Long-term trend and interannual variation in evapotranspiration of a young temperate Douglas-fir stand over 2002-2022 reveals the impacts of climate change.

The shortage of decades-long continuous measurements of ecosystem processes limits our understanding of how changing climate impacts forest ecosystems. We used continuous eddy-covariance and hydrometeorological data over 2002-2022 from a young Douglas-fir stand on Vancouver Island, Canada to assess the long-term trend and interannual variability in evapotranspiration (ET) and transpiration (T). Collectively, annual T displayed a decreasing trend over the 21 years with a rate of 1% yr-1, which is attributed to the stomatal downregulation induced by rising atmospheric CO2 concentration. Similarly, annual ET also showed a decreasing trend since evaporation stayed relatively constant. Variability in detrended annual ET was mostly controlled by the average soil water storage during the growing season (May-October). Though the duration and intensity of the drought did not increase, the drought-induced decreases in T and ET showed an increasing trend. This pattern may reflect the changes in forest structure, related to the decline in the deciduous understory cover during the stand development. These results suggest that the water-saving effect of stomatal regulation and water-related factors mostly determined the trend and variability in ET, respectively. This may also imply an increase in the limitation of water availability on ET in young forests, associated with the structural and compositional changes related to forest growth.

Evaluation of the applicability of the SWAT model in an arid piedmont plain oasis.

Hetao Oasis is located in a typical piedmont alluvial plain bounded by the Langshan Mountain Range in the north, desert in the west, and the Yellow River in the south. Agricultural activities within the oasis significantly impact the hydrological cycle and water quality in downstream locations. The research uses the Soil and Water Assessment Tool (SWAT) for a piedmont plain by defining the watershed boundary as coinciding with the natural mountain ridge, the border between the oasis and the desert, and the Yellow River. The model simulates water discharge with coefficient of determination and a Nash-Sutcliffe model efficiency of 0.78 and 0.62 during model calibration, and 0.75 and 0.69 during model validation, suggesting that delineation of the watershed as carried out in this research is suitable for piedmont plain topography. From the results, the mountains contribute 28.4% to the water discharge at the outlet of the watershed, and water-use efficiency of irrigated water is about 40%, which is consistent with field-based measurements. Methodologies used in delineating watershed boundaries and parameterizing SWAT provide a solid foundation for water balance studies in other regions of the world with similar topography.

Assessing watershed-scale impacts of best management practices and elevated atmospheric carbon dioxide concentrations on water yield.

Changes in water yield are influenced by many intersecting biophysical elements, including climate, on-land best management practices, and landcover. Large-scale reductions in water yield may present a significant threat to water supplies globally. Many of these intersecting factors are intercorrelated and confounded, making it challenging to separate the factors' individual contributions to shaping local streamflow dynamics. Comprehensive hydrological models constructed based on a well-established understanding of biophysical processes are often employed to address these matters. However, these models rarely incorporate all relevant factors influencing local hydrological processes, due to the reliance of these models on the latest, albeit limited, state-of-the-art research. For instance, complexities inherent in watershed hydrology, which involve multilayered interactions among potentially many biophysical factors, leave the direct analysis of subtle impacts on water yields measured in-situ largely intractable. Therefore, we propose an innovative approach to assess impacts of elevated atmospheric CO2 concentrations and flow diversion terraces (FDTs) on stream discharge rates at the watershed scale. Initially, we use a comprehensive hydrological model to account for the impacts of major climatic and landuse/landcover factors on changes in field-acquired measurements of water yield. Next, we employ conventional and advanced statistical methods to decompose the residuals of model predictions to facilitate the identification of subtle influences promoted by increases in atmospheric CO2 concentrations and the application of FDTs in an agriculture-dominated watershed. Through this innovative approach, we find that FDTs contributed to a watershed-wide, net water-yield reduction of 188.0 mm (or 28.9 %) from 1992 to 2014. Ongoing increases in ambient CO2 concentrations, which are responsible for an overall reduction in a watershed-level assessment of stomatal conductance, have led to a minor increase in stream discharge rates during the same 23-year period, i.e., 0.45 mm of water yield per year, or 1.6 % overall. Streamflow reductions explicitly caused by regional warming in the area alone, on account of increased evapotranspiration, may be overestimated due to the opposing, synergistic effects on water yield associated with CO2-enrichment of the lower atmosphere and the annual application of FDTs.

Using the soil and water assessment tool to estimate achievable water quality targets through implementation of beneficial management practices in an agricultural watershed.

Runoff from crop production in agricultural watersheds can cause widespread soil loss and degradation of surface water quality. Beneficial management practices (BMPs) for soil conservation are often implemented as remedial measures because BMPs can reduce soil erosion and improve water quality. However, the efficacy of BMPs may be unknown because it can be affected by many factors, such as farming practices, land-use, soil type, topography, and climatic conditions. As such, it is difficult to estimate the impacts of BMPs on water quality through field experiments alone. In this research, the Soil and Water Assessment Tool was used to estimate achievable performance targets of water quality indicators (sediment and soluble P loadings) after implementation of combinations of selected BMPs in the Black Brook Watershed in northwestern New Brunswick, Canada. Four commonly used BMPs (flow diversion terraces [FDTs], fertilizer reductions, tillage methods, and crop rotations), were considered individually and in different combinations. At the watershed level, the best achievable sediment loading was 1.9 t ha(-1) yr(-1) (89% reduction compared with default scenario), with a BMP combination of crop rotation, FDT, and no-till. The best achievable soluble P loading was 0.5 kg ha(-1) yr(-1) (62% reduction), with a BMP combination of crop rotation and FDT and fertilizer reduction. Targets estimated through nonpoint source water quality modeling can be used to evaluate BMP implementation initiatives and provide milestones for the rehabilitation of streams and rivers in agricultural regions.

Temporal heterogeneity in photosystem II photochemistry in Artemisia ordosica under a fluctuating desert environment.

Acclimation strategies in xerophytic plants to stressed environmental conditions vary with temporal scales. Our understanding of environmentally-induced variation in photosystem II (PSII) processes as a function of temporal scales is limited, as most studies have thus far been based on short-term, laboratory-controlled experiments. In a study of PSII processes, we acquired near-continuous, field-based measurements of PSII-energy partitioning in a dominant desert-shrub species, namely Artemisia ordosica, over a six-year period from 2012-2017. Continuous-wavelet transformation (CWT) and wavelet coherence analyses (WTC) were employed to examine the role of environmental variables in controlling the variation in the three main PSII-energy allocation pathways, i.e., photochemical efficiency and regulated and non-regulated thermal dissipation, i.e., Φ PSII, Φ NPQ, and Φ NO, respectively, across a time-frequency domain from hours to years. Convergent cross mapping (CCM) was subsequently used to isolate cause-and-effect interactions in PSII-energy partitioning response. The CWT method revealed that the three PSII-energy allocation pathways all had distinct daily periodicities, oscillating abruptly at intermediate timescales from days to weeks. On a diurnal scale, WTC revealed that all three pathways were influenced by photosynthetically active radiation (PAR), air temperature (T a), and vapor pressure deficit (VPD). By comparing associated time lags for the three forms of energy partitioning at diurnal scales, revealed that the sensitivity of response was more acutely influenced by PAR, declining thereafter with the other environmental variables, such that the order of influence was greatest for T a, followed by VPD, and then soil water content (SWC). PSII-energy partitioning on a seasonal scale, in contrast, displayed greater variability among the different environmental variables, e.g., Φ PSII and Φ NO being more predisposed to changes in T a, and Φ NPQ to changes in VPD. CCM confirmed the causal relationship between pairings of PSII-energy allocation pathways, according to shrub phenology. A. ordosica is shown to have an innate ability to (i) repair damaged PSII-photochemical apparatus (maximum quantum yield of PSII photochemistry, with F v/F m > 0.78), and (ii) acclimatize to excessive PAR, dry-air conditions, and prolonged drought. A. ordosica is relatively sensitive to extreme temperature and exhibits photoinhibition.

Conditional inference trees in the assessment of tree mortality rates in the transitional mixed forests of Atlantic Canada.

Long-term predictions of forest dynamics, including forecasts of tree growth and mortality, are central to sustainable forest-management planning. Although often difficult to evaluate, tree mortality rates under different abiotic and biotic conditions are vital in defining the long-term dynamics of forest ecosystems. In this study, we have modeled tree mortality rates using conditional inference trees (CTREE) and multi-year permanent sample plot data sourced from an inventory with coverage of New Brunswick (NB), Canada. The final CTREE mortality model was based on four tree- and three stand-level terms together with two climatic terms. The correlation coefficient (R2) between observed and predicted mortality rates was 0.67. High cumulative annual growing degree-days (GDD) was found to lead to increased mortality in 18 tree species, including Betula papyrifera, Picea mariana, Acer saccharum, and Larix laricina. In another ten species, including Abies balsamea, Tsuga canadensis, Fraxinus americana, and Fagus grandifolia, mortality rates tended to be higher in areas with high incident solar radiation. High amounts of precipitation in NB's humid maritime climate were also found to contribute to heightened tree mortality. The relationship between high GDD, solar radiation, and high mortality rates was particularly strong when precipitation was also low. This would suggest that although excessive soil water can contribute to heightened tree mortality by reducing the supply of air to the roots, occasional drought in NB can also contribute to increased mortality events. These results would have significant implications when considered alongside regional climate projections which generally entail both components of warming and increased precipitation.

A watershed-scale assessment of cost-effectiveness of sediment abatement with flow diversion terraces.

Soil conservation beneficial management practices (BMPs) are effective at controlling soil loss from farmlands and minimizing water pollution in agricultural watersheds. However, costs associated with implementing and maintaining these practices are high and often deter farmers from using them. Consequently, it is necessary to conduct cost-benefit analysis of BMP implementation to assist decision-makers with planning to provide the greatest level of environmental protection with limited resources and funding. The Soil and Water Assessment Tool (SWAT) was used to evaluate the efficacy of flow diversion terraces (FDT) in abating sediment yield at the outlet of Black Brook Watershed (BBW), northwestern New Brunswick. Different FDT-implementation scenarios were expressed as the ratio of land area protected by FDT to the total cultivated area. From this analysis, we found that average annual sediment yield decreased exponentially with increased FDT protection. When the proportion of FDT-protected areas was low, sediment reductions caused by FDT increased sharply with increasing use of FDT. Similarly, marginal sediment yield abatement costs (dollar per tonne of sediment reduction) increased exponentially with increasing proportion of FDT-protected area. The results indicated that increasing land protection with FDT from 6 to 50% would result in a reduction of about 2.1 tonne ha(-1) yr(-1) and costs of sediment reduction increased from $7 to $12 per tonne. Increasing FDT-protected cropland from 50 to 100%, a reduction of about 0.9 tonne of sediment ha(-1) yr(-1) would occur and the costs would increase from $12 to $53 per tonne of sediment yield reduction.

Evaluation of the suitability of six drought indices in naturally growing, transitional vegetation zones in Inner Mongolia (China).

Naturally growing vegetation often suffers from the effects of drought. There exists a vast number of drought indices (DI's) to assess the impact of drought on the growth of crops and naturally occurring vegetation. However, assessing the fitness of these indices for large areas with variable vegetation cover is often problematic because of the absence of adequate spatial information. In this study, we compared six DI's to NDVI (the normalized difference vegetation index), a common indicator of vegetation occurrence and health based on satellite-acquired reflectance data. The study area covers an aridity gradient from forests to deserts along a 2,400-km-long section across the Inner Mongolia Autonomous Region of China. On an annual timescale, standardized precipitation index (SPI) was the most appropriate in assessing drought in steppes and deserts. On a seasonal timescale, the self-calibrated Palmer drought severity index (scPDSI) displayed the greatest sensitivity during the summer, but not during the other seasons. On a monthly timescale, scPDSI demonstrated the greatest sensitivity to the various vegetation zones (i.e., forests, steppes, and deserts) in June and July. Further analysis indicated that summer drought had a lag-effect on vegetation growth, which varied from one to six months according to the specific vegetation cover. The mixed response of DI's to NDVI and the lag-effect in transitional vegetation on annual, seasonal, and monthly timescales were ascribed to differences in DI definition and the dominant plant species within the transitional cover. The current study has the potential to inform the drafting of selection criteria of DI's for the study of drought-related impact on naturally growing vegetation at timescales from month to year.

Effect of climatic variation on the morphological characteristics of 37-year-old balsam fir provenances planted in a common garden in New Brunswick, Canada.

The extent of the effect of projected changes in climate on trees remains unclear. This study investigated the effect of climatic variation on morphological traits of balsam fir [Abies balsamea (L.) Mill.] provenances sourced from locations spanning latitudes from 44° to 51°N and longitudes from 53° to 102°W across North America, growing in a common garden in eastern Canada. Lower latitude provenances performed significantly better than higher latitude provenances (p < .05) with regard to diameter at breast height (DBH), height (H), and crown width (CW), a distinction indicative of genotypic control of these traits. There was, however, no significant difference among provenances in terms of survival (p > .05), an indication of a resource allocation strategy directed at survival relative to productivity in higher latitude provenances as seen in their lower DBH, H, and CW compared to the lower latitude provenances. Temperature had a stronger relationship with DBH, H, and CW than precipitation, a reflection of adaptation to local conditions in populations of the species along latitudinal gradients. Both climatic variables had some effect on tree survival. These results suggest that the response of balsam fir to climatic variation will likely not be uniform in the species, but differ based on genetic characteristics between populations located in the northern and southern parts of the species' range. Population differences in response to climatic variation may be evident earlier in growth traits, compared to survival in balsam fir. The findings of this study will facilitate modeling in the species that is reflective of genetic variation in response to climatic conditions, and guide provenance selection for utilization in terms of productivity or resilience as well as breeding programs directed at obtaining species that possibly combine both traits.

A Wetness Index Using Terrain-Corrected Surface Temperature and Normalized Difference Vegetation Index Derived from Standard MODIS Products: An Evaluation of Its Use in a Humid Forest-Dominated Region of Eastern Canada.

In this paper we develop a method to estimate land-surface water content in amostly forest-dominated (humid) and topographically-varied region of eastern Canada. Theapproach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature (TS) and surface reflectanceas primary input. In an attempt to improve estimates of TVWI in high elevation areas, terrain-induced variations in TS are removed by applying grid, digital elevation model-basedcalculations of vertical atmospheric pressure to calculations of surface potential temperature(θS). Here, θS corrects TS to the temperature value to what it would be at mean sea level (i.e.,~101.3 kPa) in a neutral atmosphere. The vegetation component of the TVWI uses 8-daycomposites of surface reflectance in the calculation of normalized difference vegetation index(NDVI) values. TVWI and corresponding wet and dry edges are based on an interpretation ofscatterplots generated by plotting θS as a function of NDVI. A comparison of spatially-averaged field measurements of volumetric soil water content (VSWC) and TVWI for the 2003-2005 period revealed that variation with time to both was similar in magnitudes. Growing season, point mean measurements of VSWC and TVWI were 31.0% and 28.8% for 2003, 28.6% and 29.4% for 2004, and 40.0% and 38.4% for 2005, respectively. An evaluation of the long-term spatial distribution of land-surface wetness generated with the new θS-NDVI function and a process-based model of soil water content showed a strong relationship (i.e., r² = 95.7%).

Production of high-resolution forest-ecosite maps based on model predictions of soil moisture and nutrient regimes over a large forested area.

Forest ecosite reflects the local site conditions that are meaningful to forest productivity as well as basic ecological functions. Field assessments of vegetation and soil types are often used to identify forest ecosites. However, the production of high-resolution ecosite maps for large areas from interpolating field data is difficult because of high spatial variation and associated costs and time requirements. Indices of soil moisture and nutrient regimes (i.e., SMR and SNR) introduced in this study reflect the combined effects of biogeochemical and topographic factors on forest growth. The objective of this research is to present a method for creating high-resolution forest ecosite maps based on computer-generated predictions of SMR and SNR for an area in Atlantic Canada covering about 4.3 × 106 hectares (ha) of forestland. Field data from 1,507 forest ecosystem classification plots were used to assess the accuracy of the ecosite maps produced. Using model predictions of SMR and SNR alone, ecosite maps were 61 and 59% correct in identifying 10 Acadian- and Maritime-Boreal-region ecosite types, respectively. This method provides an operational framework for the production of high-resolution maps of forest ecosites over large areas without the need for data from expensive, supplementary field surveys.

Incorporating interspecific competition into species-distribution mapping by upward scaling of small-scale model projections to the landscape.

There are a number of overarching questions and debate in the scientific community concerning the importance of biotic interactions in species distribution models at large spatial scales. In this paper, we present a framework for revising the potential distribution of tree species native to the Western Ecoregion of Nova Scotia, Canada, by integrating the long-term effects of interspecific competition into an existing abiotic-factor-based definition of potential species distribution (PSD). The PSD model is developed by combining spatially explicit data of individualistic species' response to normalized incident photosynthetically active radiation, soil water content, and growing degree days. A revised PSD model adds biomass output simulated over a 100-year timeframe with a robust forest gap model and scaled up to the landscape using a forestland classification technique. To demonstrate the method, we applied the calculation to the natural range of 16 target tree species as found in 1,240 provincial forest-inventory plots. The revised PSD model, with the long-term effects of interspecific competition accounted for, predicted that eastern hemlock (Tsuga canadensis), American beech (Fagus grandifolia), white birch (Betula papyrifera), red oak (Quercus rubra), sugar maple (Acer saccharum), and trembling aspen (Populus tremuloides) would experience a significant decline in their original distribution compared with balsam fir (Abies balsamea), black spruce (Picea mariana), red spruce (Picea rubens), red maple (Acer rubrum L.), and yellow birch (Betula alleghaniensis). True model accuracy improved from 64.2% with original PSD evaluations to 81.7% with revised PSD. Kappa statistics slightly increased from 0.26 (fair) to 0.41 (moderate) for original and revised PSDs, respectively.

Carbon and biomass partitioning in balsam fir (Abies balsamea).

Balsam fir (Abies balsamea (L.) Mill) was extensively sampled to investigate the effects of forest management practices, site location, within-crown position, tree component (i.e., stem, foliage, branches and roots), and tree social classes on biomass and carbon (C) partitioning at the individual tree level and across ecological regions. The sites were located in three ecologically distinct forest regions of west-central New Brunswick, Canada. There were no significant differences in %C content of trees across ecological regions or across tree social classes. However, at the individual tree level, significant differences were evident in biomass and C allocation between different parts of the tree, between treatment types (i.e., unmanaged and pre-commercially thinned stands) and between within-crown positions, indicating the need for separate estimates of biomass and C content of tree components to obtain more precise estimates of quantities at the stand level. Calculating stand C content based on constant allocation values, as is commonly done, produced errors of up to 15% compared with the values calculated in this study. Three allometric equations of biomass and C that account for partitioning among different parts of the tree were developed and compared: (1) a third-order polynomial, (2) a modified inverse polynomial and (3) a modified Weibull equation. Diameter at breast height (DBH) was used as the only explanatory variable to describe fresh biomass, dry biomass and C content. All regressions derived showed a high correlation with DBH, with most r2 values > 0.95. A comparison of the equation results showed that the modified Weibull equation gave consistent results with the best overall fit and was the simplest of the three equations investigated. The regressions can be used to estimate forest biomass and tree C content at the stand level, given specific information on DBH.

Landscape variation in tree species richness in northern Iran forests.

Mapping landscape variation in tree species richness (SR) is essential to the long term management and conservation of forest ecosystems. The current study examines the prospect of mapping field assessments of SR in a high-elevation, deciduous forest in northern Iran as a function of 16 biophysical variables representative of the area's unique physiography, including topography and coastal placement, biophysical environment, and forests. Basic to this study is the development of moderate-resolution biophysical surfaces and associated plot-estimates for 202 permanent sampling plots. The biophysical variables include: (i) three topographic variables generated directly from the area's digital terrain model; (ii) four ecophysiologically-relevant variables derived from process models or from first principles; and (iii) seven variables of Landsat-8-acquired surface reflectance and two, of surface radiance. With symbolic regression, it was shown that only four of the 16 variables were needed to explain 85% of observed plot-level variation in SR (i.e., wind velocity, surface reflectance of blue light, and topographic wetness indices representative of soil water content), yielding mean-absolute and root-mean-squared error of 0.50 and 0.78, respectively. Overall, localised calculations of wind velocity and surface reflectance of blue light explained about 63% of observed variation in SR, with wind velocity accounting for 51% of that variation. The remaining 22% was explained by linear combinations of soil-water-related topographic indices and associated thresholds. In general, SR and diversity tended to be greatest for plots dominated by Carpinus betulus (involving ≥ 33% of all trees in a plot), than by Fagus orientalis (median difference of one species). This study provides a significant step towards describing landscape variation in SR as a function of modelled and satellite-based information and symbolic regression. Methods in this study are sufficiently general to be applicable to the characterisation of SR in other forested regions of the world, providing plot-scale data are available for model generation.

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 5-year(-1) in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling.