due to TS rainfall. Therefore, soil moisture information can be used for the assessment of whether TS...

Deltas, estuaries, and wetlands are prone to frequent coastal flooding throughout the world. In addition, a large number of people in the United States have settled in these low-lying regions. Therefore, the ecological merit of wetlands for maintaining sustainable ecosystems highlights the importance of flood risk and hazard management in these reg...

Land Surface Model (LSM) predictions are affected by unknown parameters, uncertain meteorological forcings, inaccurate initial conditions, and incomplete process representation. While parameter optimization (PO) algorithms have been used to estimate the model parameter for best model performance, the use of data assimilation (DA) has been receiving...

Compound flood hazard assessment (CFHA) and modeling are prone to various sources of uncertainty including model structure, model parameters, input/forcing data, and those associated with nonlinear interactions among flood drivers. Data assimilation (DA) is an efficient method that helps quantify and reduce uncertainty in many hydrological applicat...

The use of social media platforms such as Twitter significantly increases during natural hazards. With the emergence of several social media platforms over the past decade, many studies have investigated the applications of these platforms during calamities. This study presents a comprehensive spatiotemporal analysis of textual content from million...

Hydrodynamic modeling is a common approach for flood inundation mapping. These models use conservation of mass and momentum to simulate the river hydrodynamics during flood events. To simulate floods and estimate the spatio-temporal distribution of extreme water levels, the upstream and downstream flows are used as the minimum required boundary con...

Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reli...

Soil moisture (SM) and evapotranspiration (ET) are two important variables in hydrologic and land surface modeling with a strong relationship. In this study, the effect of multivariate assimilation of remotely sensed soil moisture and evapotranspiration on drought monitoring is examined. Numerous efforts have gone into the assimilation of satellite...

This presentation highlights how droughts affect the corn and soybean yield and acreage across different climatic zones in the Deep South region (comprising states of Alabama, Mississippi, Georgia, and Tennessee). The results highlight that both corn and soybean yields decline significantly during droughts, and farmers tend to cultivate more corn i...

An agricultural drought forecasting system that leverages dynamical land surface modeling, statistical Bayesian multivariate analysis, satellite soil moisture data assimilation, and high‐performance computing is proposed. The system is designed to generate deterministic and probabilistic drought forecasts at various lead‐times including 1, 2, and 3...

Crop yield forecasting is of great importance to crop market planning, crop insurance, harvest management, and optimal nutrient management. Commonly used approaches for crop prediction include but are not limited to conducting extensive manual surveys or using data from remote sensing. Considering the increasing amount of data provided by remote se...

Changing water quantities and location can be estimated using the Gravity Recovery and Climate Experiment (GRACE) satellites. By measuring differences in the Earth's gravity, the satellites provide monthly data on regional changes in the Earth's mass resulting from the movement of water. Studying the Southeast U.S., using the full record of the ori...

Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision making during the emergency of an upcoming flood event. Considering high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely...

Current efforts on Deep Learning-based modeling are being put for solving real world problems with complex or even not-fully understood interactions between predictors and target variables. A special artificial neural network, the Long Short-Term Memory (LSTM) is a promising data-driven modeling approach for dynamic systems yet little has been expl...

Compound flooding (CF), as a result of oceanic, hydrological, meteorological and anthropogenic drivers, is often studied with hydrodynamic models that combine either successive or concurrent processes to simulate inundation dynamics. In recent years, convolutional neural networks (CNNs) and data fusion (DF) techniques have emerged as effective alte...

The South-to-North water diversion Middle Route Project (MRP) is expected to alleviate the long-term groundwater storage (GWS) depletion in North China Plain (NCP) after the beginning of its operation in December 2014. This study aims to investigate the effect of MRP on GWS by comparing GWS changes before (2003-2014) and after (2015-2018) the MRP o...

Root zone soil moisture (RZSM) is a vital variable for vegetation growth, drought monitoring and agricultural water management. Satellite remote sensing measures soil moisture at the surface layer, while RZSM is derived usually by model-based simulations. Here, we provide the first comprehensive evaluation of eight RZSM products at a global scale,...

Wetlands are endangered ecosystems that provide vital habitats for flora and fauna worldwide. They serve as water and carbon storage units regulating the global climate and water cycle, and act as natural barriers against storm-surge among other benefits. Long-term analyses are crucial to identify wetland cover change and support wetland protection...

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions...

The forecasting of crop yields is important for optimal nutrient management, crop market planning, crop insurance, and harvest management. Common yield forecasting approaches include conducting extensive manual surveys or using data from remote sensing. With the increasing amount of data provided by remote sensing imagery, there is a need for more...

Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimati...

Precipitation predictability is likely to decrease with water cycle intensification under global warming, yet how much it will change spatiotemporally is unclear. We quantify the precipitation predictability changes under future warming using model simulations from Coupled Model Intercomparison Project Phase 5. The global‐averaged potential precipi...

The recent bushfires (2019-2020) in New South Wales (NSW) Australia were catastrophic by claiming human and animal lives, affecting ecosystems, destroying infrastructure, and more. Recent studies have investigated relationships between hydroclimatic signals and past bushfires, and very recently, a few commentary papers claimed drought and fuel mois...

Sea level rise (SLR) in the twenty-first century poses fundamental risks to coastal residents. The US Gulf of Mexico Coast (Gulf Coast) is among the regions experiencing the most rapid relative SLR. Beyond its increasing exposure to SLR and related coastal flooding, the Gulf Coast is home to a large population and displays high social vulnerability...

Floods increasingly threaten disadvantaged communities around the globe. When limited financial resources are available, nature-based and community-based incremental adaptation that codifies existing actions and behaviours can help protect people and assets through risk reduction management. These adaptation measures mainly rely on non-financial ca...