Machine Learning Approach to Simulate Soil CO2 Fluxes under Cropping Systems

With the growing number of datasets to describe greenhouse gas (GHG) emissions, there is an opportunity to develop novel predictive models that require neither the expense nor time required to make direct field measurements. This study evaluates the potential for machine learning (ML) approaches to predict soil GHG emissions without the biogeochemical expertise that is required to use many current models for simulating soil GHGs. There are ample data from field measurements now publicly available to test new modeling approaches. The objective of this paper was to develop and evaluate machine learning (ML) models using field data (soil temperature, soil moisture, soil classification, crop type, fertilization type, and air temperature) available in the Greenhouse gas Reduction through Agricultural Carbon Enhancement network (GRACEnet) database to simulate soil CO2 fluxes with different fertilization methods. Four machine learning algorithms—K nearest neighbor regression (KNN), support vector regression (SVR), random forest (RF) regression, and gradient boosted (GB) regression—were used to develop the models. The GB regression model outperformed all the other models on the training dataset with R2 = 0.88, MAE = 2177.89 g C ha−1 day−1, and RMSE 4405.43 g C ha−1 day−1. However, the RF and GB regression models both performed optimally on the unseen test dataset with R2 = 0.82. Machine learning tools were useful for developing predictors based on soil classification, soil temperature and air temperature when a large database like GRACEnet is available, but these were not highly predictive variables in correlation analysis. This study demonstrates the suitability of using tree-based ML algorithms for predictive modeling of CO2 fluxes, but no biogeochemical processes can be described with such models.

Contribution of Small Phytoplankton to Primary Production in the Northern Bering and Chukchi Seas

The northern Bering and Chukchi seas are biologically productive regions but, recently, unprecedented environmental changes have been reported. For investigating the dominant phytoplankton communities and relative contribution of small phytoplankton (<2 µm) to the total primary production in the regions, field measurements mainly for high-performance liquid chromatography (HPLC) and size-specific primary productivity were conducted in the northern Bering and Chukchi seas during summer 2016 (ARA07B) and 2017 (OS040). Diatoms and phaeocystis were dominant phytoplankton communities in 2016 whereas diatoms and Prasinophytes (Type 2) were dominant in 2017 and diatoms were found as major contributors for the small phytoplankton groups. For size-specific primary production, small phytoplankton contributed 38.0% (SD = ±19.9%) in 2016 whereas 25.0% (SD = ±12.8%) in 2017 to the total primary productivity. The small phytoplankton contribution observed in 2016 is comparable to those reported previously in the Chukchi Sea whereas the contribution in 2017 mainly in the northern Bering Sea is considerably lower than those in other arctic regions. Different biochemical compositions were distinct between small and large phytoplankton in this study, which is consistent with previous results. Significantly higher carbon (C) and nitrogen (N) contents per unit of chlorophyll-a, whereas lower C:N ratios were characteristics in small phytoplankton in comparison to large phytoplankton. Given these results, we could conclude that small phytoplankton synthesize nitrogen-rich particulate organic carbon which could be easily regenerated.

The Method for Evaluating Cross-Shore Migration of Sand Bar under the Influence of Nonlinear Waves Transformation

Sand bar migration on the gently sloping sandy bottom in the coastal zone as a result of nonlinear wave transformation and corresponding sediment transport is discussed. Wave transformation on the intermediate depth causes periodic exchange of energy in space between the first and the second wave harmonics, accompanied by changes in the wave profile asymmetry. This leads to the occurrence of periodical fluctuations in the wave-induced sediment transport. It is shown that the position of the second nonlinear wave harmonic maximum determines location of the divergence point of sediment transport on the inclined bottom profile, where it changes direction from the onshore to the offshore. Such sediment transport pattern leads to formation of an underwater sand bar. A method is proposed to predict the position of the bar on an underwater slope after a storm based on calculation of the position of the maximum amplitude of the second nonlinear harmonic. The method is validated on the base of field measurements and ERA 5 reanalysis wave data.

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

Integration of Numerical Models and InSAR Techniques to Assess Land Subsidence Due to Excessive Groundwater Abstraction in the Coastal and Lowland Regions of Semarang City

This study was carried out to assess land subsidence due to excessive groundwater abstraction in the northern region of Semarang City by integrating the application of both numerical models and geodetic measurements, particularly those based on the synthetic aperture radar interferometry (InSAR) technique. Since 1695, alluvial deposits caused by sedimentations have accumulated in the northern part of Semarang City, in turn resulting in changes in the coastline and land use up to the present. Commencing in 1900, excessive groundwater withdrawal from deep wells in the northern section of Semarang City has exacerbated natural compaction and aggravated the problem of land subsidence. In the current study, a groundwater model equivalent to the hydrogeological system in this area was developed using MODFLOW to simulate the hydromechanical coupling of groundwater flow and land subsidence. The numerical computation was performed starting with the steady-state flow model from the period of 1970 to 1990, followed by the model of transient flow and land subsidence from the period of 1990 to 2010. Our models were calibrated with deformation data from field measurements collected from various sources (e.g., leveling, GPS, and InSAR) for simulation of land subsidence, as well as with the hydraulic heads from observation wells for simulation of groundwater flow. Comparison of the results of our numerical calculations with recorded observations led to low RMSEs, yet high R2 values, mathematically indicating that the simulation outcomes are in good agreement with monitoring data. The findings in the present study also revealed that land subsidence arising from groundwater pumping poses a serious threat to the northern part of Semarang City. Two groundwater management measures are proposed and the future development of land subsidence is accordingly projected until 2050. Our study shows quantitatively that the greatest land subsidence occurs in Genuk District, with a magnitude of 36.8 mm/year. However, if the suggested groundwater management can be implemented, the rate and affected area of land subsidence can be reduced by up to 59% and 76%, respectively.

Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model

The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction.

Quantifying occurrence of deformation bands in sandstone as a function of structural and petrophysical factors and their impact on reservoir quality: an example from outcrop analog of Productive Series (Pliocene), South Caspian Basin

Deformation bands (DB) are known to influence porosity and permeability in sandstones. This study aims to predict the occurrence of DB and to quantify their impact on reservoir properties based on field measurements in the steeply dipping limb of a kilometer-scale fold in Yasamal Valley, western South Caspian Basin. An integrated approach of characterizing bands and their effect on reservoir properties included measurements of natural gamma radioactivity and permeability using portable tools, along with bed dip and the count of DB across distinct facies. A set of core analyses was performed on outcrop plugs with and without bands to estimate the alteration of rock properties at the pore scale. Interpretation of outcrop gamma-ray data indicates the absence of bands in Balakhany sandstones containing shale volume greater than 18% for unconsolidated and 32% for calcite-rich facies. A high amount of calcite cement appears to increase the number of DB. A poor, positive trend between bed dip and DB concentration was identified. We show that net to gross, defined as the thickness fraction of sandstone bound by mudstones, is among the parameters controlling the occurrence of bands. Samples containing a single DB show a 33% and 3% decrease in permeability and porosity, respectively, relative to the host rock. We reveal a new set of lithological and petrophysical factors influencing DB occurrence. This study offers a direct tool that can be applied in subsurface reservoir analogs to predict the occurrence and concentration of DB and estimate their influence on rock properties.

Appraisal of hydro-ecology, geomorphology, and sediment behavior during low and high floods in the Lower Indus River Estuary

The riverine ecosystem is beholden by the freshwater; however, morphological changes and sediment load destabilize the natural river system which deteriorates the ecology and geomorphology of the river ecosystem. The Lower Indus River Estuary (LIRE) geomorphological response was synthesized using satellite imagery (1986–2020) and evaluated against the field measurements. The estuary sinuosity index has an increasing trend from 1.84 (1986) to 1.92 (2020) and the estuary water area is increased from 101.41 km2 (1986) to 110.24 km2 (2020). The sediment load investigation at Kotri barrage indicated that the median size of bed material samples during the low-flow period falls between 0.100 and 0.203 mm and the bed material after the high flow has clay and silt (&lt;0.0623 mm) ranging from 17–95% of the total weight of samples. The vegetated land loss on the banks is positively correlated with the peak runoff at Kotri barrage (r2=0.92). The bank erosion was computed with high precision (r2=0.84) based on an improved connection of the coefficient of erodibility and excess shear stress technique. This study will be helpful for policymakers to estimate the ecological health of LIRE, and sediment fluxes play an essential role in the mega-delta system and coastal management.