An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network.
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ABSTRACT: This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since its foundation in July 2011. The sites from which measurements are being collected represent a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and a range of the most typical land use/cover types found in Wales. At present, WSMN consists of a total of nine monitoring sites across the area with a concentration of sites in three sub-areas around the region of Aberystwyth located in Mid-Wales. The dataset of composed of 0-5 (or 0-10) cm soil moisture, soil temperature, precipitation, and other ancillary data. WSMN data are provided openly to the public via the International Soil Moisture Network (ISMN) platform. At present, WSMN is also rapidly expanding thanks to funding obtained recently which allows more monitoring sites to be added to the network to the wider community interested in using its data.
Project description:The dataset presented in this article is related to the work "Evaluation and Analysis of SMAP, AMSR2, and MEaSUREs Freeze/Thaw Products in China [1]". Soil moisture and temperature are important variables of land-atmosphere energy exchange, monitoring vegetation growth, predicting drought disasters and climate and hydrological modelling [2], [3], [4], [5], [6]. This work provides detailed information on in situ soil moisture and temperature data network established in the Genhe watershed and Saihanba area in China, respectively. The Genhe watershed represents the complex surface heterogeneity in Northeast China. Therefore, data from 22 in situ sites were established in the Genhe watershed since March 2016 to improve the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Saihanba is currently China's largest manmade forest and has a unique alpine wetland and a complete aquatic ecosystem. There are 29 in situ sites deployed in Saihanba since August 2018 for studying the cold temperate continental monsoon climate and estimating forest carbon storage capacity and carbon emissions from manmade forests. Soil temperature and permittivity data in the network were measured using ECH2O EC-5TM probes (Decagon Devices, Inc., Washington, USA, https://www.metergroup.com/) and XingShiTu (XST) probes (BEIJING XST Co., Ltd., www.xingshitu.com) every 30 min at depths of 3, 5, and 10 cm for the Genhe watershed continuous automatic observation network, and depths of 5 and 10 cm for the Saihanba continuous automatic observation network. In the Genhe watershed, soil moisture and soil temperature data in the network were automatically collected using the EM50 data collection system. The Saihanba area has the XST data collection system to record soil temperature and permittivity. The permittivity data collected with the XST data collector were transformed to soil moisture data (volumetric water content) based on the formula developed by [7]. The datasets of the Genhe watershed and Saihanba area consist of raw data acquired by the data collector and processed data of soil moisture and temperature. The Saihanba dataset also includes the calibration data based on soil texture. The result of temporal variations analysis in observed data in the Genhe Watershed and the processing in observed data in the saihanba area show that the long-term in situ soil moisture and temperature datasets can be used for the validation/calibration and improvement of the soil moisture and soil freeze/thaw algorithm.
Project description:Understanding of effects of soil temperature and soil moisture on soil respiration (Rs) under future warming is critical to reduce uncertainty in predictions of feedbacks to atmospheric CO2 concentrations from grassland soil carbon. Intact cores with roots taken from a full factorial, 5-year alpine meadow warming and grazing experiment in the field were incubated at three different temperatures (i.e. 5, 15 and 25°C) with two soil moistures (i.e. 30 and 60% water holding capacity (WHC)) in our study. Another experiment of glucose-induced respiration (GIR) with 4 h of incubation was conducted to determine substrate limitation. Our results showed that high temperature increased Rs and low soil moisture limited the response of Rs to temperature only at high incubation temperature (i.e. 25°C). Temperature sensitivity (Q10) did not significantly decrease over the incubation period, suggesting that substrate depletion did not limit Rs. Meanwhile, the carbon availability index (CAI) was higher at 5°C compared with 15 and 25°C incubation, but GIR increased with increasing temperature. Therefore, our findings suggest that warming-induced decrease in Rs in the field over time may result from a decrease in soil moisture rather than from soil substrate depletion, because warming increased root biomass in the alpine meadow.
Project description: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.
Project description:While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0-10 cm, 10-30 cm, and 30-50 cm) at 0.25° spatial and daily temporal resolution over the period 2000-2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.
Project description:Current seasonal climate predictions mainly reside in the ocean anomalies. However, the prediction skills are generally limited over many extra-tropical land areas where the oceanic effects are relatively weak. In this study, we address the potential of preceding spring soil moisture condition to predict summer hot days over Northeastern China, a typical Northern Hemisphere mid-latitude region. The results show that spring soil moisture condition over Central-Eastern China is closely related with following summer hot days over Northeastern China for the period of 1979-2017. The statistical model based on the preceding spring soil moisture condition yields temporal cross-validated correlation skill of 0.57 for summer hot days over Northeastern China. The spatial pattern correlation skills of independent hindcast experiments for 2009-2017 are also high, ranging from 0.87 to 0.94. Our results can be easily applied to practical prediction of summer hot days over Northeastern China, and help to provide better climate services and reduce the detrimental effects of extreme heat over this extra-tropical region.
Project description:An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.
Project description:Precipitation and temperature are important drivers of soil respiration. The role of moisture and temperature are generally explored at seasonal or inter-annual timescales; however, significant variability also occurs on hourly to daily time-scales. We used small (1.54 m(2)), throughfall exclusion shelters to evaluate the role soil moisture and temperature as temporal controls on soil CO2 efflux from a humid tropical forest in Puerto Rico. We measured hourly soil CO2 efflux, temperature and moisture in control and exclusion plots (n = 6) for 6-months. The variance of each time series was analyzed using orthonormal wavelet transformation and Haar-wavelet coherence. We found strong negative coherence between soil moisture and soil respiration in control plots corresponding to a two-day periodicity. Across all plots, there was a significant parabolic relationship between soil moisture and soil CO2 efflux with peak soil respiration occurring at volumetric soil moisture of approximately 0.375 m(3)/m(3). We additionally found a weak positive coherence between CO2 and temperature at longer time-scales and a significant positive relationship between soil temperature and CO2 efflux when the analysis was limited to the control plots. The coherence between CO2 and both temperature and soil moisture were reduced in exclusion plots. The reduced CO2 response to temperature in exclusion plots suggests that the positive effect of temperature on CO2 is constrained by soil moisture availability.
Project description:We analyzed the effects on a soil microbial community of short-term alterations in air temperature, soil moisture and ultraviolet radiation and assessed the role of invertebrates (species Enchytraeus crypticus) in modulating the community's response to these factors. The reference soil, Lufa 2.2, was incubated for 48 h, with and without invertebrates, under the following conditions: standard (20 °C + 50% water holding capacity (WHC)); increased air temperature (15-25 °C or 20-30 °C + 50% WHC); flood (20 °C + 75% WHC); drought (20 °C + 25% WHC); and ultraviolet radiation (UV) (20 °C + 50% WHC + UV). BIOLOG EcoPlates and 16S rDNA sequencing (Illumina) were used to assess the microbial community's physiological profile and the bacterial community's structure, respectively. The bacterial abundance (estimated by 16S rDNA qPCR) did not change. Most of the conditions led to an increase in microbial activity and a decrease in diversity. The structure of the bacterial community was particularly affected by higher air temperatures (20-30 °C, without E. crypticus) and floods (with E. crypticus). Effects were observed at the class, genera and OTU levels. The presence of invertebrates mostly resulted in the attenuation of the observed effects, highlighting the importance of considering microbiome-invertebrate interactions. Considering future climate changes, the effects described here raise concern. This study provides fundamental knowledge to develop effective strategies to mitigate these negative outcomes. However, long-term studies integrating biotic and abiotic factors are needed.
Project description:Storing large amounts of organic carbon, soils are a key but uncertain component of the global carbon cycle, and accordingly, of Earth System Models (ESMs). Soil organic carbon (SOC) dynamics are regulated by a complex interplay of drivers. Climate, generally represented by temperature and moisture, is regarded as one of the fundamental controls. Here, we use 54 forest sites in Switzerland, systematically selected to span near-independent gradients in temperature and moisture, to disentangle the effects of climate, soil properties, and landform on SOC dynamics. We estimated two SOC turnover times, based on bulk soil 14C measurements (τ14C) and on a 6-month laboratory soil incubation (τi). In addition, upon incubation, we measured the 14C signature of the CO2 evolved and quantified the cumulated production of dissolved organic carbon (DOC). Our results demonstrate that τi and τ14C capture the dynamics of contrasting fractions of the SOC continuum. The 14C-based τ14C primarily reflects the dynamics of an older, stabilised pool, whereas the incubation-based τi mainly captures fresh readily available SOC. Mean site temperature did not raise as a critical driver of SOC dynamics, and site moisture was only significant for τi. However, soil pH emerged as a key control of both turnover times. The production of DOC was independent of τi and not driven by climate, but primarily by the content of clay and, secondarily by the slope of the site. At the regional scale, soil physicochemical properties and landform appear to override the effect of climate on SOC dynamics.
Project description:Soil C is the largest C pool in forest ecosystems that contributes to C sequestration and mitigates climate change. Tree diversity enhances forest productivity, so diversifying the tree species composition, notably in managed forests, could increase the quantity of organic matter being transferred to soils and alter other soil properties relevant to the C cycle.A ten-year-old tree diversity experiment was used to study the effects of tree identity and diversity (functional and taxonomic) on soils. Surface (0-10 cm) mineral soil was repeatedly measured for soil C concentration, C:N ratio, pH, moisture, and temperature in twenty-four tree species mixtures and twelve corresponding monocultures (replicated in four blocks).Soil pH, moisture, and temperature responded to tree diversity and identity. Greater productivity in above- and below-ground tree components did not increase soil C concentration. Soil pH increased and soil moisture decreased with functional diversity, more specifically, when species had different growth strategies and shade tolerances. Functional identity affected soil moisture and temperature, such that tree communities with more slow-growing and shade-tolerant species had greater soil moisture and temperature. Higher temperature was measured in communities with broadleaf-deciduous species compared to communities with coniferous-evergreen species.We conclude that long-term soil C cycling in forest plantations will likely respond to changes in soil pH, moisture, and temperature that is mediated by tree species composition, since tree species affect these soil properties through their litter quality, water uptake, and physical control of soil microclimates.