Soil Moisture Research Articles

Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture

Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0–5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5–40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7 %–13.3 % and 2.2 %–2.4 %. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas.

Generalized spatio-temporal-spectral integrated fusion for soil moisture downscaling

Soil moisture (SM) is one of the key land surface parameters, but the coarse spatial resolution of the passive microwave SM products constrains the precise monitoring of surface changes. The existing SM downscaling methods typically either utilize spatio-temporal information or leverage auxiliary parameters, without fully mining the complementary information between them. In this paper, a generalized spatio-temporal-spectral integrated fusion-based downscaling method is proposed to fully utilize the complementary features between multi-source auxiliary parameters and multi-temporal SM data. Specifically, we define the spectral characteristic of geographic objects as an assemblage of diverse attribute characteristics at specific spatio-temporal locations and scales. Based on this, the SM-related auxiliary parameter data can be treated as the generalized spectral characteristics of SM, and a generalized spatio-temporal-spectral integrated fusion framework is proposed to integrate the spatio-temporal features of the SM products and the generalized spectral features from the auxiliary parameters to generate fine spatial resolution SM data with high quality. In addition, considering the high heterogeneity of multi-source data, the proposed framework is based on a spatio-temporal constrained cycle generative adversarial network (STC-CycleGAN). The proposed STC-CycleGAN network comprises a forward integrated fusion stage and a backward spatio-temporal constraint stage, between which spatio-temporal cycle-consistent constraints are formed. Numerous experiments were conducted on Soil Moisture Active Passive (SMAP) SM products. The qualitative, quantitative, and in-situ site verification results demonstrate the capability of the proposed method to mine the complementary information of multi-source data and achieve high-accuracy downscaling of global daily SM data from 36 km to 9 km.

Stress of Soil Moisture and Temperature Exacerbates the Toxicity of Tire Wear Particles to Soil Fauna: Tracking the Role of Additives through Host Microbiota

Tire wear particles (TWPs) are considered as an emerging threat to soil fauna. However, how TWP toxicity to soil fauna responds to the stress of soil moisture and temperature remains unclear. We assessed the toxicity of environmentally relevant TWPs to the soil model species Enchytraeus crypticus under three soil moisture and two temperature gradients. Typical thermoplastic polypropylene (PP) was selected for comparison. Results showed that compared with PP, TWPs exerted stronger toxicity, including decreasing the worm growth, survival and reproduction rates, disturbing the soil and worm gut microbiota, and leaching more diverse and higher contents of additives. Stress of soil moisture and temperature exacerbated TWP toxicity mainly through affecting the leaching and transformation of additives. Fourteen mediated additives significantly contributed to the shift of the gut microbiota under soil moisture and temperature stress, among which 1,3-diphenylguanidine, N,N'-bis(methylphenyl)-1,4-benzenediamine quinone, N-tert-butyl-2-benzothiazolesulfenamide, and 2-aminobenzothiazole were identified as the main drivers. In addition, this study provided the first clear evidence that increased soil moisture and temperature promoted the transformation of additives in the soil. Our study revealed the non-negligible aggravated toxicity of TWPs to soil fauna under stress of soil moisture and temperature, providing novel insights into the environmental behavior of additives. Environmental Implication (maximum limit:100 words)Tire wear particles (TWPs) have become an emerging threat to the environment and soil is the major sink for TWPs. However, it is still unknown about whether and how the toxicity of TWPs to soil fauna responds to soil moisture and temperature. We represent the first to demonstrate that stress of soil moisture and temperature exacerbates TWP toxicity to soil fauna mainly through mediating additive leaching and transformation, and provide the first clear evidence that increased soil moisture and temperature promote the transformation of additives in the soil, helping understand the environmental behavior and risks of TWPs.

An IoT-based Intelligent Irrigation and Weather Forecasting System

Introduction:: The most crucial ingredient in agriculture is water. The amount of water that plants require must be provided to them. However, growers alternate between giving their plants more water than they truly need and giving them less and partly because they become overwatered due to meteorological circumstances like unexpected rainfall. Methods:: We employ an IoT-based intelligent irrigation system to get around this problem. It includes a centrifugal pump, a motor driver board, and a soil moisture sensor with YL69 probes. When the soil moisture level drops, the pump automatically delivers water to the plants with minimal human involvement. The electrical conductivity theory is how the sensor for soil moisture functions. A DHT11 sensor and a barometer, which provide information on the local temperature, humidity, and atmospheric pressure, are both parts of the weather monitoring system with the help of this, farmers can forecast the local weather and plan their irrigation accordingly. Results:: In this patent study, the thing speak API enables us to continually monitor information from a computer or mobile device, and the ESP8266 module links the complete system to the internet. Through this approach, water waste is reduced, and irrigation efficiency is increased while crop health and quality are preserved. Conclusion:: Overall, this research demonstrated how the Internet of Things-based intelligent irrigation systems may enhance agricultural water management. By combining soil moisture monitoring, weather monitoring, and autonomous management, we may develop irrigation techniques that are more precise, effective and patent leading to higher crop yields and sustainable agricultural practices.

Ecosystem water limitation shifts driven by soil moisture in the Loess Plateau, China

In the context of climate change, the functionality of ecosystems is primarily influenced by the availability of water and energy supply. However, there is limited research that comprehensively uses energy indicators to explore how climate change affects the water and energy limiting states of ecosystems. Here we evaluated the historical and future water and energy limitations using the Ecosystem Limitation Index (ELI) derived from evapotranspiration (ET), soil moisture (SM), net radiation (Rn), and air temperature (Ta), and conducted an in-depth analysis of the dominant factors. The results indicate that: (1) The degree of water limitation deepened initially and then weakened. Over 68 % of the region became drier initially, while over 83 % became wetter later. (2) In terms of area, soil moisture emerged as a critical factor influencing the variations in water and energy constraints within the Loess Plateau. Further research revealed the range of critical soil moisture (CSM) for the transition of water-energy limitation state is 0.286 mm3mm−3, and it varies with changes in temperature, soil texture, vegetation cover, and season. (3) Future projections suggest a transition towards heightened water limitations across the Loess Plateau. These findings underscore the efficacy of ELI in assessing and predicting dynamic ecosystem changes, offering valuable insights into the impacts of climate change on water and energy cycles within semi-arid ecosystems.

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2% and 12.7%, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

Influence of stand characteristics and management activities on aboveground carbon storage in Japanese cedar and cypress plantations: Sustainable management implications

Tree plantations substantially sequester carbon in aboveground biomass and soil and serve as an alternative to forests in mitigating global warming. This study investigated the relationship between aboveground carbon and stand characteristics and management activities in two Japanese plantations (cedar and cypress) for their sustainable management viewpoint, which has not been studied before. Aboveground carbon stock ranged from 3.96 to 248.53 Mg C ha-1 in 9- to 188-year-old Japanese cedar and 7.09 to 119.17 Mg C ha-1 in 11- to 129-year-old Japanese cypress plantations. Annual aboveground carbon storage differed significantly between the two species. Aboveground carbon storage in Japanese cedar was affected by stand characteristics (e.g. soil moisture, electrical conductivity, soil texture especially sand and clay, site quality, and stem density) and management activities (e.g. initial silvicultural practices, stem exclusion practices, pest and wildlife management and land management). However, the same in Japanese cypress was affected by stand characteristics (e.g. soil moisture, electrical conductivity, soil texture especially sand, bulk density, site quality, stem density, and elevation) along with management activities (e.g. initial silvicultural practices, stem exclusion practices and land management). Therefore, continuous monitoring and periodic assessment of the stand characteristics and management activities may be taken into consideration in future policy-making decisions to promote aboveground carbon storage and climate change mitigation ability of these plantations in Japan on a sustained basis.

Limitation of summer extreme high temperatures on radial growth relieve with increasing latitude in subtropics

Global warming has induced an increase in the intensity and frequency of summer extreme high temperature events in Chinese subtropical forest, which contributes to a large component of net primary ecological production among global forests. However, how summer extreme high temperature events would influence tree radial growth in these humid subtropical forest remains unclear. We investigated the non-linear response of tree radial growth to temperature, soil moisture and their mixed effect across broad latitude gradients in Chinese subtropical forests, using a method of modelling cambial growth kinetics of Schima superba. The results showed that radial growth was constrained by summer extreme high temperatures and this limiting effect relieved from south to north. However, we also found a growth constraint imposed by soil moisture during spring and autumn, which intensified toward northern sites. Coincidentally, the radial growth pattern showed a distinct transition from bimodal to unimodal from South to North sites, with no significant changes in cambium phenology over the time scale from 1986 to 2014 being observed. In general, we determined that limitation of extreme high temperatures on radial growth relieve with increasing latitude in subtropics. This study provides a theoretical basis for promoting the accurate assessment of carbon sink potential and for predicting future forest dynamics in subtropics.

Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor

Studying the influence of vegetation dynamics on water storage is fundamental for efficiently managing ecosystems in dryland areas. Therefore, this study was designed to investigate the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage (TWS) trends across the China-Pakistan Economic Corridor (CPEC). Exploring vegetation growth's effect on TWS utilizes remote sensing and statistical methods such as generalized additive model (GAM), random forest, Mann-Kendall test, Sen's slope, and Partial correlation coefficient. Our results revealed a consistent increase in vegetation cover in semi-arid and dry sub-humid regions, especially in croplands, from 1986 to 2020. However, a noticeable finding was that TWS has declined by approximately 39.65 % in the study area. On the other hand, forests have displayed resilience by mitigating the effects of climate change and human activities through hydraulic memory effects. Additionally, soil moisture has decreased in various land cover types, with croplands experiencing the most significant decrease. Similarly, increased vegetation growth in greening drylands significantly negatively impacts terrestrial water storage, with correlations of −0.31 for terrestrial water storage anomaly (TWSA), −0.26 for root zone soil moisture (RZSM), and −0.29 for surface soil moisture (SSM). Meanwhile, evapotranspiration (ET) also had negative correlations with TWSA, RZSM, and SSM, with standardized coefficients of −0.23, −0.16, and −0.11, respectively. These findings exposed the study area's vegetation interaction with land cover and hydrological dynamics. Addressing these hydrological imbalances will help ensure sustainable ecological management in the study area.

Convolutional Neural Networks accurately predict soil matric potential from soil, weather, and satellite-derived data

Being able to predict soil moisture dynamics offers water managers the possibility to better plan irrigation events and prevent soil moisture deficits from reaching levels that reduce crop production. Machine learning (ML) model predictions can potentially assist farmers in managing irrigation water more efficiently. In this study, we aimed to assess the accuracy of a set of ML models in predicting soil matric potential seven days ahead in gravity-surface irrigated cotton paddocks and evaluate the models’ performance for longer term predictions (14 days). The ML models used past soil moisture, weather, and satellite-derived crop-related data as features for the input parameters. Input data were structured in tuples that were organised following a 20-day ‘window’ approach that ‘slid’ one position forward after each training round. A convolutional neural network (CNN) model outperformed a Long Short-Term Memory, Dense Multilayer Perceptron, and Linear Regression model, the latter of which produced the least accurate predictions. The accuracy of the soil matric potential predictions with the CNN model was stable over time (R2 ≥ 0.92 and root mean square deviation ≤ 7.5 kPa). However, less accurate predictions were obtained for a short period after emergence and at crop senescence. This study demonstrates the feasibility of producing accurate predictions of soil matric potential in cotton fields at 0.20 m soil depth with a CNN model, which can be integrated into irrigation decision support systems.

Process and mechanism analysis of typical permafrost area surface deformation at two sites along the Qinghai-Tibet highway based on long-term leveling observation

Surface deformation indicates permafrost changes and multi-year surveys can reflect its characteristics and causes. In this study, surface deformations in Wudaoliang (WDL) and Xidatan (XDT) were determined based on long-term leveling measurements, hydrothermal data, precipitation and soil sampling. In-situ observations can be used to study the influence of hydrothermal changes in the active layer on the surface. The amplitude and onset date of seasonal deformation are different due to the soil moisture (SM) and soil texture in the two permafrost regions. High SM and fine-grained soils can cause significant seasonal surface deformation. A good correlation existed between the freeze-thaw front migration rate and deformation in the active layer with a uniform distribution of SM. The inter-annual subsidence rate in the WDL was higher than in the XDT, owing to the different rates of ground ice thawing near the permafrost table. The long-term subsidence rate positively correlates with the increased seasonal deformation amplitude, especially in fine-grained soils.

PHYTOCHEMICAL VARIABILITY OF SELECTED MEDICINAL PLANTS FROM DIFFERENT AGRO-CLIMATIC ZONES IN KENYA

Medicinal properties of plants are a factor of their photochemical content and profile. Agro-climatic factors such as temperature, sunlight, water availability, and soil composition can influence the profile of bioactive phytochemicals in plants and hence their medicinal potential. The study investigated the phytochemical variability in leaf extracts of selected medicinal plants, mango, guava, and avocado, from different agro-climatic zones in Kenya. Fresh healthy leaves were sampled from two hundred and twenty-seven mango (83), guava (85), and avocado (59) accessions in nine different areas. Methanol extracts of the leaves were prepared and assayed for total phenol content (TPC), total flavonoid content (TFC), and antioxidant activity. The TPC and TFC distribution in the extracts did not correlate with the agro-climate zones. However, patterns were observed that are attributable to geographical and specific agro-climatic parameters. Further, correlation analyses showed that specific agro-climatic parameters significantly affected TPC and TFC. Specifically, guava TPC and avocado TPC and mango TFC, had moderately negative correlation with rainfall and soil moisture. Mango TFC had moderate correlation with altitude and temperature. However, none of the correlations was high (r ≥ 0.5), suggesting effect of other confounding factors. No discernable trend was observed with the antioxidant properties. Though some geographical patterns and correlations with agro-climatic parameters were established in the study, TPC, TFC, and antioxidant activity and agro-climatic zones may not be conclusively used to characterize these medicinal plants. Future studies may narrow on specific bioactive molecules and specific agro-climatic parameters.

Optimization of leaching level and alternating drip irrigation start time improved water saving, yield enhancement, and salt leaching

Shortage of water resources and salinization of soil are two important factors restricting the sustainable development of agriculture in Xinjiang. The current drip irrigation strategy cannot meet the high efficiency water saving and salt leaching requirement, seriously limiting sustainable development. Therefore, in 2020 and 2021, a field experiment was conducted with three leaching levels (120, 240 and 360 mm) and four alternate drip irrigation (ADI) start time (no ADI, seedling, budding and flowering). The field experiment was combined with HYDRUS to investigate the effect of water saving and salt leaching by combining ADI with leaching during the reproductive period. The results showed that the HYDRUS-2D model simulated soil moisture and salinity with high accuracy under different irrigation conditions, with R2 ranging from 0.63 to 0.93, MRE from 2.45 % to 11.66 %, and RMSE from 1.88 % to 4.41 % for soil moisture and 0.29 g kg−1 to 0.97 g kg−1 for soil salinity, respectively. Optimization of irrigation method, leaching level and ADI start time all improved water and salinity stress characteristics. Although initially high soil salinity resulted in the conversion of some severe stress to moderate due to the irrigation, these disadvantages were offset by reductions in severe stress duration and area. Water-salt production model constructed based on water and salt stress characteristics demonstrated good yield simulation performance. According to HYDRUS, water-salt production model and comprehensive evaluation, starting ADI at the 3rd irrigation event with a leaching level of 290 mm was found to be optimal considering yield, water use efficiency, soil desalination and desalination efficiency. Considering the practicability, the recommended optimum ADI leaching level was 262–318 mm, with ADI initiated between the 2nd and 4th irrigation event. The study provided a vital theoretical basis for efficient water-salt management, supporting sustainable development in saline-alkali cotton fields in Xinjiang under limited water resources.

Reconstruction of Historical Site-Scale Dust Optical Depth (DOD) Time Series from Surface Dust Records and Satellite Retrievals in Northern China: Application to the Evaluation of DOD in CMIP6 Historical Simulations

Abstract The biases generated by state-of-the-art climate models in simulating dust optical depth (DOD) remain to be detailed. Here, a site-scale DOD dataset in March–August over northern China (NC) during 1980–2001 was reconstructed using the empirical relationship between MODIS-retrieved DOD and dust-event frequencies during 2001–21. Then, through the combined use of MODIS-based and reconstructed DOD, we evaluated the reproducibility of DOD from 10 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the historical period (1980–2001 and 2002–14) and under different Shared Socioeconomic Pathways (SSPs) during 2015–21. The results demonstrate that CMIP6 models and multimodel ensemble mean (MEM) are capable of capturing the spatial pattern of DOD, but with considerable uncertainty and intermodel variability in magnitude. Regionally averaged DOD is underestimated by 56.09% during 1980–2001 and overestimated by 30.97% during 2002–14 in MEM over NC. Simultaneously, the intermodel standard deviations are greater than MEM during 2002–14, suggesting large discrepancies among individual models. Very few models accurately capture the trends in DOD, which can mainly be attributed to the different trends in simulated wind speed (WS), soil moisture, and vegetation cover, and their contributions to dust evolution. Under four SSPs, despite the best correlation between SSP1-2.6-modeled and MODIS DOD over Gobi Desert (GD), overestimation of DOD is still observed. More models under SSP1-2.6 capture the positive DOD trend, mainly attributable to positive changes in simulated WS over GD.

Estimating crop leaf area index and chlorophyll content using a deep learning-based hyperspectral analysis method

The crop leaf area index (LAI) and leaf chlorophyll content (LCC) are essential indicators that reflect crop growth status, and their accurate estimation is helpful for agricultural management decision-making. Traditional hyperspectral estimation methods for crop LAI and LCC from canopy spectra face challenges due to intricate soil backgrounds, canopy structural environments, and varying observational conditions. This paper proposes an LAI and LCC estimation method based on hyperspectral remote sensing, a radiation transfer model (RTM), and a leaf area index and leaf chlorophyll content deep learning network (LACNet). The LACNet architecture was developed utilizing deep and shallow feature fusion, blocks, and a hyperspectral-to-image transform (HIT) concept, aiming to improve LAI and LCC estimation. We used a field-based spectrometer to collect a dataset comprising 1,234 spectral measurements across five crop types: wheat, maize, potato, rice, and soybean. We used properties optique spectrales des feuilles and scattering by arbitrarily inclined leaves (PROSAIL) to generate a simulated spectra dataset (n = 145,152) representing complex farmland conditions for the five abovementioned crops, considering the variations in soil type, soil moisture, LAI, LCC, etc. The LACNet deep learning model sequentially uses RTM simulated and field-based spectra datasets for training, achieving higher universality and validation accuracy. We also analyzed the LACNet model’s interpretability for LAI and LCC estimation based on the gradient-weighted class activation mapping theory. From our research, we drew the following conclusions: (1) The shallow network features are sensitive to the LAI and LCC in the entire visible band, consistent with our correlation analysis results, while the deep network sensitive areas are mainly concentrated in the RE + VIS and RE + NIR regions of the HIT images. (2) The LACNet deep learning model (LAI: coefficient of determination (R2) = 0.770, root mean square error (RMSE) = 0.968 m2/m2; LCC: R2 = 0.765, RMSE = 4.547 Dualex readings) can provide higher crop LAI and LCC estimation accuracy than widely used spectral feature and statistical regression methods (LCC: R2 = 0.491–0.620, RMSE = 5.804–6.691 Dualex readings; LAI: R2 = 0.476–0.716, RMSE = 1.089–1.482 m2/m2). The results of this study highlight the potential of the LACNet deep learning model as an effective and robust tool for accurately estimating crop LAI and LCC.

Optimizing CLM4.5 simulations of the active layer: The role of gravel in Tibetan Plateau permafrost

Gravel is widely distributed in soils of the Tibetan Plateau (TP), where permafrost also occurs over approximately half of the total area. Gravel has different thermal and hydrological properties to those of fine-grained soils, which may have a considerable impact on hydrothermal transport within TP soils. However, few land models consider gravel. Here, we incorporated the thermal and hydraulic properties of gravel into the Community Land Model and explored the effect of differing gravel contents on land surface processes. We found that all parameters affecting soil hydrothermal transport were sensitive to gravel content. Soil thermal and hydraulic conductivities increased with increasing gravel content, ultimately leading to variations in soil temperature, soil water content, and radiation fluxes with changing gravel content. Soil containing gravel exhibited higher infiltration and greater total water storage capacities. We also compared different model schemes in an attempt to improve the simulation of active layers in permafrost regions with high gravel content on the TP. The scheme in which both gravel and freeze–thaw parameterizations were applied (i.e., SP3) improved both the latent heat flux and ground heat flux transfer. The SP3 scheme performed best in terms of soil temperature and soil moisture simulations at both the Nagqu and Madoi field stations, demonstrating a particular ability to better characterize soil moisture processes as a function of temperature during soil freezing and thawing.

Construction and verification of distributed hydrothermal coupling model in the source area of the Yangtze River

Study regionThe source area of the Yangtze River, a typical catchment in the cryosphere on the Tibet Plateau, was used to develop and validate a distributed hydrothermal coupling model. Study focusClimate change has caused significant changes in hydrological processes in the cryosphere, and related research has become hot topic. The source area of the Yangtze River (SAYR) is a key catchment for studies of hydrological processes in the cryosphere, which contains widespread glacier, snow, and permafrost. However, the current hydrological modeling of the SAYR rarely depicts the process of glacier/snow and permafrost runoff from the perspective of coupled water and heat transfer, resulting in distortion of simulations of hydrological processes. Therefore, we developed a distributed hydrothermal coupling model, namely WEP-SAYR, based on the WEP-L (Water and energy transfer process in large river basins) model by introducing modules for glacier and snow melt and permafrost freezing and thawing. New hydrological insights for the regionIn the WEP-SAYR model, the soil hydrothermal transfer equations were improved, and a freezing point equation for permafrost was introduced. In addition, the glacier and snow meltwater processes were described using the temperature index model. Compared to previously applied models, the WEP-SAYR portrays in more detail glacier/snow melting, dynamic changes in permafrost water and heat coupling, and runoff dynamics, with physically meaningful and easily accessible model parameters. The model can describe the soil temperature and moisture changes in soil layers at different depths from 0 to 140cm. Moreover, the model has a good accuracy in simulating the daily/monthly runoff and evaporation. The Nash-Sutcliffe efficiency exceeded 0.75, and the relative error was controlled within ±20%. The results showed that the WEP-SAYR model balances the efficiency of hydrological simulation in large scale catchments and the accurate portrayal of the cryosphere elements, which provides a reference for hydrological analysis of other catchments in the cryosphere.

Simulating coefficient of soil moisture content uniformity of sprinkler irrigation systems using a COMSOL-3D model

Water distribution uniformity is important for assessing the hydraulic performance of sprinkler nozzles and designing sprinkler irrigation systems. However, current studies rarely consider the redistribution abilities of sprinkler water in the soil and judge whether a sprinkler system is qualified based only on the coefficient of uniformity on the ground (CUg), which leads to a serious underestimation of the actual irrigation effect of sprinkler irrigation projects. Hence, this study establishes and validates a COMSOL-3D model to simulate the movement of soil water during sprinkler irrigation. Moreover, the effects of the soil hydraulic and irrigation parameters on the coefficient of soil moisture content uniformity (CUs) were explored. The results showed that 1) the Nash-Sutcliffe efficiency coefficient (NSE) of the vertical wetting front (VWF) and soil moisture content (SMC) in the two sets of validation experiments was 0.965 and 0.862 and 0.867 and 0.900, respectively. 2) Owing to the differences in soil saturated hydraulic conductivity, after the end of irrigation, the VWF transport rates in vertical direction differed among the three textured soils, with loam exhibiting the highest rate, followed clay loam and silty clay. However, their CUs increased with increasing water transport time, indicating that the water distribution within the sprinkler-wetted zone became increasingly homogeneous as the sprinkler irrigation water was transported and diffused from the top to bottom in the soil. 3) Dimensional analyses showed that CUs were strongly influenced by the initial soil moisture content, irrigation time, and water transport time. 4) In Case 4, when CUg was reduced from 81.9% to 60%, CUs only decreased from 83.2% to 81.4% after 48h of irrigation, indicating that the sprinkler irrigation water had good redistributive ability. This study provides a reference for rationally reducing the CUs of sprinkler irrigation projects and maximising project investments and planting interest.

Enhanced soil stabilisation and growth of Lolium perenne through combined seeding with Cynodon dactylon

Herbaceous plants play a crucial role in soil stabilisation, and combined seeding is often employed in ecosystem management to promote biodiversity. This study investigated the influence of combined seeding of Ryegrass (Lolium perenne) and Bermuda (Cynodon dactylon) on plant growth and soil stabilisation through in-situ sampling and indoor experimental measurements. Four experimental plots were established: a. bare soil, b. L. perenne single species, c. C. dactylon single species, d. combined L. perenne and C. dactylon. The results indicate that combined seeding inhibited the development of L. perenne and C. dactylon root depth by 14.50% and 29.20%, respectively. However, shoot height, total leaf area, total root length and total root surface area increased in L. perenne under combined seeding, while these parameters decreased for C. dactylon. Combined seeding significantly enhanced the resistance to breakage in tension and tensile strength of L. perenne, with no significant impact on C. dactylon. Plant roots notably increased soil cohesion, with a respective increase of 37.08%, 26.98%, and 50.81% in cohesion for L. perenne single species plot, C. dactylon single species plot, and combined L. perenne and C. dactylon plot compared to bare soil. The root content in combined seeding significantly increased, with an increase of 27.17% and 65.20% compared to single seeding of L. perenne and C. dactylon, respectively. Additionally, under the influence of roots, the soil moisture content in the combined seeding plot was lower than in the single species and bare soil plots. These findings highlight that combined seeding enhanced plant competition, improved soil shear strength, and provided significant ecological benefits, offering insights for vegetation-based slope design.

Aqua-stream: an IoT based smart water management system for sustainable living

<p class="03IJISAEAbstract">Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.</p>