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"Ecohydrology Mathematical models."
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Temperature Is Likely an Important Omission in Interpreting Vegetation Optical Depth
Vegetation optical depth (VOD) satellite microwave retrievals provide significant insights into vegetation water content and responses to hydroclimatic changes. While VOD variations are commonly linked to dry biomass and live fuel moisture content (LFMC), the impact of canopy temperature (Tc) remains overlooked in large‐scale studies. Here, we investigated the impact of Tc on L‐band (1.4 GHz) and X‐band (10.7 GHz) VOD at diurnal and seasonal timescales. Synthetic benchmark VOD was created using realistic fields of Tc, LFMC, and biomass in an electromagnetic model. Perturbation experiments revealed that Tc strongly affects diurnal VOD variations at both L‐band and X‐band. Seasonally, while biomass emerges as the largest contributor to VOD variations in 70% (at X‐band) and 90% (at L‐band) of our study region, Tc and LFMC still play substantial roles. The findings stress the importance of refining retrieval algorithms to distinguish Tc, LFMC, and biomass effects for future VOD applications in ecohydrology. Plain Language Summary Satellite measurements known as vegetation optical depth (VOD) are sensitive to how much water plants contain (i.e., their hydration level). Because of this, VOD allows researchers to study how ecosystems respond to changes in weather, climate, and other factors. However, besides water, other variables like the temperature of the plants and their biomass can also change satellite VOD readings. In this study, we used computer simulations to examine how canopy temperature, relative water content, and plant biomass may affect these satellite readings. We found that canopy temperature has a surprisingly strong impact on VOD variations, especially at the diurnal timescale. Our results highlight the need to account for plant temperature influence in future VOD applications. Key Points A simulation approach was used to investigate how canopy temperature, live fuel moisture content, and dry biomass affect VOD variations The impact of canopy temperature on VOD variations is more substantial than previously assumed, both at diurnal and seasonal timescales To interpret VOD dynamics, algorithms should differentiate between temperature, live fuel moisture content, and biomass contributions
Journal Article
A Fully Coupled Numerical Solution of Water, Vapor, Heat, and Water Stable Isotope Transport in Soil
Modeling water stable isotope transport in soil is crucial to sharpen our understanding of water cycles in terrestrial ecosystems. Although several models for soil water isotope transport have been developed, many rely on a semi‐coupled numerical approach, solving isotope transport only after obtaining solutions from water and heat transport equations. However, this approach may increase instability and errors of model. Here, we developed an algorithm that solves one‐dimensional water, heat, and isotope transport equations with a fully coupled method (MOIST). Our results showed that MOIST is more stable under various spatial and temporal discretization than semi‐coupled method and has good agreement with semi‐analytical solutions of isotope transport. We also validated MOIST with long‐term measurements from a lysimeter study under three scenarios with soil hydraulic parameters calibrated by HYDRUS‐1D in the first two scenarios and by MOIST in the last scenario. In scenario 1, MOIST showed an overall NSE, KGE, and MAE of simulated δ18O of 0.47, 0.58, and 0.92‰, respectively, compared to the 0.31, 0.60, and 1.00‰ from HYDRUS‐1D; In scenario 2, these indices of MOIST were 0.33, 0.52, and 1.04‰, respectively, compared to the 0.19, 0.58, and 1.15‰ from HYDRUS‐1D; In scenario 3, calibrated MOIST exhibited the highest NSE (0.48) and KGE (0.76), the smallest MAE (0.90) among all scenarios. These findings indicate MOIST has better performance in simulating water flow and isotope transport in simplified ecosystems than HYDRUS‐1D, suggesting the great potential of MOIST in furthering our understandings of ecohydrological processes in terrestrial ecosystems.
Journal Article
Uncertainty in hydrological signatures
Information about rainfall–runoff processes is essential for hydrological analyses, modelling and water-management applications. A hydrological, or diagnostic, signature quantifies such information from observed data as an index value. Signatures are widely used, e.g. for catchment classification, model calibration and change detection. Uncertainties in the observed data – including measurement inaccuracy and representativeness as well as errors relating to data management – propagate to the signature values and reduce their information content. Subjective choices in the calculation method are a further source of uncertainty. We review the uncertainties relevant to different signatures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall–runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment. We found that the uncertainties were often large (i.e. typical intervals of ±10–40 % relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncertainty types such as rating-curve form. We found that signatures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment comparisons or inferences about dominant processes.
Journal Article
Monitoring and modeling water-vegetation interactions in groundwater-dependent ecosystems
In many regions around the world, groundwater is the key source of water for some vegetation species, and its availability and dynamics can define vegetation composition and distribution. In recent years the interaction between groundwater and vegetation has seen a renewed attention because of the impact of groundwater extraction on natural ecosystems' health and increasing interest in the restoration of riparian zones and wetlands. The literature provides studies that approach this problem from very different angles. Information on the vegetation species that are likely to depend on groundwater and the physical characteristics of such species can be found in a large body of literature in ecology and plant physiology. Environmental engineers, hydrologists, and geoscientists are more focused on ecosystem restoration and the estimation of a catchment's water balance, for which the groundwater transpired by vegetation might be an important component. Here we join together these different bodies of literature with the aim of providing the state of knowledge on groundwater‐dependent vegetation. We describe the physiological features that characterize groundwater‐dependent vegetation, review different methods to study vegetation water use in the field, discuss recent advances in the understanding of how groundwater levels might determine vegetation composition, and present a summary of the available mathematical models that include the interaction between groundwater levels and vegetative water use. Several future research directions are identified, such as the quantification and modeling of the partitioning of transpiration between unsaturated and saturated zones and the development of integrated models able to link hydrology, ecology, and geomorphology. Key Points The root system of phreatophytes is key for their interaction with groundwater In many areas, water table levels can explain vegetation patterns Integrated ecogeomorphology models are required for water management purposes
Journal Article
Reassessment of the 2010–2011 Haiti cholera outbreak and rainfall-driven multiseason projections
Mathematical models can provide key insights into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. We study the ex post reliability of predictions of the 2010–2011 Haiti cholera outbreak from four independent modeling studies that appeared almost simultaneously during the unfolding epidemic. We consider the impact of different approaches to the modeling of spatial spread of Vibrio cholerae and mechanisms of cholera transmission, accounting for the dynamics of susceptible and infected individuals within different local human communities. To explain resurgences of the epidemic, we go on to include waning immunity and a mechanism explicitly accounting for rainfall as a driver of enhanced disease transmission. The formal comparative analysis is carried out via the Akaike information criterion (AIC) to measure the added information provided by each process modeled, discounting for the added parameters. A generalized model for Haitian epidemic cholera and the related uncertainty is thus proposed and applied to the year-long dataset of reported cases now available. The model allows us to draw predictions on longer-term epidemic cholera in Haiti from multiseason Monte Carlo runs, carried out up to January 2014 by using suitable rainfall fields forecasts. Lessons learned and open issues are discussed and placed in perspective. We conclude that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control.
Journal Article
Investigating the root plasticity response of Centaurea jacea to soil water availability changes from isotopic analysis
• Root water uptake is a key ecohydrological process for which a physically based understanding has been developed in the past decades. However, due to methodological constraints, knowledge gaps remain about the plastic response of whole plant root systems to a rapidly changing environment.
• We designed a laboratory system for nondestructive monitoring of stable isotopic composition in plant transpiration of a herbaceous species (Centaurea jacea) and of soil water across depths, taking advantage of newly developed in situ methods. Daily root water uptake profiles were obtained using a statistical Bayesian multisource mixing model.
• Fast shifts in the isotopic composition of both soil and transpiration water could be observed with the setup and translated into dynamic and pronounced shifts of the root water uptake profile, even in well watered conditions.
• The incorporation of plant physiological and soil physical information into statistical modelling improved the model output. A simple exercise of water balance closure underlined the nonunique relationship between root water uptake profile on the one hand, and water content and root distribution profiles on the other, illustrating the continuous adaption of the plant water uptake as a function of its root hydraulic architecture and soil water availability during the experiment.
Journal Article
Stochastic Ecohydrological Perspective on Semi‐Distributed Rainfall‐Runoff Dynamics
Quantifying watershed process variability consistently with climate change and ecohydrological dynamics remains a central challenge in hydrology. Stochastic ecohydrology characterizes hydrologic variability through probability distributions that link climate, hydrology, and ecology. However, these approaches are often limited to small spatial scales (e.g., point or plot level) or focus on specific fluxes (e.g., streamflow), without accounting for the entire water balance at the basin scale. While semi‐distributed models account for spatial heterogeneity and upscaled hydrologic fluxes, they lack the analytical simplicity of stochastic ecohydrology or the SCS‐CN method and, perhaps more importantly, do not directly characterize probability distributions that integrate the effects of past random variability in hydroclimatic conditions. This hinders an efficient characterization of hydrological statistics at the watershed scale. To overcome these limitations, we merge stochastic ecohydrology, the spatial upscaling of semi‐distributed modeling, and the SCS‐CN rainfall‐runoff partitioning. The resulting unified model analytically characterizes watershed ecohydrological and hydrological statistics using probability density functions (PDFs) that are functions of climate and watershed model parameters (e.g., baseflow coefficient)—something unattainable with the Monte Carlo methods of traditional stochastic hydrology. Calibrated across 81 watersheds in Florida and southern Louisiana, the model PDFs precisely capture the long‐term average water balance and runoff variance, as well as the runoff quantiles with a median Nash–Sutcliffe efficiency of 0.98. These results also advance the SCS‐CN method by providing an analytical PDF for the Curve Number (CN), explicitly linked to climate variables, baseflow, and the long‐term water balance partitioning described by the Budyko curve.
Journal Article
Coupled ecohydrology and plant hydraulics modeling predicts ponderosa pine seedling mortality and lower treeline in the US Northern Rocky Mountains
• We modeled hydraulic stress in ponderosa pine seedlings at multiple scales to examine its influence on mortality and forest extent at the lower treeline in the northern Rockies.
• We combined a mechanistic ecohydrologic model with a vegetation dynamic stress index incorporating intensity, duration and frequency of hydraulic stress events, to examine mortality from loss of hydraulic conductivity. We calibrated our model using a glasshouse dry-down experiment and tested it using in situ monitoring data on seedling mortality from reforestation efforts. We then simulated hydraulic stress and mortality in seedlings within the Bitterroot River watershed of Montana.
• We show that cumulative hydraulic stress, its legacy and its consequences for mortality are predictable and can be modeled at local to landscape scales. We demonstrate that topographic controls on the distribution and availability of water and energy drive spatial patterns of hydraulic stress. Low-elevation, south-facing, nonconvergent locations with limited upslope water subsidies experienced the highest rates of modeled mortality.
• Simulated mortality in seedlings from 2001 to 2015 correlated with the current distribution of forest cover near the lower treeline, suggesting that hydraulic stress limits recruitment and ultimately constrains the low-elevation extent of conifer forests within the region.
Journal Article
Technical note: Seamless extraction and analysis of river networks in R
Spatially explicit mathematical models are key to a mechanistic understanding of environmental processes in rivers. Such models necessitate extended information on networks' morphology, which is often retrieved from geographic information system (GIS) software, thus hindering the establishment of replicable script-based workflows. Here I present rivnet, an R package for GIS-free extraction and analysis of river networks based on digital elevation models (DEMs). The package exploits TauDEM's flow direction algorithm in user-provided or online accessible DEMs, and allows for computing covariate values and assigning hydraulic variables across any network node. The package is designed so as to require minimal user input while allowing for customization for experienced users. It is specifically intended for application in models of ecohydrological, ecological or biogeochemical processes in rivers. As such, rivnet aims to make river network analysis accessible to users unfamiliar with GIS-based and geomorphological methods and therefore enhance the use of spatially explicit models in rivers.
Journal Article