Hydrological Drought Forecasting Using a Deep Transformer Model

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were collected from these two stations. The two deep learning models were used to predict stage data for five different time steps: 30, 60, 90, 120, and 180 days. A drought series was created from the forecasted values using a monthly fixed threshold of the 75th percentile (75Q). The transformer model outperformed the LSTM model for all of the timescales at both locations when considering the following averages: MSE = 0.11, MAE = 0.21, RSME = 0.31, and R2 = 0.92 for the Chattahoochee station, and MSE = 0.06, MAE = 0.19, RSME = 0.23, and R2 = 0.93 for the Blountstown station. The transformer model exhibited greater accuracy in generating the same drought series as the observed data after applying the 75Q threshold, with few exceptions. Considering the evaluation criteria, the transformer deep learning model accurately forecasts hydrological drought in the Apalachicola River, which could be helpful for drought planning and mitigation in this area of contested water resources, and likely has broad applicability elsewhere

A User-Centered Mapping Design for Geomorphological Hazard Thematic Map

Numerous studies have concentrated on developing user-centered designs for hazard zone maps but rarely for hazard-oriented geomorphological maps, named as Geomorphological Hazard Thematic Maps (GHTMs) in this study, which provide more detailed information about natural hazards. This study developed a user-centered mapping design for GHTMs for nonexperts in geomorphology. We invited civil engineers and high school educators to evaluate a sample GHTM\u27s design in group and focus group panel interviews. The civil engineers preferred maps with more geomorphological features, whereas the educators preferred simple designs. Both groups indicated that the inclusion of essential facilities and road networks is essential. The map was also adjusted by adding hillshade layer and by changing the symbology for mass wasting, fault scarps, and fluvial features to increase clarity and simplicity. This case study is the first step toward developing user-centered mapping designs for hazard communication that will deepen their understanding of natural hazards

Geovisualization geoscience of large river floodplains

Alluvial river landscapes of the lower Mississippi and Atchafalaya rivers in the south-central USA are flood prone and have shifted historically in position and form, resulting in interventions for flood reduction, navigation, and water supply. Fisk mapped these landscapes in the middle of the twentieth century as a series of artistic colourful map plates. Selected areas are revisited with modern data sets (LiDAR from 2003, hydrographic surveys from 2006 and 2007) including two sites along the Mississippi River (near the Old River juncture and near Morganza Floodway) and one in the middle Atchafalaya River. By using 2D and 3D geovisualization, we find that the extent, variety, and dimensions of anthropogenic landforms have grown in prominence since Fisk’s mapping. The volumes of the highest positive landforms are quantified to provide some indication of direct and indirect anthropogenic activity in these landscapes

Effects of antibiotics and metals on lung and intestinal microbiome dysbiosis after sub-chronic lower-level exposure of air pollution in ageing rats

We investigated the effects of antibiotics, drugs, and metals on lung and intestinal microbiomes after sub-chronic exposure of low-level air pollution in ageing rats. Male 1.5-year-old Fischer 344 ageing rats were exposed to low-level traffic-related air pollution via whole-body exposure system for 3 months with/without high-efficiency particulate air (HEPA) filtration (gaseous vs. particulate matter with aerodynamic diameter of ≤2.5 µm (PM2.5) pollution). Lung functions, antibiotics, drugs, and metals in lungs were examined and linked to lung and fecal microbiome analyses by high-throughput sequencing analysis of 16 s ribosomal (r)DNA. Rats were exposed to 8.7 μg/m3 PM2.5, 10.1 ppb NO2, 1.6 ppb SO2, and 23.9 ppb O3 in average during the study period. Air pollution exposure decreased forced vital capacity (FVC), peak expiratory flow (PEF), forced expiratory volume in 20 ms (FEV20), and FEF at 25∼75% of FVC (FEF25–75). Air pollution exposure increased antibiotics and drugs (benzotriazole, methamphetamine, methyl-1 H-benzotriazole, ketamine, ampicillin, ciprofloxacin, pentoxifylline, erythromycin, clarithromycin, ceftriaxone, penicillin G, and penicillin V) and altered metals (V, Cr, Cu, Zn, and Ba) levels in lungs. Fusobacteria and Verrucomicrobia at phylum level were increased in lung microbiome by air pollution, whereas increased alpha diversity, Bacteroidetes and Proteobacteria and decreased Firmicutes at phylum level were occurred in intestinal microbiome. Lung function decline was correlated with increasing antibiotics, drugs, and metals in lungs as well as lung and intestinal microbiome dysbiosis. The antibiotics, drugs, and Cr, Co, Ca, and Cu levels in lung were correlated with lung and intestinal microbiome dysbiosis. The lung microbiome was correlated with intestinal microbiome at several phylum and family levels after air pollution exposure. Our results revealed that antibiotics, drugs, and metals in the lung caused lung and intestinal microbiome dysbiosis in ageing rats exposed to air pollution, which may lead to lung function decline