To solve the problem of estimating and verifying stream flow without direct observation data, we estimated stream flow in ungauged zones by coupling a hydrological model with a hydrodynamic model, using the Poyang Lake basin as a test case. To simulate the stream flow of the ungauged zone, we built a soil and water assessment tool (SWAT) model for the entire catchment area covering the upstream gauged area and ungauged zone, and then calibrated the SWAT model using the data in the gauged area. To verify the results, we built two hydrodynamic scenarios (the original and adjusted scenarios) for Poyang Lake using the Delft3D model. In the original scenario, the upstream boundary condition is the observed stream flow from the upstream gauged area, while, in the adjusted scenario, it is the sum of the observed stream flow from the gauged area and the simulated stream flow from the ungauged zone. The experimental results showed that there is a stronger correlation and lower bias (R² = 0.81, PBIAS = 10.00 %) between the observed and simulated stream flow in the adjusted scenario compared to that (R² = 0.77, PBIAS = 20.10 %) in the original scenario, suggesting the simulated stream flow of the ungauged zone is reasonable. Using this method, we estimated the stream flow of the Poyang Lake ungauged zone as 16.4 ± 6.2 billion m³ a⁻¹, representing ∼ 11.24 % of the annual total water yield of the entire watershed. Of the annual water yield, 70 % (11.48 billion m³ a⁻¹) is concentrated in the wet season, while 30 % (4.92 billion m³ a⁻¹) comes from the dry season. The ungauged stream flow significantly improves the water balance with the closing error decreased by 13.48 billion m³ a⁻¹ (10.10 % of the total annual water resource) from 30.20 ± 9.1 billion m³ a⁻¹ (20.10 % of the total annual water resource) to 16.72 ± 8.53 billion m³ a⁻¹ (10.00 % of the total annual water resource). The method can be extended to other lake, river, or ocean basins where observation data is unavailable.

Background: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people’s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. Objective: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. Methods: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. Results: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. Conclusions: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner.

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.

To bridge the gap between large-scale GCM (global climate model) outputs and regional-scale climate requirements of hydrological models, a spatiotemporally distributed downscaling model (STDDM) was developed. The STDDM was done in three stages: (1) up-sampling grid-observations and GCM simulations for spatially continuous finer grids, (2) creating the mapping relationship between the observations and the simulations differently in space and time, and (3) correcting the simulation and producing downscaled data to a spatially continuous grid scale. We applied the STDDM to precipitation downscaling in the Poyang Lake watershed using the MRI-CGCM3 (Meteorological Research Institute Coupled Ocean–Atmosphere General Circulation Model 3), with an acceptable uncertainty of ≤ 4.9 %. Then we created future precipitation changes from 1998 to 2100 (1998–2012 in the historical scenario and 2013–2100 in the RCP8.5 scenario). The precipitation changes increased heterogeneities in temporal and spatial distribution under future climate warming. In terms of temporal patterns, the wet season become wetter, while the dry season become drier. The frequency of extreme precipitation increased, while that of the moderate precipitation decreased. Total precipitation increased, while rainy days decreased. The maximum continuous dry days and the maximum daily precipitation both increased. In terms of spatial patterns, the dry area exhibited a drier condition during the dry season, and the wet area exhibited a wetter condition during the wet season. Analysis with temperature increment showed precipitation changes can be significantly explained by climate warming, with p<0.05 and R≥0.56. The precipitation changes indicated that the downscaling method is reasonable, and the STDDM could be successfully applied to the basin-scale region based on a GCM. The results implied an increasing risk of floods and droughts under global warming, which were a reference for water balance analysis and water resource planning.

Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Estimation) to facilitate further usage and research by the wider academic community.

Studies on human mobility have a long history with increasingly strong interdisciplinary connections across social science, environmental science, information and technology, computer science, engineering, and health science. However, what is lacking in the current research is a synthesis of the studies to identify the evolutional pathways and future research directions. To address this gap, we conduct a systematic review of human mobility-related studies published from 1990 to 2020. Drawing on the selected publications retrieved from the Web of Science, we provide a bibliometric analysis and network visualisation using CiteSpace and VOSviewer on the number of publications and year published, authors and their countries and afflictions, citations, topics, abstracts, keywords, and journals. Our findings show that human mobility-related studies have become increasingly interdisciplinary and multi-dimensional, which have been strengthened by the use of the so-called ‘big data’ from multiple sources, the development of computer technologies, the innovation of modelling approaches, and the novel applications in various areas. Based on our synthesis of the work by top cited authors we identify four directions for future research relating to data sources, modelling methods, applications, and technologies. We advocate for more in-depth research on human mobility using multi-source big data, improving modelling methods and integrating advanced technologies including artificial intelligence, and machine and deep learning to address real-world problems and contribute to social good.