Hydrologic sources of carbon cycling uncertainty throughout the terrestrial–aquatic continuum

Linking hydrologic interactions with global carbon cycling will reduce the uncertainty associated with scaling-up empirical studies and facilitate the incorporation of terrestrial–aquatic linkages within global and regional change models. Much of the uncertainty in estimates of carbon fluxes associated with precipitation and hydrologic transport results from the extensive spatial and temporal heterogeneity in both intrinsic functioning and anthropogenic modification of hydrological cycles. To better understand this variation we developed a landscape ecological approach to coupled hydrologic–carbon cycling that merges local mechanisms with multiple-scale spatial heterogeneity. This spatially explicit framework is applied to examine variability in hydrologic influences on carbon cycling along a continental scale water availability gradient with an explicit consideration of human sources of variability. Hydrologic variation is an important component of the uncertainty in carbon cycling; accounting for this variation will improve understanding of current conditions and projections of future ecosystem responses to global change.     

Modeling Effects of Forest Cover Reduction on Larval Walleye Survival in Lake Erie Tributary Spawning Basins

An integrated modeling approach is used to link land use to river discharge, and then to survival of larval walleye that hatch in northern Ohio streams draining into Lake Erie (USA). First, to link land use and river discharge, the parameters of a simple hydrologic model are statistically related to watershed landscape attributes, including forest cover. One such relationship allows estimation of the change in daily river discharge that could result from a reduction in basin-scale forest cover. Second, to represent the river discharge-larval survival link, we reexamine a dataset from Mion and others to propose a relationship between daily flow velocity, water temperature, and walleye larval survival. Together, these linked models provide estimates of the reduction in larval survival due to reduction in forest cover, along with the uncertainty of those estimates. For the Grand River watershed, decreasing forest cover from 45.2 to 30% is projected to reduce average larval survival by about 45%. In the adjacent Chagrin River, dropping cover from 62.5 to 30% reduces survival by almost 60%. The greater rate of reduction of survival in the Chagrin River as forest levels fall is explained by a relatively greater increase in storm flows for the Chagrin, due to more frequently saturated soils. Therefore, forest preservation in the Chagrin River watershed is projected to be more effective in preserving walleye larval tributary habitat.     

Estimating carbon carrying capacity in natural forest ecosystems across heterogeneous landscapes: addressing sources of error

Evaluating contributions of forest ecosystems to climate change mitigation requires well‐calibrated carbon cycle models with quantified baseline carbon stocks. An appropriate baseline for carbon accounting of natural forests at landscape scales is carbon carrying capacity (CCC); defined as the mass of carbon stored in an ecosystem under prevailing environmental conditions and natural disturbance regimes but excluding anthropogenic disturbance. Carbon models require empirical measurements for input and calibration, such as net primary production (NPP) and total ecosystem carbon stock (equivalent to CCC at equilibrium). We sought to improve model calibration by addressing three sources of errors that cause uncertainty in carbon accounting across heterogeneous landscapes: (1) data‐model representation, (2) data‐object representation, (3) up‐scaling. We derived spatially explicit empirical models based on environmental variables across landscape scales to estimate NPP (based on a synthesis of global site data of NPP and gross primary productivity, n=27), and CCC (based on site data of carbon stocks in natural eucalypt forests of southeast Australia, n=284). The models significantly improved predictions, each accounting for 51% of the variance. Our methods to reduce uncertainty in baseline carbon stocks, such as using appropriate calibration data from sites with minimal human disturbance, measurements of large trees and incorporating environmental variability across the landscape, have generic application to other regions and ecosystem types. These analyses resulted in forest CCC in southeast Australia (mean total biomass of 360 t C ha^?1, with cool moist temperate forests up to 1000 t C ha^?1) that are larger than estimates from other national and international (average biome 202 t C ha^?1) carbon accounting systems. Reducing uncertainty in estimates of carbon stocks in natural forests is important to allow accurate accounting for losses of carbon due to human activities and sequestration of carbon by forest growth.     

流域景观格局与河流水质的多变量相关分析

流域内的景观格局改变是人类活动的宏观表现,会对河流水质产生显著影响,因此明确影响水质变化的关键景观因子,对于深入了解景观对水质的影响机制具有重要的研究价值。选择广东省淡水河流域为研究对象,以2007年ALOS卫星影像以及水质监测数据为基础,运用空间分析和多变量分析方法,分析淡水河流域景观格局与河流水质的相关关系。用包括流域和河岸带尺度的景观组成和空间结构信息的景观指数表征景观格局,用Spearman秩相关分析、多元线性逐步回归模型和典型相关分析(CCA)研究景观指数和水质指标的相关关系。研究结果表明:林地、城镇用地和农业用地占淡水河流域总面积超过90%,其中城镇用地超过20%。多元线性逐步回归分析和CCA结果说明水质指标受到多个景观指数的综合影响,反映了景观格局对水质的复杂影响机制。流域景观格局对河流水质有显著影响,流域尺度的景观指数比河岸带尺度的景观指数对水质影响更大。城镇用地比例是影响耗氧污染物和营养盐等污染物浓度最重要的景观指数,林地和农业用地对水质的影响较小。另外,景观破碎化对pH值、溶解氧和重金属等水质指标有显著影响。CCA的第一排序轴解释了景观指数与水质指标相关性的54.0%,前两排序轴累积能解释景观指数与水质指标相关性的87.6%,前两轴分别主要表达了城市化水平和景观破碎化水平的变化梯度。淡水河流域的景观格局特征从上游到下游呈现出城市—城乡交错—农村的景观梯度,水质变化也对应了这个梯度的变化,说明人类活动引起的流域土地覆盖及土地管理措施变化会对水质变化产生显著影响。     

流域尺度上的景观格局与河流水质关系研究进展

利用景观生态学原理研究流域尺度上土地利用及其空间格局对河流水质的影响,已成为流域环境研究中的热点问题。在综合评价国内外土地利用变化与河流水质关系研究的基础上,阐述了景观格局在流域水环境研究中的重要性,并根据国内外研究进展,对景观格局与水质关系的研究方法和手段进行了分类分析,同时也对流域尺度上的景观-水质模型研究进展也进行了分析总结,最后指出了景观格局与水质关系研究的核心问题和未来研究的热点方向。     

SWAT模型对景观格局变化的敏感性分析——以丹江口库区老灌河流域为例

景观的空间配置与类型组成能够对流域的产流、产沙及非点源污染产生影响。在以往SWAT模型研究中,往往默认水文模型考虑了该影响。为分析SWAT模型对不同景观格局变化的敏感性,根据老灌河流域2000年土地利用在各子流域的组成,模拟研究区更为破碎、复杂的景观空间配置,通过设置多套试验参数,利用SWAT模型生成基于不同景观格局的模拟结果。结果表明,SWAT模型不能反映除坡度和面积变化之外的景观水平下各斑块之间因景观空间格局改变对流域产流、产沙以及非点源污染的影响;模型通过其他参数的调整,弥补了模型分析数据的不足,使实测数据与模型部分结果高度吻合。这表明,一个能够反映流域部分水文特征的SWAT模型,未必是对研究区真实情形的模拟,而是各个参数间平衡的结果。因此,在利用SWAT模型分析模拟景观变化时,不应默认模型能够模拟景观空间格局改变对流域水文过程的影响,同时研究者可以通过划分坡度带,提高模型对不同坡度土地利用的敏感性。     

Dealing with uncertainty in landscape genetic resistance models: a case of three co‐occurring marsupials

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel inference as an improvement over single model‐based inference. We illustrate the approach empirically using co‐occurring, woodland‐preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground‐dwelling antechinus (Antechinus flavipes). First, we use maximum‐likelihood and a bootstrap procedure to identify the best‐supported isolation‐by‐resistance model out of 56 models defined by linear and non‐linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small data sets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision‐making, where dealing with uncertainty is critical.     

Modeling indirect N2O emissions along the N cascade from cropland soils to rivers

The frequently observed discrepancy between estimations of N₂O emissions at regional or global scale based either on field data or inventories (bottom-up) or on direct atmospheric observations (top-down) suggests that riparian areas and river surfaces play a significant role as hot spots of emission. We developed a modeling procedure to assess N₂O emissions occurring during the transfer of water masses from the subroot water pool of the watershed to the outlet of the river drainage network, including their passage through riparian wetlands. The model was applied to three river basins of increasing size located in the sedimentary geological area of the Paris basin (France) and validated by its capability to predict river N₂O concentrations and fluxes across the river–atmosphere interface. At the scale of the Seine watershed, indirect emissions, i.e. emissions linked to agricultural practices but occurring elsewhere than directly at the field plot, are estimated to represent approximately 20% of the direct emissions from the watershed soils, in good agreement with previous estimates based on empirical accounting approaches. Denitrification in riparian zones is responsible for the largest share of these indirect emissions. The model results are very sensitive to the value of the ratio of N₂O versus (N₂ + N₂O), in the final products of denitrification in rivers and wetlands. By calibration on river N₂O concentrations, a value of 0.015 ± 0.05 is proposed for this ratio, in agreement with recent studies. This represents the main uncertainty factor of the model. In basins with conditions prone to increasing the value of this ratio, higher proportions of indirect N₂O emissions might possibly be observed.

     

Landscape,regional and global estimates of nitrogen flux from land to sea: Errors and uncertainties

Regional to global scale modelling of N fluxfrom land to ocean has progressed to datethrough the development of simple empiricalmodels representing bulk N flux rates fromlarge watersheds, regions, or continents on thebasis of a limited selection of modelparameters. Watershed scale N flux modellinghas developed a range of physically-basedapproaches ranging from models where N fluxrates are predicted through a physicalrepresentation of the processes involved,through to catchment scale models which providea simplified representation of true systemsbehaviour. Generally, these watershed scalemodels describe within their structure thedominant process controls on N flux at thecatchment or watershed scale, and take intoaccount variations in the extent to which theseprocesses control N flux rates as a function oflandscape sensitivity to N cycling and export. This paper addresses the nature of the errorsand uncertainties inherent in existing regionalto global scale models, and the nature of errorpropagation associated with upscaling fromsmall catchment to regional scale through asuite of spatial aggregation and conceptuallumping experiments conducted on a validatedwatershed scale model, the export coefficientmodel. Results from the analysis support thefindings of other researchers developingmacroscale models in allied research fields. Conclusions from the study confirm thatreliable and accurate regional scale N fluxmodelling needs to take account of theheterogeneity of landscapes and the impact thatthis has on N cycling processes withinhomogenous landscape units.     

The application of Artificial Neural Network (ANN) model to the simulation of denitrification rates in mesocosm-scale wetlands

Denitrification and its regulating factors are of great importance to aquatic ecosystems, as denitrification is a critical process to nitrogen removal. Additionally, a by-product of denitrification, nitrous oxide, is a much more potent greenhouse gas than carbon dioxide. However, the estimation of denitrification rates is usually clouded with uncertainty, mainly due to high spatial and temporal variations, as well as complex regulating factors within wetlands. This hampers the development of general mechanistic models for denitrification as well, as most previously developed models were empirical or exhibited low predictability with numerous assumptions. In this study, we tested Artificial Neural Network (ANN) as an alternative to classic empirical models for simulating denitrification rates in wetlands. ANN, multiple linear regression (MLR) with two different methods, and simplified mechanistic models were applied to estimate the denitrification rates of 2-year observations in a mesocosm-scale constructed wetland system. MLR and simplified mechanistic models resulted in lower prediction power and higher residuals compared to ANN. Although the stepwise linear regression model estimated similar average values of denitrification rates, it could not capture the fluctuation patterns accurately. In contrast, ANN model achieved a fairly high predictability, with an R² of 0.78 for model validation, 0.93 for model calibration (training), and a low root mean square error (RMSE) together with low bias, indicating a high capacity to simulate the dynamics of denitrification. According to a sensitivity analysis of the ANN, non-linear relationships between input variables and denitrification rates were well explained. In addition, we found that water temperature, denitrifying enzyme activity (DEA), and DO accounted for 70% of denitrification rates. Our results suggest that the ANN developed in this study has a greater performance in simulating variations in denitrification rates than multivariate linear regressions or simplified nonlinear mechanistic model.     

Assessing the Role of Parameter and Input Uncertainty in Ecohydrologic Modeling: Implications for a Semi-arid and Urbanizing Coastal California Catchment

Ecohydrologic models are a key tool in understanding plant–water interactions and their vulnerability to environmental change. Although implications of uncertainty in these models are often assessed within a strictly hydrologic context (for example, runoff modeling), the implications of uncertainty for estimation of vegetation water use are less frequently considered. We assess the influence of commonly used model parameters and inputs on predictions of catchment-scale evapotranspiration (ET) and runoff. By clarifying the implications of uncertainty, we identify strategies for insuring that the quality of data used to drive models is considered in interpretation of model predictions. Our assessment also provides insight into unique features of semi-arid, urbanizing watersheds that shape ET patterns. We consider four sources of uncertainty: soil parameters, irrigation inputs, and spatial extrapolation of both point precipitation and air temperature for an urbanizing, semi-arid coastal catchment in Santa Barbara, CA. Our results highlight a seasonal transition from soil parameters to irrigation inputs as key controls on ET. Both ET and runoff show substantial sensitivity to uncertainty in soil parameters, even after parameters have been calibrated against observed streamflow. Sensitivity to uncertainty in precipitation manifested primarily in winter runoff predictions, whereas sensitivity to irrigation manifested exclusively in modeled summer ET. Neither ET nor runoff was highly sensitive to uncertainty in spatial interpolation of temperature. Results argue that efforts to improve ecohydrologic modeling of vegetation water use and associated water-limited ecological processes in these semi-arid regions should focus on improving estimates of anthropogenic outdoor water use and explicit accounting of soil parameter uncertainty.     

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale‐selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection – a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM‐based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species’ detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species’ scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.     

流域水文模型在面源污染模拟与管控中的应用研究进展

面源污染是影响流域水环境和水安全的重要污染来源,对其进行有效防控需要对其负荷以及防控措施效果进行科学高效精准的预测。流域水文模型(Hydrological Simulation Program-FORTRAN,HSPF)具有突出的综合性和灵活性,是面源污染模型的典范。近年来,HSPF模型应用于我国流域面源污染相关的研究和实践有了飞速发展,但同样也面临着模型机理和参数本地化、模型构建精细化、模型结构不确定性较大等方面的挑战。围绕该模型在面源污染模拟与管控中的研究进展,对其在变化环境下的模拟方法和成果,以及应对参数识别、不确定性分析、措施效果评估和总量控制的思路和方法等方面进行了总结,并分析了现代化环境模拟形势下HSPF模型的延伸发展。结合模型相关研究的总结,强调了面向我国流域特色的本地化模型改进、服务河长制精细监管的大尺度精细化模拟、以及模型与大数据统计及人工智能耦合的互馈集合模拟等后续研究是需要重点关注的发展动向。     

农村多水塘系统水环境过程研究进展

农村多水塘系统由于其不可替代的水资源蓄积和营养物去除功能,广泛分布于我国东部和南部地区。在分析多水塘系统水环境过程研究进展的基础上,指出了目前多水塘系统水环境过程研究中的不足及未来发展趋势。关于多水塘系统的研究主要从两个尺度展开,分别是生态系统尺度和景观尺度。(1)基于生态系统尺度的多水塘系统水环境过程研究主要表现在两方面。首先,多水塘系统在改变区域水文情势上发挥着重大作用。多水塘系统能有效降低流速,且增加地表径流的滞留时间;其次是对多水塘系统水质的研究,主要包括水塘对污染物截留降解能力的研究、水塘底泥和水体之间营养物形态转化和输移机制的研究。(2)基于景观尺度的多水塘系统水环境过程模型研究主要包括构建经验模型和机制模型两方面。经验模型主要是利用统计分析方法分析景观格局与水环境之间关系;适用于农村多水塘系统的水环境机制模型主要包括国外的SWAT、HSPF、DRAINWAT和TOPMODEL模型。农村多水塘系统的研究可以为建设生态新农村提供科学依据。     

Denitrification in the river network of a mixed land use watershed: unpacking the complexities

River networks have the potential to permanently remove nitrogen through denitrification. Few studies have measured denitrification rates within an entire river network or assessed how land use affect rates at larger spatial scales. We sampled 108 sites throughout the network of the Fox River watershed, Wisconsin, to determine if land use influence sediment denitrification rates, and to identify zones of elevated sediment denitrification rates (hot spots) within the river network. Partial least squares regression models identified variables from four levels of organization (river bed sediment, water column, riparian zone, and watershed) that best predicted denitrification rates throughout the river network. Nitrate availability was the most important predictor of denitrification rates, while land cover was not always a good predictor of local-scale nitrate concentrations. Thus, land cover and denitrification rate were not strongly related across the Fox River watershed. A direct relationship between denitrification rate and watershed land cover occurred only in the Wolf River sub-watershed, the least anthropogenically disturbed of the sub-watersheds. Denitrification hot spots were located throughout the river network, regardless of watershed land use, with hot spot location being determined primarily by nitrate availability. In the Fox River watershed, when nitrate was abundant, river bed sediment character influenced denitrification rate, with higher denitrification rates at sites with fine, organic sediments. These findings suggest that denitrification occurring throughout an entire river network, from headwater streams to larger rivers, can help reduce nitrogen loads to downstream water bodies.

     

Using simplified watershed hydrology to define spatially explicit ‘zones of influence’

Riparian areas represent dynamic spatial gradients characterized by a varying degree of terrestrial–aquatic interaction. Many studies have considered riparian zones to be discrete watershed sub-portions (e.g., 100-m riparian buffers), whereas I introduce ‘zones of influence’ that are subsets of the riparian zone. The purpose of this study is to introduce the concept of hydrologically defined influence zones using a simple hydrologic model to delimit land-cover. I describe a method for identifying zones of influence using watershed hydrologic patterns to delimit zones along a near-stream continuum between a downstream point (e.g., sample reach) and the watershed boundary. Using hydrologic modeling equations and GIS, travel time was calculated for every 30 × 30-m cell in 10 watersheds providing spatially explicit estimates of watershed hydrology and enabling us to calculate the travel time required for rainfall in any watershed cell to reach the watershed terminus. Shorter-duration travel times (i.e., 30–60 min) described smaller areas than longer-duration travel times (i.e., 210–300 min). This method is an alternative method to delimit near stream areas when quantifying watershed influence. Handling editor: K. Martens     

Testing scale dependent assumptions in regional ecosystem simulations

Abstract. We present a Regional Ecosystem Simulation System (RESSys) which uses satellite data to define vegetation properties, topographic and soil data to define site characteristics, and a climate generator program to build a topographically sensitive microclimate map. We use a 150-km² mountainous forested watershed in Glacier National Park to test the consequences of modeling various ecosystems processes using different versions of RESSys with increasing simplification of the landscape: (1) spatial scaling generated using 30 m x 30 m Landsat Thematic Mapper data versus 1 km x 1 km Advanced Very High Resolution Radiometer data for vegetation definition; (2) modeling hydrologic dynamics produced by using a topographic routing model versus a simple soil ‘bucket’ model; (3) variable landscape partitioning based on patterns of topographic complexity; and (4) representation of annual net primary productivity (ANPP) using an absorbed photosynthetic active radiation (APAR) model. We evaluate results of these simulations by comparison with average values and areal distributions of photosynthesis, evapotranspiration, hydrologic outflow, and ANPP. Our primary goal is to test whether areal average flux of carbon and water can be scaled linearly over a complex landscape. We found that daily photosynthesis could be predictably estimated between modeling scales with correlation coefficients ranging between 0.89 to 0.99. ANPP was highly correlated among the modeling scales with maximum differences between ANPP prediction of ca. 0.5Mg C ha^-1 yr^-1. Evapotranspiration was similarly predictable between scales but was influenced by differences associated with hydrologic modeling. Hydrologic outflow was not highly correlated between different modeling scales as a function of the different hydrologic models used at different scales.     

Bayesian Analysis for Uncertainty and Risk in a Groundwater Numerical Model's Predictions

Groundwater modeling typically relies on some hypothesis and approximations of reality, as the real hydrologic systems are far more complex than we can mathematically characterize. This kind of a model's errors cannot be neglected in the uncertainty analysis for a model's predictions in practical issues. As the scale and complexity increase, the associated uncertainties boost dramatically. In this study, a Bayesian uncertainty analysis method for a deterministic model's predictions is presented. The geostatistics of hydrogeologic parameters obtained from site characterization are treated as the prior parameter distribution in the Bayes’ theorem. Then the Markov-Chain Monte Carlo method is used to generate the posterior statistical distribution of the model's predictions, conditional to the observed hydrologic system behaviors. Finally, a series of synthetic examples are given by applying this method to a MODFLOW pumping test model, to test its capability and efficiency in order to assess various sources of the model's prediction uncertainty. The impacts of the model's parameter sensitivity, simplification, and observation errors to predict uncertainty are evaluated, respectively. The results are analyzed statistically to provide deterministic predictions with associated prediction errors. Risk analysis is also derived from the Bayesian results to draw tradeoff curves for decision-making about exploitation of groundwater resources.     

Catchment Legacies and Time Lags: A Parsimonious Watershed Model to Predict the Effects of Legacy Storage on Nitrogen Export

Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures.