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1. 4-State Hidden Markov Model Using PMDI and Temperature for Climate-Informed Scenario Generation

   https://doi.org/10.4211/hs.a46a305cb4b4430e9643178fe11f7cf3  

   Tezcan, Burcu; Garcia, Margaret (June 2025, HydroShare)

   This repository contains R scripts for implementing a computationally efficient 4-state Hidden Markov Model (HMM) that uses temperature as a covariate to generate ensembles of plausible Palmer Modified Drought Index (PMDI) scenarios across the Western U.S. The model uses paleo PMDI data, which spans from 1500 to 1980 with a matrix grid of 1823 x 481 (e.g., 1823 grid-cells and 481 years). Similarly, paleo temperature data covers the same period, arranged in a matrix grid of 1637 grid cells by 481 years. To address the high dimensionality of the datasets, Principal Component Analysis (PCA) is applied to each variable, and the first six principal components (PCs) from both PMDI and temperature are retained as input to the HMM. The trained HMM is then used to simulate future PMDI scenarios by leveraging bias-corrected CMIP6 temperature projections under the Shared Socioeconomic Pathway (SSP) 2–4.5 scenario. The HMM framework is designed to capture the spatiotemporal variability and regime-shifting behavior of hydroclimatic patterns. It provides critical insights into the spatial correlation of wet and dry conditions across the Western U.S., supporting regional drought risk assessment and long-term water resource planning. For a more detailed description of the model, please refer to the following paper: Tezcan, B., & Garcia, M. (2025). Training a hidden Markov model with PMDI and temperature to create climate informed scenarios. Frontiers in Water, 7, Article 1472695. https://doi.org/10.3389/frwa.2025.1472695 

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2. Diel Greenhouse Gas Emissions Demonstrate a Strong Response to Vegetation Patch Types in a Freshwater Wetland

   https://doi.org/10.1029/2024JG008193  

   Taylor, Aileen K; Sharp, Sean J; Stewart, Graham A; Williams, Michael R; McCarty, Greg W; Palmer, Margaret A (November 2024, Journal of Geophysical Research: Biogeosciences)

   Abstract Wetland methane (CH₄) fluxes are highly variable over spatial and temporal scales due to variations in CH₄production, oxidation, and transport. While some aspects of temporal variability in CH₄fluxes are well documented, diel variability is poorly constrained, and studies report conflicting findings, making it difficult to generalize. Topographic, geochemical, hydroclimatic, and vegetative variability can result in characteristically different “patches” that likely influence differences in diel patterns. We investigated diel patterns of CH₄fluxes from a large seasonal‐mineral soil wetland in Maryland (USA) across three functionally unique patches: two with vegetation (emergent and submerged aquatic vegetation) and one without (open water) during the summer of 2021. To explore the relationships between vegetation, environmental conditions, and flux patterns, we also measured physiochemical variables (air and water temperature, pH, relative humidity, PAR, dissolved oxygen, and water depth). To our knowledge, this is the first study comparing diel variability using chambers across such distinct vegetation patch types. We found that diel patterns were strongly linked to patch types: CH₄fluxes from the emergent vegetation did not display a consistent diel pattern, while fluxes from the submerged vegetation and no vegetation patches frequently peaked at 13:00 and 05:00, respectively. These differences could be a direct result of vegetation impact on production, oxidation, and/or transport of CH₄or on conditions covarying with patch type. This study contributes to the growing understanding of how CH₄fluxes vary spatially over diel cycles and emphasizes the importance of considering spatially varying diel patterns when estimating fluxes. 

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3. Mapping physiology: biophysical mechanisms define scales of climate change impacts

   https://doi.org/10.1093/conphys/coz028  

   Choi, Francis; Gouhier, Tarik; Lima, Fernando; Rilov, Gil; Seabra, Rui; Helmuth, Brian; Seebacher, Frank (January 2019, Conservation Physiology)

   Abstract The rocky intertidal zone is a highly dynamic and thermally variable ecosystem, where the combined influences of solar radiation, air temperature and topography can lead to differences greater than 15°C over the scale of centimetres during aerial exposure at low tide. For most intertidal organisms this small-scale heterogeneity in microclimates can have enormous influences on survival and physiological performance. However, the potential ecological importance of environmental heterogeneity in determining ecological responses to climate change remains poorly understood. We present a novel framework for generating spatially explicit models of microclimate heterogeneity and patterns of thermal physiology among interacting organisms. We used drone photogrammetry to create a topographic map (digital elevation model) at a resolution of 2 × 2 cm from an intertidal site in Massachusetts, which was then fed into to a model of incident solar radiation based on sky view factor and solar position. These data were in turn used to drive a heat budget model that estimated hourly surface temperatures over the course of a year (2017). Body temperature layers were then converted to thermal performance layers for organisms, using thermal performance curves, creating ‘physiological landscapes’ that display spatially and temporally explicit patterns of ‘microrefugia’. Our framework shows how non-linear interactions between these layers lead to predictions about organismal performance and survivorship that are distinct from those made using any individual layer (e.g. topography, temperature) alone. We propose a new metric for quantifying the ‘thermal roughness’ of a site (RqT, the root mean square of spatial deviations in temperature), which can be used to quantify spatial and temporal variability in temperature and performance at the site level. These methods facilitate an exploration of the role of micro-topographic variability in driving organismal vulnerability to environmental change using both spatially explicit and frequency-based approaches. 

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4. Spatial patterns of tree and shrub biomass in a deciduous forest using leaf-off and leaf-on lidar

   https://doi.org/10.1139/cjfr-2018-0033  

   Brubaker, Kristen M.; Johnson, Quincey K.; Kaye, Margot W. (September 2018, Canadian Journal of Forest Research)

   Understanding patterns of aboveground carbon storage across forest types is increasingly important as managers adapt to threats of global change. We combined field measures of aboveground biomass with lidar to model fine-scale biomass in deciduous forests located in two watersheds; one watershed was underlain by sandstone and the other by shale. We measured tree and shrub biomass across three topographic positions for both watersheds and analyzed biomass using mixed models. The watershed underlain by shale had 60% more aboveground biomass than the sandstone watershed. Although spatial patterns of biomass were different across watersheds, both had higher (between about 40% and 55%) biomass values at the toe-slope position than at the ridge-top position. To model fine-scale spatial patterns of biomass, we tested the effectiveness of leaf-on and leaf-off lidar combined with topographic metrics to develop a spatially explicit random forest model of tree and shrub biomass across both watersheds. Leaf-on variables were more important for modeling shrub biomass, while leaf-off variables were more effective at modeling tree biomass. Our model of tree and shrub biomass reflects the distribution of biomass across both watersheds at a fine scale and highlights the potential of abiotic factors such as topography and bedrock to affect carbon storage. 

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5. Sampling Regime Effects on Detecting Spatial Stability of Water Quality

   https://doi.org/10.1029/2024WR038844  

   Dai, Dan; Fernandez, Nicolas; Cohen, Matthew J; Jawitz, James W (September 2025, Water Resources Research)

   Abstract Protecting surface water quality can be complicated by high spatiotemporal variability. Pollutant sources and transport pathways may be identified through sufficiently high‐density monitoring sites and high‐frequency sampling, but practical considerations necessitate tradeoffs between spatial and temporal resolution in water quality monitoring network design. We examined how tradeoffs in sampling density and frequency affect measures of spatiotemporal variability in water quality, emphasizing pattern stability over time. We quantified the spatial stability of stream water quality across >250 monitoring sites in the intensively monitored watershed draining to Lake Okeechobee, FL using Spearman's rank correlations between instantaneous observations and site long‐term means for each parameter. We found that water quality spatial patterns for geogenic, biogenic, and anthropogenic parameters were generally stable on decadal timescales for all solutes, and that sampling densely in space yields more information than sampling frequently in time. Variations in spatial stability decreased with increased sampling density but not with greater sampling frequency, attesting to the dominance of spatial variability over temporal variability. For nutrients, the spatial coefficient of variation (CV) was approximately double the temporal CV. Spatial stability of most solutes was similar across flow conditions, but high‐flow monitoring allows for more sites that effectively capture the long‐term spatial patterns of nutrient sources. Water quality monitoring regimes can be optimized for efficiency in capturing water quality patterns and should be adjusted to focus more on spatial variation. We discuss potential improvements for water quality monitoring, particularly in watersheds where scarce resources necessitate tradeoffs between sampling density and frequency. 

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