Agricultural and Forest Meteorology最新文献

Models based on the complementary relationship for estimating evaporation typically incorporate two parameters, one for adjusting the relationship's shape and the other for formulating potential evaporation ($E_{po}$). In practical applications, single-parameter versions are often derived by fixing one of these parameters. But there is ongoing debate about which parameter to fix and under what conditions. To address these crucial questions in the application of generalized complementary models, we conducted a comprehensive comparison of the consequences arising from the simplification of three prominent two-parameter generalized complementary models (H2012 by Han et al. (2012), B2015 by Brutsaert (2015), and S2022 by Szilagyi et al. (2022)) to their respective single-parameter versions. This analysis utilized data from 24 grassland and 19 forest flux sites, showcasing varying land-atmosphere coupling dynamics. The results underscore the robustness of the two-parameter scheme in accommodating diverse land-atmosphere coupling. The choice of which parameter to fix depends on the land-atmosphere coupling strength. Under conditions where evaporation is closely coupled with the land surface, as observed over grasslands, fixing the $E_{po}$-related parameter while leaving the shape parameter for calibration (as in the simplification of H2012) preserves the dependence of the shape parameter on land surface wetness, albeit with an acceptable reduction in performance. In contrast, when evaporation is closely coupled to the outer atmosphere, as observed over forests, fixing the shape parameter but leaving the $E_{po}$-related parameter for calibration (as in the simplifications of B2015 and S2022) maintains the physical correlations between the $E_{po}$-related parameters and the major atmospheric factors, also with an acceptable reduction in performance. These findings provide valuable insights for the parameterization of complementary models, aiding in the selection of appropriate parameter-fixing strategies based on the prevailing land-atmosphere coupling conditions.

In Part 1 of this series (Garcia et al., 2022), we introduced a novel individual-based model for the simulation of dispersal flight of adult spruce budworm (SBW: Choristoneura fumiferana) and demonstrated the results of that model under real weather conditions for two nights in July 2013 on which SBW mass dispersal events were observed by weather radar in southern Quebec, Canada. Here, following the selection of one uncertain parameter value using empirical measurements, we used those radar observations for the quantitative calibration of two uncertain flight model variables in our individual-based SBW–pyATM model, one that describes the conversion of moth wingbeat to flight speed, and a second that allows the moth to conserve energy during flight. For these experiments, we adapted a grid-based metric from meteorology that has previously been used to calibrate and validate precipitation forecasts by comparison with radar data. Through thousands of flight simulations for the night of 15–16 July 2013, examining each of these parameters separately and in conjunction, we arrived at optimal values that produce a spatiotemporal distribution of SBW moth dispersal that most closely matches the radar observations for that night. We then applied those calibrated parameter values to simulations of SBW dispersal on the night of 14–15 July 2013 and found a lesser but still reasonable resemblance to weather radar observations on that night as well. These two parameters have significant effects on the speed, altitude, and distance of dispersal, and are thus critical to the goal of estimating when and where SBW males and females land, with subsequent effects on reproductive behavior and the spatial redistribution of SBW populations over a dispersal season.

The impact of diverse mulching factors on crop growth depends on their influences on heat transfer, while the precise effects of these factors on heat transfer remain unclear. To address this knowledge gap, we employed the CropSMPAC model to simulate energy fluxes and soil temperature under varying mulching conditions. Our study integrated a soil column experiment and a three–year field experiment. The soil column experiment encompassed 13 treatments, incorporating 3 plastic film colors, i.e., transparent film (TM), black film (BM), and silver–grey film (GM), and 2 mulching ratios (f_m), i.e., 100 % and 75 %, as well as 2 distances between soil and film (Z_sm), i.e., 0 and 5 mm, along with a control treatment (no mulching). The filed experiment comprised 2 treatments, i.e., film mulching (FM) and no mulching (NM), utilizing TM with a f_m of 97.98 % and Z_sm of 5 mm under FM condition. Results demonstrated the robust performance of the CropSMPAC model in predicting hourly soil surface temperature, hourly soil temperature in the night at 10 cm depth, daily soil water content at 10 cm depth across varying mulching scenarios. Furthermore, the model aptly captured soil temperature, net radiation flux (R_n) and soil heat flux (G) during the maize growth stages under both FM and NM conditions. For daily soil temperature at 10 cm depth, the root mean square error (RMSE) were 1.71 and 2.71 °C, Nash–Sutcliffe efficiency coefficient (NSE) were 0.79 and 0.55, and determination coefficient (R²) were 0.76 and 0.75 for FM and NM, respectively. Corresponding values for daily R_n were 37.3 and 42.7 W m^–2 (RMSE), 0.56 and 0.47 (NSE), and 0.72 and 0.66 (R²), they were 8.5 and 6.9 W m^–2 (RMSE), 0.44 and 0.56 (NSE), and 0.62 and 0.72 (R²) for daily G. Both measurements and simulations revealed that TM increased soil temperature in the daytime and night. In contrast, BM and GM raised soil temperature only in the night. The soil temperature under f_m of 100 % was higher than under f_m of 75 % for both TM and BM. Film mulching with Z_sm of 5 mm contributed to an increase in soil temperature compared with Z_sm of 0 mm for TM, while led to a reduction for BM. Additionally, a dense crop canopy helped mitigate the fluctuations in G and soil temperature, and the warming effect of plastic film mulching also weakened with the increase of canopy coverage.

Measurements of atmosphere-surface exchange are largely limited by the availability of fast-response gas analyzers; this limitation hampers our understanding of the role of terrestrial ecosystems in atmospheric chemistry and global change. Current micrometeorological methods, compatible with slow-response gas analyzers, are difficult to implement, or rely on empirical parameters that introduce large systematic errors.

Here, we develop a new micrometeorological method, optimized for slow-response gas analyzers, that directly measures exchange rates of different atmospheric constituents, with minimal requirements. The new method requires only the sampling of air at a constant rate and directing it into one of two reservoirs, depending on the direction of the vertical wind velocity. An integral component of the new technique is an error diffusion algorithm that minimizes the biases in the measured fluxes and achieves direct flux estimates.

We demonstrate that the new method provides an unbiased estimate of the flux, with accuracy within 0.1% of the reference eddy covariance flux, and importantly, allows for significant enhancements in the signal-to-noise ratio of measured scalars without compromising accuracy. Our new method provides a simple and reliable way to address complex environmental questions and offers a promising avenue for advancing our understanding of ecological systems and atmospheric chemistry.

The continuous economic and ecological construction in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) has caused frequent temporal and spatial changes in local forests, thus affecting the regional climate. Yet few studies have addressed the temperature feedback through biophysical mechanisms due to forest change in two urban agglomerations of China. We compared MODIS and Landsat-based land cover data to detect a more accurate forest cover change. We then used the moving window strategy and spatiotemporal pattern change analysis method to quantify and compare the actual impact of forest cover change on temperature and the differences in driving factors (e.g., evapotranspiration (ET), albedo, and precipitation) from 2010 to 2020. The results showed that Landsat-based land cover data performed well. The conversion from forest to cropland was dominated in YRD and PRD, followed by the conversion of cropland to forest, with a small proportion of forest converting to impervious surface. The afforested areas in the two regions showed a diurnal cooling effect (-0.18 ± 0.07 °C and -0.10 ± 0.13 °C, respectively), which was greater than the air temperature. Forest converting to impervious surfaces led to stronger warming (0.39 ± 0.37 °C in YRD) than that of cropland (0.05 ± 0.03 °C in YRD and 0.07 ± 0.06 °C in PRD). The daytime LST variations can be explained by ET and inconsistent albedo effects. Seasonally, the cooling effects induced by afforestation predominated during the growing season (spring and summer), accompanied by the relatively high ET. This study shows that rational afforestation and control of deforestation are helpful to achieve sustainable forest management in urban agglomerations and to regulate climate warming.

Surface energy fluxes, mainly encompassing the net radiation (R_n), latent heat flux (LE), sensible heat flux (H_s), and soil heat flux (G_s), play an important role in the land-atmosphere interactions. However, almost all sites face the problem of energy imbalance, and advection fluxes associated with large inhomogeneous surfaces have been ignored, especially in arid oasis areas. In this study, a three-year continuous measurement of energy fluxes with an eddy covariance system was conducted in a drip-irrigated oasis agroecosystem in Northwest China. Reanalysis data including air temperature (T_a), soil moisture (θ), and leaf area index (LAI) in our cropland and surrounding deserts were also collected. The results showed that multi-year mean turbulent fluxes (LE+H_s) accounted for 75 ± 8 % (mean ± standard deviation) of available energy (R_n–G_s). To be precise, LE took up 72 ± 10 % of available energy, and 7.8 ± 2.8 % of it was induced by higher sensible heat advection, proving a pronounced advection effect in this study. When advection was present, most likely during the heading stage, the threshold value for the Priestley–Taylor parameter α, an indicator to reflect the advection effect, fell in the range of 0.88–1.34. Additionally, after a significant irrigation event, α showed a good linear relationship with differences in air temperature (ΔT_a), soil moisture (Δθ), and leaf area index (ΔLAI) between our cropland and surrounding deserts. It's worth mentioning that Δθ was the most significant factor, showing a negative correlation with the advection effect. This study has deepened our understanding of the energy balance in oasis agriculture, emphasizing that the advection effect should not be overlooked.

Canopy temperature (T_c) plays an important role in regulating the rates of mass and energy fluxes at the leaf surface. Better understanding of the relationship between T_c and water availability may enable more accurate monitoring of ecosystem functioning in a changing climate. Here, we used high spatiotemporal resolution thermal infrared cameras deployed at three eddy covariance flux tower sites along a water- to energy-limited gradient – including a predominately water-limited grassland/shrubland site, a seasonally water-limited evergreen needleleaf forest, and a predominantly energy-limited deciduous broadleaf forest – to determine T_c seasonality and its relationship with gross primary productivity (GPP) and environmental drivers. We found midday T_c was generally warmer than air temperature (T_air) during the growing season (T_c:T_air slope: 1.14–1.27) for all sites. Water-limited sites exhibited higher positive T_c deviations from T_air (2.30 ± 0.06 °C) compared to the energy-limited site (1.29 ± 0.09 °C) partly due to their reduced latent heat fluxes during water-limited periods. We further found that the T_c:T_air slope increased with site aridity, namely for 1.14 slope for the grassland, 1.15 for the evergreen forest, and 1.27 for the broadleaf forest. Peak GPP occurred when T_c was higher than T_air across all sites, with peak GPP at the grassland site occurring at +1.1 °C (T_c-T_air) and peak GPP at the broadleaf evergreen site occurring at +2.2 °C (T_c-T_air). T_c-T_air dynamics were mostly associated with soil water content at water-limited sites where canopies undergo a substantial cooling during the transition from dormancy to the peak GPP, while net radiation played a crucial role at the energy-limited site where the canopy heats up compared to T_air over the same phenological transition. Our findings provide novel insights into T_c-ecosystem water availability links, highlighting the drivers of T_c-T_air across diverse ecosystems in various phenological stages, which has implications for ecosystem management in a changing climate.

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km² boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

The slow temperature acclimation of photosynthesis has been confirmed through early field experiments and studies. However, this effect is difficult to characterize and quantify with some simple and easily accessible indicators. As a result, the impact of slow temperature acclimation of photosynthesis on gross primary production (GPP) estimation has often been overlooked or not integrated into most GPP models. In this study, we used a theorical variable-state of acclimation (S), to characterize the slow temperature acclimation. This variable represents the temperature to which the photosynthetic machinery adapts and is defined as a function of air temperature ($T_a$) and time constant (τ) required for vegetation to respond to temperature, to discuss its impact on GPP simulation. We used FLUXNET2015 dataset to calculate S and established a GPP model using S and shortwave radiation (SW) based on random forest algorithm (S model). As a comparison, we directly used $T_a$ and SW to build the other GPP model ($T_a$ model). Moreover, the divergent temperature acclimation capacities of plants are crucial to predict and make preparations for likely temperature stress in the future. Therefore, the spatial distribution of $\tau$ values was also mapped using satellite sun induced chlorophyll fluorescence (SIF) and $T_a$ datasets. The results indicated that: (1) taking into account the slow temperature acclimation of photosynthesis led to a more precise estimation of GPP which mainly reflected in reduction of excessive fluctuations in GPP predictions; (2) considering the slow temperature acclimation of photosynthesis can reduce the sensitivity of vegetation to temperature; (3) the improvement of S model in GPP estimations was different in different vegetation growth stages which was more significant in the springtime recovery stage; (4) $\tau$ values had significant spatial distribution which was strongly affected by the determinants of vegetation growth and seasonal variations in temperature.

Evaporative Stress Index (ESI), also sometimes referred as Evaporative Stress Ratio (ESR), has been widely used as an indicator of vegetation evaporative stress, and is often used to track forest and agriculture droughts. Lower the stress, higher is the value of ESI or ESR. The goal of this study is to assess the suitability of these indices for tracking vegetation evaporative stress. As the dynamics of water loss from vegetation through transpiration (T) can be different than that of evapotranspiration (ET) from the ecosystem, it is hypothesized that ESI or ESR may not be sufficiently representative of the vegetation evaporative stress. Using eddy covariance flux tower data of 518 site years, distributed across 49-sites and 9 land covers globally, our findings reveal underestimation of vegetation evaporative stress by ESI during periods of high vapor pressure deficit (VPD) and overestimation during dry, low-VPD periods. The results highlight the need to improve representativeness of ESI for monitoring vegetation evaporative stress. Notably, this may entail accurate estimation of ecosystem T in systems lacking in-situ data, a challenge that warrants further attention.