Ecohydrology最新文献

Headwater streams influence the carbon cycle, but their productivity estimation remains challenging. We propose the use of dissolved oxygen data (% saturation, DOsat) and on-site photosynthetically active radiation (PAR) data to develop DOsat~PAR curves as an analogy to the well-known photosynthesis–irradiance (P–E) curves. The premise of our research is that although these curves are simple, they provide detailed information of stream ecosystem productivity dynamics. We used data from two streams in the Oregon Coast Range to investigate daily gross primary productivity (GPP). We used properties of the light-limited portion of the DOsat~PAR regression curves to produce a model to estimate GPP. We found that the slope of the DO–PAR relation varied widely between 1.6 × 10⁻⁴ and 0.045 and had strong correlations (r² > 0.78). The data from one of the two study sites (Oak Creek) was used for model development while the data from the other site (South Fork Mill Creek) was used for model validation. The model's ability to quantify the effects of a discrete storm event on stream productivity was tested by comparing GPP estimates calculated through a Bayesian framework (streamMetabolizer) and our raw data-driven estimates of GPP which were based on the variability of the DOsat~PAR regression curves. The proposed methodology was successful in estimating GPP in headwaters. We foresee that this method may be used to assess disturbances and construct a baseline understanding of productivity dynamics in other headwater ecosystems that is independent of the methodological challenges of the current stream metabolism models.

Hydrologic stress is increasing in Fremont cottonwood (Populus fremontii) forests across the southwestern United States because of increased temperature and streamflow diversion. The spatial variability of this stress is large yet poorly understood. Along the Yampa and Green Rivers in Colorado and Utah, vapour pressure deficit and flow diversions increase downstream. To investigate effects of this gradient on cottonwoods, we measured the percent live canopy and height of randomly selected trees at three sites: Deerlodge Park on the Yampa River (DLP), Island Park on the upper Green (ILP) and Canyonlands National Park on the lower Green (CAN). From these same trees, we took increment cores to understand differences in tree growth in each forest over time. We then related tree metrics to local water availability, streamflow and climatic data. Cottonwoods at CAN were shorter and had lower percent live canopy and growth rate than similarly aged trees upstream. CAN trees that grew higher above the water surface also tended to have lower tree growth, height and live canopy percentage. Furthermore, the correlation between tree growth and maximum vapour pressure deficit showed a much stronger negative shift since 1990 at CAN than at the other sites. All of these differences suggest higher hydrologic stress at CAN, which we attribute to the combined effects of peak flow declines from Flaming Gorge Reservoir, flow diversion and the higher and increasing vapour pressure deficit at CAN. Further research on the variability of hydrologic stress on cottonwoods could help managers anticipate and mitigate the effects of drought stress in these iconic forests.

More than half of water supply in the western United States is sourced from forested lands that are increasingly under wildfire risk. Studies have begun to isolate the effects of wildfire on streamflow, but they have typically used coarse temporal resolutions that cannot account for the numerous, interconnected watershed processes that control the responses to rainfall events. In this study, we employed a method to isolate fine-scale (daily) effects of fire. Wildfire effects were estimated as the difference between measured post-fire streamflow and unburned scenarios of post-fire streamflow simulated by a hydrologic model calibrated to pre-fire conditions. The method was applied to track hydrologic recovery after wildfires in six burned watersheds across the western United States: North Eagle Creek, NM (2012 Little Bear Fire), Lopez Creek, CA (1985 Las Pilitas Fire), City Creek, Devil Canyon Creek, East Twin Creek, and Plunge Creek, CA (2003 Old Fire). All six watersheds experienced prolonged increases of post-fire streamflow, with the most consistent changes occurring during periods of low streamflow. Following 6 years of increased streamflow, Lopez Creek experienced 6 years of reduced streamflow before returning to the pre-fire hydrologic regime. North Eagle Creek and the four watersheds affected by the Old Fire continued to have elevated streamflow 9 and 18 years post-fire, respectively, without returning to the pre-fire hydrologic regime.

The spatiotemporal variability of groundwater level is an important property of peatland hydrology that directly alternates water storage. Nonetheless, the current understanding of the variations of groundwater level over long periods of time remains limited. In this study, we investigated two peatland watersheds (0.151 km² for Watershed 1 and 0.844 km² for Watershed 2) in the Zoige Basin in the upper watershed of the Yellow River to monitor temporal variability of groundwater level using self-recorded water loggers over 4 years (2017–2021). The main results demonstrate that (1) groundwater level variations were controlled by gully drainage in sites adjacent to the gully but were more affected by rainfall in sites distant from the gully. The groundwater level near the gully downcut was lower than that near the gully without complete downcutting through the pear layer, with a maximum difference of 58.3 cm, indicating the longitudinal effect of groundwater level in the watershed. (2) Because the rainfall had a lag effect on the groundwater level, the length of lag gradually decreased with increased rainfall intensity (i.e., the lag time for sites distant from the gully was about 18 min shorter than that of sites close to the gully in Watershed 1). (3) The peak values of the groundwater level occurred simultaneously with the maximum and minimum rainfall in Watershed 2, and the peak occurrence time was related to the ratio of precipitation to evaporation. In the downstream sites, the groundwater level fluctuated more than the upstream ones in Watershed 2. Moreover, the average groundwater level in the upstream sites was 14.3 cm higher than that of the middle ones, indicating a decreasing trend of water storage along the gully. (4) The differences in groundwater level between wet and dry seasons were clear, but the difference was smaller in the upstream sites due to limited gully incision and higher water storage within the peat layer. Additionally, groundwater level changes were more extreme on rainy days during both the wet and dry seasons, but the different intensities of rainfall resulted in stable groundwater in the dry season and an oscillating groundwater level in the wet season in Watershed 2. This study uncovers the groundwater dynamics in the two peatland watersheds, which is of great significance for understanding runoff variation, ecohydrological processes, and wetland shrinkage.

Patterns of alpine plant productivity are extremely variable in space and time. Complex topography drives variations in temperature, wind, and solar radiation. Surface and subsurface flow paths route water between landscape patches. Redistribution of snow creates scour zones and deep drifts, which drives variation in water availability and growing season length. Hence, the distribution of snow likely plays a central role in patterns of alpine plant productivity. Given that these processes operate at sub-1 m to sub-10 m spatial scales and are dynamic across daily to weekly time scales, historical studies using manual survey techniques have not afforded a comprehensive assessment of the influence of snow distribution on plant productivity. To address this knowledge gap, we used weekly estimates of normalised difference vegetation index (NDVI), snow extent, and peak snow depth, acquired from drone surveys at 25 cm resolution. We derived six snowpack-related and topographic variables that may influence vegetation productivity and analysed these with respect to the timing and magnitude of peak productivity. Peak NDVI and peak NDVI timing were most highly correlated with maximum snow depth, and snow-off-date. We observed up to a ~30% reduction in peak NDVI for areas with deeper and later snow cover, and a ~11-day delay in the timing of peak NDVI in association with later snow-off-date. Our findings leverage a novel approach to quantify the importance of snow distribution in driving alpine vegetation productivity and provide a space for time proxy of potential changes in a warmer, lower snow future.

In high mountain areas, snowmelt water is a key—yet fading—hydrological resource, but its importance for soil recharge and tree root water uptake is understudied. In these environments, heterogeneous terrains enhance a highly variable availability of soil and groundwater resources that can be accessed by plants. We conducted a tracer-based study on a subalpine forest in the Italian Alps. We investigated the isotopic composition (²H and ¹⁸O) of snowmelt, precipitation, spring water, soil water—at different locations and depths—and xylem water of twigs taken from alpine larch, Swiss stone pine and alpenrose plants during bi-weekly field campaigns (growing seasons of 2020 and 2021). Mixing models based on δ¹⁸O revealed a large contribution of snowmelt to soil and xylem water, particularly during early summer. We investigated the contribution of water from different soil depths to xylem water, using the sap flow records to date back the end-member signatures. We found a flexible use of shallow and deeper soil water by the investigated plants, with groundwater more likely used by larger trees and during the late summer. Results based on isotopic data were combined with geophysical observations of the subsurface structure to develop a conceptual model about the different exploitation of water by plants depending on their location (shallow soil on a slope vs. a saturated area). Our study highlights the relevance of snowmelt in high-elevation terrestrial ecosystems, where heterogeneous substrates shape the water availability at different depths and, in turn, water uptake by plants.

Ecosystem management depends on transforming qualitative observations (e.g., slow-moving shallow conditions provide nursery refugia for silvery minnow larvae) into management actions to increase habitat quantity or improve habitat quality. To be effective, decision metrics that are developed for management objectives should be validated with field observations. Model assumptions, precision and parameter importance can be refined by comparing the fidelity of selected parameters computed as habitat quality metrics and the correlation of these metrics to real-world observations. Validated environmental metrics are more credible for management and can be compatible with ecosystem monitoring and project design processes. In this study, streamflow monitoring data and hydraulic modelling are used to quantify fish habitat extent for 15 years of spring runoff. The spring runoff event coincides with larval maturation to a free-swimming juvenile phase for the silvery minnow, a critical period in Rio Grande habitat management. Different methods to estimate habitat availability (i.e., hydrology statistics, inundation extents based on hydraulic modelling and areal habitat availability based on different formulations of a habitat suitability index curve) were used to test the efficacy of different metrics relative to species population monitoring. This analysis finds that flow–ecology relationships based on hydraulic modelling or hydrology statistics are both effective and highly correlated to larval production. The investigation shows how seasonal hydrologic characterization and hydraulic discretization have varying levels of correlation with seasonal fish production. This study demonstrates how hydraulic modelling data and hydrologic characterization of riverine environments can be used to validate or develop conceptual ecological models.

This study provides a new perspective on understanding the intricacies of water-mediated connectivity in ecosystems, bridging landscape ecology and geomorphology through network science. We highlight dryland and river-floodplain ecosystems as distinct examples of contrasting water-controlled systems. We (1) discuss central considerations in developing structural connectivity and functional connectivity networks of water-mediated connectivity; (2) quantify the emergent patterns in these networks; and (3) evaluate the capacity of network science tools for investigating connectivity characteristics. With a focus on strength (weights) and direction, connectivity is quantified using seven parameters at both network and node levels. We find that link density, betweenness centrality and page rank centrality are highly sensitive to directionality; global efficiency and degree centrality are particularly sensitive to weights; and relative node efficiency remains unaffected by weights and directions. Our study underscores how network science approaches can transform how we quantify and understand water-mediated connectivity, especially in consideration of the role(s) of weights and directionality. This interdisciplinary perspective, linking ecology, hydrology and geomorphology, has implications for both theoretical insights and practical applications in environmental management and conservation efforts.

The provision of drinking water, agricultural, and industrial applications by reservoirs has made lake exploration and monitoring unavoidable. The features of the ecosystem, particularly physical and chemical elements, influence the evaluation of the quality of water resources. Lakes undergo extensive qualitative changes due to their vast amount of water. In general, these bodies of water represent geological conditions as well as water contamination produced by natural and human activities. In the present research, the prediction of the amount of phycocyanin (fPC) in the water of Lake Michigan has been implemented employing four tree-based machine learning techniques based on seasonality factors. Phycocyanin has significant effects on quality parameters such as turbidity, chlorophyll concentration, algal bloom, and dissolved oxygen in water by affecting the photosynthesis process of algae. Therefore, in this study, the prediction of the amount of phycocyanin dissolved in the lake water using the mentioned variables, along with the temperature of the water, specific conductance, and pH, has been able to interpret the quality of the water and the occurrence of phenomena such as algal blooms. The results of the models in predicting fPCs equal to 0.44 and 0.55 μg/L were consistent with the natural conditions of the lake, and it seems that ensemble tree–based models, along with the biological index of fPC, formed the right combination of input and output parameters in modeling and obtained the lowest prediction error (root-mean-square error [RMSE] boosted trees = 0.0140 and RMSE random forests = 0.0141 μg/L).