Soil Moisture Temperature Research Articles

Soil moisture–temperature coupling over India through multi-scale observations

ABSTRACT Soil moisture–temperature coupling has been shown to affect the severity and occurrences of extreme temperature events like heatwaves and droughts. In this study, we quantify the soil moisture–temperature coupling over India at daily and climatological scales. Our results show maximum climatological coupling over north and central India during pre-monsoon months. The coupling strength is related to soil moisture content. Daily coupling is calculated for two heatwave events (2009 and 2015) to determine whether land–atmosphere coupling enhances the heatwave event. We observed strong soil moisture–temperature coupling during the 2009 heatwave, whereas weak coupling is observed in the 2015 heatwave. Our results suggest that soil moisture anomalies could enhance heat anomalies and temperature anomalies through the reduction in latent heat flux, leading to enhanced soil moisture–temperature coupling. Thus our study highlights the importance of soil moisture and associated coupling in understanding and predicting heatwave events.

Middle concentration of microplastics decreasing soil moisture-temperature and the germination rate and early height of lettuce (Lactuca sativa var. ramosa Hort.) in Mollisols

Microplastics (MPs) have been widely found in soils, however, the mechanism of MPs influencing plant growth is still debated and possibly attributed to the soil environment changed by MPs. In this study, 0.0 %, 0.1 %, 0.5 %, 1.0 %, 2.0 %, and 5.0 % (w/w) content of low-density polyethylene MPs (LDPE-MPs) with the particle sizes of 75–2000 μm was used to test how MPs alter the germination and the early growth of lettuce (Lactuca sativa var. ramosa Hort.) in Mollisols under both natural condition and regular incubation condition. Soil temperature (ST), soil moisture (SM) and the ratio of cracks area to surface soil area (CA) and cracks length to surface soil area (CL) were monitored. As well, the dynamics of water and nutrient infiltration reported by our previous publication were combined to analyze the relationship between soil properties and crop growth influenced by MP concentration. The main results showed that: (1) compared with CK (0.0 %), the germination and plant height of lettuce were lowest in treatments with the middle concentration of MPs (0.5 % and 1 %, w/w), but was highest in treatments of high concentration of MPs (5.0 %, w/w) during the whole 14 days of incubation; (2) increasing MP concentration weakened the influence of SM on ST in Mollisols; (3) the average of SM and ST were highest at 5 % of MP concentration, while was lowest at 0.5 % and 1 % of MP concentration from the 2nd to the 9th day; (3) compared with CK and other treatments, the CA and CL were lowest in 1.0 % MP concentration, but were highest in 0.1 % and 5.0 % of MP concentration. This study provides insight that middle, rather than high and low levels of MP concentration, significantly decrease the SM and ST and increase nitrogen leaching which further leads to negative impacts on emergent and early growth of crops in soils with heavy texture (Mollisols).

Using Machine Learning With Partial Dependence Analysis to Investigate Coupling Between Soil Moisture and Near‐Surface Temperature

AbstractSoil moisture (SM) influences near‐surface air temperature by partitioning downwelling radiation into latent and sensible heat fluxes, through which dry soils generally lead to higher temperatures. The strength of this coupled soil moisture‐temperature (SM‐T) relationship is not spatially uniform, and numerous methods have been developed to assess SM‐T coupling strength across the globe. These methods tend to involve either idealized climate‐model experiments or linear statistical methods which cannot fully capture nonlinear SM‐T coupling. In this study, we propose a nonlinear machine‐learning (ML)‐based approach for analyzing SM‐T coupling and apply this method to various mid‐latitude regions using historical reanalysis datasets. We first train convolutional neural networks (CNNs) to predict daily maximum near‐surface air temperature (TMAX) given daily SM and geopotential height fields. We then use partial dependence analysis to isolate the average sensitivity of each CNN's TMAX prediction to the SM input under daily atmospheric conditions. The resulting SM‐T relationships broadly agree with previous assessments of SM‐T coupling strength. Over many regions, we find nonlinear relationships between the CNN's TMAX prediction and the SM input map. These nonlinearities suggest that the coupled interactions governing SM‐T relationships vary under different SM conditions, but these variations are regionally dependent. We also apply this method to test the influence of SM memory on SM‐T coupling and find that our results are consistent with previous studies. Although our study focuses specifically on local SM‐T coupling, our ML‐based method can be extended to investigate other coupled interactions within the climate system using observed or model‐derived datasets.

Temperature-Soil Moisture Dryness Index for Remote Sensing of Surface Soil Moisture Assessment

Temperature-Soil Moisture Dryness Index for Remote Sensing of Surface Soil Moisture Assessment

Mesoclimate regulation induced by landscape restoration and water harvesting in agroecosystems of the horn of Africa

When changes are made in a landscape, these changes may impact on the local climate. In arid and semi-arid agroecosystems, measures such as Landscape Restoration and Water Harvesting (LRWH) are implemented in order to revert land degradation and increase agricultural yields, by means of reducing runoff losses and increasing soil moisture. The present work analyses to what extent storing soil moisture, with adequate land and water management practices, can reduce temperatures in the hot months after the rainy season in a semi-arid climate. Since it is reported that Soil Moisture (SM) deficit can enhance heatwaves in diverse regions of the world because of Soil Moisture-Temperature Coupling (SMTC), it is hypothesized that increasing soil water availability, during the dry and hot periods, can mitigate hot temperatures. The analysis was carried for Enabered catchment, in Tigray Region, Ethiopia, where the rainy season runs from June to September. Here, large scale LRWH implementation ended in 2008. Using remote sensing data, the present study evaluates (1) to what extent LRWH implementation can enhance SM conservation at agroecosystem scale; (2) to what extent LRWH implementation can mitigate temperatures in the dry season at agroecosystem scale; and (3) the evidence of SMTC effects. Results show an increased capacity of the catchment to maintain SM accumulated in the rainy season and consequent reduced temperatures. Increase of SM was especially significant for September (P < 0.01), while temperature decrease was evident in October (P < 0.01) and November (P < 0.05), with decreases of Land Surface Temperatures (LST) up to 1.74 °C A simple, parsimonious linear regression model demonstrates that SMTC is evident at catchment scale and that the implementation of LRWH measures provides a climate regulation effect in the watershed. The present work can reinforce the call for an increased adoption of water harvesting, land restoration and green water management, in order to increase the resilience of agricultural ecosystem and food security in arid and semi-arid areas.

High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region

High-resolution soil moisture/temperature (SM/ST) are critical components of the growing demand for fine-scale products over the Indian monsoon region (IMR) which has diverse land-surface characteristics. This demand is fueled by findings that improved representation of land-state help improve rainfall/flood prediction. Here we report on the development of a high-resolution (4 km and 3 hourly) SM/ST product for 2001–2014 during Indian monsoon seasons (June–September). First, the quality of atmospheric fields from five reanalysis sources was examined to identify realistic forcing to a land data assimilation system (LDAS). The evaluation of developed SM/ST against observations highlighted the importance of quality forcing fields. There is a significant relation between the forcing error and the errors in the SM/ST. A combination of forcing fields was used to develop 14-years of SM/ST data. This dataset captured inter-annual, intra-seasonal, and diurnal variations under different monsoon conditions. When the mesoscale model was initialized using the SM/ST data, improved simulations of heavy rain events was evident, demonstrating the value of the data over IMR.

Smart Green House Based on IOT

In conventional strategy for cultivating, human works was necessary to see the greenhouse at particular point want to observe all the required levels physically. The regular technique is observed to be slow and require a large amount of effort and energy. Along these lines this analysis around building up a framework that can consequently screen and anticipate various changes in light, temperature Soil moisture and humidity levels of the greenhouse. The goal of the survey is that to build up a programmed monitoring device observing framework utilizing sensors and send email warnings and messages to the mobiles. The recommended framework has an estimation which equipped for identifying the levels of light, temperature,soil moisture and humidity. The framework additionally had an instrument to caution agriculturists with respect to the limitation change in the conservatory then safeguard measures can be taken in advance. In this examination, a few experiments was directed to a particular final aim to demonstrate the suitability of framework. Test outcomes showed that the dependability of the framework in spreading data straightforwardly to the agriculturists could be picked up astoundingly in different conditions

Spatiotemporal Heterogeneity of Soil Available Nitrogen During Crop Growth Stages on Mollisol Slopes of Northeast China

AbstractSpatiotemporal heterogeneity of soil available nitrogen (AN) (sum of NO3−–N and NH4+–N) is the essential basis for soil management and highly correlates to crop yield. Both geostatistical and traditional analyses were used to describe the spatiotemporal distribution of AN in the 0–20‐cm soil depth on typical Mollisol slopes (S1 and S2) in Northeast China. The concentration of NO3−–N dynamics at slope positions was typically opposite to NH4+–N. The peak values of AN typically moved from the summit of the slope to the bottom from spring to autumn and were mainly influenced by the content of NO3−–N (S1, 7·9–18·9 mg kg−1; S2, 1·2–103·6 mg kg−1), both of NO3−–N (S1, 3·9–8·3 mg kg−1; S2, 2·2–28·0 mg kg−1) and NH4+–N (S1, 21·4–30·5 mg kg−1; S2, 2·1–23·3 mg kg−1), and NH4+–N (S1, 10·5–28·9 mg kg−1; S2, 5·0–39·0 mg kg−1) in the seedling stage, vegetative growth stage, and reproductive growth stage, respectively. The spatial autocorrelation of AN was strong and was mainly influenced by structural factors during crop growth stages. This was mainly determined by soil erosion–deposition (SED) and soil temperature–moisture (STM) in the seedling stage; this was also mainly influenced by SED, STM, crop type, and crop growth in the vegetative growth stage and by early STM and early SED in the reproductive growth stage. Generally, the content of AN, NO3−–N, and NH4+–N on the whole slope was mainly determined by the early SED and local fertilizer application, while their spatiotemporal heterogeneity, especially the evenness, was mainly changed by SED, STM, crop growth, and crop types on the slope scale. In order to increase more crop yields, additional N fertilizer application on both the summit and the bottom during the vegetative growth stage and conservation tillage systems or additional soil amendments on the back slopes was necessary. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Development of the Flux-Adjusting Surface Data Assimilation System for Mesoscale Models

Abstract The flux-adjusting surface data assimilation system (FASDAS) is developed to provide continuous adjustments for initial soil moisture and temperature and for surface air temperature and water vapor mixing ratio for mesoscale models. In the FASDAS approach, surface air temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface observations. Then, the difference between the analyzed surface observations and model predictions of surface layer temperature and water vapor mixing ratio are converted into respective heat fluxes, referred to as adjustment heat fluxes of sensible and latent heat. These adjustment heat fluxes are then used in the prognostic equations for soil temperature and moisture via indirect assimilation in the form of several new adjustment evaporative fluxes. Thus, simulated surface fluxes for the subsequent model time step are affected such that the predicted surface air temperature and water vapor mixing ratio conform more closely to observations. The simultaneous application of indirect and direct data assimilation maintains greater consistency between the soil temperature–moisture and the surface layer mass-field variables. The FASDAS is coupled to a land surface submodel in a three-dimensional mesoscale model and tests are performed for a 10-day period with three one-way nested domains. The FASDAS is applied in the analysis nudging mode for two coarse-resolution nested domains and in the observational nudging mode for a fine-resolution nested domain. Further, the effects of FASDAS on two different initial specifications of a three-dimensional soil moisture field are also studied. Results indicate that the FASDAS consistently improved the accuracy of the model simulations.

Identifying habitat types in a disturbed area of the forest-steppe ecotone of Patagonia

We surveyed an old ranch (22,000 ha) in the eastern catchment of lake Nahuel Huapi (Argentina), representing both landscape and historical use patterns of the forest-steppe ecotone in the Andes foothills. The objective was to describe plant-environment relations and to develop a procedure to classify habitat types for conservation aims. Floristic and landscape variables were recorded in 72 upland sites. Vegetation types were classified with Two way indicator Species Analysis and environmental gradients detected with Detrended Canonical Correspondence Analysis. Vegetation types were tested for environmental homogeneity with Discriminant Analysis. A key to classify habitat types was built out from the classification functions obtained by DA analysis. Floristic classifi cation resulted in 7 steppe types and 4 shrubland/woodland types. Vegetation samples were placed along two main gradients, one related to topography and the other to the type of substrate. Steppe types occur in all positions of the topographic gradient forming a temperature-soil moisture continuum, but only (except one of them) on accumu lated substrates. Islands of woody types are confined to relatively warm positions of the topographic gradient and to deflated, rocky substrates. This distribution pattern would be caused by frost sensibility of forest species, and by the different strategies of water capture of woody and herbaceous plants. Three pairs of plant assemblages were not discriminated, allowing classification of 8 habitat types out of the 11 communities. Undiscriminated communities (3 pairs) and misclassified samples (29%) were discussed as the result of disturbances.

Factors Affecting the Suppression of Pea(Pisum sativum)Root Rot(Aphanomyces euteiches)by Dinitroaniline Herbicides

Field and greenhouse studies have shown that dinitroaniline herbicides suppress root rot of peas (Pisum sativumL.) caused by the fungusAphanomyces euteichesDrechs. This investigation was conducted to identify factors which could enhance root rot suppression. In a temperature-soil moisture growth chamber study, dinitroaniline herbicides significantly reduced disease severity symptoms at all temperature and soil moisture regimes except 32 C and 0.75 field capacity. Dinitroaniline herbicides increased pea fresh weight significantly only under those conditions which favored severe disease development. Maximum root rot suppression occurred at 24 C and 1.25 field capacity. In a greenhouse study of the interaction of root rot suppression with pea varieties, dinitroaniline herbicides significantly increased growth of all 12 pea varieties tested including a root rot tolerant wild type of pea. A series of experiments explored the effect of uniformity, time, and depth of dinitroaniline herbicide incorporation on root rot suppression. In greenhouse studies, non-uniform incorporation significantly reduced root rot suppression indicating the need for thorough incorporation to achieve maximum root rot suppression. The combination of trifluralin (α,α,α-trifluoro-2,6-dinitro-N,N-dipropyl-p-toluidine) + oryzalin (3,5-dinitro-N4,N4dipropylsulfanilamide) was more effective than either trifluralin or oryzalin alone under non-uniform incorporation conditions but not under uniform incorporation conditions. Field studies showed that herbicide incorporation 2 weeks prior to planting may improve pea yield by 1.12 kg/ha of trifluralin or 0.56 + 0.56 kg/ha of trifluralin + oryzalin but not by 0.56 kg/ha of trifluralin. Herbicide incorporation to 15.2 cm rather than the normal 7.6 cm, increased pea yield at the 1.12 kg/ha rate of trifluralin or oryzalin or at 0.56 + 0.56 kg/ha of trifluralin + oryzalin. At the 0.56 kg/ha rate of trifluralin or oryzalin, herbicide dilution offset any benefit from deeper soil incorporation.