A5079914897- Hydrology and Watershed Management Studies
- Hydrological Forecasting Using AI
- Groundwater flow and contamination studies
- Air Quality and Health Impacts
- Flood Risk Assessment and Management
- Meteorological Phenomena and Simulations
- Climate variability and models
- Climate Change and Health Impacts
- COVID-19 epidemiological studies
- Hydrology and Drought Analysis
- COVID-19 impact on air quality
- Soil Moisture and Remote Sensing
- Precipitation Measurement and Analysis
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Noise Effects and Management
- Air Quality Monitoring and Forecasting
- Groundwater and Isotope Geochemistry
- Seismic Imaging and Inversion Techniques
- Engineering Education and Pedagogy
- Maritime Navigation and Safety
- Energy and Environment Impacts
- Reservoir Engineering and Simulation Methods
- Remote Sensing and LiDAR Applications
- Health, psychology, and well-being
- Climate change and permafrost
Princeton University
2023-2025
Chengdu University of Traditional Chinese Medicine
2024
Forschungszentrum Jülich
2020-2023
Lanzhou University
2019-2020
Abstract Object Meteorological parameters are the important factors influencing infectious diseases like severe acute respiratory syndrome (SARS). This study aims to explore association between coronavirus disease (COVID-19) death and weather parameters. Methods In this study, we collected daily number of COVID-19, meteorological air pollutant data from 20 January, 2020 29 February, in Wuhan, China. Then, generalized additive model was applied impact temperature, humidity diurnal temperature...
Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, the existing global- to continental-scale hydrological models applied at coarse resolution neglect more complex processes such as lateral surface groundwater flow, thereby not capturing smaller-scale processes. Applications high-resolution physically based integrated often limited watershed scales, neglecting...
Abstract Background A great number of studies have confirmed that children are a particularly vulnerable population to air pollution. Methods In the present study, 332,337 outpatient visits 15 hospitals for respiratory diseases among (0–13 years), as well simultaneous meteorological and pollution data, were obtained from 2014 2016 in Lanzhou, China. The generalized additive model was used examine effects pollutants on children’s visits, including stratified analysis age, gender season....
Groundwater is becoming more important in sustainable water management, particularly the context of climate change and intensive human interventions. Given that groundwater varies space time, it to predict both its dynamic processes static patterns. However, lack reliable data restricts development large-scale monitoring systems linking observations with modeling at spatial scales relevant for local decision making. In this study, we leverage existing physically-based table depth Contiguous...
<title>Abstract</title> Groundwater is the largest accessible freshwater on Earth, yet its quantity and distribution remains unknown. Here we develop a high resolution (approximately 30m) estimate of water table depth over continental United States using machine learning that includes groundwater pumping uncertainty. This represents our most accurate to date. We calculate total storage results show coarse products are systematically biased in their estimates locally, where decisions made, as...
Objective Coronavirus disease 2019 (COVID-19) is a serious infectious disease, which has caused great number of deaths and health problems worldwide. This study aims to examine the effects airborne particulate matter (PM) pollution on COVID-19 across China. Methods In this study, we obtained confirmed cases COVID-19, data ambient PM with aerodynamic diameter ≤ 2.5 μm (PM ) 10 ), temperature (AT), absolute humidity (AH) migration scale index (MSI) in 72 cities China (excluded Wuhan city)...
With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Observing System for Europe SAR (ROSE-L) and its integration into existing C-band missions such as Sentinel-1, multi-frequency observations with high temporal spatial resolution will become available. The SARSense campaign was conducted between June August 2019 to investigate potential estimating soil plant parameters at agricultural test site in Selhausen (Germany). It included C- air- space-borne accompanied by...
Abstract Water table depth (WTD) has a substantial impact on the connection between groundwater dynamics and land surface processes. Due to scarcity of WTD observations, physically‐based models are growing in their ability map at large scales; however, they still challenged represent simulated compared well observations. In this study, we develop purely data‐driven approach estimating continental scale. We apply random forest (RF) model estimate over most contiguous United States (CONUS)...
The aim of this study was to explore the mediating role psychological capital in relationship between perceived social support and presenteeism among nurses.
Abstract. Many European countries rely on groundwater for public and industrial water supply. Due to a scarcity of near-real-time table depth (wtd) observations, establishing spatially consistent monitoring system at the continental scale is challenge. Hence, it necessary develop alternative methods estimating wtd anomalies (wtda) using other hydrometeorological observations routinely available near real time. In this work, we explore potential Long Short-Term Memory (LSTM) networks...
Abstract Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model emulate flows simulated by the integrated ParFlow‐CLM across contiguous US. We compare convolutional neural networks like ResNet UNet autoregressively against our architecture called Forced SpatioTemporal RNN (FSTR). The FSTR incorporates separate encoding of initial conditions,...
The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water anomalies ( wtd a ) over Europe facilitate European management under drought conditions. Long Short-Term Memory (LSTM) networks are deep learning technology exploit long-short-term dependencies in the input-output relationship, which have been observed response dynamics atmospheric and land surface processes. Here, we introduced...
Cold spells and heat waves in a changing climate are well known as great public-health concerns due to their adverse effects on human health. However, very few studies have quantified health impacts of cold the region Northwestern China. The purpose present study was evaluate years life lost (YLL) Lanzhou, city with temperate continental climate. We compiled daily dataset including deaths, weather variables, air pollutants China, from 2014-2017. used distributed lag non-linear model estimate...
Abstract The main challenge of pan-European groundwater (GW) monitoring is the sparsity collated water table depth ( wtd ) observations. anomaly a measure increased due to droughts. Combining long short-term memory (LSTM) networks and transfer learning (TL), we propose an AI-based methodology LSTM-TL produce reliable estimates at European scale in absence consistent observational data sets. core idea modeled relationship between input hydrometeorological forcings observation-based...
Abstract. Many European countries mainly rely on groundwater for domestic water use. Due to a scarcity of near real-time table depth (wtd) observations, establishing spatially consistent monitoring system at the continental scale is challenge. Hence, it necessary develop alternative methods estimate wtd anomalies (wtda) using other hydrometeorological observations routinely available real-time. In this work, we explore potential Long Short-Term Memory (LSTM) networks produce monthly wtda,...
Groundwater is our largest freshwater reservoir, playing a key role in supplying drinking water, guaranteeing food security, supporting biodiversity, and sustaining surface water bodies. While we have table depth (WTD) observations at approximately one million wells over the contiguous US (CONUS), WTD data are sparse city or individual farm level, where local decisions often made. To address challenge, introduce novel product for CONUS, that consists of hyper-resolution (1 arcsec, ~30 m)...
Abstract Groundwater is our largest freshwater reservoir, playing an important role in the global hydrologic cycle. Lack of reliable groundwater data restricts development monitoring systems linking observations with modeling at spatial scales relevant for local decision making. Despite growing interests machine learning (ML) resource modeling, taking ML models to scale still outstanding due sparse data. The contiguous US (CONUS) has extensive information covering a wide range hydrogeologic...