Land Model Research Articles

Comparison Between Iterative Least Square and Nonparametric Epanechnikov Kernel in Semivariogram Modeling, Case study: Urban Land Cover in East Java Province

Landcover is an example of spatial data that contains location coordinate information along with the variables measured at each location, namely height, slope, and curvature. The spatial relationship between locations can be measured using a semivariogram. Semivariogram is a statistic used to measure the spatial correlation of pairs of locations separated by a certain distance and angle. In estimating semivariogram parameters, namely, nugget, sill, and range, there are two methods, namely parametric via iterative least squares and nonparametric via kernel functions. These two methods will be compared for semivariogram modeling of built-up land in East Java province. The best method is selected based on the smallest SSE value. For each physical factor, the best model with the smallest SSE is the Epanechnikov kernel function, with 3.6 for the elevation SSE, 8.84 ⋅ 10−7 for the slope SSE, and 2.15 ⋅ 10−21 for the curvature SSE. So, it is concluded in this case that the nonparametric kernel Epanechnikov method is much better than the parametric method using Gaussian and exponential models.

Integrated climate, land, energy, and water framework to support the Nationally Determined Contribution updating process

System models help policymakers to produce net zero decarbonization pathways. In 2019, Costa Rica launched its National Decarbonization Plan (NDP) – constructed using a detailed Open-Source energy Modelling System (OSeMOSYS-CR) model and simplified ones for non-energy sectors. The country then developed an integrated Climate, Land, Energy and Water (CLEW) model to capture synergies across sectors and guide future climate related processes. This resulted in the CLEW-CR model. In 2020, the country updated its Nationally Determined Contributions (NDCs), deploying a highly participatory process supported by the CLEW-CR model to guide the discussions with over 150 stakeholders. This paper presents the framework adopted to develop the CLEW-CR model (from data collection to scenarios creation and cost-benefit analysis) and describes its use to align its NDCs with its NDP. Over 15 different scenarios were produced to inform the country's dialogue during the updating process of its NDCs demonstrating the flexibility of this model to support multiple policy questions. This paper shows that deploying the NDC by 2030 and the NDP by 2050 would result in a net economic benefit of $55 billion – about 85 % of Costa Rica's GDP, highlighting the economic viability of Costa Rica's commitment to a sustainable future.

Evaluation of land use change, modeling and prediction of areas susceptible to physical development of the city (Case Study: Nurabad Mamasani Town)

Evaluation of land use change, modeling and prediction of areas susceptible to physical development of the city (Case Study: Nurabad Mamasani Town)

Diurnal Carbon Monoxide Retrieval from FY-4B/GIIRS Using a Novel Machine Learning Method

Carbon monoxide (CO) is one of the primary reactive trace gases in the Earth’s atmosphere and plays an important role in atmospheric chemistry. The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4 series satellites is currently the only geostationary hyperspectral thermal infrared sensor capable of monitoring the unprecedented hourly CO concentrations in East Asia during both daytime and nighttime. In this study, we presented a radiative transfer model-driven machine learning approach to quickly convert CO spectral features extracted from FY-4B/GIIRS into CO total columns. We built machine learning models for land and ocean regions separately from July 2022 to June 2023, and these models reproduced more than 97.77% (land) and 98.49% (ocean) of the CO column variance in the training set. We estimated the absolute uncertainty of the retrieved CO column based on error propagation theory and found that it is dominated by GIIRS measurement noise. We compared the machine learning retrieval results with optimal estimation and ground-based Fourier transform infrared measurements, and the results reveal the consistent spatial distribution and temporal variation across these different datasets. Our results confirm that the machine learning method has the potential to provide reliable CO products without the computationally intensive iterative process required by traditional retrieval methods. The diel cycle and monthly variation of CO over land and ocean demonstrate the value of GIIRS in monitoring the long-range transport of anthropogenic pollutants and biomass burning emissions.

Positive asymmetric responses indicate larger carbon sink with increase in precipitation variability in global terrestrial ecosystems

<p>Climate changes have caused high inter-annual variability in precipitation. However, how the terrestrial ecosystem responds to precipitation variability remains unclear. Using global remote sensing data and a meta-analysis by synthesizing 800 pairwise observations of experimental manipulations worldwide, we quantified the responses of the terrestrial ecosystem net carbon productivity (NEP) to precipitation variability. The results indicate that NEP displays a positive asymmetry in response to precipitation change, e.g., the magnitude of the increase in NEP (33.4%) under water-addition treatments is larger than that of the decline in NEP (-24.62%) under water-reduction treatments. The positive asymmetry of NEP in arid regions (< 500 mm) is larger than that in humid regions (> 500 mm). The former is mainly due to the positive asymmetry in vegetation productivity, while the latter results from the respiration process, i.e., the decrease in soil respiration in water-reduction treatments is stronger than in water-addition treatments. Furthermore, land models reproduce a positive NEP asymmetry in response to precipitation change, but display poor performance in ecosystem respiration (ER) responses owing to uncertainties in simulating soil water content (SWC). The positive asymmetry of NEP in this study implies that the increase in precipitation variability (except extreme anomalies) is conducive to high carbon sink in the global terrestrial ecosystem. Meanwhile, the performance of the models when simulating SWC in response to precipitation in humid regions needs to be further improved to better predict the carbon sink in the terrestrial ecosystem.</p>

Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery

AbstractTropical cyclones are an important cause of forest disturbance, and major storms caused severe structural damage and elevated tree mortality in coastal tropical forests. Model capabilities that can be used to understand post‐hurricane forest recovery are still limited. We use a vegetation demography model, the Functionally Assembled Terrestrial Ecosystem Simulator, coupled with the Energy Exascale Earth System Model Land Model (ELM‐FATES) to study the processes and the key factors regulating post‐hurricane forest recovery. We implemented hurricane‐induced forest damage, including defoliation, structural biomass reduction, and tree mortality, performed ensemble model simulations, and used random forest feature importance. For the simulation in the Luquillo Experimental Forest, Puerto Rico, we identified factors controlling the post‐hurricane forest recovery, and quantified the sensitivity of key model parameters to the post‐hurricane forest recovery. The results indicate a tendency for the Bisley forests to shift toward the light demanding plant functional type (PFT) when the pre‐hurricane biomass between the light demanding and shade tolerant PFTs is nearly equal and forests experience hurricane disturbance with mortality >60% for both the two PFTs. Under more realistic conditions where the shade tolerant PFT is initially dominant, mortality >80% is required for a shift toward dominance of the light demanding PFT at Bisley. Hurricane mortality and background mortality are the two major factors regulating post‐hurricane forest recovery in simulations. This research improves understanding of the ELM‐FATES model behavior associated with hurricane disturbance and provides guidance for dynamic vegetation model development in representing hurricane induced forest damage with varied intensities.

PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning

AbstractDetecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P‐waves and 0.12 s for S‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments.

Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2

Abstract. In the Energy Exascale Earth System Model (E3SM) Multi-scale Modeling Framework (MMF), where parameterizations of convection and turbulence are replaced by a 2-D cloud-resolving model (CRM), there are multiple options to represent land–atmosphere interactions. Here, we propose three different coupling strategies, namely the (1) coupling of a single land surface model to the global grid (MMF), (2) coupling a single land copy directly to the embedded CRM (SFLX2CRM), and (3) coupling a single copy of land model to each column of the CRM grid (MAML). In the MAML (Multi-Atmosphere Multi-Land) framework, a land model is coupled to CRM at the CRM-grid scale by coupling an individual copy of a land model to each CRM grid. Therefore, we can represent intra-CRM heterogeneity in the land–atmosphere interaction processes. There are 5-year global simulations run using these three coupling strategies, and we find some regional differences but overall small changes with respect to whether a land model is coupled to CRM or a global atmosphere. In MAML, the spatial heterogeneity within CRM induces stronger turbulence, which leads to the changes in soil moisture, surface heat fluxes, and precipitation. However, the differences in the MAML from the other two cases are rather weak, suggesting that the impact of using MAML does not justify the increase in cost.

Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)

Abstract. Farmers around the world time the planting of their crops to optimize growing season conditions and choose varieties that grow slowly enough to take advantage of the entire growing season while minimizing the risk of late-season kill. As climate changes, these strategies will be an important component of agricultural adaptation. Thus, it is critical that the global models used to project crop productivity under future conditions are able to realistically simulate growing season timing. This is especially important for climate- and hydrosphere-coupled crop models, where the intra-annual timing of crop growth and management affects regional weather and water availability. We have improved the crop module of the Community Land Model (CLM) to allow the use of externally specified crop planting dates and maturity requirements. In this way, CLM can use alternative algorithms for future crop calendars that are potentially more accurate and/or flexible than the built-in methods. Using observation-derived planting and maturity inputs reduces bias in the mean simulated global yield of sugarcane and cotton but increases bias for corn, spring wheat, and especially rice. These inputs also reduce simulated global irrigation demand by 15 %, much of which is associated with particular regions of corn and rice cultivation. Finally, we discuss how our results suggest areas for improvement in CLM and, potentially, similar crop models.

Comparative analysis of land use change prediction models for land and fine wetland types: Taking the wetland cities Changshu and Haikou as examples

With the development of social economy and the influence of human activities, land use has undergone great changes. Land use prediction models have gradually emerged for more rational planning and development of the region. This study took Changshu and Haikou, two wetland cities, as examples to explore the prediction effects of land and fine wetland types in PLUS, FLUS and CLUES models, and combined CA-Markov model to make a detailed summary of the composition, use requirements, advantages and disadvantages of each model. The results showed that: (1) From 2015 to 2020, the urban area in Changshu and Haikou showed a significant increase, with growth rates of 16.61% and 21.37%, respectively. However, the total area of wetland remained above 10%, which was not greatly affected; (2) The average overall accuracy of the PLUS, FLUS and CLUES models in the prediction of Changshu and Haikou was 0.85, 0.86, and 0.84, respectively, while Kappa was 0.79, 0.81, and 0.78, respectively. Especially, the prediction in the fine wetland types also achieved high accuracy. (3) FLUS and CLUES had more stable results in the comparison of spatial results, deviation on the prediction of some land use categories was easily born in PLUS. Each model has its own advantages and disadvantages in practical application and operation. The research results of this paper are intended to let more people understand different land use prediction models and provide reference basis for the improvement and application of the models.

Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014

AbstractLow snow levels over the past few decades and predictions of a low‐to‐no snow future have spurred research into snow droughts, which pose a threat to water security and management. Systematic data‐model comparisons of snow drought have been lacking, hindering our understanding of the drivers of snow drought in the past. To address this gap, we analyzed snow drought events using standardized snow water equivalent index derived from monthly results of four numerical experiments using the E3SM Land Model (ELM) and ERA5‐Land data during the period of 1980–2014. Additionally, we compared snow drought duration calculated from models with those from the ERA5‐Land data during selected El Niño‐Southern Oscillation (ENSO) years. The numerical experiments were conducted with ELM driven by two prescribed atmospheric forcings, and with the coupled land‐atmosphere configuration of E3SM with and without plant hydraulics scheme feedback. Analysis reveals that 20%–30% of snow droughts occur due to factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., especially in high latitudes (50° North). Furthermore, our study highlights the exacerbating effect of ENSO events on snow drought conditions in various regions, despite some discrepancies between model and ERA5‐Land results. We also identified limitations of the coupled land‐atmosphere models in our current configuration in capturing the spatial patterns of snow droughts. This study underscores the challenge of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought.

Global impacts of vegetation clumping on regulating land surface heat fluxes

The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m−2 (∼18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m−2 (∼65%), resulting in a total sensible heat decrease of 1.0 W m−2 (∼3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m−2 (∼2%), primarily due to increased evapotranspiration (up to 11 W m−2) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. These changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.

Impacts of Land Use/Land Cover Distributions on Permafrost Simulations on Tibetan Plateau

The Tibetan Plateau (TP) is distributed with large areas of permafrost, which have received increasing attention as the climate warms. Accurately modeling the extent of permafrost and permafrost changes is now an important challenge for climate change research and climate modeling in this region. Uncertainty in land use and land cover (LULC), which is important information characterizing surface conditions, directly affects the accuracy of the simulation of permafrost changes in land surface models. In order to investigate the effect of LULC uncertainty on permafrost simulation, we conducted simulation experiments on the TP using the Community Land Model, version 5 (CLM5) with five high-resolution LULC products in this study. Firstly, we evaluated the simulation results using shallow soil temperature data and deep borehole data at several sites. The results show that the model performs well in simulating shallow soil temperatures and deep soil temperature profiles. The effect of different land use products on the shallow soil temperature and deep soil temperature contours is not obvious due to the small differences in land use products at these sites. Although there is little difference in the simulating results of different land use products when compared to the permafrost distribution map, the differences are noticeable for the simulation of the active layer. Land cover had a greater impact on soil temperature simulations in regions with greater land use inconsistency, such as at the junction of bare soil and grassland in the northwestern part of the TP, as well as in the southeast region with complex topography. The main way in which this effect occurs is that land cover affects the net surface radiation, which in turn causes differences in soil temperature simulations. In addition, we discuss other factors affecting permafrost simulation results and point out that increasing the model plant function types as well as carefully selecting LULC products is one of the most important ways to improve the simulation performance of land-surface models in permafrost regions.

Land Surface Temperature Sensitivity to Changes in Vegetation Phenology Over Northern Deciduous Forests

AbstractChanges in vegetation phenology are important contributors to seasonal climate change over the northern land surface. However, the biogeophysical effects of phenological shifts on land surface temperature (LST) remain uncertain. Here, we demonstrated the sensitivity of LST to advanced start of growing season (SOS) and delayed end of growing season (EOS) during corresponding green‐up and senescence periods over northern deciduous forests. Moreover, the mechanism responsible for LST sensitivity was assessed by quantifying the contributions of biogeophysical properties to LST sensitivity by applying a two resistance mechanism method to an experimental set using Community Land Model version 5. LST sensitivities to advanced SOS and delayed EOS were −2.1 × 10−2 K day−1 and −2.8 × 10−2 K day−1, respectively, indicating cooling effects by phenological shifts. The different responses of the aerodynamic resistance to SOS and EOS could explain the higher LST sensitivity to EOS than that to SOS. The decrease in the aerodynamic resistance in response to delayed EOS was three times larger than that to advanced SOS, thereby inducing a larger surface cooling effect during senescence period through enhanced turbulent heat transfer. Thus, the reduced aerodynamic resistance could explain the cooling effect of the phenological shift, which is an important consideration for projecting seasonal climate change.

Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning

Remote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various environments and scales. Predicting susceptibility to LULC change is crucial for policy formulation and land management. However, the use of machine learning (ML) for modelling LULC change is limited. This study modelled LULC change susceptibility in the Okavango basin using ML techniques. Areas with high LULC change susceptibility are termed priority management areas (PMAs) in this study. Trajectories of LULC change between 1996 and 2020 are derived from existing LULC change maps of the Okavango basin. Overlay analysis is then used to detect patches of LULC change transitions. Three LULC transitional categories are adopted for modelling PMAs, namely 1) from natural to anthropogenic classes (Category A); 2) from anthropogenic to natural classes (Category B); and 3) from natural to another natural class (Category C). An ensemble of ML algorithms is calibrated with categories of LULC change and social-ecological drivers of change to produce maps showing the susceptibility of LULC change in the basin. Thereafter, thresholding is done on probability maps of susceptibility to LULC change based on the maximum sum of sensitivity and specificity (max SSS) to delineate PMAs. Results for trajectories of LULC change indicate that anthropogenic activities (croplands, built-up areas, and barelands) generally expanded, displacing natural areas (wetlands, woodlands, water, and shrubland) from 1996 to 2020. Regarding PMAs, anthropogenic-related PMAs (Category A ∼34 560 km2) covered a larger area compared to the natural ones (Categories B∼33 407 km2) and (Categories C∼15 040 km2). The findings of this study emphasize the value of ensemble ML modelling in identifying PMAs and guiding transboundary land use planning. Overall, this study highlights the role of anthropogenic activities in driving land use changes in Transboundary Drainage Basins (TDBs) and suggests a need to promote sustainable practices in predicted PMAs through comprehensive planning to ensure water availability in the Okavango basin.

Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model

Terrestrial water storage (TWS) encompasses various components such as snow water, surface water, canopy water, soil water, and groundwater. Therefore, TWS is strongly affected by several factors including climate change, floods, drought, and anthropogenic activities. Predicting and quantifying TWS is critical in understanding terrestrial water cycles, as well as for managing water resources at the regional and global scale. TWS can be monitored with the Gravity Recovery and Climate Experiment (GRACE) satellites and estimated using land surface models (LSMs) on various spatial scales. The present study demonstrated how large-scale TWS data assimilation affects model performance by assessing the variations in TWS and related hydrological variables. Moreover, unlike traditional physical-based models, the assimilation of TWS data into an LSM enabled the assessment of anthropogenic impacts. TWS data from GRACE was assimilated into the Community Land Model version 5 (CLM5) with the CLM5-BGC biogeochemistry module covering the East Asia region. The model was forced using 40-ensemble meteorological forcing data from 2003 to 2010 and GRACE TWS data were assimilated using the ensemble adjustment Kalman filter (EAKF). Data assimilation markedly improved the performance of the model, as demonstrated by a decrease in the root mean square error from 0.71 mm/month in the free run (FR) without data assimilation to 0.48 mm/month in the data assimilation (DA) run, in addition to an increase in the correlation coefficient from 0.55 in FR to 0.69 in DA. Additionally, unlike the FR model, the DA model effectively captured the observed negative trends of the TWS, soil moisture, and evapotranspiration in Northern India and the Northern China Plain. Finally, our findings also demonstrated that the anthropogenic contribution to the negative trend of the TWS over Northern India and Northern China Plain was marginally increased in the DA model compared to the FR model. Collectively, our findings demonstrated that TWS data assimilation enables a more realistic evaluation of the long-term and large-scale changes in hydrologic variables.

Carbon dioxide exchange in an idealized valley

On a global scale the budget of carbon dioxide (CO2) bears a substantial uncertainty, which is commonly understood to be mainly due to land-surface exchange processes. In this paper it is examined to what extent mountainous model topography can amplify these land-surface exchange processes. The underlying hypothesis is that, on the meso-scale, topography adds additional atmospheric mechanisms that drive the exchange of CO2 at the surface.This model sensitivity study represents an advancement in model development through the implementation of a pioneering combination of the Weather Research and Forecasting (WRF) model, coupled with the Community Land Model (CLM4) to account for photosynthesis, and the semi-empirical TPGPP-LAI model to address ecosystem respiration. An idealized sine-shaped valley is investigated to examine the effect of complex topography on the CO2 budget compared to flat terrain. The systematic variation of meteorological initial conditions and plant functional types create an ensemble that unveils the fundamental factors which dominate the differences of CO2 fluxes between simulations with topography compared to a flat reference surface.During daytime, when there is photosynthesis, the vegetation temperature of the hypothetical sub-grid scale valley is on average 0.7 K colder and its vapor pressure deficit is 0.8 hPa lower which results in stronger net ecosystem exchange. During nighttime the valley is 0.6 K warmer than the plain, which increases ecosystem respiration compared to the plain. Over 24 h the valley is 0.1 K colder, but it acts as a sink of CO2 as the two opposing processes, during daytime and at night, result in 0.2 gC m−2 d−1 stronger net ecosystem exchange in the valley. During daytime differences are caused implicitly by meso-scale circulations through a secondary effect, as slope-winds shape the vertical temperature and humidity profiles, whereas at night the larger surface area of the valley is the prime cause for the differences. In contrast, the effect of differences in ambient CO2 on the ecosystem exchange is negligible.

Spatial variations in water use efficiency across global terrestrial ecosystems

Ecosystem water use efficiency (WUE) is an important property for understanding the coupling between ecosystem carbon and water cycles and for constraining land models, whereas the mechanism of its spatial variations remains unclear. Here we proposed a framework to decompose ecosystem WUE into component processes, with which the physiological and non-physiological processes controlling ecosystem WUE were included. Further, with the data of measurements by eddy covariance at global Fluxnet sites, we investigated the spatial patterns and determinants of ecosystem WUE with the framework proposed. Our results showed that crop exhibited the highest value of WUE, followed by forest, grassland, wetland, and shrub. The cross-site difference in WUE was mainly determined by that of gross primary productivity (GPP) and the physiological component, i.e., the ratio of GPP to plant transpiration. Spatially, ecosystem WUE showed an overall pattern of increase with air temperature, peaking at ca. 10 ℃ and decreased thereafter. Mean annual precipitation was positively correlated with WUE when < 1000 mm. Moreover, leaf area index (LAI) and the ratio of ambient to intercellular CO2 concentration at canopy level (Ci/Ca) were the two key factors dominating the spatial ecosystem WUE patterns. LAI affected ecosystem WUE mainly through biophysically controlling the fraction of soil water evaporation. Our findings highlight the importance of LAI and canopy level Ci/Ca on controlling the spatial variations in ecosystem WUE, which warrants more attentions and investigations in ecosystem models.

Flexible Foliar Stoichiometry Reduces the Magnitude of the Global Land Carbon Sink

AbstractIncreased plant growth under elevated carbon dioxide (CO2) slows the pace of climate warming and underlies projections of terrestrial carbon (C) and climate dynamics. However, this important ecosystem service may be diminished by concurrent changes to vegetation carbon‐to‐nitrogen (C:N) ratios. Despite clear observational evidence of increasing foliar C:N under elevated CO2, our understanding of potential ecological consequences of foliar stoichiometric flexibility is incomplete. Here, we illustrate that when we incorporated CO2‐driven increases in foliar stoichiometry into the Community Land Model the projected land C sink decreased two‐fold by the end of the century compared to simulations with fixed foliar chemistry. Further, CO2‐driven increases in foliar C:N profoundly altered Earth's hydrologic cycle, reducing evapotranspiration and increasing runoff, and reduced belowground N cycling rates. These findings underscore the urgency of further research to examine both the direct and indirect effects of changing foliar stoichiometry on soil N cycling and plant productivity.

Spatiotemporal variability in the C-factor: An analysis using high resolution satellite imagery

Estimating the cover and management factor (C-factor) for Universal Soil Loss Equation (USLE) that varies spatially and temporally within a watershed is time-consuming and resource-intensive. The Normalized Difference Vegetation Index (NDVI) approach can offer a potential alternative for this process. The current study examines nine NDVI models to compare and evaluate their performance in estimating the C-factor values for an agricultural watershed in southwestern Ontario, Canada. Satellite imagery from 2013 to 2020 was used to analyze the models’ similarities and differences on a detailed spatial and temporal scale. The results showed different C-factor values for each model, reflecting that they were developed for different geographical areas and purposes. While the Karaburun model differed from all other models on an annual basis, a detailed combined analysis of different spatial and temporal scales revealed that it was similar to other models. Seasonal analysis was found to be adequate for the current study, as it reduced the resources required and provided an overall view of the vegetation situation. However, a detailed monthly analysis may be necessary when investigating a specific season. The current analysis found that the summer months of June, July, and August have similar trends when comparing different models for different land uses and individual months, which aligns with the seasonal analysis. In conclusion, the current study highlights the importance of incorporating spatial and temporal scales in hydrological modeling and provides valuable insight into the applicability of different NDVI models for estimating the C-factor for southwestern Ontario watersheds. These findings can help inform future research and aid in developing accurate models for estimating soil erosion in this region. The results also emphasize that the NDVI approach has the potential for estimating the USLE C-factor and improving the estimation of soil erosion from agricultural watersheds by incorporating a variable C-factor over time and space. However, further research is needed to validate each model and determine which model best suits the study area.