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1. Model Informed Data Collection in Coupled Human-Water Systems: An Exploratory Application of a Hydrological and Agent-Based Model

   Mohajer Iravanloo, B.; Yu, D.; Janssen, M.; Garcia, M. (April 2021, European Geophysical Union)

   Hydrological systems in the Anthropocene have shown substantial shifts from their natural processes due to human modifications. Consequently, deploying coupled human-water modeling is a critical tool to analyze observed changes. However, the development of socio-hydrological models often requires extensive qualitative data collection in the field and analysis. Despite the advances in developing inter-disciplinary methodologies in utilizing qualitative data for coupled human-water modeling, there is a need to identify influential parameters in these systems to inform data collection. Here, we present an exploratory socio-hydrological model to systemically investigate the feedback system of public infrastructure providers, resource users, and the dynamics of water scarcity at the catchment scale to inform data collection and analysis in the field. Specifically, we propose a novel socio-hydrological model by employing and integrating a top-down hydrological model and an extension of Aqua.MORE Model (an Agent-Based Model designed to simulate dynamics of water supply and demand). Specifically, we model alternate behavioral theories of human decision-making to represent the agents" behavior. Then, we perform sensitivity analysis techniques to identify key socio-economic and behavioral parameters affecting emergence patterns in a stylized human-dominated catchment. We apply the proposed methodology to the Lake Mendocino Watershed in Northern California, US. The results will potentially point which parameters are influential and how they could be mapped to a particular interview or survey question. This study will help us to identify features of decision-making behavior for inclusion in fieldwork, that be might be overlooked in the absence of the proposed modeling. We anticipate that the proposed approach also contributes to the current Panta Rhei Research Initiative of the International Association of Hydrological Sciences (IAHS) which aims at improving the interpretation of the hydrological processes governing the socio-hydrological systems by focusing on their changing dynamics in connection with rapidly changing human systems. 

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2. Multi-Scale Temporal Patterns in Stream Biogeochemistry Indicate Linked Permafrost and Ecological Dynamics of Boreal Catchments

   https://doi.org/10.1007/s10021-021-00709-6  

   Webster, Alex J.; Douglas, Thomas A.; Regier, Peter; Scheuerell, Mark D.; Harms, Tamara K. (October 2021, Ecosystems)

   Temporal patterns in stream chemistry provide integrated signals describing the hydrological and ecological state of whole catchments. However, stream chemistry integrates multi-scale signals of processes occurring in both the catchment and stream. Deconvoluting these signals could identify mechanisms of solute transport and transformation and provide a basis for monitoring ecosystem change. We applied trend analysis, wavelet decomposition, multivariate autoregressive state-space modeling, and analysis of concentration–discharge relationships to assess temporal patterns in high-frequency (15 min) stream chemistry from permafrost-influenced boreal catchments in Interior Alaska at diel, storm, and seasonal time scales. We compared catchments that varied in spatial extent of permafrost to identify characteristic biogeochemical signals. Catchments with higher spatial extents of permafrost were characterized by increasing nitrate concentration through the thaw season, an abrupt increase in nitrate and fluorescent dissolved organic matter (fDOM) and declining conductivity in late summer, and flushing of nitrate and fDOM during summer rainstorms. In contrast, these patterns were absent, of lower magnitude, or reversed in catchments with lower permafrost extent. Solute dynamics revealed a positive influence of permafrost on fDOM export and the role of shallow, seasonally dynamic flowpaths in delivering solutes from high-permafrost catchments to streams. Lower spatial extent of permafrost resulted in static delivery of nitrate and limited transport of fDOM to streams. Shifts in concentration–discharge relationships and seasonal trends in stream chemistry toward less temporally dynamic patterns might therefore indicate reorganized catchment hydrology and biogeochemistry due to permafrost thaw. 

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3. Spatiotemporal trends in particle-associated microbial communities in a chlorinated drinking water distribution system

   https://doi.org/10.1371/journal.pwat.0000183  

   Ferrebee, Madison; Osborne, Erika; Garner, Emily (November 2023, PLOS Water)

   Kumar, Bimlesh (Ed.)

   Various spatiotemporal, hydraulic, and water quality parameters can affect the microbial community composition of water within drinking water distribution systems (DWDSs). Although some relationships between various paravmeters and microbial growth are known, the effects of spatial and temporal trends on particle-associated microbial communities in chlorinated DWDSs remain poorly understood. The objectives of this study were to characterize the microbial community composition of both particle-associated bacteria (PAB) and total bacteria (TB) within a full-scale chlorinated DWDS, and assess relationships between microbiavvl community and various spatiotemporal, hydraulic, and water quality parameters. Bulk water samples were collected from the treatment plant, a storage tank, and 12 other sites in a rural chlorinated DWDS at varying distances from the treatment plant on four sampling dates spanning six months. Amplicon sequencing targeting the 16S rRNA gene was performed to characterize the microbial community. Gammaproteobacteria dominated the DWDS, and hydraulic parameters were well-correlated with differences in microbial communities between sites. Results indicate that hydraulic changes may have led to the detachment of biofilms and loose deposits, subsequently affecting the microbial community composition at each site. Spatial variations in microbial community were stronger than temporal variations, differing from similar studies and indicating that the highly varied hydraulic conditions within this system may intensify spatial variations. Genera containing pathogenic species were detected, withLegionellaandPseudomonasdetected at every site at least once andMycobacteriumdetected at most sites. However, only one sample had quantifiablePseudomonas aeruginosathrough quantitative polymerase chain reaction (qPCR), and no samples had quantifiableLegionella pneumophilaorMycobacterium avium, indicating a low human health risk. This study establishes spatial variations in PAB associated with varied hydraulic conditions as an important factor driving microbial community within a chlorinated DWDS. 

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4. HISDAC-ES: historical settlement data compilation for Spain (1900–2020)

   https://doi.org/10.5194/essd-15-4713-2023  

   Uhl, Johannes H.; Royé, Dominic; Burghardt, Keith; Aldrey Vázquez, José A.; Borobio Sanchiz, Manuel; Leyk, Stefan (January 2023, Earth System Science Data)

   Abstract. Multi-temporal measurements quantifying the changes to the Earth's surface are critical for understanding many natural, anthropogenic, and social processes. Researchers typically use remotely sensed Earth observation data to quantify and characterize such changes in land use and land cover (LULC). However, such data sources are limited in their availability prior to the 1980s. While an observational window of 40 to 50 years is sufficient to study most recent LULC changes, processes such as urbanization, land development, and the evolution of urban and coupled nature–human systems often operate over longer time periods covering several decades or even centuries. Thus, to quantify and better understand such processes, alternative historical–geospatial data sources are required that extend farther back in time. However, such data are rare, and processing is labor-intensive, often involving manual work. To overcome the resulting lack in quantitative knowledge of urban systems and the built environment prior to the 1980s, we leverage cadastral data with rich thematic property attribution, such as building usage and construction year. We scraped, harmonized, and processed over 12 000 000 building footprints including construction years to create a multi-faceted series of gridded surfaces, describing the evolution of human settlements in Spain from 1900 to 2020, at 100 m spatial and 5-year temporal resolution. These surfaces include measures of building density, built-up intensity, and built-up land use. We evaluated our data against a variety of data sources including remotely sensed human settlement data and land cover data, model-based historical land use depictions, and historical maps and historical aerial imagery and find high levels of agreement. This new data product, the Historical Settlement Data Compilation for Spain (HISDAC-ES), is publicly available (https://doi.org/10.6084/m9.figshare.22009643, Uhl et al., 2023a) and represents a rich source for quantitative, long-term analyses of the built environment and related processes over large spatial and temporal extents and at fine resolutions. 

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5. Multi-scale dynamic spatiotemporal graph attention network for forecasting karst spring discharge

   https://doi.org/10.1016/j.jhydrol.2025.133289  

   Zhou, Renjie (October 2025, Journal of Hydrology)

   Karst aquifers are important groundwater resources that supply drinking water for approximately 25 % of the world’s population. Their complex hydrogeological structures, dual-flow regimes, and highly heterogeneous flow pose significant challenges for accurate hydrodynamic modeling and sustainable management. Traditional modeling approaches often struggle to capture the intricate spatial dependencies and multi-scale temporal patterns inherent in karst systems, particularly the interactions between rapid conduit flow and slower matrix flow. This study proposes a novel multi-scale dynamic graph attention network integrated with long short-term memory model (GAT-LSTM) to innovatively learn and integrate spatial and temporal dependencies in karst systems for forecasting spring discharge. The model introduces several innovative components: (1) graph-based neural networks with dynamic edge-weighting mechanism are proposed to learn and update spatial dependencies based on both geographic distances and learned hydrological relationships, (2) a multi-head attention mechanism is adopted to capture different aspects of spatial relationships simultaneously, and (3) a hierarchical temporal architecture is incorporated to process hydrological temporal patterns at both monthly and seasonal scales with an adaptive fusion mechanism for final results. These features enable the proposed model to effectively account for the dual-flow dynamics in karst systems, where rapid conduit flow and slower matrix flow coexist. The newly proposed model is applied to the Barton Springs of the Edwards Aquifer in Texas. The results demonstrate that it can obtain more accurate and robust prediction performance across various time steps compared to traditional temporal and spatial deep learning approaches. Based on the multi-scale GAT-LSTM model, a comprehensive ablation analysis and permutation feature important are conducted to analyze the relative contribution of various input variables on the final prediction. These findings highlight the intricate nature of karst systems and demonstrate that effective spring discharge prediction requires comprehensive monitoring networks encompassing both primary recharge contributors and supplementary hydrological features that may serve as valuable indicators of system-wide conditions. 

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