Inland Water Bodies Research Articles

Inventory of methane and nitrous oxide emissions from freshwater aquaculture in China

Aquaculture is a major emission source of atmospheric methane (CH4) and nitrous oxide (N2O). However, its contribution remains highly uncertain because the source has been neglected in global and national greenhouse gas inventories. Here, we present an inventory of CH4 and N2O fluxes from five freshwater aquaculture systems in China, which accounted for more than half of global freshwater aquaculture production during 2000-2020. We show that total CH4 and N2O emissions were 2.5 (0.6-4.2) Tg CH4 yr-1 and 18.3 (3.8-32.2) Gg N2O yr-1, respectively, with 75% coming from ponds and paddy fields. CH4 and N2O effluxes from freshwater aquaculture were 5 and 2 times higher than the average from other inland water bodies, respectively. Aquaculture accounts for half of the national inland water emissions, and outweighs the land soil methane sink. We suggest that tracking aquacultural emissions and their reduction through aerated systems could provide additional opportunity to reduce agricultural non-CO2 emissions without compromising food security.

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

Evaluating SWOT's interferometric capabilities for mapping intertidal topography

The Surface Water and Ocean Topography (SWOT) mission, originally designed for observing ocean and inland water bodies, can be a valuable tool for mapping the topography of intertidal flats. This study provides the first demonstration of SWOT's ability to measure intertidal topography using simultaneous interferometric acquisitions. Observations acquired between April and July 2023, during the calibration/validation (CALVAL) phase of the mission, were used. SWOT-derived intertidal Digital Elevation Models (DEMs) were generated from level-2 High-Rate Pixel Cloud (L2_HR_PIXC) products and compared to airborne and UAV LiDAR-derived DEMs. Our results indicate a high reliability of the SWOT observations with a mean absolute error lower than 0.49 m, reaching 0.13 m in specific areas. This validation confirms SWOT's contribution for coastal studies beyond water level mapping, showcasing its broader applicability in the monitoring and management of coastal and estuarine environments.

Microplastic evidence assessment from water and sediment sampling in a shallow tropical lake.

Microplastics (MPs) severely threaten inland waterbodies due to the direct impact of human activities. In the present study, spatial and temporal patterns of MPs in a shallow tropical lake were assessed, describing their size, morphology, and polymer types. Water and sediment samples were collected from Lake Chapala during three seasons, and MPs were quantified with a stereomicroscope. The structure, elemental composition, and polymeric composition were determined via environmental scanning electron microscopy and Fourier transform infrared spectroscopy. The highest average concentration of microplastics in Lake Chapala was detected during the low-water period in April 2022 (2.35 items/L), exceeding the July 2022 rainy season concentration (1.8 items/L) by 0.25 items/L, and sediment concentrations were also higher in April 2022 (219 items/kg) compared to July 2022 (210 items/kg). This study highlights the significant pollution of Lake Chapala with microplastics, emphasizing the need for urgent measures to manage plastic waste and mitigate its environmental impact on aquatic ecosystems. PRACTITIONER POINTS: Microplastic contamination was evaluated in Lake Chapala. The distribution profiles of microplastics were different in each area. Heavy metals osmium, tellurium, and rhodium were found associated with the PMs. Polymers were found in this study.

Unlocking the potential of CYGNSS for pan-tropical inland water mapping through multi-source data and transformer

Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

Acquisition of Bathymetry for Inland Shallow and Ultra-Shallow Water Bodies Using PlanetScope Satellite Imagery

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters.

Studies of lentic inland water ecosystems in the Galapagos archipelago: Current state of knowledge and bibliometric analysis.

ABSTRACT The Galapagos Archipelago - due to its remoteness, isolation, recent volcanic origin, and endemic fauna and flora - is iconic for inspiring Darwin's evolutionary theory. Since Darwin’s day researchers have focused on the Archipelago’s terrestrial and marine species. Its location in an important climate regulating body of water, coupled with a low level of human activity for most of its history, attracted several paleolimnologists to the inland waters of the Archipelago. Studies of the many lagoons, crater lakes, and other inland waters were mainly in support of paleolimnological research and are therefore limited in scope. Because of the presence of permanent human settlements on four of the Archipelago’s islands, plus increased tourism, the limited number of permanent freshwater bodies are under unprecedented environmental stress. This bibliometric analysis assembles what is known about Galapagos lentic inland water bodies. It shows that studies on biodiversity, water quality, and threats to these ecosystems are few and fragmented across time and space.

Inland water body surface area is associated with High Pathogenic Avian Influenza outbreaks in Iowa

Inland water body surface area is associated with High Pathogenic Avian Influenza outbreaks in Iowa

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.

Evaluation of GPM IMERG Early, Late, and Final Run in Representing Extreme Rainfall Indices in Southwestern Iran

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Effective monitoring and management of inland waterbodies depend on reliable assessments of water quality through remote sensing technologies. Match-up analysis plays a significant role in investigating the comparability between in situ and remote sensing data of physical and biogeochemical variables. By exploring different spatial aggregations and temporal windows, we aimed to identify which configurations are most effective and which are less effective for the assessment of remotely sensed water quality data within the context of governmental monitoring programs. Therefore, in this study, remote sensing data products, including the variables of Secchi depth, chlorophyll-a, and turbidity, derived from the Copernicus satellites Sentinel-2 and Sentinel-3, were compared with in situ laboratory data from >100 waterbodies (lakes and reservoirs) in Germany, covering a period of 5 years (2016–2020). Processing was carried out using two different processing schemes, CyanoAlert from Brockmann Consult GmbH and eoapp AQUA from EOMAP GmbH & Co. KG, in order to analyze the influence of different processors on the results. To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches.

Effect of a dense inflow on the stratification of a steep‐sided lake

AbstractWe detail the effect of a small stream of dense inflow that significantly altered the stratification and water quality in a constructed water body in northern British Columbia, Canada. As the dense inflow passed through the epilimnion of the steep‐sided lake, it entrained relatively large quantities of water. The resulting mixture of dense inflow and entrained epilimnetic water sank to the bottom of the lake. The removal of water from the epilimnion due to entrainment reduced the epilimnetic thickness. This opposes the normal process of epilimnetic deepening due to wind and convective cooling. The flux of fluid entrained into the dense inflow was calculated to be between 4 and 14 times the inflow, depending primarily on the thickness of the epilimnion. The entrainment had four major effects: (1) it reduced the residence time of the epilimnion from half a year to less than a month; (2) it removed the freshwater cap that resulted from spring ice melt; (3) it enabled fall turnover, which further enhanced deep oxygen content and helped to prevent meromixis from developing in the lake; and (4) it produced a rapid decline in contaminant (i.e., zinc) concentrations in the epilimnion, which received dissolved metals inputs from oxidized sulfide minerals exposed in the subaerial walls of the lake. Given the wide variety of inflows to inland water bodies, some of which are at least seasonally dense, an understanding of the mechanisms detailed here can inform lake management in general, and more specifically, management of water quality in mine‐impacted water bodies.

Enhanced water level monitoring for small and complex inland water bodies using multi-satellite remote sensing

Water level monitoring in lakes and reservoirs is essential for effective water resource management, especially in remote areas where traditional ground sensors are costly and difficult to maintain. Remote sensing offers an alternative, but improving the quality, resolution, and accuracy of satellite data remains crucial. This paper introduces MoRLa (Measurement of Reservoir Level from Altimetry), a data filtering procedure designed to enhance satellite altimetry retrievals. MoRLa increases the acceptance of satellite observations and improves the quality of water level estimates by using physical characteristics of water bodies to exclude non-conforming measurements. Unlike previous studies with static masks, MoRLa employs a dynamic filter adaptable to actual water levels at specific times. Tested on reservoirs in the Korean Peninsula, including the Hwang-Gang dam, MoRLa shows significant improvements in water level measurements using Cryosat-2, ICESat-2, and Sentinel-3A and B satellites.

Dynamics and source characterization of chromophoric dissolved organic matter (CDOM) in a tropical monsoon driven lagoon

While there is a significant body of research on the dynamics of chromophoric dissolved organic matter (CDOM) in coastal and offshore waters, our understanding of CDOM dynamics in tropical inland water bodies remains limited. To bridge this knowledge gap, a monthly in-situ investigation was carried out at 33 stations along a monsoon driven lagoon, Chilika, on the southeast coast of India for one year i.e., from July 2018 to June 2019. CDOM absorption at 440 nm [aCDOM(440)] data were analyzed as a proxy for CDOM concentration which varied between a range of 0.04–65.14 m−1 with an average value of 2.77 ± 5.23 m−1. A gradient in aCDOM(440) was observed from the river discharge dominated shallower northern sector (4.91 ± 8.32) m−1 to the more isolated and less fresh water influenced deeper southern sector (1.21 ± 1.55) m−1. Spectral slope (S), spectral slope ratio (SR), and molecular weight proxy (M) were computed to understand the possible source and fate of CDOM in the lagoon. The average spectral slope of S280–500 and S350–500 vary between 0.002 and 0.096 nm−1 and 0.001–0.095 nm−1, respectively. The (SR) and (M) values ranged between of 0.01–6.81 and 0.31–52.28. It was observed that large-sized, high molecular weight CDOM from terrestrial origin was prevalent during monsoon with lower (S280–500), (SR), and (M)values. In contrast, lower molecular weight CDOM fractions were prevalent during pre- and post-monsoon which were mainly of autochthonous origin with higher (S350–500), (SR), and (M) values. Our findings indicate significant spatio-temporal variations of CDOM in Chilika Lagoon, influenced by the monsoon-driven influx of freshwater and the mixing of fresh and marine water. In-situ changes in CDOM are likely shaped by microbial breakdown and photodegradation.

Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping

Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how Generative Topographic Mapping (GTM), a probabilistic realization of the self-organizing map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply GTM to visualize the distribution of captured reflectance spectra, revealing the small-scale spatial variability of the water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of nearshore algae, as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.

Exploring the topographical pattern beneath the water surface: Global bathymetric volume-area-height curves (BVAH) of inland surface water bodies

Global inland surface water bodies such as lakes and reservoirs, important components of the hydrosphere and ecosphere, are increasingly affected by climate change. Generating bathymetric volume-area-height (BVAH) curves for global inland surface water bodies can enhance our understanding of their topography and climate impacts. However, accurately quantifying the topographic patterns of these water bodies remains challenging due to the difficulties in collecting comprehensive bathymetric data. Therefore, we collected and processed over 2000 bathymetric maps of global water bodies from over 50 different data sources and then developed the BVAH model. Finally, the BVAH hydrological curves of 16671 global inland surface water bodies (larger than 10 km2) were generated. The results include but are not limited to (1) For most targeted water bodies, area (A) and volume (V) exhibit significant power function relationships with surface heights (H), with optimal power values quantified as 1.42 for A and 2.42 for V. (2) The BVAH model outperforms GLOBathy in estimating area and volume changes, achieving higher correlation coefficients (CC) of approximately 0.962 for the area and 0.991 for volume, and demonstrating lower percentages of root mean squared errors (PRMSE) around 10.9% for the area and 4.8% for volume. (3) In the case study of the Tibetan Plateau and various large global reservoirs, the BVAH curve database can capture dynamic volume changes. As a unified simulation of the bathymetric topographical patterns, our bathymetric dataset and corresponding BVAH curve database have great potential to contribute to effective water resource management and ecological conservation efforts worldwide.

Tugwi Mukosi Dam and Climate Change: Unlocking Economic Value from Field Cropping for Chivi and Masvingo Rural Districts

Climate change is real and water harvesting is a promising strategy for food and income insecurity for struggling communities. This study proposes how crop agricultural crop value can be unlocked by Masvingo and Chivi rural district communities around and downstream Tugwi Mukosi Dam (TMD). The TMD is Zimbabwe’s largest inland water body is located at the confluence of Tugwi-Mukosi rivers. The dam is on the boundary of in the two districts of Masvingo Province. The study applied both qualitative and quantitative data from Zimbabwe’s Ministry of Lands, Water, Agriculture Fisheries and Rural Development, Tongaat Hulett Zimbabwe Limited, local leadership (elected and traditional), development agencies in Chivi, and Masvingo districts, the Zimbabwe National Water Authority (ZINWA), as well as agronomists from a local university. Study participants were purposively selected. Interviews, observation and questionnaires were the main data collection instruments. Findings from this study established that local communities around and downstream this dam still suffer severe food and income insecurity despite being on the fringes of this mega water body. It also established vast agricultural crop potential for local communities given supportive national policy and capital investment by the government. It established that the dam was planned and constructed to serve economic interests of corporates only. The study recommends liberalisation of the ZINWA policy to allow local communities to benefit fully from the dam, particularly now as the districts suffer the effects of climate change.

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

On the Method for Solving the Problem of Ice Cover Deformation under an Arbitrary Moving Load

Introduction. The development of the polar areas of the World Ocean and the need to solve various problems associated with a large number of freezing inland water bodies issue new challenges for science. These challenges include the problem of studying the behavior of ice cover when exposed to various types of loads. Of great interest is the consideration of problems about the action of a moving load on the ice cover. A moving load simulates the effect of moving vehicles on ice. However, in papers devoted to the above problems, cases of load movement along a straight-line trajectory are considered. The objective of this research is to develop a method for studying the behavior of ice cover under the action of a load moving arbitrarily.Materials and Methods. The article proposes a method for solving the problem of the action of a force moving along an arbitrary trajectory on the ice cover of a reservoir of finite depth. The problem amounts to solving a system of two differential equations. The first of them models the behavior of the ice cover, and it is the equation of vibrations of a viscoelastic plate. The second equation simulates the behavior of fluid in a state of potential flow, and it is Laplace's equation. To solve the system of differential equations, integral transformations in time, space and variables were used. The resulting solution was expressed through an iterated integral, which was calculated using numerical methods.Results. The development and implementation of the method resulted in solving the problem of the movement of a concentrated force along an ice cover according to an arbitrary law. At the same time, studies were carried out on the behavior of displacements and stresses in the ice cover depending on the speed and acceleration of the movement of the vertical load, on the depth of the reservoir, and on the viscoelastic properties of ice. In addition, the distribution of the velocity vector of fluid particles along the depth of the reservoir was calculated.Discussion and Conclusion. The proposed method is very effective for solving problems of moving loads acting on the ice cover of a reservoir of finite depth. It provides solving problems about the action of a load moving along an ice cover along a complex trajectory. The results obtained can be used to calculate the stress and displacement of the ice cover during the laying of ice roads or the construction of airfields on the ice.

Ecological Dynamics of Water Hyacinth in Coimbatore Lakes: Implications for Biomass, Carbon Stock, and Nutrient Management

In aquatic ecosystems, water hyacinth (Eichhornia crassipes) often proliferates, impacting water quality and ecological balance. This study investigates the density, biomass, carbon stock, and nutrient concentrations of E. crassipes across five significant lakes in Coimbatore: Krishnampathy, Kurichi, Ukkadam, Singanallur, and Sulur. Field surveys, laboratory analyses and statistical methods were adopted in this study. The highest density of E. crassipes was observed in Krishnampathy Lake (59±2.5, no./m2). The biomass values (kg/m2) ranged from 1.00 to 7.33 for leaf, 3.23 to 8.03 for stalk, and from 1.47 to 10.80 for root samples. The carbon stock values (kg/m2) ranged from 0.51 to 3.74 for leaf, 1.49 to 3.73 for stalk, and from 0.68 to 4.19 for root samples, revealing the species potential role in reducing atmospheric carbon in mitigation of climate change. We have studied the macro and micro-nutrient concentrations present in E. crassipes, and also we checked the relationship between chlorophyll content and nutrient concentration with the carbon stock of different plant parts of E. crassipes. This study contributes to a better understanding of the ecological dynamics of water hyacinth in inland water bodies and its potential implications for carbon cycling and nutrient dynamics.