Temporal vs. Spatial Heterogeneity in Wetlandscape Water Quality

Temporal vs. Spatial Heterogeneity in Wetlandscape Water Quality | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 January 2026 V1 Latest version Share on Temporal vs. Spatial Heterogeneity in Wetlandscape Water Quality Authors : Esther Lee 0000-0002-5659-4800 [email protected] , Olivia Cacciatore , Joshua M. Epstein 0000-0001-9283-3111 , James Jawitz 0000-0002-6745-0765 , and Matt Cohen 0000-0001-5674-1850 Authors Info & Affiliations https://doi.org/10.22541/au.176863528.84575212/v1 169 views 82 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Quantifying the relative importance of spatial vs. temporal variance informs efficient water quality measurements at all scales. We examined water quality variability across three US coastal plain wetlandscapes to understand when and where solutes vary in these headwater landscapes. These wetlandscapes (<10 km 2 ) are minimally impacted forested systems with numerous similarly situated small depressional wetlands, suggesting comparative spatial homogeneity of solute composition, and are hydrologically dynamic, suggesting significant temporal heterogeneity. We quantified spatial and temporal variance in water quality across 16 wetlands in each wetlandscape using repeated (n=4 to 6) field measurements of >20 solutes—including anions, cations, nutrients, organic matter quality metrics, and physio-chemical parameters. We found an even balance between spatial and temporal variance for ions and organic solutes, but dominance of spatial variance for nutrients, implying local source heterogeneity is at least as important as hydrological and seasonal variation in controlling landscape solute patterns. Models predicting temporal variation based on wetland hydrologic and seasonal drivers (mean R 2 = 0.61) outperformed models predicting spatial variation using landscape/network position and geomorphic attributes (mean R 2 = 0.22). This implies consistently and markedly larger unexplained variance in space than in time, suggesting that increasing sampling locations (spatial density) is more consequential for capturing environmental variation than increasing sampling frequency (temporal density). We compared wetlandscape-scale variance patterns with water quality observations synthesized at larger scales and observed increasing spatial variation with larger extent, but surprisingly consistent temporal variance at all scales. This framework underscores the utility of low-frequency, high density measurements for maximizing information content from water quality monitoring programs. Temporal vs. Spatial Heterogeneity in Wetlandscape Water Quality Esther Lee 1,2,* , Olivia Cacciatore 1,3 , Joshua M. Epstein 1 , James W. Jawitz 4 , and Matthew J. Cohen 1,5 1 School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA 2 School of Spatial Environment System Engineering, Handong Global University, 558 Handong-ro, Buk-gu, Pohang, Gyeongbuk 37554, South Korea 3 Arcadis, USA, Inc. 4 Soil, Water, and Ecosystem Sciences, University of Florida, Gainesville, FL, USA 5 Water Institute, University of Florida, Gainesville FL USA Keywords: solute composition, spatio-temporal variability, variance partitioning, environmental processes, wetland water quality *Corresponding author: Esther Lee ( [email protected] ) Acknowledgments Funding support provided by National Science Foundation (#EAR2129926) USDA NRCS (Project #NR203A750027C005), and USDA CRIS (Project #FLA-FOR-005834). The authors thank Paul Decker, Carlos Quintero, Tony Davanzo, and Kenyon Watkins for field assistance, and Andy Rappe, Lisa Huey, Scott Sager and the US National Park Service for assisting with site access, archival data, and site selection. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Abstract Quantifying the relative importance of spatial vs. temporal variance informs efficient water quality measurements at all scales. We examined water quality variability across three US coastal plain wetlandscapes to understand when and where solutes vary in these headwater landscapes. These wetlandscapes (<10 km 2 ) are minimally impacted forested systems with numerous similarly situated small depressional wetlands, suggesting comparative spatial homogeneity of solute composition, and are hydrologically dynamic, suggesting significant temporal heterogeneity. We quantified spatial and temporal variance in water quality across 16 wetlands in each wetlandscape using repeated (n=4 to 6) field measurements of >20 solutes—including anions, cations, nutrients, organic matter quality metrics, and physio-chemical parameters. We found an even balance between spatial and temporal variance for ions and organic solutes, but dominance of spatial variance for nutrients, implying local source heterogeneity is at least as important as hydrological and seasonal variation in controlling landscape solute patterns. Models predicting temporal variation based on wetland hydrologic and seasonal drivers (mean R 2 = 0.61) outperformed models predicting spatial variation using landscape/network position and geomorphic attributes (mean R 2 = 0.22). This implies consistently and markedly larger unexplained variance in space than in time, suggesting that increasing sampling locations (spatial density) is more consequential for capturing environmental variation than increasing sampling frequency (temporal density). We compared wetlandscape-scale variance patterns with water quality observations synthesized at larger scales and observed increasing spatial variation with larger extent, but surprisingly consistent temporal variance at all scales. This framework underscores the utility of low-frequency, high density measurements for maximizing information content from water quality monitoring programs. Introduction Understanding space-time variability patterns in hydrologic, chemical or biological functions is a key challenge in environmental sampling (Hutchins et al., 1999; Smart et al., 2001; Likens and Buso, 2006; Temnerud et al., 2010). There is a critical need to strategically design sampling regimes that explicitly account for uncertainties—focusing beyond what has been abundantly measured or is readily predicted—to instead monitor what remains uncertain or unpredictable in either temporal or spatial domain. The ratio of temporal to spatial variability has been introduced as a framework to inform investments in ecological and environmental measurements (Abbott et al., 2018; Hammond & Kolasa, 2014), and has been quantified at various spatial scales, ranging across headwater catchments (Egusa et al., 2019; Shogren et al., 2019), watersheds (Abbott et al., 2018; Gu et al., 2021), and even nations (Dupas et al., 2019). However, differences in study extent, sampled analytes, and landscape characteristics present challenges for cross-study comparisons and limit the ability to extract generalizable insights. Wetlandscapes— mosaics of uplands and embedded wetlands—often serve as source areas for many headwater catchments in low-gradient watersheds (Thorslund et al., 2017). These wetland systems are episodically hydrologically connected (Lee et al. 2023, Epstein and Cohen, 2025) and thus play a critical role in regulating water storage and release, thereby mediating nutrient and carbon fluxes, and filtering pollutants prior to downstream transport (Cheng & Basu, 2017). Assessing time-space variability across wetlandscapes helps quantify landscape hydrochemical functions and informs effective water quality monitoring (Epting et al., 2018). While headwater wetlandscapes are not well studied, headwater streams may offer a useful analogy. These systems differ markedly in space because of the heterogeneity of the small areas they drain (Abbott et al., 2018) and are temporally dynamic because of their relatively limited storage (Zipper et al. 2021). Moreover, higher order streams exhibit reduced solute spatial heterogeneity in hydrochemical signatures (Burt & Pinay, 2005; Creed et al., 2015; Lefebvre et al., 2007), especially when the length scales of solute-generating variation are short (Abbott et al. 2018). At stream-reach length scales, spatial variation in solute composition declines (McGuire et al. 2014), indicating that while headwater streams may differ between catchments, spatial variance within catchments (or, by extension, headwater wetlandscapes) may be far smaller, particularly when landscape attributes ( e.g. , land cover, lithology, soils) are relatively homogeneous. We therefore expect spatial variation among wetlands in relatively homogeneous wetlandscapes to be small compared to the temporal variation driven by hydrological forcing. Enumerating how this pattern changes with increasing spatial extent at the landscape scale emerges as a gap in our understanding about the functional measurement of solute heterogeneity (Schauer et al. 2025). Seasonal climate forcing, sporadic surface water connectivity, and biogeochemical processes introduce temporal variability in water quality in wetlandscapes and beyond (Raymond et al., 2016; Lee et al., 2023). Wetland hydrologic connectivity to the flow network (Powers et al., 2012) controls water residence time (Rains et al., 2016) and downstream biogeochemical functions (Leibowitz, 2003; Marton et al., 2015). For example, frequent wetland spillage decreases specific conductance (SpC; Leibowitz & Vining, 2003; Thorslund et al. 2018), dissolved organic carbon (DOC; Hosen et al., 2018), and total dissolved nitrogen (TDN; Cook, 2001; Yu et al., 2015). However, it remains unclear whether highly synchronous hydrologic connectivity patterns ( e.g. , Klammler et al. 2020, Epstein and Cohen, 2025) within a wetlandscape drive synchronous temporal patterns in solute composition. While highly synchronized solute concentrations ( e.g., nutrients and carbon) have been observed with stable seasonal to decadal hydrologic patterns in small agricultural catchments (Abbott et al. 2018), transferability of this pattern to low intensity wetlandscapes is untested. In this context, we evaluate the relative contribution of spatial vs. temporal variance to wetlandscape solute patterns and identify the predictability of spatio-temporal variance based on readily available environmental attributes. Recent systematic efforts to quantify how water quality varies across space and time have provided new insights at the national scale (Dupas et al., 2019; Schauer et al., 2025) and across large watersheds (Dai et al., 2025), but a gap remains at finer scales, limiting our ability to understand underlying processes governing wetlandscape hydrological and biogeochemical functions. Closing this gap will advance targeted monitoring strategies that optimize labor and cost invested in sampling (Gu et al., 2021). The aims of our study were (i) to evaluate the relative importance of spatial and temporal variance in solute concentrations among wetlands within contrasting wetlandscapes, (ii) assess how predictable variation in each solute is based on hydrologic, geomorphic, and climatic attributes, and (iii) synthesize studies to evaluate scale-dependence of space vs. time variance patterns in water quality. We accomplished this by measuring variation in multiple solutes over time and space in wetlands in each of three coastal plain wetlandscapes in Florida, US. Measurements of multiple solutes enabled quantification of spatial vs. temporal variability dominance across different groups ( e.g. , nutrients, ions, organic matter quality). We sought to identify key environmental attributes controlling solute variation and assess our capacity to predict wetland solute composition. We assessed model residual variance to develop a framework that prioritizes true uncertainty ( i.e. , what we neither know nor can readily predict) in environmental processes that regulate wetlandscape water quality. By understanding space-time variability at the local (wetlandscape) scale we seek to guide effective water quality sampling design to optimize information trade-offs between spatially extensive vs. high frequency monitoring. Finally, we compared our wetlandscape variance patterns with similar observations of solute variation at larger spatial extents to better understand how space-time patterns of water quality vary with scale. Site Description Wetlandscapes are composed of numerous wetlands, often with similar geomorphology and hydrology, embedded within a mosaic of uplands (Rains et al., 2016; Thorslund et al., 2017). We studied three minimally impacted wetlandscapes in the US southeastern coastal plain spanning a gradient of hydrologic connectivity (Fig. 1), from principally via surface flow paths in Big Cypress National Preserve (BICY) to almost exclusively via groundwater flow paths in Ordway Swisher Biological Station (OSBS), with mixed connectivity via continuous subsurface and episodic surface flow paths in Austin Cary Forest (ACF), typical of coastal plain wetlandscapes ( i.e. , flatwoods) (Lee et al. 2023). We selected 16 wetlands in each setting for water quality sampling based on dichotomous classifications of size (large vs. small), network position (headwater vs. downstream), distance to adjacent wetland features (near vs. far), and water provenance (clear vs. highly tannic, in the case of OSBS). In each case, we have prior insights about hydrological processes (Lee et al., 2023, Epstein and Cohen, 2025), but limited information about water quality. BICY is a low relief (mean slope = 0.02 m km -1 ) karst-plain wetlandscape located in southwest Florida with a shallow confining unit beneath Pleistocene limestone. Embedded depressional “cypress dome” wetlands form via feedbacks between water storage and limestone dissolution (Watts et al., 2014) yielding wetlands of similar size and depth, evenly spaced across the wetlandscape (Quintero & Cohen, 2019). Wetland hydrologic processes in BICY are tightly coupled and strongly seasonal, with water level variation in any single wetland strongly predictive of water storage and flux across the entire landscape (Klammler et al., 2020). The positive water balance (P > PET, Florida Automated Weather Network) and predominance of surface hydrologic connectivity results in the wetland stage converging to the surface connectivity elevation threshold ( i.e. , spill depth; Lee et al., 2023). These wetlands are important landscape reservoirs for organic matter and aquatic refugia in the dry season and with water enriched in weathering products and organic matter, but persistently low in nutrients (especially phosphorus) and dissolved solids (Zhang et al., 2019). ACF is a flatwood wetlandscape, composed of Plio-Pleistocene sands over a Miocene clay confining unit (the Hawthorn Group). The ACF landscape is a mosaic of managed pinelands and embedded depressional wetlands that store water for prolonged periods. The wetlands in ACF store highly acidic tannic-rich water (Haag & Lee, 2010) and are subject to spatial variation in forest management ( i.e. , density, stand age) as well as drainage features ( e.g. , ditches). Nearly all the wetlands connect annually via surface flow paths, but these connections are spatially heterogeneous in length and timing. Water quality in managed flatwoods like ACF is good, with low alkalinity, highly colored waters that are generally low in nutrients, with expected variation with hydrological state and season (Lee et al., 2023; Haag & Lee, 2010). OSBS is a sandhill lake wetlandscape characterized by relic dune features with higher relief and thick high-permeability sands above the Hawthorn Formation which restricts vertical drainage. The wetlandscape has numerous embedded wetlands dominated by herbaceous hydrophytes within a low-density longleaf pine upland mosaic (FNAI, 2010). Wetland connectivity is dominated by subsurface flow with limited surface water connectivity because of the deep, highly transmissive sandy aquifer media. Water bodies are characterized into dark and clear water flow paths (Epstein and Cohen, 2025), with dark water systems receiving source water from expansive bottomland wetlands. The clear water depressions source water from the sand aquifer with commensurate low dissolved solids. Dissolved organic matter (DOM) is visually distinct between dark and clear wetlands making this an important factor for site selection. The entire landscape of OSBS is nutrient poor, leading to persistent low nutrient concentrations (OSBS, 2024). 3. Methods 3.1. Wetland Hydrology and Topography We monitored sub-daily water levels in each of 48 wetlands (16 per wetlandscape) over 2-3 years in slotted and mesh-wrapped PVC piezometer wells, installed following ERDC TN-WRAP-00-02 guidelines (Sprecher, 2000). HOBO U20 total pressure transducers (Onset Computer Corporation, Bourne MA, USA) were suspended in wells measuring total pressure hourly; barometric pressure was subtracted from this total pressure to yield water depth above the sensor, which was converted to local water depth at the well using the measured sensor depth below the ground surface (McLaughlin and Cohen 2011; Lee et al., 2023). This local water depth was converted to water depths in the deepest part of each wetland based on high-resolution light detection and ranging (LiDAR) digital elevation models (DEMs) from which we determined the difference in elevation between the well and the deepest part of each wetland. The LiDAR DEMs for each wetlandscape (Fig. 1) were used to extract geographic, geometric, and hydrographic characteristics of each instrumented wetland using ArcMap (ESRI ArcMap 10.6; Redlands, CA). Wetland geomorphic depth was quantified as the elevation above the deepest portion of the basin at which water connects via surface pathways to an adjacent depression (merge) or activates landscape-flow (spill). Extraction of wetland depth is described in Lee et al. (2023). Wetland area is the footprint of inundation at the wetland spill elevation. Wetland bathymetry was characterized by an exponent ( b ) in a power function relating wetland stage to volume ( i.e., V = a h b ; Hayashi and Van der Kamp, 2000). Nearest neighbor distances between wetlands were the minimum Euclidean distance between wetland centroids. Figure 1. Topographic context of three Florida wetlandscapes (wetlands denoted in light red) and selected study wetlands (colored shaded areas): a) Big Cypress National Preserve (BICY), b) Austin Cary Forest (ACF), and c) Ordway Swisher Biological Station (OSBS). 3.2. Water Quality Measurement We sampled 21 solutes including major ions ( e.g., Na + , K + , Ca 2+ , Mg 2+ , Cl - ), nutrients ( e.g., NO 3 - , SO 4 2- , NH 4 + ), organic ( e.g., TOC, aromaticity), and physio-chemical characteristics (pH, temperature, dissolved oxygen) (Table S1). Surface water quality ( i.e. , physio-chemical characteristics) was measured in situ at each wetland center using a calibrated multiparameter sonde (YSI Professional Plus Series model 10102030), while other solutes were measured from periodic grab samples collected during site visits. Samples were collected 5-7 times over the study (Jan. 2018-Nov. 2020) to capture seasonal and hydrographic variation, particularly in BICY where regular hydrologic variation has been observed (McLaughlin et al., 2019; Lee et al., 2023; Epstein and Cohen, 2025). Water samples were filtered using 0.45-μm syringe filters and stored in 20 mL Nalgene plastic vials; five vials were frozen until analysis, and one was refrigerated and analyzed for DOM quality within 10 weeks of sampling. Major ions, including Cl - , are abundant dissolved constituents in surface water, varying in response to interactions with sediments and underlying bedrock, as well as evaporative enrichment. Anion and cation concentrations were measured using Dionex ICS-2100 and ICS-1600 Ion Chromatographs, respectively. Anions were measured prior to samples being acidified for cation analysis. Charge balance errors on sample replicates were below 5%. Nitrogen and phosphorus are important controls on primary productivity in aquatic ecosystems. We analyzed three nitrogen compounds (NO 3 - , NH 4 + , and TKN) as well as soluble reactive phosphorus (SRP) on an air-segmented continuous autoflow analyzer using standard colorimetry methods (EPA 350.1 for NO 3 - and NH 4 + , 351.2 for TKN, and 365.1 for SRP). DOM characteristics indicate wetland biogeochemical processes, residence time, and hydroclimatic influences (O’Donnell et al., 2012; Osburn et al., 2018; Wilson & Xenopoulos, 2009). We measured total organic carbon concentrations (TOC) and a suite of chromophoric dissolved organic carbon (CDOM) quality proxies, including the Fluorescence Index (FI), Biological Index (BIX), Humification Index (HIX), Specific UV Absorbance (SUVA 254 ), and Spectral Slope Ratio (Sr). TOC concentrations were measured using Shimadzu TOC-L on samples acidified prior to analysis to halt microbial activity and decomposition. Fluorescence properties were analyzed using a Horiba Aqualog spectrofluorometer using an integration time of 10 seconds and 4-pixel increment for the excitation/emission spectra between 240 and 800 nm. The excitation emission matrices (EEMs) from this analysis were interpreted using parallel factor analysis (Stedmon and Bro, 2008; Yamashita et al., 2008). FI is calculated based on two points in the fulvic-acid influenced region of EEMs, and indicates the degree of allochthonous (or autochthonous) influence to DOM processing. BIX is a measure of DOM produced by microorganisms and HIX is a measure of DOM containing recalcitrant humic substances. SUVA 254 measures aromaticity and reactivity and is calculated by dividing absorbance at 254 nm by TOC concentrations. Sr is an indicator of molecular weight distribution and is related to photobleaching and algal productivity. 3.3. Data Analysis 3.3.1. Comparing Temporal vs. Spatial Variation We quantified spatial and temporal variability using the means of the local coefficients of variation of sampled solute composition in space and time (Schauer et al. 2025), following concepts developed in prior studies (Abbott et al., 2018; Dupas et al., 2019; Wheeler and Ledford, 2023): \(\displaystyle\overline{\text{CV}_{s}}=\frac{1}{n_{i}}\sum_{i=1}^{n_{i}}\frac{\sigma_{\text{WQ}\left[i,\ *\right]}}{\mu_{\text{WQ}\left[i,*\right]}}\) (1) \(\displaystyle\overline{\text{CV}_{t}}=\frac{1}{n_{j}}\sum_{j=1}^{n_{j}}\frac{\sigma_{\text{WQ}\left[*,j\right]}}{\mu_{\text{WQ}\left[*,j\right]}}\) (2) where \(n\) is the total number of samples and \(WQ\) is the space-time matrix of concentration with size \(n_{i}\times n_{j}\), where \(i\)and \(j\) denote sampling time and site respectively.\(\text{WQ}\left[i,\ *\right]\) is a vector representing solute concentrations of the sampling event ( i.e., across different sampling events) and\(\text{WQ}\left[*,j\right]\) is a vector representing the concentrations measured from the same site ( i.e., across different sampling events). Spatial variability (\(\overline{\text{CV}_{s}}\)) was computed for each solute using the site mean (\(\mu_{\text{WQ}\left[i,*\right]}\)) and standard deviation (\(\sigma_{\text{WQ}\left[i,\ *\right]}\)) for each sampling event ( i.e., across sites). The temporal mean (\(\mu_{\text{WQ}\left[*,j\right]}\)) and standard deviation (\(\sigma_{\text{WQ}\left[*,j\right]}\)) for each site across sampling dates yields the temporal variability (\(\overline{\text{CV}_{t}}\)). Solutes with \(\overline{\text{CV}_{t}}\) or\(\overline{\text{CV}_{s}}\) less than 0.5 are designated as low heterogeneity in time or space, respectively (Musolff et al., 2017). The ratio between temporal and spatial coefficients of variation (\(\overline{\text{CV}_{t}}\) : \(\overline{\text{CV}_{s}}\)) indicates the dominance of spatial vs. temporal variability for each solute (Schauer et al., 2025). A value of 1.0 indicates solute variation occurs equally in space and time, while values above (temporal variation dominates) or below (spatial variation dominates) 1.0 suggest unequal variation. We used a Kruskal-Wallis test to assess whether variability ratios (\(\overline{\text{CV}_{t}}\) : \(\overline{\text{CV}_{s}}\)) exhibit statistically different central tendencies across wetlandscapes. We ranked all ratios and evaluated the differences in the distribution of rank sums among wetlandscapes. The test statistic ( H , approximated by a chi-square distribution) quantifies between-group differences relative to within-group variability. A significant p-value indicates at least one wetlandscape differs from the others, rejecting the null hypothesis of equal medians (or identical distributions). To further identify which groups differ, we conducted a Dunn post-hoc pairwise comparison test. To examine the scale effect on the spatio-temporal variability, we compared the space-time variance patterns observed in these wetlandscapes (~10 1 km 2 ) with patterns across different spatial extents in Florida (state scale, Cohen, 2018), Iran (regional scale, ~10 5 km 2 ; Razmkhah et al., 2010), and France (national scale, ~10 6 km 2 ; Dupas et al., 2019; Table S2). 3.3.2. Predicting Spatial and Temporal Variation We used general linear models enabling the use of both continuous and factor predictor variables to regress observed solute concentrations against environmental attributes that describe both temporal and spatial variation. For temporal variation, we considered regionally averaged dynamic water depth and a measure of the annual variation in radiation (a sinusoidal pattern as a function of day of year). The former is derived from a single time series consisting of average water depths across the study wetlands, representing a parameter that can be derived even when water level measurements are not available across multiple wetlands—as is often the case in less well-studied wetlandscapes. The latter is from latitude-specific models of top-of-atmosphere insolation (Liang et al., 2024). For spatial predictors, we considered wetland size, wetland depth, wetland basin shape ( b ), and distance to nearest neighboring wetland as predictor variables characterizing spatial heterogeneity. Prior to regression, we performed principal components analysis (PCA) on the predictor variables to remove highly correlated (Pearson r > |0.7|) variables. For example, we selected wetland area but not wetland depth or stage-volume exponent, because these three variables were duplicative (Fig. S1), and wetland size is easiest to measure. The fractions of observed spatial and temporal variance unexplained by the model fits are expressed as the complement of the coefficient of determination: \(\displaystyle 1-R_{s}^{2}\) and \(\displaystyle 1-R_{t}^{2}\), respectively. We computed the residual unexplained spatial and temporal variability (\(\displaystyle\overline{\text{CV}_{s,\ \epsilon}}\) and\(\displaystyle\overline{\text{CV}_{t,\epsilon}}\)) as the ratios of the unexplained standard deviations (the square root of the product of the observed and unexplained variances) and the observed mean across sampling time or across sites. \(\displaystyle\overline{\text{CV}_{t,\ \epsilon}}=\frac{\sigma_{\epsilon,WQ\left[*,\ j\right]}}{\mu_{\text{WQ}\left[*,j\right]}}=\overline{\text{CV}_{t}}\sqrt{1-R_{t}^{2}}\) (8) Unexplained variation provides an informative rationale for additional monitoring. Where residual variation is low, more intensive monitoring may not be necessary or informative, even where raw variation is high. While including land cover is likely to improve model performance and reduce uncertainty in spatial water quality variance, we omitted this since land use and management intensity were uniform across our wetlandscapes. We also excluded stream network topology ( e.g., landscape patches of solute sinks and sources; Abbott et al., 2018) since including this variable showed no improvement in model performance. Results 4.1. Heterogeneity in Wetland Hydrology Although the study wetlandscapes have similar hydroclimate and dominant vegetation species, their wetland types, geologic contexts, and geomorphic environments differ. The mean wetland depth in OSBS (264 cm\(\displaystyle\pm\) 95.0) is greater than in BICY (37.2 cm \(\displaystyle\pm\) 14.2), and mean stage is similarly higher in OSBS (Table 1). Wetland water levels in BICY are highly synchronous (mean temporal correlation between wetlands, r = 0.99), whereas wetland water levels in OSBS are more asynchronous, with lower temporal correlation, r = 0.80 (Epstein and Cohen, 2025). Table 1. Summary of geographic, climatologic, and geomorphic information for study wetlandscapes: Big Cypress National Preserve (BICY), Austin Cary Forest (ACF), and Ordway Swisher Biological Station (OSBS). Long-term climatology data provided by Florida Automated Weather Network (FAWN). Wetland ecotype cypress dome pine flatwood depression dark water / clear sandhill Geologic substrate Pleistocene limestone Plio-Pleistocene sands Plio-Pleistocene sands Dominant vegetation woody woody woody/ herbaceous Geographic coordinates 25.99 N, 80.93 W 29.75 N, 82.21 W 29.71 N, 81.99 W Mean wetland stage (\(\pm\ \)SD) (cm) 28.6 (\(\pm\) 12.1) 44.6 (\(\pm\) 23.2) 178 (\(\pm\) 80.2) Mean spill depth (\(\pm\) SD) (cm) 37.2 (\(\pm\) 14.2) 51.8 (\(\pm\) 27.3) 264 (\(\pm\) 95.0) Study period Dec 2018 - Oct 2020 Oct 2018 - Oct 2020 Jan 2018 - Nov 2020 Long-term annual precipitation (mm) 1253 1290 1254 Long-term annual evapotranspiration (mm) 1184 1014 1032 4.2. Spatial vs. Temporal Heterogeneity in Water Quality We observed systematic differences in spatial vs. temporal heterogeneity across wetlandscapes, with shallow\(\displaystyle\overline{\text{CV}_{t}}\ :\ \overline{\text{CV}_{s}}\) slope in OSBS (0.61 ± 0.09), a slope identical to 1.0 in ACF (0.98 ± 0.06), and a slope marginally above 1.0 in BICY (1.09 ± 0.01). The ratio in OSBS is significantly different from both BICY (p-value = 0.012) and ACF (p-value = 0.019) from pairwise Dunn comparisons; while the ratios in BICY and ACF were not significantly different. While the space-time variance patterns varied across different solute functional groups (Fig. 2b-e), there was consistency between wetlandscapes, with the majority of the solutes (15 out of 21) suggesting primacy of spatial variability ( i.e. , \(\displaystyle\overline{\text{CV}_{t}}\) :\(\displaystyle\overline{\text{CV}_{s}}\) < 1) in OSBS, and most of the solutes (14 out of 21) indicating dominant temporal variation in BICY (Fig. 2a). Figure 2. (a) Relative dominance of temporal vs. spatial variability in solute composition in three contrasting wetlandscapes: Big Cypress National Preserve (BICY; blue; slope = 1.09 \(\pm\) 0.01), Austin Cary Forest (ACF; yellow; slope = 0.98 \(\pm\) 0.06), and Ordway Swisher Biological Station (OSBS, red; slope = 0.61 \(\pm\) 0.09). Spatial and temporal variation patterns for (a) all solutes, with colored lines depicting individual wetlandscape relationships between \(\overline{\text{CV}_{t}}\) and \(\overline{\text{CV}_{s}}\) and disaggregated for (b) ions, (c) nutrients, (d) organic, and (e) physio-chemical functional groups. Shaded area denotes low temporal and spatial variability ( \(\overline{\text{CV}}\) < 0.5) and grey dashed lines indicate the 1:1 line of equal spatial and temporal variability. Examining variability of individual solute and water quality parameters provides insight into the relative variability in geochemical processes in wetlandscapes. Ions ( e.g., Cl - and SO 4 2- ) and nutrients ( e.g., NO 3 - , NH 4 + , and SRP) show greater overall variability (i.e., most \(\overline{\text{CV}_{t}}\) and\(\overline{\text{CV}_{s}}\) values exceed 0.5, Fig. 2b-c), whereas organic ( e.g., FI, BIX, and Sr) and physio-chemical ( e.g., pH, SpC, Temp) measurements exhibited lower variability (\(\overline{\text{CV}}\) < 0.5; Fig. 2e-f). Solute-specific patterns reveal high temporal to spatial \(\overline{\text{CV}}\) ratio for DO (2.49\(\pm\)0.42) and SO 4 2- (1.17\(\pm\)0.27), indicating dominant temporal heterogeneity, while spatial variation dominated SRP, especially in OSBS, with a\(\overline{\text{CV}}\) ratio < 1.0 (0.83\(\pm\)0.65). Water quality spatial variability increased across different spatial extents, with highest values at national scale (France, intermediate at sub-national scale (Florida and Iran, lowest at local scale (Florida wetlandscapes, temporal variance was more consistent across scales, albeit with higher temporal variability at the wetlandscape scale. Spatial and temporal variance were evenly balanced for data aggregated across the three wetlandscapes in Florida, but we observed increasing dominance of spatial variation at larger scales (Fig. 3a). The steady decrease in median \(\overline{\text{CV}_{t}}\) : \(\overline{\text{CV}_{s}}\) with increasing spatial extent from near unity for Florida wetlandscapes, to 0.75, 0.53, and 0.14 for streams in Florida, Iran, and France suggests a systematic scaling pattern worthy of further investigation. The increasing dominance in spatial solute variance ( i.e. lower\(\overline{\text{CV}_{t}}\) : \(\overline{\text{CV}_{s}}\)) at larger spatial extents is coupled with modest evidence of greater stability in time (Abbott et al., 2018). Spatial variance is most dominant in ions and nutrients (Fig. 3b, c), whereas organic and physio-chemical measurements show lower overall variance (Fig. 3d). This likely arises because over large study areas, sampling encounters more diverse solute-generating conditions, leading to greater spatial variation. The weaker mechanism of declining temporal variation with scale may involve increased likelihood of sampling larger order rivers, with the attendant blending of spatial and temporal signals that implies (Abbott et al. 2018). Specific examination of the role of stream order is recommended for future research. Environmental attributes explain some of the spatial vs. temporal total variance in solute concentrations, indicating meaningful reductions in both spatial and temporal uncertainty with simple feature and temporal attributes. Model predictions of temporal variance (\(R_{t}^{2}\)) were markedly better than for spatial variance (\(R_{s}^{2}\)), with 82%, 53%, and 49% of temporal variability explained by landscape water depth and seasonal radiation in BICY, ACF, and OSBS wetlandscapes, respectively. In contrast, only 22%, 24%, and 20% of spatial variability was explained by wetland size and separation distance. Model performance is illustrated in Figure 4 by partitioning predictable vs residual variance and moving the space-time position for each solute towards the origin in \(\overline{\text{CV}_{t}}\) :\(\overline{\text{CV}_{s}}\) plots in proportion to model performance. The resulting slopes of regression lines in space-time plots were far shallower for residual variation, compared to total observed variation. Specifically, slopes reduced from 1.09 ± 0.01 to 0.24 ± 0.05 in BICY, from 0.98 ± 0.06 to 0.57 ± 0.09 in ACF, and from 0.61 ± 0.09 to 0.30 ± 0.08 in OSBS; all changes were statistically significant. Figure 3. (a) Temporal vs. spatial variability in composition of all solutes in our study wetlandscapes (black circles) compared to solutes across state (Florida – purple), regional (Iran – blue), and national (France – yellow) scales (colored squares). Colored lines show the general relationships between temporal and spatial variability at each scale, and the grey dashed line denotes equal spatial and temporal variability (i.e., 1:1). We disaggregated the solutes for (b) ions, (c) nutrients, and (d) organic and physio-chemical functional groups. The grey shaded area denotes low temporal and spatial variability ( \(\overline{\text{CV}}\) < 0.5). Figure 4. (a) Conceptual illustration of the residual temporal and spatial variance (gray circle), after subtracting from the observed variance (white circle) the predictable portion from wetland and time attributes. Dashed grey line denotes equivalence (i.e., 1:1), and the grey shaded region denotes low variance ( \(\displaystyle\overline{\text{CV}}\) < 0.5). Larger residual uncertainty in space suggests sampling at more sites while larger residual uncertainty in time support higher frequency sampling. Partitioning of observed (white circles) and residual variance (colored circles) for (b) Big Cypress National Preserve (BICY), (c) Austin Cary Forest (ACF), and (d) Ordway Swisher Biological Station (OSBS). Colored lines are linear regressions between spatial and temporal variation, with lighter lines for the observed data and darker lines for the residual variation. The high predictability of temporal variability in BICY suggests strong climate and hydrological controls in solute compositions (Fig. 4b). Because this variation is readily predictable, the high observed temporal variation does not necessarily require more frequent sampling. In ACF, hydrology and landscape attributes reduce uncertainty in both spatial and temporal variance, explaining considerable solute variability except for SO 4 2- and NO 3 - . Models for these two solutes leave larger residuals in space than time variation, indicating that spatially dense sampling is preferable for capturing unpredictable variability. Only half of temporal variability (\(R_{t}^{2}\)=0.49) and one-fifth of the spatial variability (\(R_{s}^{2}\)=0.2) are predictable in OSBS (Fig. 4d), resulting in large residual variability and strongly implying that dense sampling is required to characterize solute patterns there. SRP, K + , and SO 4 2- exhibited particularly high residual spatial variability, whereas SUVA 254 exhibited relatively high residual temporal variability. This implies that the sampling density in time and space required to effectively characterize extant variation differs by solute, complicating design of efficient monitoring schemes. Discussion 5.1. Characterizing Spatial vs. Temporal Heterogeneity in Water Quality High solute variability was observed across wetlands in these three wetlandscapes, with total variance evenly balanced between space and time. Spatial heterogeneity was moderately dominant in OSBS, where connectivity occurs nearly exclusively via subsurface pathways (Lee et al., 2023), with median spatial vs. temporal solute variation ratio (\(\overline{\text{CV}_{t}}\ :\ \overline{\text{CV}_{s}}\)) of 0.86. In contrast, in BICY and ACF, where surface connectivity is frequent and sometimes prolonged, temporal variability was dominant, with the median\(\overline{\text{CV}_{t}}\ :\ \overline{\text{CV}_{s}}\) of 1.37 and 1.21, respectively (Fig. 2a). That temporal variability (\(\overline{\text{CV}_{t}}\)) was surprisingly consistent across the three wetlandscapes (0.52, 0.42 and 0.44 for BICY, ACF and OSBS, respectively) implies that asynchrony in hydrological connectivity (Epstein et al., 2025) regulates the relative importance of spatial variation (\(\overline{\text{CV}_{s}}\) values are 0.24, 0.29 and 0.59). This hypothesis can be tested at watershed scales based on metrics of synchrony among headwater streams, and may help guide water quality monitoring designs in contrasting climate regimes. When temporal variability dominates, such as in BICY and ACF, spatial persistence (relative ordering of sites) is low, obscuring spatial patterns (Dai et al. 2025). As such, high\(\overline{\text{CV}_{t}}\ :\ \overline{\text{CV}_{s}}\) ratio indicates reduced spatial stability (Dupas et al., 2019), which may imply elevated sampling frequency necessary to adequately capture temporal solute variation. In contrast, in the OSBS wetlandscape where spatial variation exceeds temporal variation, spatial patterns are likely stable, and sampling regimens should emphasize low frequency sampling of many wetlands rather than frequent sampling—targeting few wetlands. We note, however, that each solute differs in the relative importance of spatial vs. temporal variability, implying differential sampling designs based on the solute of interest. For example, nutrients consistently show strong dominance of spatial variance (Fig. 2c), emphasizing a need for spatially dense sampling, while ions tended to exhibit greater temporal variation, perhaps in response to evaporative enrichment as a dominant process (Fig. 2b). In contrast to these two groups with high overall variation, organic solutes (Fig. 2d) and physio-chemical parameters (Fig. 2e, with DO as a notable exception) exhibit low overall variability, and limited differences between spatial and temporal components, suggesting lower sampling effort in both space and time may be acceptable to capture variability in most of these constituents. While the complexities of customizing sampling regimes to specific solutes are obvious, efficiency gains implied by this result may be important to consider. We observed strong influence of spatial extent on the relative importance of space vs. time variance, with spatial variation increasing in relative importance at larger scales (Fig. 3). This may arise from two overlapping processes. First, this may reflect the role of storage in attenuating temporal variance signals in larger systems. Generally, higher temporal variability is observed in headwater streams due to catchment smaller size with lower buffering capacity and higher biogeochemical reactivity (Creed et al., 2015; Vannote et al., 1980). This effect seems relatively modest in our data, however, because the magnitude of temporal variation is surprisingly consistent between wetlandscapes, and between scales. A second process to explain the advancing dominance of spatial variance at larger spatial extents is that broader sampling encounters increasingly diverse land use, lithology, climate, and topography conditions. The varied ecoregions across France (Dupas et al., 2019; Abbott et al., 2018) are understandably more spatially diverse that the comparatively lower heterogeneity in Florida, or indeed in a single landscape. As such, high spatial heterogeneity at a larger extent arises from the stability imprinted on water quality patterns from large scale spatial structures ( e.g. , differences in basement rock types). As such, characterizing spatial patterns at large scales is expedited because each observation is representative of highly persistent patterns (Abbott et al., 2018). Where the emphasis is on large scale ( e.g. , national or continental scale) patterns, spatially extensive sampling with reduced sampling frequency emerges as the key design element (Fig. 3; Schauer et al. 2025). In general, with the emergence of high frequency water quality sensors, the emphasis of sampling aquatic systems has been on capturing temporal patterns (Rode et al. 2016) at the frequency of hydrological variation. Our results offer an important counterpoint to that, suggesting that temporal variation is relatively well constrained, and at large scales may be reasonably considered small compared to spatial variation. This relative dominance of spatial vs. temporal heterogeneity has important implications, including for preserving wildlife aquatic habitat, understanding change in hydrochemistry, and designing effective sampling of environmental processes (Dupas et al., 2019; Elser and Bennett 2011). While the extremes of temporal fluctuation in water quality, not mean values, may regulate habitat quality for aquatic organisms (Davis et al., 2010), the spatial variation in water quality impacts the mosaic of conditions that support meta-ecosystem and biogeochemical functions. As such, even at the most local scale, a renewed emphasis on the causes and consequences of spatial variation is likely to yield important management-relevant insights that a few sensors capturing temporal patterns cannot. Clearly, the design of any sampling procedure depends on the focal question, but the evidence from these wetlandscapes, and even more so from variance patterns over larger extents, is that spatial controls are prominent and ignored at our peril. 5.2. Diagnosing Uncertainty in Temporal vs. Spatial Heterogeneity While spatial and temporal variance components are broadly equal at the wetlandscape scale, we observed clear evidence that spatial variability in wetland solute composition was more poorly explained than temporal variation (Fig. 4). Most of the temporal variance in solute composition was explained by hydrologic attributes and seasonality, implying inherently regular and stable seasonal and event driven patterns (\(\displaystyle R_{t}^{2}\) = 0.82, 0.53, 0.49 in BICY, ACF, and OSBS, respectively; Fig. 4). In contrast, spatial variance was far less explicable by selected geogenic and environmental attributes (\(\displaystyle R_{s}^{2}\) = 0.22, 0.24, and 0.20 in BICY, ACF, and OSBS, respectively). In each wetlandscape, the residual temporal variation was far smaller than the residual spatial variation, especially in BICY (Fig. 4b) but evident in all three wetlandscapes. This suggests that our models predicting water quality temporal variation with modest inputs (seasonality and hydrological state) successfully attenuate uncertainty, while our spatial models based on landscape attributes are less effective. The significant reduction in \(\displaystyle\overline{\text{CV}_{s}}\) vs.\(\displaystyle\overline{\text{CV}_{t}}\) slopes in Fig. 4 (1.09 to 0.24 in BICY, 0.98 to 0.57 in ACF and 0.61 to 0.30 in OSBS) quantify this effect. Given these results, conclusions from the observed variance patterns about the prioritization of spatially dense sampling over high temporal frequency sampling are strongly reinforced. Wetlandscape-scale solute uncertainty is overwhelmingly a matter of differences between sites, and not between times. High predictability of temporal variation arises from coherent seasonal and event-driven solute dynamics. For example, solutes in BICY closely follow highly regular seasonal hydrologic patterns, and while the magnitude of variation is large, the temporal uncertainty ( i.e. , unpredictable variation) is small. Given the small magnitude of spatial variation in BICY (\(\displaystyle\overline{\text{CV}_{s}}\) < 0.5; Fig. 4b), sampling needs in this wetlandscape are generally low. In this landscape, similarly sized wetlands are highly hydrologically synchronized (Epstein and Cohen, 2025), spatial heterogeneity is small, and temporal variation is predictable. As such, ACF emerges as the wetlandscape with the largest residual spatial vs. temporal variability, and the highest \(\displaystyle\overline{\text{CV}_{t,\ \epsilon}}\):\(\displaystyle\overline{\text{CV}_{s,\ \epsilon}}\) ratio. Flatwoods landscapes are abundant across the US southeastern coastal plain, and characterizing water quality there appears to be a sampling design challenge. The general conclusion of our work that spatial variation dominates holds in the flatwoods (ACF), but less so than other settings, suggesting that sampling protocols need to be balanced. Both the observed and residual variation for solutes in OSBS retained an emphasis on spatial sampling, but some solutes ( e.g., SRP, SO 4 2- ) show particularly high dominance of residual spatial variance, likely driven by the contrasting provenance of water between dark (nutrient rich, especially for P and N) and clear (nutrient poor) flow paths in this sandhill landscape (OSBS, 2024). Contrasting dissolved organic matter conditions in OSBS dominates spatial heterogeneity, suggesting a synoptic—rather than high-frequency—sampling approach that is stratified between features in different flow regimes is the most effective monitoring approach. Increasingly accessible water quality and environmental datasets ( e.g. , Fernandez et al. 2025) establish a foundation for characterizing spatial vs. temporal variability across different spatial extents, beyond local site-specific studies, (Kirchner and Neal, 2013; Abbott et al., 2016). Schauer et al. (2025) illustrate the value of this rigorous synthesis of existing data. Here we add that characterizing residual uncertainty in water quality after applying simple models informs sampling design to navigate the persistent trade-offs in investing resources in spatially extensive vs. temporally intensive sampling. Effective monitoring design is needed to understand and manage environmental processes, especially in settings with degrading water quality and limited sampling capacity (Seitzinger et al 2010; Crocker and Bartram 2014). Our results suggest that prioritizing spatially extensive screening is the most efficient way to utilize limited resources at all spatial extents, identifying locations in need of remediation or restoration environmental agencies (Abbott et al., 2018). Testing the generality of our proposed model residual assessment at different spatial extents ( i.e., local, regional, global) will improve understanding of space-time variance in water quality and environmental processes and inform design of efficient water quality sampling networks. Conclusion Characterizing the relative importance of spatial vs. temporal variability provides guidance to improve water quality sampling efficiency. Our results suggest that at the smallest spatial extent (Florida wetlandscapes, <10 1 km 2 ) for which space vs. time water quality variation has been assessed, spatial variation and temporal variation are nearly equal (\(\displaystyle\overline{\text{CV}_{t}}\) : \(\displaystyle\overline{\text{CV}_{s}}\) = 1.05 \(\displaystyle\pm\) 0.07), but that with increasing spatial monitoring extent, spatial variation increasingly dominates (\(\displaystyle\overline{\text{CV}_{t}}\) :\(\displaystyle\overline{\text{CV}_{s}}\) = 0.19 \(\displaystyle\pm\) 0.11 in France; underscoring a critical emphasis on capturing spatial variation when interpreting water quality patterns. In hydrologically diverse settings, spatial variability dominates due to differences in flow paths, connectivity, and source inputs, whereas in more hydrologically homogeneous environments ( i.e. , local wetlandscapes), temporal variability, which is surprisingly consistent across scales, is of elevated importance, driven by synchronized responses to shared climatic and hydrological forcing. This highlights the need to consider hydrological similarity when interpreting spatiotemporal water quality patterns and designing monitoring frameworks. We suggest that variation left unexplained after considering hydrologic, seasonal, geogenic, and environmental attributes is ultimately more useful for characterizing the value of additional sampling, and that this analysis in our study wetlandscapes strongly prioritizes spatial over temporal sampling, even though raw spatial and temporal variance was similar. We foresee utility of this variance partitioning approach in characterizing space-time water quality structure across diverse ecohydrological and environmental settings. Author Contributions: Conceptualization: Esther Lee, Joshua M. Epstein, James W. Jawitz, Matthew J. Cohen Data curation: Olivia Cacciatore, Joshua M. Epstein, Matthew J. Cohen Formal analysis: Esther Lee, Joshua M. Epstein, Matthew J. Cohen Funding acquisition: James W. Jawitz, Matthew J. Cohen Investigation: Esther Lee, James W. Jawitz, Matthew J. Cohen Methodology: Esther Lee, Joshua M. Epstein, James W. Jawitz, Matthew J. Cohen Project Administration: James W. Jawitz, Matthew J. Cohen Resources: Esther Lee, Olivia Cacciatore, Joshua M. Epstein, Matthew J. Cohen Supervision: James W. Jawitz, Matthew J. Cohen Validation: Esther Lee, James W. Jawitz, Matthew J. Cohen References Abbott, B.W., Baranov, V., Mendoza-Lera, C., Nikolakopoulou, M., Harjung, A. & Kolbe, T. et al. (2016). Using multi-tracer inference to move beyond single-catchment ecohydrology. Earth-Sci. Rev., 160, 19–42. Abbott, B. W., Gruau, G., Zarnetske, J. P., Moatar, F., Barbe, L., Thomas, Z., … & Pinay, G. (2018). Unexpected spatial stability of water chemistry in headwater stream networks. Ecology letters , 21 (2), 296-308. Burt, T.P. & Pinay, G. (2005). Linking hydrology and biogeochemistry in complex landscapes. Prog. Phys. Geogr., 29, 297–316. Cheng, F. Y., & Basu, N. B. (2017). Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing. Water Resources Research, 53(6), 5038–5056. Cook, B. J. (2001). Temporary hydrologic connections make “isolated” wetlands function at the landscape scale. University of Montana, 95. Creed, I.F., McKnight, D.M., Pellerin, B.A., Green, M.B., Bergamaschi, B.A. & Aiken, G.R. et al. (2015). The river as a chemostat: fresh perspectives on dissolved organic matter flowing down the river continuum. Can. J. Fish Aquat. Sci., 72, 1272–1285. Crocker, J., & Bartram, J. (2014). Comparison and cost analysis of drinking water quality monitoring requirements versus practice in seven developing countries. International journal of environmental research and public health , 11 (7), 7333-7346. Dai, D., Fernandez, N., Cohen, M. J., & Jawitz, J. W. (2025). Sampling regime effects on detecting spatial stability of water quality. Water Resources Research , 61 (9), e2024WR038844. Davis, J., Sim, L. & Chambers, J. (2010). Multiple stressors and regime shifts in shallow aquatic ecosystems in antipodean landscapes. Freshw. Biol., 55, 5–18. Diamond, J. S., & Cohen, M. J. (2018). Complex patterns of catchment solute–discharge relationships for coastal plain rivers. Hydrological Processes , 32 (3), 388-401. Dupas, R., Minaudo, C., & Abbott, B. W. (2019). Stability of spatial patterns in water chemistry across temperate ecoregions. Environmental Research Letters, 14(7), 074015. Egusa, T., Kumagai, T. O., Oda, T., Gomi, T., & Ohte, N. (2019). Contrasting patterns in the decrease of spatial variability with increasing catchment area between stream discharge and water chemistry. Water Resources Research , 55 (8), 7419-7435. Elser, J., & Bennett, E. (2011). A broken biogeochemical cycle. Nature , 478 (7367), 29-31. Epstein, J. M., & Cohen, M. J. (2025). Wetland Hydrologic Synchrony and Functional Redundancy Across Contrasting Wetlandscapes. Hydrological Processes , 39 (9), e70244. Epting, S. M., Hosen, J. D., Alexander, L. C., Lang, M. W., Armstrong, A. W., & Palmer, M. A. (2018). Landscape metrics as predictors of hydrologic connectivity between Coastal Plain forested wetlands and streams. Hydrological Processes, 32(4), 516–532. Fernandez, N., Cohen, M.J. and Jawitz, J.W. (2025). ChemLotUS: A Benchmark Data Set of Lotic Chemistry Across US River Networks. Water Resources Research , 61 (5), p.e2024WR039355. Florida Natural Areas Inventory. (2010). https://www.fnai.org/species-communities/natcom-guide. Gu, S., Casquin, A., Dupas, R., Abbott, B. W., Petitjean, P., Durand, P., & Gruau, G. (2021). Spatial persistence of water chemistry patterns across flow conditions in a mesoscale agricultural catchment. Water Resources Research , 57 (7), e2020WR029053. Haag, K., & Lee, T. (2010). Hydrology and Ecology of Freshwater Wetlands in Central Florida- A Primer. US Department of the Interior & USGS. Hammond, M. P., & Kolasa, J. (2014). Spatial variation as a tool for inferring temporal variation and diagnosing types of mechanisms in ecosystems. PloS one , 9 (2), e89245. Hayashi, M., & Van der Kamp, G. (2000). Simple equations to represent the volume–area–depth relations of shallow wetlands in small topographic depressions. Journal of hydrology , 237 (1-2), 74-85. Hosen, J. D., Armstrong, A. W., & Palmer, M. A. (2018). Dissolved organic matter variations in coastal plain wetland watersheds: The integrated role of hydrological connectivity, land use, and seasonality. Hydrological Processes , 32 (11), 1664-1681. Hutchins MG, Smith B, Rawlins BG, Lister TR. 1999. Temporal and spatial variability of stream waters in Wales, the Welsh borders and part of the West Midlands, UK. 1. Major ion concentrations. Water Research 33: 3479 – 3491. Kirchner, J.W. & Neal, C. (2013). Universal fractal scaling in stream chemistry and its implications for solute transport and water quality trend detection. Proc. Natl Acad. Sci., 110, 12213–12218. Klammler, H., Quintero, C. J., Jawitz, J. W., McLaughlin, D. L., & Cohen, M. J. (2020). Local storage dynamics of individual wetlands predict wetlandscape discharge. Water Resources Research , 56 (11), e2020WR027581. Lee, E., Epstein, J. M., & Cohen, M. J. (2023). Patterns of wetland hydrologic connectivity across coastal-plain wetlandscapes. Water Resources Research , 59 , e2023WR034553. Lefebvre, S., Clement, J.C., Pinay, G., Thenail, C., Durand, P. & Marmonier, P. (2007). 15n-nitrate signature in low-order streams: effects of land cover and agricultural practices. Ecol. Appl. 17, 2333– 2346. Leibowitz, S. G., & Vining, K. C. (2003). Temporal connectivity in a prairie pothole complex. Wetlands, 23(1), 13–25. Liang, H., Jiang, B., Liang, S., Wen, J., He, T., Zhang, X., … & Yin, X. (2024). A novel Terrain Correction Sinusoidal Model for improving estimation of daily clear-sky downward shortwave radiation. IEEE Transactions on Geoscience and Remote Sensing . Likens GE, Buso DC. (2006). Variation in streamwater chemistry throughout the Hubbard Brook Valley.Biogeochemistry78: 1 – 30. McGuire, K. J., Torgersen, C. E., Likens, G. E., Buso, D. C., Lowe, W. H., & Bailey, S. W. (2014). Network analysis reveals multiscale controls on streamwater chemistry. Proceedings of the National Academy of Sciences , 111 (19), 7030-7035. McLaughlin, D.L. and Cohen, M.J. (2011). Thermal artifacts in measurements of fine-scale water level variation. Water Resources Research 47: W09601. O’Donnell, J., Aiken, G. R., Walvoord, M., & Butler, K. (2012). Dissolved organic matter composition of winter flow in the Yukon River basin: Implications of permafrost thaw and increased groundwater discharge. Global Biogeochemical Cycles, 26(4). Ordway-Swisher Biological Station (OSBS). 2024. “Hydrology.” http://ordway-swisher.ufl.edu/Hydrology.aspx. Osburn, C., Oviedo-Vargas, D., Barnett, E., Dierick, D., Oberbauer, S., & Genereux, D. (2018). Regional Groundwater and Storms Are Hydrologic Controls on the Quality and Export of Dissolved Organic Matter in Two Tropical Rainforest Streams, Costa Rica. Journal of Geophysical Research: Biogeosciences, 123(3), 850–866. Pinay, G., Peiffer, S., De Dreuzy, J.-R., Krause, S., Hannah, D.M. & Fleckenstein, J.H. et al. (2015). Upscaling nitrogen removal capacity from local hotspots to low stream orders’ drainage basins. Ecosystems, 18, 1101–1120. Quintero, C. J., & Cohen, M. J. (2019). Scale-Dependent Patterning of Wetland Depressions in a Low-Relief Karst Landscape. Journal of Geophysical Research: Earth Surface, 124(8), 2101–2117. Rains, M. C., Leibowitz, S. G., Cohen, M. J., Creed, I. F., Golden, H. E., Jawitz, J. W., Kalla, P., Lane, C. R., Lang, M. W., & McLaughlin, D. L. (2016). Geographically isolated wetlands are part of the hydrological landscape: Invited Commentary. Hydrological Processes, 30(1), 153–160. Raymond, P.A., Saiers, J.E. & Sobczak, W.V. (2016). Hydrological and biogeochemical controls on watershed dissolved organic matter transport: pulse-shunt concept. Ecology, 97, 5–16. Rode, M., Wade, A.J., Cohen, M.J., Hensley, R.T., Bowes, M.J., Kirchner, J.W., Arhonditsis, G.B., Jordan, P., Kronvang, B., Halliday, S.J. and Skeffington, R.A., (2016). Sensors in the Stream: The High-Frequency Wave of the Present. Environmental science & technology , 50 (19),10297-10307. Schauer, L. S., Jawitz, J. W., Cohen, M. J., & Musolff, A. (2025). Spatial and Temporal Variability of River Water Quality. Hydrological Processes , 39 (5), e70154. Seitzinger, S. P., Mayorga, E., Bouwman, A. F., Kroeze, C., Beusen, A. H., Billen, G., … & Harrison, J. A. (2010). Global river nutrient export: A scenario analysis of past and future trends. Global biogeochemical cycles , 24 (4). Shogren, A. J., Zarnetske, J. P., Abbott, B. W., Iannucci, F., Frei, R. J., Griffin, N. A., & Bowden, W. B. (2019). Revealing biogeochemical signatures of Arctic landscapes with river chemistry. Scientific Reports , 9 (1), 12894. Smart RP, Soulsby C, Cresser MS, Wade AJ, Townend J, Billett MF,Langan S. 2001. Riparian zone influence on stream water chemistry at different spatial scales: a GIS-based modelling approach, an example for the Dee, NE Scotland. Science of the Total Environment280:173 – 193. Sprecher, S. W. (2000). Installing Monitoring Wells/Piezometers in Wetlands, ERDC TN-WRAP-00-02, U.S. Army Research and Development Center, Vicksburg, MS. Stedmon, C.A. & Bro, R., (2008). Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography: Methods , 6 (11), 572-579. Temnerud, J., Fölster, J., Buffam, I., Laudon, H., Erlandsson, M., & Bishop, K. (2010). Can the distribution of headwater stream chemistry be predicted from downstream observations?. Hydrological Processes , 24 (16), 2269-2276. Thorslund, J., Cohen, M. J., Jawitz, J. W., Destouni, G., Creed, I. F., Rains, M. C., Badiou, P., & Jarsjö, J. (2018). Solute evidence for hydrological connectivity of geographically isolated wetlands. Land Degradation & Development. https://doi.org/10.1002/ldr.3145 Thorslund, J., Jarsjo, J., Jaramillo, F., Jawitz, J. W., Manzoni, S., Basu, N. B., Chalov, S. R., Cohen, M. J., Creed, I. F., Goldenberg, R., Hylin, A., Kalantari, Z., Koussis, A. D., Lyon, S. W., Mazi, K., Mard, J., Persson, K., Pietro, J., Prieto, C., Quin, A., Van Meter, K., and Destouni, G. (2017). Wetlands as large-scale nature-based solutions: Status and challenges for research, engineering and management, Ecological Engineering, 108:489–497. Yamashita, Y., Jaffé, R., Maie, N. and Tanoue, E. (2008). Assessing the dynamics of dissolved organic matter (DOM) in coastal environments by excitation emission matrix fluorescence and parallel factor analysis (EEM‐PARAFAC). Limnology and oceanography , 53 (5), 1900-1908. Yu, X., Hawley-Howard, J., Pitt, A. L., Wang, J. J., Baldwin, R. F., & Chow, A. T. (2015). Water quality of small seasonal wetlands in the Piedmont ecoregion, South Carolina, USA: effects of land use and hydrological connectivity. Water Research , 73 , 98-108. Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R. & Cushing, C.E. (1980). The river continuum concept. Can. J. Fish Aquat. Sci., 37, 130–137. Watts, A. C., Watts, D. L., Cohen, M. J., Heffernan, J. B., McLaughlin, D. L., Martin, J. B., & Kobziar, L. N. (2014). Evidence of biogeomorphic patterning in a low‐relief karst landscape. Earth Surface Processes and Landforms , 39 (15), 2027-2037. Wheeler, C. T., & Ledford, S. H. (2023). Stability in headwater chemical signatures across a dynamic flow regime in a highly urbanized Piedmont catchment. Hydrological Processes , 37 (2), e14825. Wilson, H. F., & Xenopoulos, M. A. (2009). Effects of agricultural land use on the composition of fluvial dissolved organic matter. Nature Geoscience, 2(1), 37–41. Zhang, X., Bianchi, T. S., Cohen, M. J., Martin, J. B., Quintero, C. J., Brown, A. L., … & Kenney, W. F. (2019). Initiation and development of wetlands in southern Florida karst landscape associated with accumulation of organic matter and vegetation evolution. Journal of Geophysical Research: Biogeosciences, 124(6), 1604-1617. Zipper, S. C., Hammond, J. C., Shanafield, M., Zimmer, M., Datry, T., Jones, C. N., … & Allen, D. C. (2021). Pervasive changes in stream intermittency across the United States. Environmental Research Letters , 16 (8), 084033. Supplementary Material File (fig1 wetland map_labeled_cropped.tif) Download 35.06 MB Information & Authors Information Version history V1 Version 1 17 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords environmental processes solute composition spatio-temporal variability variance partitioning wetland water quality Authors Affiliations Esther Lee 0000-0002-5659-4800 [email protected] University of Florida School of Forest Fisheries and Geomatics Sciences View all articles by this author Olivia Cacciatore University of Florida School of Forest Fisheries and Geomatics Sciences View all articles by this author Joshua M. Epstein 0000-0001-9283-3111 University of Florida School of Forest Fisheries and Geomatics Sciences View all articles by this author James Jawitz 0000-0002-6745-0765 University of Florida View all articles by this author Matt Cohen 0000-0001-5674-1850 University of Florida School of Forest Fisheries and Geomatics Sciences View all articles by this author Metrics & Citations Metrics Article Usage 169 views 82 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Esther Lee, Olivia Cacciatore, Joshua M. Epstein, et al. Temporal vs. Spatial Heterogeneity in Wetlandscape Water Quality. Authorea . 17 January 2026. DOI: https://doi.org/10.22541/au.176863528.84575212/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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