Eleven years of mountain weather, snow, soil moisture and stream flow data from the rain-snow transition zone – the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA

Godsey, Sarah E.; Marks, Danny; Kormos, Patrick R.; Seyfried, M. S.; Enslin, Clarissa L.; Winstral, A. H.; McNamara, J. P.; Link, Timothy E.

doi:10.5194/essd-2017-112

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Meteorological and snow depth data from Johnston Draw during this time are described by and available through Godsey et al (2018). We used hourly snow depth measured with sonic depth sensors (Judd Communications) at all six stations, and T a measured at Stations 2, 4, 2b, and 4b to analyze SA effects on those variables.…”

Section: Hydrometeorological Datamentioning

confidence: 99%

Slope and aspect controls on soil climate: Field documentation and implications for large‐scale simulation of critical zone processes

Seyfried¹,

Flerchinger²,

Bryden

et al. 2021

Vadose Zone Journal

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Soil climate, as quantified by soil temperature (T S ) and water content (θ), exerts important controls on critical zone processes. It may be sensitive to variations in local slope and aspect (SA), but this attribute remains poorly quantified at the local scale and unresolved in large-scale models. Estimation of SA effects on soil climate across multiple scales may facilitated using topographically modified, incoming clear-sky solar radiation (S R,CS,T ). We established six paired automated soil climate monitoring stations on opposing north-facing (NF) and south-facing (SF) slopes (4 yr) and collected spatial T S and θ data within the hectare surrounding four stations (2 yr) to measure SA effects on soil climate. Results were compared with physically based simulations and evaluated in the context of S R,CS,T . Spatial θ data were more variable than Ts, and both were consistent with values from continuous monitoring stations. On average, the SF T S was much greater (4.7 ˚C) and the annual summer drought longer (36 d) than on the adjacent NF aspect. Seasonal variations of T S and θ were different from each other and also different from S R,CS,T . Local conditions, including snow cover, precipitation patterns, and soil properties, largely controlled seasonal variations of T S and θ, which cannot be predicted from S R,CS,T . This indicates that realistic simulation of many critical zone processes requires high-resolution inputs.Simulations captured first-order SA effects and could be useful for estimating SA effects in lieu of field monitoring.

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Section: Hydrometeorological Datamentioning

confidence: 99%

Slope and aspect controls on soil climate: Field documentation and implications for large‐scale simulation of critical zone processes

Seyfried¹,

Flerchinger²,

Bryden

et al. 2021

Vadose Zone Journal

Self Cite

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Critical Zone Science Informs Landscape Management Through Quantification of Slow Episodic Constraints on Ecosystem Services

Lohse,

Commendador,

Glossner

et al. 2024

Advances in Critical Zone Science

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An Innovative Smart and Sustainable Low-Cost Irrigation System for Anomaly Detection Using Deep Learning

Benameur,

Dahane,

Kechar

et al. 2024

Sensors

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The agricultural sector faces several difficulties today in ensuring the safety of food supply, including water scarcity. This study presents the design and development of a low-cost and full-featured fog-IoT/AI system targeted towards smallholder farmer communities (SFCs). However, the smallholder community is hesitant to adopt technology-based solutions. There are many overwhelming reasons for this, but the high cost, implementation complexity, and malfunctioning sensors cause inappropriate decisions. The PRIMA INTEL-IRRIS project aims to make digital and innovative agricultural technologies more appealing and available to these communities by advancing the intelligent irrigation “in-the-box” concept. Considered a vital resource, collected data are used to detect anomalies or abnormal behavior, providing information about an occurrence or a node failure. To prevent agro-field data leakage, this paper presents an innovative, smart, and sustainable low-cost irrigation system that employs artificial intelligence (AI) techniques to analyze anomalies and problems in water usage. The sensor anomaly can be detected using an autoencoder (AE) and a generative adversarial network (GAN). We will feed the autoencoders’ anomaly detection models with time series records from the datasets and replace detected anomalies with the reconstructed outputs. When integrated with an IoT platform, this methodology is a tool for easing the labeling of sensor anomalies and can help create supervised datasets for future research. In addition, anomalies can be corrected by prediction models based on deep learning approaches, applying CNN/BiLSTM architecture. The results show that AEs outperform the GANs, achieving an accuracy of 90%, 95%, and 97% for soil moisture, air temperature, and air humidity, respectively. The proposed system is designed to ensure that the data are of high quality and reliable enough to make sound decisions compared to the existing platforms.

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Eleven years of mountain weather, snow, soil moisture and stream flow data from the rain-snow transition zone – the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA

Cited by 4 publications

References 12 publications

Slope and aspect controls on soil climate: Field documentation and implications for large‐scale simulation of critical zone processes

Slope and aspect controls on soil climate: Field documentation and implications for large‐scale simulation of critical zone processes

Critical Zone Science Informs Landscape Management Through Quantification of Slow Episodic Constraints on Ecosystem Services

An Innovative Smart and Sustainable Low-Cost Irrigation System for Anomaly Detection Using Deep Learning

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