Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects

Simulating adaptive grazing management on soil organic carbon in the Southeast U.S.A. using MEMS 2

Grazing lands play a significant role in global carbon (C) dynamics, holding substantial soil organic carbon (SOC) stocks. However, historical mismanagement (e.g., overgrazing and land-use change) has led to substantial SOC losses. Regenerative practices, such as adaptive multi-paddock (AMP) grazing, offer a promising avenue to improve soil health and help combat climate change by increasing SOC accrual, both in its particulate (POC) and mineral-associated (MAOC) organic C components. Because adaptive grazing patterns emerge from the combination of different levers such as frequency, intensity, and timing of grazing, studying AMP grazing management in experimental trials and representing it in models remains challenging. Existing ecosystem models lack the capacity to predict how different adaptive grazing levers affect SOC storage and its distribution between POC and MAOC and along the soil profile accurately. Therefore, they cannot adequately assist decision-makers in effectively optimizing adaptive practices based on SOC outcomes. Here, we address this critical gap by developing version 2.34 of the MEMS 2 model. This version advances the previous by incorporating perennial grass growth and grazing submodules to simulate grass green-up and dormancy, reserve organ dynamics, the influence of standing dead plant mass on new plant growth, grass and supplemental feed consumption by animals, and their feces and urine input to soil. Using data from grazing experiments in the southeastern United States and experimental SOC data from two conventional and three AMP grazing sites in Mississippi, we tested the capacity of MEMS 2.34 to simulate grass forage production, total SOC, POC, and MAOC dynamics to 1-m depth. Further, we manipulated grazing management levers, i.e., timing, intensity, and frequency, to do a sensitivity analysis of their effects on SOC dynamics in the long term. Our findings indicate that the model can represent bahiagrass forage production (BIAS = 9.51 g C m-2, RRMSE = 0.27, RMSE = 65.57 g C m-2, R2 = 0.72) and accurately captured the dynamics of SOC fractions across sites and depths (0-15 cm: RRMSE = 0.05; 15-100 cm: RRMSE = 1.08-2.07), aligning with patterns observed in the measured data. The model best captured SOC and MAOC stocks across AMP sites in the 0-15 cm layer, while POC was best predicted at-depth. Otherwise, the model tended to overestimate SOC and MAOC below 15 cm, and POC in the topsoil. Our simulations indicate that grazing frequency and intensity were key levers for enhancing SOC stocks compared to the current management baseline, with decreasing grazing intensity yielding the highest SOC after 50 years (63.7-65.9 Mg C ha-1). By enhancing our understanding of the effects of adaptive grazing management on SOC pools in the southeastern U.S., MEMS 2.34 offers a valuable tool for researchers, producers, and policymakers to make AMP grazing management decisions based on potential SOC outcomes.

SWAT model applications: From hydrological processes to ecosystem services

Ecosystem services in bolstering human well-being and steering environmental management garnered increasing recognition. In this realm, the Soil and Water Assessment Tool (SWAT) rose as an instrumental tool in ecosystem services. The heterogeneous applications of SWAT across diverse studies underscore an imperative for bibliometric analysis to decipher these evolving trends. This study endeavors to execute a comprehensive analysis of SWAT's application for ecosystem services, delineating key thematic development and exploring its utilization in ecosystem services. We conducted a comprehensive literature review by searching the Web of Science database, retrieving a total of 534 articles. The CiteSpace facilitated our co-citation analysis, enabling the identification of seminal publications and burgeoning themes within SWAT. Our analysis delineated thematic development in SWAT pertaining to ecosystem services. Initially concentrated on hydrological processes, the focus progressively broadened to encompass comprehensive ecosystem services evaluations. We examined 81 peer-reviewed publications directly related to ecosystem services, and most of them addressed certain ecosystem services, such as water yield, soil retention, regulation of water flow, food, and carbon storage. SWAT holds a unique advantage in quantifying water-related processes. Future studies should focus more on ecosystem service flows based on SWAT, which contributes to elucidating the relationship between nature and humans, facilitating comprehensive ecosystem management.

Unraveling the impact of wildfires on permafrost ecosystems: Vulnerability, implications, and management strategies

Permafrost regions play an important role in global carbon and nitrogen cycling, storing enormous amounts of organic carbon and preserving a delicate balance of nutrient dynamics. However, the increasing frequency and severity of wildfires in these regions pose significant challenges to the stability of these ecosystems. This review examines the effects of fire on chemical, biological, and physical properties of permafrost regions. The physical, chemical, and pedological properties of frozen soil are impacted by fires, leading to changes in soil structure, porosity, and hydrological functioning. The combustion of organic matter during fires releases carbon and nitrogen, contributing to greenhouse gas emissions and nutrient loss. Understanding the interactions between fire severity, ecosystem processes, and the implications for permafrost regions is crucial for predicting the impacts of wildfires and developing effective strategies for ecosystem protection and agricultural productivity in frozen soils. By synthesizing available knowledge and research findings, this review enhances our understanding of fire severity's implications for permafrost ecosystems and offers insights into effective fire management strategies.

Techno-economic analysis of phytoremediation: A strategic rethinking

Phytoremediation is a cost-effective and environmentally sound approach, which uses plants to immobilize/stabilize, extract, decay, or lessen toxicity and contaminants. Despite successful evidence of field application, such as natural attenuations, and self-purification, the main barriers remain from a "promising" to a "commercial" approach. Therefore, the ultimate goal of this paper is to examine factors that contribute to phytoremediation's underutilization and discuss the real costs of phytoremediation when the time and land values are considered. We revisit mechanisms and processes of phytoremediation. We synthesize existing information and understanding based on previous works done on phytoremediation and its applications to provide the technical assessment and perspective views in the commercial acceptance of phytoremediation. The results show that phytoremediation is the most suitable for remote regions with low land values. Since these regions allow a longer period to be restored, land vegetation covers can be established in more or less time like natural attenuation. Since the length of phytoremediation is an inherent limitation, this inherent disadvantage limits its adoption in developed business regions, such as growing urban areas. Because high land values could not be recovered in the short term, phytoremediation is not cost-effective in those regions. We examine the potential measures that can enhance the performance of phytoremediation, such as soil amendments, and agricultural practices. The results obtained through review can clarify where/what conditions phytoremediation can provide the most suitable solutions at a large scale. Finally, we identify the main barriers and knowledge gaps to establishing a vegetation cover in large-scale applications and highlight the research priorities for increased acceptance of phytoremediation.

Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data

Confidence in model estimates of soil CO2 flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data-model fusion with modified versions of the CN-SIM model and a 571-day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These "lagged-peak" samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest-performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co-metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth-system models.

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects

Land-use and land-cover change (LULCC) are of importance in natural resource management, environmental modelling and assessment, and agricultural production management. However, LULCC detection and modelling is a complex, data-driven process in the remote sensing field due to the processing of massive historical and current data, real-time interaction of scenario data, and spatial environmental data. In this paper, we review principles and methods of LULCC modelling, using machine learning and beyond, such as traditional cellular automata (CA). Then, we examine the characteristics, capabilities, limitations, and perspectives of machine learning. Machine learning has not yet been dramatic in modelling LULCC, such as urbanization prediction and crop yield prediction because competition and transition between land cover types are dynamic at a local scale under varying natural drivers and human activities. Upcoming challenges of machine learning in modelling LULCC remain in the detection and prediction of LULC evolutionary processes if considering their applicability and feasibility, such as the spatio-temporal transition mechanisms to describe occurrence, transition, spreading, and spatial patterns of changes, availability of training data of all the change drivers, particularly sequence data, and identification and inclusion of local ecological, hydrological, and social-economic drivers in addressing the spectral feature change. This review points out the need for multidisciplinary research beyond image processing and pattern recognition of machine learning in accelerating and advancing studies of LULCC modelling. Despite this, we believe that machine learning has strong potentials to incorporate new exploratory variables in modelling LULCC through expanding remote sensing big data and advancing transient algorithms.

Carbon variation of dry grasslands in Central Asia in response to climate controls and grazing appropriation

Quantification of grassland carbon (C) variations is necessary for understanding how grazing and climate change interact to regulate carbon capture and release. Central Asia (CA) has the largest temperate grassland belt in the world and unique temperate dryland ecosystems, which experienced severe climate change and grazing-induced disturbances. However, the impact of grazing on C dynamics is highly uncertain owing to climate variations. Here, an arid ecosystem model (AEM) supplemented with a grazing module that specifically addressed physiological and ecological characteristics of dryland vegetation was developed to quantitatively simulate grassland C dynamics in response to changes in precipitation, temperature, grazing intensity, and CO₂ level in the past decades. The regional simulation results showed that net primary productivity (NPP) was affected mainly by precipitation (in 59% of the studied area). Grazing had a negative effect on NPP and C stocks, whereas overcompensation occurred in 25.71% of the studied area, mainly in the dry western parts. The complex interaction effects of climate, CO₂, and grazing negatively affected productivity, with a grassland NPP decrease of - 1.14 g C/m²/a and high interannual variability. We found that the temporal pattern of cumulative C sequestration, especially total C and vegetation C (VEGC), closely followed the annual fluctuations of precipitation. VEGC stocks decreased from 182.22 to 177.82 g C/m², with a very low value between 1998 and 2008, when precipitation significantly decreased. The results indicate that southern Xinjiang and the Turgay Plateau of Kazakhstan are ecologically fragile areas due to grassland degradation.

Initial soil conditions outweigh management in a cool-season dairy farm's carbon sequestration potential

Pastures and rangelands are a dominant portion of global agricultural land and have the potential to sequester carbon (C) in soils, mitigating climate change. Management intensive grazing (MIG), or high density grazing with rotations through paddocks with long rest periods, has been highlighted as a method of enhancing soil C in pastures by increasing forage production. However, few studies have examined the soil C storage potential of pastures under MIG in the northeastern United States, where the dairy industry comprises a large portion of agricultural use and the regional agricultural economy. Here we present a 12-year study conducted in this region using a combination of field data and the denitrification and decomposition (DNDCv9.5) model to analyze changes in soil C and nitrogen (N) over time, and the climate impacts as they relate to soil carbon dioxide (CO₂) and nitrous oxide (N₂O) fluxes. Field measurements showed: (1) increases in soil C in grazed fields under MIG (P = 0.03) with no significant increase in hayed fields (P = 0.55); and (2) that the change in soil C was negatively correlated to initial soil C content (P = 0.006). Modeled simulations also showed fields that started with relatively less soil C had significant gains in C over the course of the study, with no significant change in fields with higher initial levels of soil C. Sensitivity analyses showed the physiochemical status of soils (i.e., soil C and clay content) had greater influence over C storage than the intensity of grazing. More extensive grazing methods showed very little change in soil C storage or CO₂ and N₂O fluxes with modeled continuous grazing trending towards declines in soil C. Our study highlights the importance of considering both initial system conditions as well as management when analyzing the potential for long-term soil C storage.

Modelling groundwater quality of the Athabasca River Basin in the subarctic region using a modified SWAT model

Groundwater is a vital resource for human welfare. However, due to various factors, groundwater pollution is a paramount environmental concern. It is challenging to simulate groundwater quality dynamics with the Soil and Water Assessment Tool (SWAT) because it does not adequately model nutrient percolation processes in the soil. The objectives of this study were to extend the SWAT module to simulate groundwater quality for the parameters nitrate and Total Dissolved Solids (TDS). The results of the SWAT model for the Athabasca River Basin in Canada revealed a linear relationship between observed and calculated groundwater quality. This result achieved satisfactory values for coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS). For nitrate, the model performance measures R² ranged from 0.66–0.83 during calibration and NSE from 0.61–0.83. R² is 0.71 during validation and NSE ranged from 0.69–0.75. Likewise, for TDS, the model performance measures R² ranged from 0.61–0.82 during calibration and from 0.58–0.62 during validation. When coupled with soil zone and land surface processes, nitrate and TDS concentrations in groundwater can be simulated with the SWAT model. This indicated that SWAT may be helpful in evaluating adaptive management scenarios. Hence, the extended SWAT model could be a powerful tool for regional-scale modelling of nutrient loads, and to support and effective surface and groundwater management.

Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020

Grassland remote sensing (GRS) is an important research topic that applies remote sensing technology to grassland ecosystems, reflects the number of grassland resources and grassland health promptly, and provides inversion information used in sustainable development management. A scientometrics analysis based on Science Citation Index-Expanded (SCI-E) was performed to understand the research trends and areas of focus in GRS research studies. A total of 2692 papers related to GRS research studies and 82,208 references published from 1980 to 2020 were selected as the research objects. A comprehensive overview of the field based on the annual documents, research areas, institutions, influential journals, core authors, and temporal trends in keywords were presented in this study. The results showed that the annual number of documents increased exponentially, and more than 100 papers were published each year since 2010. Remote sensing, environmental sciences, and ecology were the most popular Web of Science research areas. The journal Remote Sensing was one of the most popular for researchers to publish documents and shows high development and publishing potential in GRS research studies. The institution with the greatest research documents and most citations was the Chinese Academy of Sciences. Guo X.L., Hill M.J., and Zhang L. were the most productive authors across the 40-year study period in terms of the number of articles published. Seven clusters of research areas were identified that generated contributions to this topic by keyword co-occurrence analysis. We also detected 17 main future directions of GRS research studies by document co-citation analysis. Emerging or underutilized methodologies and technologies, such as unmanned aerial systems (UASs), cloud computing, and deep learning, will continue to further enhance GRS research in the process of achieving sustainable development goals. These results can help related researchers better understand the past and future of GRS research studies.

Modelling Watershed and River Basin Processes in Cold Climate Regions: A Review

Watersheds in cold regions provide water, food, biodiversity and ecosystem service. However, the increasing demand for water resources and climate change challenge our ability to provide clean freshwater. Particularly, watersheds in cold regions are more sensitive to changing climate due to their glaciers’ retreat and permafrost. This review revisits watershed system and processes. We analyze principles of watershed modelling and characteristics of watersheds in cold regions. Then, we show observed evidence of their impacts of cold processes on hydrological and biogeochemical processes and ecosystems, and review the watershed modeling and their applications in cold regions. Finally, we identify the knowledge gaps in modeling river basins according to model structures and representations of processes and point out research priorities in future model development.