Soil Model Research Articles

Implications of climate and litter quality for simulations of litterbag decomposition at high latitudes

Abstract. Litter decomposition is a vital part of the carbon cycle and is thoroughly studied both in the field and with models. Although temporally and spatially limited, litterbag decomposition experiments are often used to calibrate and evaluate soil models, coupled to land models, that are intended for use on large scales. We used the microbially explicit soil decomposition model MIMICS+ to replicate two high-latitude litterbag decomposition experiments of different spatial and temporal scales. We investigated how well the model represented observed mass loss in terms of the controlling factors of climate and litter quality and their relative importance with time. In addition to default model forcing, we used measured and site-specific model-derived microclimatic variables (soil moisture and temperature), hypothesizing that this would improve model results. We found that MIMICS+ represented mass loss after 1, 3, and 6 years well across a climatic gradient of Canadian sites but had more variable results for 1-year mass loss across a climate grid in southern Norway. In terms of litter quality, the litter metabolic fraction had more influence on modeled mass loss than the carbon-to-nitrogen ratio of the litter. Using alternative microclimate sources led to up to 23 % more mass remaining and down to 22 % less mass remaining compared to the simulations using default model inputs. None of the input alternatives significantly improved results compared to using the default model setup. We discuss possible causes for our findings and suggest measures to better utilize short-term field experiments to inform microbially explicit decomposition models.

Thermo-hydro-mechanical behaviour of energy tunnel in unsaturated soils

Soils surrounding energy tunnels are often unsaturated, particularly in areas with a low water table. All studies of the mechanical behaviour of energy tunnels focused on saturated or dry soils. Through numerical simulations using a coupled thermo-hydro-mechanical model for unsaturated soils, this study aims to improve the understanding of the interactions between energy tunnels and unsaturated ground. The influences of water table position and soil type on the mechanical behaviour of energy tunnels were investigated. Results show that the thermally-induced lining convergence of energy tunnels does not change much with the water table position. Nevertheless, the other mechanical responses (e.g., settlements of ground and tunnel and lining stress changes) highly depend on the water table position. These mechanical responses are more significant when the water table is higher than the tunnel center, except for the larger settlements at a lower water table in clay. Furthermore, the above responses are underestimated if soils above the water table are assumed to be completely dry. The degree of underestimation is most significant for the clay, followed by silt and sand, e.g., a maximum of 54%, 23% and 10% in the change of circumferential stress, respectively. These results highlight that the unsaturated soil model should be used to evaluate the thermo-hydro-mechanical responses of energy tunnels in unsaturated soils, particularly for the clay ground.

Analisis Daya Dukung Lateral Bored Pile Ø 80 Cm dengan Menggunakan Uji Beban Lateral dan Menggunakan Metode Elemen Hingga pada Proyek Menara BRI - Medan

Bored pile foundations are deep foundations that are often used in the construction of large construction sites located in dense areas with the consideration of reducing noise and the influence of vibrations that would occur if pile foundations were used. This analysis aims to calculate the lateral bearing capacity of the bored pile foundation based on the results of analytical calculations using the Broms method, the Davisson method, the Chin method, the Mazurkiewich method, and analyzing the displacement of the bored pile foundation based on loading tests in the field. and the results of soil modeling with Allpile and finite element methods with the Mohr-Coulomb soil model, and the Hardening Soil model. Based on the analysis that has been carried out, the ultimate bearing capacity of the bored pile based on SPT data using the Broms method is 18.92 tons, while the results of the interpretation of the loading test using the Davisson method are 18 tons, the Chin method is 18.98 tons, and the Mazurkiewich method is 19 tons. For the large deflection of a single bored pile with a load of 200% with the Allpile program the large deflection that occurs is 4.25 mm and analysis based on FEM PLAXIS 3D using soil modeling with Mohr - Coulomb large deflection of 3.0 mm and soil modeling with Hardening Soil 2.99 mm.

Filling the gaps in soil data: A multi-model framework for addressing data gaps using pedotransfer functions and machine-learning with uncertainty estimates to estimate bulk density

Legacy soil datasets are a valuable resource and should be used to the greatest extent possible. However, such datasets may be incomplete, and lack observations for every attribute, as the dataset may be compiled from multiple studies. To use these datasets in soil mapping and modeling work, it is useful to fill the gaps in the dataset with estimated values. Machine learning is an approach that can provide estimates with high accuracy. In this study, the machine learner Random Forest (RF) was used to estimate bulk density values in an existing dataset from the province of British Columbia (BC), Canada, which was used as a case study dataset. As the dataset had missing observations across all attributes, multiple models needed to be generated and tested to determine the accuracy of the estimated values produced. A total of 512 models were tested using RF, which were then ranked based on the concordance correlation coefficient (CCC) of their estimates; the CCC of all models ranged from 0.51 to 0.92. The estimates of 27 of these models were then used to fill the missing observations in the dataset; the accuracy of these models ranged from a CCC of 0.63 to 0.92. Further, uncertainty estimates for the predictions were generated using quantile regression, which was coupled with the RF approach. Each model tested therefore had an accuracy measurement and an uncertainty estimate. This approach, of using multiple models developed in RF, can be applied to other legacy soil datasets with inconsistently missing values to produce estimates which can fill the missing observations, and produce uncertainty estimates for those estimates.

Comparative study on dynamic responses of ballasted and ballastless tracks at critical velocity

High-speed railways are becoming ubiquitous worldwide, demanding increased operational speeds. Yet, as trains near critical speeds, dynamic stress and embankment displacement escalate, jeopardizing safety. While significant strides have been made in studying these critical speeds, capturing the dynamic characteristics of all track responses remains elusive. This study presents a coupled train-track-ground model employing a 2.5D finite element approach integrated with a nonlinear soil model to investigate the influence of embankment and foundation properties on the critical speeds for both ballasted and ballastless tracks. The research resulted in the development of unified Dynamic Amplification Factor curves that consistently represent the dynamic behavior of various tracks as observed in multiple studies. Additionally, the applicability of simplified theoretical models for calculating critical speeds, such as the elastic half-space foundation and track-foundation models, was assessed. The findings suggest that the simplified models are suitable only under specific conditions. This research provides valuable perspectives on optimizing train speeds and ensuring the safety of diverse track types.

Modeling the response of a shallow foundation using advanced soil constitutive models

Shallow foundations are a common type of foundation used for many significant structures. These foundations can be subjected to vertical loads, moments, and base shear, which induce stresses and strains in the supporting soil. Consequently, understanding soil behaviour is crucial in geotechnical engineering. This paper presents a numerical analysis of a shallow foundation using various soil behaviour laws, utilizing finite element methods implemented in Plaxis software. The primary objective of this study is to conduct numerical modeling of a foundation under a centered load. Specifically, it aims to compare the results obtained using different constitutive models, including the Mohr-Coulomb (MC) model, the Soft Soil model, and the modified Cam-Clay model. By employing these models, we aim to evaluate their effectiveness in predicting soil response under load and to identify the most accurate model for specific soil conditions. The comparative analysis provides insights into the advantages and limitations of each model, highlighting the importance of selecting appropriate soil behaviour laws for accurate geotechnical design. Furthermore, this study underscores the significance of advanced numerical tools in enhancing our understanding of soil-structure interaction and improving the reliability of foundation design. The findings contribute to the broader field of geotechnical engineering by offering guidance on the selection of constitutive models for various soil types and loading conditions, ultimately aiding in the design of safer and more efficient shallow foundations. This research also emphasizes the role of precise modeling in predicting settlement and bearing capacity, which are critical factors in the performance and longevity of shallow foundations. Overall, this study aims to improve foundation design practices significantly.

Development of SADS (Soil and Air Detector System) to Support Palm Oil Industries in Indonesia

The issue of environmental damage due to the expansion of palm oil plantations in Indonesia has become a global issue. It has often hampered the development of the downstream palm oil industries in Indonesia. The European market was once closed to palm oil and derivative products from Indonesia. In fact, this industry has become a source of income and employment for millions of Indonesians. Therefore, palm oil industry must be supported with sustainable efforts. One of them is developing SADS (Soil and Air Detector System) model. Which is a tool for detecting soil and air environmental conditions around palm oil plantations. The parameters are soil PH, CO2 levels, temperature and humidity, and sunlight. The measurement results from this tool can be accessed and displayed in real-time via the internet. This is research aims to build a smart system as a solution to environmental problems around palm oil plantations. This is useful for knowing and observing the condition of palm oil plantations and as a basis for taking mitigation actions if the condition is in a critical state

Investigation of the vibration isolation effect of composite vibration isolation walls on ground surface vibrations in deep tunnels of suburban railways

To investigate the vibration isolation effect of composite vibration isolation walls on surface vibrations in suburban railway deep tunnels under various influencing factors, an integrated numerical model of the train was initially developed. This model solved the wheel-rail interaction force and was applied to a three-dimensional volume coupling model of the track soil. Subsequently, the model's reliability was validated through comparison with measured data. Afterward, the vibration isolation effects of various types of EPS material vibration isolation walls were examined, with a focus on exploring the impact of thickness, material proportion, and relative positioning of the materials within the vibration isolation wall composed of EPS material and concrete. Research indicates that with an increase in the burial depth of a single material vibration isolation wall, its effective vibration isolation frequency range gradually widens. When the burial depth of the vibration isolation wall exceeds the tunnel burial depth, the vibration isolation effect is optimal. Composite vibration isolation walls, with thicknesses smaller than single-material vibration isolation walls, exhibit superior vibration isolation effects compared to their single-material counterparts. The effective vibration isolation frequency band of composite vibration isolation walls differs from that of single-material vibration isolation walls. Using the optimal-size vibration isolation wall of a single material as a composite vibration isolation wall enhances the vibration isolation effect of peak acceleration in the frequency domain by 16.58% and peak velocity by 16.95%. Moreover, frequency domain peak displacement experiences a 30.73% improvement in the vibration isolation effect.

Assessment of sediment yield and accumulation in reservoir: The case of Gibe One Reservoir, Southwestern Ethiopia

Soil erosion and sediment buildup are the factors that speed up the decline in capacity and function of reservoirs, agricultural products, and water resources. In order to simulate sediment and runoff and map high sediment-yielding sub-basins in the Gibe Gojeb catchment in southwest Ethiopia, this study used the Soil and Water Assessment Tool (SWAT) model. Using data on sediment and river flow, calibration and validation were carried out. Between 2003 and 2016, the catchment produced an average annual sediment loading of 62.5 tons ha−1 yr−1, with loading fluctuations ranging from 0.2 to 108.4 tons ha−1 yr−1. The acceptable sediment yield threshold value ranges from 12.3 to 108.4 tons ha−1 yr−1 for 56 sub-basins, and from 0.2 to 10 tons ha−1 yr−1 for 5 sub-basins. The most significant sub-basins with very high to extremely severe sediment yields were sub-basins 1 to 30, 32 to 44, 47, 48, 50, 51, and 53 to 61. After thirteen years of operation, the yearly amount of 58,802 tons of sediment transferred from the catchment and deposited into Gibe One reservoir has decreased the capacity by 5.7 %. The accumulation of sediment in a reservoir has an impact on its functionality, power production, and capacity, affecting the safety of dams and the environment. The study's findings enhanced our comprehension of sediment accumulation in reservoirs and furnished us with the necessary information regarding reservoir safety, integrated soil, and water management.

Poroelastic effects of chemical loading in consolidation of residual soils

An analytical chemo-hydromechanical model of residual soils was presented in this study. The modeling procedure utilized a unique phenomenological approach, which highlighted some of the macro-mechanical influences responsible for the behavior of residual soils. The macromechanical analysis yielded an extended poroelastic theory. The developed model was applied in solving a typical civil engineering problem of soil consolidation. For a chemical loading treatment, the problem was using requisite boundary conditions. Thereafter, the mathematical software Mathematica was used for solving the ensuing diffusion equations using physico-chemical properties typical of a residual soil sample. Even though the chemical loading was instantaneous, it took some time before its effect could be felt everywhere according to the poroelastic theory. From obtained results the evolution of the generated pore pressure showed increasing segments of the soil returning to their initial state after the passage of the pore pressure front. The vertical displacement showed a slight increase or elevation of the soil surface. The flux of water flow in the soil was initially positive before turning negative, which can be explained by initial successful penetration of the infiltrating liquid until it met increasingly tortuous paths as result of lower permeability. The volume (per unit area) of water leaving the soil after chemical loading demonstrated that a portion of the water flux associated with the infiltrating liquid ends up leaving the soil through the sides. The developed model was later compared with a similar porochemoelastic model found in literature with reasonable agreement.

Spatiotemporal evolution of runoff and sediment and their dominant driving factors in the Lower Jinsha River basin

The Jinsha River Basin (JRB) contributes a significant amount of sediment to the Yangtze River; however, an imbalance exists between runoff and sediment. The underlying mechanisms and primary factors driving this imbalance remain unclear. In this study, the Shapley Additive Explanation (SHAP) and Geographical Detector Model (GDM) were employed to quantify the importance of the driving factors for water yield (WYLD) and sediment yield (SYLD) using the Soil and Water Assessment Tool (SWAT) model in the JRB. The results indicated that the SWAT model performed well in simulating runoff and sediment, with R2 > 0.61 and NSE > 0.5. Based on the simulated data, SYLD exhibited strong spatiotemporal linkages with WYLD. Temporally, both sediment and runoff showed decreasing trends, with the sediment decrease being more pronounced. Spatially, WYLD and SYLD displayed similar distribution patterns, with low values in the southwest and high values in the northeast. By quantifying the driving factors, we found that climatic factors, including precipitation and potential evapotranspiration, were the main influencing factors for WYLD and SYLD across the entire region, though their contributions to the two variables differed. For WYLD, climatic factors accounted for 70 % of the total influencing factors, whereas their contribution to SYLD was 50 %. Furthermore, soil type and land-use type played significant roles in the SYLD, with importance values of 16 % and 12 %, respectively. Under the influence of surface conditions, the proportion of SYLD in the JRB to the total SYLD in the Yangtze River Basin was greater than that of WYLD. The findings of this study provide scientific evidence and technical support for local environmental impact assessments and the formulation of soil and water conservation plans.

Investigation of the impact of widely used pesticides on conjugative transfer of multidrug resistance plasmids

Plasmid-mediated conjugative transfer has emerged as a major driver accounting for the dissemination of antibiotic resistance genes (ARGs). In addition to the use of antimicrobial agents, there is growing evidence that non-antibiotic factors also play an important role. Pesticides are widely used to protect crops against vectors of diseases, and are indispensable agents in agricultural production, whereas the impact of pesticide pollution on the transmission of antimicrobial resistance remains poorly understood. Here we reveal that the pesticides at environmentally relevant concentrations, especially cyromazine (Cyr) and kresoxim-methyl (Kre), greatly facilitate the conjugative transfer of antibiotic-resistance plasmids carrying clinically important ARGs. Mechanistic studies indicate that Cyr and Kre treatments trigger reactive oxygen species (ROS) production and SOS response, increase membrane permeability, upregulate bacterial proton motive force (PMF) and promote ATP supply. Further non-targeted metabolomics and biochemical analysis demonstrate that the addition of Cyr and Kre accelerates tricarboxylic acid (TCA) cycle and electron transport chain (ETC), thereby activating bacterial energy metabolism. In the constructed soil model, we prove that two pesticides contribute to the dissemination of resistance plasmids in the soil microbiota. 16S rRNA sequencing analyses indicate that pesticides alter transconjugant microbial communities, and enable more opportunistic pathogens, such as Pseudomonas and Enterobacter, to acquire the multidrug resistance plasmids. Collectively, our work indicates the potential risk in accelerating the spread of antimicrobial resistance owing to pesticide pollution, highlighting the importance of continuous surveillance of pesticide residues in complex environmental settings.

Impact on Hydrological Alteration due to Climate Change in Seti Watershed

This study provides streamflow and hydrologic alternation in the climate change context in Seti watershed in Nepal, which will assist planners in better managing streamflow and preparing for climate change. The Soil and Water Assessment Tool (SWAT) model was utilized in this study to simulate the hydrological dynamics of the watershed. The model has been calibrated and validated, with satisfactory performance. Future climate is projected based on the latest set of Shared Socio-economic Pathways (SSP) scenarios from CMIP6 global climate model outputs. The response of streamflow to future climate in three future periods (near, 2025-2050), (mid, 2051-2075), and (far, 2076-2100) is analyzed by comparing to a baseline. Results showed projected decrease of up to 20% during winter in both scenarios, and increase of up to 158% (SP245) and 203% (SSP585) during pre-monsoon, increase of up to 120% (SSP245) and 185% (SSP585) during monsoon, and increase of up to 103% (SSP245) and 144% (SSP585) during post-monsoon seasons.

Calculating the Soil Freezing Characteristic Curve of Saline Soil With Equivalent State of Bulk Solution

AbstractThe relationship between unfrozen water content and temperature, called as soil freezing characteristic curve (SFCC), is of importance for hydrologic, engineering, environmental issues related to frozen soil. The SFCC of saline soil is essentially a result of phase equilibrium of pore solution, which is similar but not identical to that of bulk solution. However, there is still a vacancy of study on the phase equilibrium of pore solution in frozen soil. In this study, image transformation was used to establish the relationship of phase equilibrium between bulk solution and pore solution, with four introduced parameters. Then, the new model of SFCC for saline soil was proposed based on the equivalent state of bulk solution with Pitzer model and SFCC of nonsaline soil. The model was validated by the experimental data from published articles and showed good performance in calculating SFCC of saline soils regardless of soil type, phase transition path, and soil initial water‐salt condition, and some advantages when compared to other three models. All the four introduced parameters have clear physical meanings and their relationships with soil type and initial salt concentration were discussed. Finally, the evolution of phase diagram from bulk solution to pore solution at icing stage was figured out. Further studies are needed for their relationship at salt crystallization stage. Shifting the research perspective from unfrozen water content to pore solution, this study gives a new approach to research of freezing characteristic of saline soil and could promote hydrological and engineering research in cold regions.

Understanding the effects of afforestation on water quantity and quality at watershed scale by considering the influences of tree species and local moisture recycling

Forest restoration emerges as a sustainable practice to counteract biodiversity loss and enhance ecosystem services such as carbon sequestration and water quality improvement. However, more research is necessary on the hydrological effects of forest restoration under different strategies such as tree species options. In this study, we investigate how afforestation with longleaf pine (Pinus palustris) and loblolly pine (Pinus taeda L.) may affect the full hydrologic cycles including precipitation (P) and water quality across two large watersheds: the Alabama-Coosa-Tallapoosa (ACT) and Tombigbee-Black Warrior (TBW) river basins in the southeast United States. To capture the impacts of afforestation on precipitation, we leveraged the Soil and Water Assessment Tool (SWAT) model and a local moisture recycling ratio (LMR) dataset to establish a relationship between model-simulated evapotranspiration (ET) and LMR. Longleaf pine and loblolly pine have contrasting forest structure characteristics that affect model parameters and hydrological responses. Results showed that afforestation with longleaf pine increased mean annual ET by 3 % (25 mm/year) and 6 % (48 mm/year) across the ACT and TBW watersheds, respectively. As a result, mean annual streamflow decreased by 3.3 % (18 mm/year) and 1.6 % (11 mm/year) in the ACT and TBW watersheds, respectively. In contrast, afforestation with loblolly pine led to larger increases in mean annual ET of 17 % (131 mm/year)and 10 % (79 mm/year) in affected areas in the ACT and TBW watersheds, respectively. As a result, mean annual streamflow decreased by 5.2 % (29 mm/year) and 2.8 % (19 mm/year) at the watershed level in the ACT and TBW watersheds, respectively. Overall, the afforestation scenarios led to decreases in watershed-scale sediment and nutrient exports, especially under longleaf pine afforestation. Large-scale afforestation had negligible effects on precipitation via local moisture recycling at the watershed scale. Our study indicates that the choices of tree species and forest structure are important to water yield in the southeastern U.S. and that moisture recycling has minor influences on the local water cycle of the study region.

Longevity and persistence of arrowleaf sida seeds in soil and emergence modeling using thermal and hydrothermal time models

Arrowleaf sida (Sida rhombifolia L.) is a weed that has a fibrous stem, aggressive root system and waxy leaves that can hinder herbicide absorption. Based on knowledge of the environmental requirements for germination of this species, it is possible to predict emergence to assist in integrated management. The aim of this investigation was to characterize the air temperature and soil water potential requirements for arrowleaf sida germination, to model the emergence in the field using thermal and hydrothermal time models and to evaluate the longevity of the arrowleaf sida seed bank. Laboratory experiments were conducted to determine the temperature and water potential for seed germination by testing eight temperatures (10, 15, 20, 25, 30, 35, 40, and 45 °C) and 10 water potentials (0, −0.05, −0.1, −0.2, −0.4, −0.6, −0.9, −1.2, −1.5, and −2.0 MPa). Field experiments were conducted between 2014 and 2018 to model the emergence using three sampling times (starting on 10/20, 11/10, and 12/01), during which soybean seeds were sown. To evaluate the longevity and persistence of the seed bank, a factorial experiment was conducted, in which the first factor included three burial depths (0, 3, and 6 cm) and the second factor included five collection times (0, 1, 4, 10, and 16 months) after seed burial. The base, optimal and maximum temperatures for arrowleaf sida germination were 10.0, 24.8, and 42.5 °C, respectively, and the base water potential was −1.20 MPa. It is possible to predict the emergence of arrowleaf sida in the field by hydrothermal and thermal time models under different environmental conditions. In addition, this species has a persistent seed bank, with 30% of seeds being viable after 16 months of burial.

Separating the impact of climate change and human activities on the connection between meteorological and hydrological drought

AbstractUnderstanding the transition from meteorological to hydrological drought is essential for accurately predicting hydrological droughts. However, the factors driving this transition are intricate, and a comprehensive understanding of how direct human activities influence this shift in drought is lacking. In this study, we initially explored the spatiotemporal correlation between the occurrence of meteorological and hydrological droughts. Subsequently, we formulated multiple hydrological replenishment scenarios using the soil and water assessment tool (SWAT) model to assess the environmental impact of the transition from meteorological to hydrological droughts. The Xijiang River Basin (XRB), the primary tributary of the Pearl River basin, was selected as the study area. Our results identified 91 meteorological droughts in the XRB, and only 66 hydrological droughts from 1978 to 2018. The transition rate from meteorological to hydrological drought demonstrated large spatial variability, with a basin‐average rate of 56% and the lowest transition rate of 45% in the headstream The transition from meteorological to hydrological drought was mostly rapid between November and December (~2 months), but can be prolonged during spring (about 3–5 months) and winter (around 7–9 months). Additionally, analysis of regeneration scenarios indicated that human activity has mitigated drought severity over recent decades. The primary driver affecting drought duration and frequency during the transition from meteorological to hydrological droughts shows conspicuous spatial disparities in densely populated areas. Although human activity significantly contributes to drought duration and severity compared to climate change during the transition in the headstream, its effects are more pronounced downstream in terms of drought duration and frequency. One plausible explanation is that increased water consumption downstream has considerably prolonged drought progression, whereas water management has counteractive effects on drought progression due to climate change in the headstream. Our findings offer valuable insights into the transition process from meteorological to hydrological droughts in the presence of extensive human activities.

Responses of runoff to changes in climate and human activities in the Liuhe River Basin, China

Since the 1950s, numerous soil and water conservation measures have been implemented to control severe soil erosion in the Liuhe River Basin (LRB), China. While these measures have protected the upstream soil and water ecological environment, they have led to a sharp reduction in the downstream flow and the deterioration of the river ecological environment. Therefore, it is important to evaluate the impact of soil and water conservation measures on hydrological processes to assess long-term runoff changes. Using the Soil and Water Assessment Tool (SWAT) models and sensitivity analyses based on the Budyko hypothesis, this study quantitatively evaluated the effects of climate change, direct water withdrawal, and soil and water conservation measures on runoff in the LRB during different periods, including different responses to runoff discharge, hydrological regime, and flood processes. The runoff series were divided into a baseline period (1956–1969) and two altered periods, i.e., period 1 (1970–1999) and period 2 (2000–2020). Human activities were the main cause of the decrease in runoff during the altered periods, contributing 86.03% (−29.61 mm), while the contribution of climate change was only 13.70% (−4.70 mm). The impact of climate change manifests as a decrease in flood volume caused by a reduction in precipitation during the flood season. Analysis of two flood cases indicated a 66.00%–84.00% reduction in basin runoff capacity due to soil and water conservation measures in the upstream area. Soil and water conservation measures reduced the peak flow and total flood volume in the upstream runoff area by 77.98% and 55.16%, respectively, even with nearly double the precipitation. The runoff coefficient in the reservoir area without soil and water conservation measures was 4.0 times that in the conservation area. These results contribute to the re-evaluation of soil and water conservation hydrological effects and provide important guidance for water resource planning and water conservation policy formulation in the LRB.

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.

A thermal conductivity model for alpine meadow soils on the Tibetan Plateau and validation analysis

Under the background of global warming, natural disasters such as landslides and mudslides are becoming more frequent on the Tibetan Plateau. It is important to study the thermal behavior of alpine meadow soils, which are widely distributed on the Tibetan Plateau due to the high altitude and extreme climate. In this paper, a quantitative mathematical model of thermal conductivity for alpine meadow soils is presented, which successfully describes the influence of plant roots and organic matter on the thermal behavior of alpine meadow soils. A series of thermal conductivity experiments are carried out on thirty alpine meadow soils collected from Nangqian district, located east of Tibetan Plateau, China. The results of the proposed model are compared with the experimental measured data to validate the model efficiency and satisfactory agreement is achieved. Furthermore, Root mean square error (RMSE) and bias are also used to evaluate the performance of the proposed model and other available models. The results of the validation analysis show that the proposed model, with an RMSE of 0.041 W m−1 K−1 and a bias of 0.005 W m−1 K−1, provides reliable and accurate estimated values of thermal conductivity for alpine meadow soils. This study is beneficial for understanding the thermal behaviors of soil-root system on the Tibetan Plateau.