Process-based Model Research Articles

Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model

ContextProcess-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).ObjectiveWe calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.MethodsWe conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.Results and conclusionsThe APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.

Environmental determinants of aerobic methane oxidation in a tropical river network

Aerobic methane oxidation (MOX) significantly reduces methane (CH4) emissions from inland water bodies and is, therefore, an important determinant of global CH4 budget. Yet, the magnitude and controls of MOX rates in rivers – a quantitatively significant natural source of atmospheric CH4 – are poorly constrained. Here, we conducted a series of incubation experiments to understand the magnitude and environmental controls of MOX rates in tropical fluvial systems. We observed a large variability in MOX rate (0.03 - 3.45 μmol l-1d-1) shaped by a suit of environmental variables. Accordingly, we developed an empirical model for MOX that incorporate key environmental drivers, including temperature, CH4, total phosphorus, and dissolved oxygen (O2) concentrations, based on the results of our incubation experiments. We show that temperature dependency of MOX (activation energy: 0.66 ± 0.18 eV) is lower than that of sediment methanogenesis (0.71 ± 0.21 eV) in the studied tropical fluvial network. Furthermore, we observed a non-linear relationship between O2 concentration and MOX, with the highest MOX rate occuring ∼135 μmol O2l-1, above or below this “optimal O2” concentration, MOX rate shows a gradual decline. Together, our results suggest that the relatively lower temperature response of MOX compared to methanogenesis along with the projected decrease of O2 concentration due to organic pollution may cause elevated CH4 emission from tropical southeast Asian rivers. Since estimation of CH4 oxidation is often neglected in routine CH4 monitoring programs, the model developed here may help to integrate MOX rate into process-based models for fluvial CH4 budget.

Modeling platform to assess the effectiveness of single and integrated Ixodes scapularis tick control methods

BackgroundLyme disease continues to expand in Canada and the USA and no single intervention is likely to curb the epidemic.MethodsWe propose a platform to quantitatively assess the effectiveness of a subset of Ixodes scapularis tick management approaches. The platform allows us to assess the impact of different control treatments, conducted either individually (single interventions) or in combination (combined efforts), with varying timings and durations. Interventions include three low environmental toxicity measures in differing combinations, namely reductions in white-tailed deer (Odocoileus virginianus) populations, broadcast area-application of the entomopathogenic fungus Metarhizium anisopliae, and fipronil-based rodent-targeted bait boxes. To assess the impact of these control efforts, we calibrated a process-based mathematical model to data collected from residential properties in the town of Redding, southwestern Connecticut, where an integrated tick management program to reduce I.xodes scapularis nymphs was conducted from 2013 through 2016. We estimated parameters mechanistically for each of the three treatments, simulated multiple combinations and timings of interventions, and computed the resulting percent reduction of the nymphal peak and of the area under the phenology curve.ResultsSimulation outputs suggest that the three-treatment combination and the bait boxes–deer reduction combination had the overall highest impacts on suppressing I. scapularis nymphs. All (single or combined) interventions were more efficacious when implemented for a higher number of years. When implemented for at least 4 years, most interventions (except the single application of the entomopathogenic fungus) were predicted to strongly reduce the nymphal peak compared with the no intervention scenario. Finally, we determined the optimal period to apply the entomopathogenic fungus in residential yards, depending on the number of applications.ConclusionsComputer simulation is a powerful tool to identify the optimal deployment of individual and combined tick management approaches, which can synergistically contribute to short-to-long-term, costeffective, and sustainable control of tick-borne diseases in integrated tick management (ITM) interventions.Graphical

Algorithm for estimating cultivar-specific parameters in crop models for newer crop cultivars

Model parameters in mechanistic process-based crop models are broadly classified into general parameters (environmental, management, and parameters related to soil processes), species-specific parameters, and cultivar-dependent parameters. While general and species-specific parameters typically remain unchanged, identifying cultivar-dependent parameters is crucial during calibration. With the continuous development of new cultivars, updating cultivar-specific parameters in crop models is necessary. However, gathering data on the behavior of these newer cultivars and calibrating the model to accommodate them is challenging. Currently, there is no standard approach for determining cultivar-specific parameters in crop models due to the difficulty of identifying variations in the growth and development process in newer cultivars, as well as the variations in the model structure, correlations between sub-model components and between different parameters in the crop models. The present study develops a standard methodology for determining cultivar-dependent parameters based on experiments and incorporating them into process-based crop growth models. The developed methodology is demonstrated using the cotton crop and the GOSSYM, a mechanistic process-based cotton crop simulation model. Experiments are conducted on 40 major cotton cultivars grown in the USA to quantify their growth and development under the same environmental and management conditions. Cultivar-dependent parameters are derived and integrated into the GOSSYM model based on the experimental data. By establishing a standardized methodology and demonstrating its application in the context of cotton and the GOSSYM model, this study provides practical guidance for incorporating cultivar-dependent parameters into crop simulation models. Researchers and agronomists can effectively utilize this methodology to integrate new cultivars into their crop models.

Levelized Cost and Carbon Intensity of Solar Hydrogen Production from Water Electrolysis Using a Scalable and Intrinsically Safe Photocatalytic Z-Scheme Raceway System

The realization of an environmentally sustainable and widely-adopted hydrogen economy may require lowering hydrogen production costs of production pathways with ultra-low greenhouse gas emissions to $1/kg H2. The allocation of new or existing renewable electricity generation solely to hydrogen production remains contentious due to disputes regarding emissions accounting. Photoelectrochemical (PEC) hydrogen production technologies offer a unique solution, as hydrogen is produced directly from solar energy and water, without the need for electricity generation. However, cost projections for past photoelectrochemical designs have suggested that they are not cost competitive compared to conventional electrolysis systems manufactured at scale. Herein, we offer the first illustrative benchmark of cost and carbon intensity of hydrogen produced in a Type 2 Z-scheme photocatalytic reactor design that employs suspended semiconducting nanoparticles organized in two stacked baggies in a raceway design.To explore the near-term and future cost projection for hydrogen production via Type 2 photocatalytic Z-scheme raceways, the authors developed a bottom-up total installed capital cost model. This project cost model incorporates: 1) raceway reactor cost, derived from a Design for Manufacturing and Assembly (DFMA) process-based cost model, 2) mechanical balance of plant, including process equipment, piping, valves, and instrumentation, derived from equipment quotes, scaling, database values, and Aspen estimates; 3) electrical balance of plant, including wiring, derived from time and material cost correlations; 4) Site Preparation, focused on green field installation; and 5) Construction Overhead, including engineering, procurement, and construction (EPC) costs and project contingency. The capital cost model is fed into a Levelized Cost of Hydrogen (LCOH) discounted cash flow model that accounts for electricity and water consumption, in addition to other operating costs, including labor, maintenance, and raceway replacements.A hybrid life-cycle approach was used to develop an inventory of greenhouse gas emissions from all materials and energy flows associated with the facility life cycle. These flows are converted to emissions based on specific emission factors, or by using physical units-based input–output LCA models associated with the broader economic impacts of fuel and materials production, use, and end use.Estimates for levelized cost of hydrogen suggest that a 50 metric ton H2 per day (MTD) raceway plant can approach $2.50/kg H2 at an 8% solar-to-hydrogen efficiency with further cost improvements possible through increased performance and larger plant scales. Carbon intensity is estimated to be well under clean hydrogen targets for global warming potential of <2-4 kg CO2e/kg H2 produced. The results suggest a highly competitive and scalable technology, that justifies further experimental validation and prototyping in the field. Figure 1

Simulation of Flow and Salinity in a Large Seasonally Managed Wetland Complex

Seasonally managed wetlands in the San Joaquin River (SJR) watershed in California provide important benefits to wildlife and humans but are threatened through anthropogenic activity. Wetlands in the SJR are subject to salinity regulation, which poses challenges for wetland management. Salinity management in the SJR basin is supported by a process-based model, the Watershed Analysis Risk Management Framework (WARMF). Wetlands are simulated with a “bathtub” analog where water levels are assumed to be the same over one model compartment and the storage volume depends on depth. The complexity and extent of hydrological features pose challenges for input data acquisition. Two approaches to estimating inflow and pond depth and determining water sources were assessed. Approach 1 used mostly monitored data, while Approach 2 used wetland manager knowledge. Approach 2 predicted outflow and salinity better than Approach 1, and an important benefit was the simulation of water reuse within the wetland complex, which was previously not implemented. Approach 1 is generally suited for estimating pond depth when a model compartment represents one wetland, while Approach 2 is suited for wetlands with large spatial extent, many hydrological features, and managed flows. The improved model will support wetland management.

Net evaporation-induced mangrove area loss across low-lying Caribbean islands

Abstract Although mangroves provide many beneficial ecosystem services, such as blue carbon storage and coastal protection, they are currently under threat due to changes in climate conditions, such as prolonged drought exposure. Under drought conditions, evaporation exceeds precipitation and high soil salinities can lead to stunted growth and die-back. To quantify this interplay, we developed a database for low-lying and uninhabited mangrove islands in the Caribbean under various evaporation and precipitation regimes. We extracted physical and biological information from each island using remote sensing techniques and coupled it with a process-based model. We used this database to develop a model that explains both the spatial variability in vegetated area across the Caribbean—as a function of rates of evaporation and precipitation—and porewater salinity concentration and dispersion from island edge towards the interior of mangrove islands. We then used this validated model to predict mangrove area loss associated with increases in evaporation to precipitation rates by 2100 for different Shared Socioeconomic Pathways (SSP). Less wealthy Caribbean regions such as Belize, Puerto Rico, and Venezuela are disproportionally affected, with mangrove area losses ranging from 3%–7% for SSP 2.6 and 13%–21% for SSP 7.0. Furthermore, foregone carbon sequestration in lost biomass under SSP 4.5 and 7.0 scenarios could compromise the ability of low-lying Caribbean mangrove islands to vertically adjust to sea level rise.

Transfer entropy on collective motion with undeclared loose leader–follower (LLF) structure

During an emergency or evacuation, individuals focus on the movement of their neighbors and follow those in sight, even without social affiliation, forming the undeclared Loose Leader-Follower (LLF) group, rather than the usual assumption of following certain rules in most of evacuation models. Inspired by the real-life experience and experimental data, the study develops a novel stochastic process-based evacuation model, integrated the LLF structure in collective motion to clarify the mechanism underlying individual interaction and refine the walking process according to attainable information. Transfer entropy (TE), a model-free measure of the direction of information flow, is used to detect the role of the individuals in the LLF structure from the time-series reconstructed motion information (e.g., the walking coordinate series). To reduce the error of the role detection, according to the 3σ principle for TE values, the detection threshold is decided based on the surrogate walking data. For simulation of their motion decision in the LLF structure, the TE is integrated into the walking behavior mode (TE-POMDP) using the Partially Observable Markov Decision Process (POMDP), that produces their motion behavior under the typical attained information condition. The results show that the TE-POMDP can provide more realistic trajectories. Additionally, we find that a ‘Delta-like’ formation of leading group distribution was presented and remained stable in the procession after a short period of confusion in early stage.

Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mainstream tools for streamflow simulation. However, their inherent limitations, such as time-consuming and large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise use of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming calibration algorithms to reduce computational costs. Then, XGBoost combines BTOP simulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inputs and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jialing River basin, China. The results show that the performance of BTOP_XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21%, and 83%, respectively. Meanwhile, the computational time of BTOP_XGB is saved by >90% compared to the original calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model. This study simplifies the complexity of the hydrological model and enhances the stability of machine learning, jointly improving the reliability of streamflow simulation. The hybrid approach provides a potential shortcut for streamflow simulation over basins with large areas or limited observed data.

On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN-1) in ERAN.

Mapping and assessing marine ecosystem services supply in the Baltic Sea

Coastal and marine ecosystems supply multiple Ecosystem Services (ES). Nevertheless, these ecosystems are among the most impacted by human activities, harming the ES sustainable supply. Since ES are a spatial phenomenon, mapping can contribute to understand ES supply. For this, we use quantitative spatio-temporal frameworks to map and assess the supply of one provisioning (food from fisheries) and two regulating ES (nursery habitats and nutrient regulation), considering two periods: Baltic Sea Holistic Assessment (HOLAS) 2 (2011–2016) and 3 (2016–2021). The ES supply was assessed following a process-based modelling approach, using bio-physical indicators as proxies. The three ES models were applied and validated, showing moderate results. For fisheries and nursery ES the results showed a significantly higher supply in HOLAS 3 than in 2, and for nutrient ES the opposite. This indicates that the assessed ES changed due to environmental activities. The Anselin Local Moran's results showed that most ES index values aggregate in the High-High cluster; Moran's I and semi-variogram results showed a clustered pattern; and the Getis Ord* analysis showed that hot and cold spots corresponded to high and low supply areas. For fisheries, high ES supply areas were located in the central-southern part of the Baltic Sea, while low-supply regions were located in the northern part. For nursery ES, high supply areas were located in the southwestern Finnish and western Estonian coasts. For nutrient ES, high supply areas occurred in the central- and eastern-southern parts close to the coast. Correlations showed a statistically significant negative correlation between fisheries and nursery ES and a significant positive correlation between fisheries and nutrient ES. No statistically significant correlations were observed between nursery and nutrient ES supply. The results obtained are essential to support coastal and marine management and planning in the Baltic Sea as well as international environmental policies and directives.

Optimized structure design for binary particle mixing in rotating drums using a combined DEM and gaussian process-based model

Particle mixing is a crucial operation in various industrial production processes. However, phenomena like segregation or local accumulation can arise, especially when particles differ in properties like radius and density. Numerical simulation of particles using Discrete Element Method (DEM) allows for the manipulation of control variables in batches, generating a large amount of data and facilitating quantitative research. In this study, the mixing behaviors of binary particles in rotating drums are systematically investigated. The DEM model is first validated with experimental data and then rotating drums with varying obstacles, rotation speeds, particle radii, and densities are simulated. Moreover, a Gaussian process-based optimization is conducted by correlating Lacey mixing index (MI) and parameterized shape of obstacle to find the optimized mixing condition. Experimental validations are further performed on the optimized condition to verify the design. It is shown that this integrated approach holds significant potential for enhancing the efficiency, effectiveness of industrial mixing processes and the consideration of energy consumption when balancing the mixing efficiency and optimal rotating speed.

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time-consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data-driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short-Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5-year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub-basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT-LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling. PRACTITIONER POINTS: We aimed to enhance process-based models for watershed water-quality modeling. The Soil and Water Assessment Tool-Long Short-Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub-basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.

Reconciling the EU forest, biodiversity, and climate strategies.

Forests provide important ecosystem services (ESs), including climate change mitigation, local climate regulation, habitat for biodiversity, wood and non-wood products, energy, and recreation. Simultaneously, forests are increasingly affected by climate change and need to be adapted to future environmental conditions. Current legislation, including the European Union (EU) Biodiversity Strategy, EU Forest Strategy, and national laws, aims to protect forest landscapes, enhance ESs, adapt forests to climate change, and leverage forest products for climate change mitigation and the bioeconomy. However, reconciling all these competing demands poses a tremendous task for policymakers, forest managers, conservation agencies, and other stakeholders, especially given the uncertainty associated with future climate impacts. Here, we used process-based ecosystem modeling and robust multi-criteria optimization to develop forest management portfolios that provide multiple ESs across a wide range of climate scenarios. We included constraints to strictly protect 10% of Europe's land area and to provide stable harvest levels under every climate scenario. The optimization showed only limited options to improve ES provision within these constraints. Consequently, management portfolios suffered from low diversity, which contradicts the goal of multi-functionality and exposes regions to significant risk due to a lack of risk diversification. Additionally, certain regions, especially those in the north, would need to prioritize timber provision to compensate for reduced harvests elsewhere. This conflicts with EU LULUCF targets for increased forest carbon sinks in all member states and prevents an equal distribution of strictly protected areas, introducing a bias as to which forest ecosystems are more protected than others. Thus, coordinated strategies at the European level are imperative to address these challenges effectively. We suggest that the implementation of the EU Biodiversity Strategy, EU Forest Strategy, and targets for forest carbon sinks require complementary measures to alleviate the conflicting demands on forests.

Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector.

Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N2O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N2O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process-based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low-ambition nitrogen regulation policies, EF would increase from 1.18%-1.22% in 2010 to 1.27%-1.34% by 2050, representing a relative increase of 4.4%-11.4% and exceeding the IPCC tier-1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%-0.35% (relative increase of 11.9%-17%). In contrast, high-ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying-wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N2O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N2O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio-economic assessments and policy-making efforts.

Comparison of methods to calculate groundwater recharge for karst aquifers under a Mediterranean climate

Karst aquifers can be particularly vulnerable to human activities and climate change due to their relatively high degree of connection with the surface. This study utilized an ensemble of event-based recharge calculation methods to address the problem of structural uncertainty for the example of the Western Mountain Aquifer (WMA), a Mediterranean karst aquifer located in Israel and the West Bank. Spatially distributed recharge estimates derived from the Soil and Water Assessment Tool (SWAT) and the process-based infiltration model (PIM) were compared to site-specific, empirical regression models. The SWAT and PIM mean annual recharge estimates ranged from 32–34.6% of precipitation, almost equating to the results of empirical regression models (32–36%). Future recharge predictions under the influence of climate change were quantified by parameterizing the SWAT and PIM methods with a downscaled regional climate model of Israel. SWAT predicts a 23% decrease in recharge by 2051–2070 relative to 1981–2001. In contrast, PIM shows a 9% decrease, possibly due to the representation of infiltration through preferential flow pathways and exclusion of surface runoff processes. These divergent projections underline key methodological differences in the representation of hydrological processes. Nevertheless, both methods effectively provided good estimates of groundwater recharge. The recharge rates estimated from the various methods were integrated into MODFLOW to assess their relative impacts on groundwater storage dynamics. The ensemble of MODFLOW projected groundwater storage outputs can provide guidance for sustainable groundwater management in the region.

Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m−2 day−1), ANHP increased by 9.39 kg ha−1, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha−1 more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C3 mid-grasses in taller structured ecosites compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R2 values varied by ecosite ranging from 0.49 to 0.67, while RAP R2 values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP.

Hydro-pedotransfer functions: a roadmap for future development

Abstract. Hydro-pedotransfer functions (PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties (SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards–Richardson equation are mostly limited to predicting parameters of the van Genuchten–Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium (ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.

Autumn phenology consistently delays in subtropical forests in China based on a new process-based model integrating temperature, photoperiod and precipitation

Abstract Climate warming has delayed vegetation autumn phenology, which in turn influences terrestrial carbon and water cycles and their feedback to the climate. However, the performance of autumn phenology models, especially for subtropical forests, remains poor. In this study, we extracted the end-of-photosynthetic-growing-season (EOPS) dates in subtropical China over the period 2001-2018 based on high-resolution solar-induced chlorophyll fluorescence (SIF) dataset using three fitting methods. We developed a new autumn phenology model (DMP model) that integrates precipitation and photoperiod into the classic cold-degree days model, and the new model outperformed the classic model reducing the RMSE by approximately 2 days. We found that the EOPS date was delayed by an average of 4.1 days per decade in the subtropical forests of China from 2001 to 2018, and the precipitation (partial correlation coefficient, r = 0.45), rather temperature (r = 0.29) determined the EOPS processes. We further studied future EOPS using the DMP model and found the EOPS will delay by 1.0 days per decade under the SSP2-4.5 scenario and 2.7 days per decade under the SSP5-8.5 scenario from 2030 to 2100. Our study highlighted the role of precipitation in regulating EOPS in the subtropical forests of China and provided valuable insight for integrating multiple climatic determinants into autumn phenology models.

Use of artificial neural networks with the physiological principles to predict growth model

Process-based models have become essential tools for forest planning and management due to their ability to assess the impacts of climate and operational changes on productivity. Physiological Principles Predicting Growth (3-PG) is a process-based/hybrid model widely used, associated with allometric models to estimate dendrometric variables. However, the accuracy of the model outputs still needs to be improved, especially those of interest to forest management. In this study, we tested the efficiency of artificial neural networks (ANNs) to estimate the average diameter at 1.30 m height (dbh), total height, stand volume, and root, stem, and foliage biomass. We also evaluated the prediction of two parameters that estimate the proportion of biomass allocated aboveground (ap and np). A database from published 3-PG parameterizations was generated and used to train the ANNs. Two networks were validated against observed data from two municipalities in the MG state of Brazil. Potential productivity maps at six and seven years were generated for Brazil, to evaluate the performance of the 3-PG + ANN model. All ANNs presented satisfactory statistical results in predicting the ap and np parameters, foliage biomass, dbh, total height and stand volume. The use of artificial neural networks integrated with the 3-PG model or another process-based model is a promising alternative to improve the accuracy of estimates, especially those of interest for forest management. Artificial neural networks offer a simple, flexible way to insert important variables into a process-based model. The parameters and 3-PG outputs can be estimated efficiently using artificial neural networks.