Marit G. A. Hendrickx, Jan Diels, Pieter Janssens, Steffen Schlüter, Jan Vanderborght
When estimating field-scale average soil moisture from sensors measuring at fixed positions, spatial variability in soil moisture leads to “measurement errors” of the spatial mean, which may persist over time due to persistent soil moisture patterns resulting in autocorrelated measurement errors. The uncertainty of parameters that are derived from such measurements may be underestimated when they are assumed to be independent. Temporal autocorrelation models assume stationary random errors, but such error models are not necessarily applicable to soil moisture measurements. As an alternative, we propose a mechanistic error model that is based on the spatial variability of the water retention curve and assumes a uniform water potential. We tested whether spatial soil moisture variability and its temporal covariance could be predicted based on (1) mean soil moisture, (2) water retention variability, and (3) (co)variances of the van Genuchten parameters using a first-order expansion of the retention curve. The proposed models were tested in a numerical and a field experiment. For the field experiment, in situ sensor measurements and water retention curves were obtained in a field plot. Both experiments showed that water retention variability under a uniform water potential is a good predictor for spatial soil moisture variability, and that soil moisture errors are strongly correlated in time and neglecting them would be an incorrect assumption. The temporal error covariance could be predicted as a function of the mean moisture contents at two observation times. Further research is required to assess the impact of these temporal correlations on soil moisture predictions.
{"title":"Temporal covariance of spatial soil moisture variations: A mechanistic error modeling approach","authors":"Marit G. A. Hendrickx, Jan Diels, Pieter Janssens, Steffen Schlüter, Jan Vanderborght","doi":"10.1002/vzj2.20295","DOIUrl":"https://doi.org/10.1002/vzj2.20295","url":null,"abstract":"When estimating field-scale average soil moisture from sensors measuring at fixed positions, spatial variability in soil moisture leads to “measurement errors” of the spatial mean, which may persist over time due to persistent soil moisture patterns resulting in autocorrelated measurement errors. The uncertainty of parameters that are derived from such measurements may be underestimated when they are assumed to be independent. Temporal autocorrelation models assume stationary random errors, but such error models are not necessarily applicable to soil moisture measurements. As an alternative, we propose a mechanistic error model that is based on the spatial variability of the water retention curve and assumes a uniform water potential. We tested whether spatial soil moisture variability and its temporal covariance could be predicted based on (1) mean soil moisture, (2) water retention variability, and (3) (co)variances of the van Genuchten parameters using a first-order expansion of the retention curve. The proposed models were tested in a numerical and a field experiment. For the field experiment, in situ sensor measurements and water retention curves were obtained in a field plot. Both experiments showed that water retention variability under a uniform water potential is a good predictor for spatial soil moisture variability, and that soil moisture errors are strongly correlated in time and neglecting them would be an incorrect assumption. The temporal error covariance could be predicted as a function of the mean moisture contents at two observation times. Further research is required to assess the impact of these temporal correlations on soil moisture predictions.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"1 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139053275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inmaculada Lebron, David M. Cooper, Michele A. Brentegani, Laura A. Bentley, Gloria Dos Santos Pereira, Patrick Keenan, Jack Bernard Cosby, Bridget Emmet, David A. Robinson
Soils and the vadose zone are the major terrestrial repository of carbon (C) in the form of soil organic matter (SOM), more resistant black carbon (BC), and inorganic carbonate. Differentiating between these pools is important for assessing vulnerability to degradation and changes in the C cycle affecting soil health and climate regulation. Major monitoring programs from field to continent are now being undertaken to track changes in soil carbon (SC). Inexpensive, robust measures that can differentiate small changes in the C pools in a single measurement are highly desirable for long-term monitoring. In this study, we assess the accuracy and precision of thermo-gravimetric analysis (TGA) using organic matter standards, clay minerals, and soils from a national data set. We investigate the use of TGA to routinely differentiate between C pools, something no single measurement has yet achieved. Based on the kinetic nature of thermal oxidation of SC combined with the different thermodynamic stabilities of the molecules, we designed a new method to quantify the inorganic and organic SC and further separate the organic biogeochemically active SOM (as loss on ignition, LOI) from the resistant BC in soils. We analyze the TGA spectrums of a national soil monitoring data set (n = 456) and measure total carbon (TC) using thermal oxidation and also demonstrate a TC/LOI relationship of 0.55 for soils ranging from mineral soils to peat for the United Kingdom consistent with previous monitoring campaigns.
{"title":"Soil carbon determination for long-term monitoring revisited using thermo-gravimetric analysis","authors":"Inmaculada Lebron, David M. Cooper, Michele A. Brentegani, Laura A. Bentley, Gloria Dos Santos Pereira, Patrick Keenan, Jack Bernard Cosby, Bridget Emmet, David A. Robinson","doi":"10.1002/vzj2.20300","DOIUrl":"https://doi.org/10.1002/vzj2.20300","url":null,"abstract":"Soils and the vadose zone are the major terrestrial repository of carbon (C) in the form of soil organic matter (SOM), more resistant black carbon (BC), and inorganic carbonate. Differentiating between these pools is important for assessing vulnerability to degradation and changes in the C cycle affecting soil health and climate regulation. Major monitoring programs from field to continent are now being undertaken to track changes in soil carbon (SC). Inexpensive, robust measures that can differentiate small changes in the C pools in a single measurement are highly desirable for long-term monitoring. In this study, we assess the accuracy and precision of thermo-gravimetric analysis (TGA) using organic matter standards, clay minerals, and soils from a national data set. We investigate the use of TGA to routinely differentiate between C pools, something no single measurement has yet achieved. Based on the kinetic nature of thermal oxidation of SC combined with the different thermodynamic stabilities of the molecules, we designed a new method to quantify the inorganic and organic SC and further separate the organic biogeochemically active SOM (as loss on ignition, LOI) from the resistant BC in soils. We analyze the TGA spectrums of a national soil monitoring data set (<i>n</i> = 456) and measure total carbon (TC) using thermal oxidation and also demonstrate a TC/LOI relationship of 0.55 for soils ranging from mineral soils to peat for the United Kingdom consistent with previous monitoring campaigns.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"13 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139036446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rajsekhar Kandala, Harrie-Jan Hendricks Franssen, Abhijit Chaudhuri, M. Sekhar
This synthetic study explores the value of near-surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS-1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi-arid to sub-humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.
{"title":"The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model","authors":"Rajsekhar Kandala, Harrie-Jan Hendricks Franssen, Abhijit Chaudhuri, M. Sekhar","doi":"10.1002/vzj2.20298","DOIUrl":"https://doi.org/10.1002/vzj2.20298","url":null,"abstract":"This synthetic study explores the value of near-surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS-1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm<sup>3</sup>/cm<sup>3</sup> for univariate soil temperature assimilation and 0.01 cm<sup>3</sup>/cm<sup>3</sup> for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi-arid to sub-humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"9 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lena Lärm, Felix Maximilian Bauer, Jan van der Kruk, Jan Vanderborght, Shehan Morandage, Harry Vereecken, Andrea Schnepf, Anja Klotzsche
Non-invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in-situ time-lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the “trend-corrected spatial permittivity deviation of vegetated field,” allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non-root-related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend-corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross-correlated with the root volume fraction, resulting in a coefficient of determination (R2) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability.
{"title":"Linking horizontal crosshole GPR variability with root image information for maize crops","authors":"Lena Lärm, Felix Maximilian Bauer, Jan van der Kruk, Jan Vanderborght, Shehan Morandage, Harry Vereecken, Andrea Schnepf, Anja Klotzsche","doi":"10.1002/vzj2.20293","DOIUrl":"https://doi.org/10.1002/vzj2.20293","url":null,"abstract":"Non-invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in-situ time-lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the “trend-corrected spatial permittivity deviation of vegetated field,” allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non-root-related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend-corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross-correlated with the root volume fraction, resulting in a coefficient of determination (<i>R</i><sup>2</sup>) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"59 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138505129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model-based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot-scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO-56 model), and (4) evaluating the performance of ERT-based ET computation. Our results showed that ERT was able to capture decimeter-scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point-based sensing, which can further our understanding of the hydraulic dynamics in the soil-plant-atmosphere continuum.
{"title":"Improving evapotranspiration computation with electrical resistivity tomography in a maize field","authors":"Chunwei Chou, Luca Peruzzo, Nicola Falco, Zhao Hao, Benjamin Mary, Jiannan Wang, Yuxin Wu","doi":"10.1002/vzj2.20290","DOIUrl":"https://doi.org/10.1002/vzj2.20290","url":null,"abstract":"Hydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model-based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot-scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO-56 model), and (4) evaluating the performance of ERT-based ET computation. Our results showed that ERT was able to capture decimeter-scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point-based sensing, which can further our understanding of the hydraulic dynamics in the soil-plant-atmosphere continuum.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"733 ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138505160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kenneth S. Tramm, Jason T. Minter, Catherine A. Seaton
Risk-based corrective action (RBCA) programs employ conservative models to develop default values for soil screening, which simplify the risk assessment process. However, for several naturally occurring metals (e.g., arsenic and lead), these published screening values are often unrealistic and well below the documented background levels in soil. This can lead to confusion among the regulated community and inexperienced regulators, as it will inappropriately identify naturally occurring conditions as a release (false positive or Type I error). An effective RBCA program requires the incorporation of defensible background threshold values (BTVs) in the screening process. Recent datasets and BTV development methods are available to enhance existing RBCA programs and reduce the occurrence of Type I errors. This review evaluated the role “background” currently plays in the Texas Risk Reduction Program (TRRP) and offers defensible approaches in minimizing Type I errors estimated by one Texas municipality to directly result in an unnecessary expenditure of over $250,000 annually to address this confusion in the form of additional assessment, remediation, soil management, and even disposal requirements. The same BTV development process demonstrated in this Texas case study can also inform risk assessment efforts in other areas where BTVs can supplement existing RBCA programs.
{"title":"Importance of background threshold value development within risk-based corrective action programs","authors":"Kenneth S. Tramm, Jason T. Minter, Catherine A. Seaton","doi":"10.1002/vzj2.20294","DOIUrl":"https://doi.org/10.1002/vzj2.20294","url":null,"abstract":"Risk-based corrective action (RBCA) programs employ conservative models to develop default values for soil screening, which simplify the risk assessment process. However, for several naturally occurring metals (e.g., arsenic and lead), these published screening values are often unrealistic and well below the documented background levels in soil. This can lead to confusion among the regulated community and inexperienced regulators, as it will inappropriately identify naturally occurring conditions as a release (false positive or Type I error). An effective RBCA program requires the incorporation of defensible background threshold values (BTVs) in the screening process. Recent datasets and BTV development methods are available to enhance existing RBCA programs and reduce the occurrence of Type I errors. This review evaluated the role “background” currently plays in the Texas Risk Reduction Program (TRRP) and offers defensible approaches in minimizing Type I errors estimated by one Texas municipality to directly result in an unnecessary expenditure of over $250,000 annually to address this confusion in the form of additional assessment, remediation, soil management, and even disposal requirements. The same BTV development process demonstrated in this Texas case study can also inform risk assessment efforts in other areas where BTVs can supplement existing RBCA programs.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"725 ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138505161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The cosmic‐ray neutron probe (CRNP) is a mesoscale and noninvasive method for measuring soil moisture and has been widely studied and applied. However, studies of its applicability in rocky mountainous areas are still challenging in complex topography and high gravel content. In this study, a field experiment was carried out to assess the applicability of the CRNP for measuring soil moisture in rocky areas of Taihang Mountains of North China. The results showed that the Pearson correlation coefficient and the root mean square error between the soil moisture from CRNP and the drying method are 0.911 and 0.025 m 3 m −3 , respectively, indicating that the CRNP can estimate the average soil moisture well in the study area. Compared with the capacitive sensor, the CRNP overestimated soil moisture when small rainfall events occurred, which was caused by the interception of canopy and litter. The nonlinear weighting method performed better than the linear weighting method in representing average soil moisture within the CRNP footprint. The high gravel content that contained high lattice water content reduced the penetration depth of CRNP. Biomass reduces the accuracy of the CRNP by affecting the neutron intensity. In summary, CRNP can measure soil moisture accurately in rocky areas of the Taihang Mountains, especially in dry environments with low biomass.
宇宙射线中子探针(CRNP)是一种中尺度、无创的土壤湿度测量方法，已经得到了广泛的研究和应用。然而，在地形复杂、含砾量高的岩石山区，其适用性研究仍具有挑战性。本研究通过田间试验，对CRNP在太行山岩石区土壤水分测量中的适用性进行了评价。结果表明，CRNP与干燥法土壤湿度的Pearson相关系数和均方根误差分别为0.911和0.025 m 3 m−3，表明CRNP能较好地估算研究区土壤平均湿度。与电容式传感器相比，CRNP在小降雨时高估了土壤水分，这是由于冠层和凋落物的截留造成的。非线性加权法比线性加权法更能表征CRNP足迹内的平均土壤水分。砾石含量高，晶格含水量高，降低了CRNP的渗透深度。生物量通过影响中子强度降低了CRNP的准确性。综上所述，CRNP可以准确测量太行山岩石区土壤水分，特别是在低生物量的干燥环境中。
{"title":"Application of cosmic‐ray neutron probes for measuring soil moisture in rocky areas of the Taihang Mountains, North China","authors":"Zhihua Zhang, Huidi Ou, Yuefeng Shi, Youliang Ye, Yuqiang Sang, Siyi Zhang, Jinsong Zhang","doi":"10.1002/vzj2.20291","DOIUrl":"https://doi.org/10.1002/vzj2.20291","url":null,"abstract":"Abstract The cosmic‐ray neutron probe (CRNP) is a mesoscale and noninvasive method for measuring soil moisture and has been widely studied and applied. However, studies of its applicability in rocky mountainous areas are still challenging in complex topography and high gravel content. In this study, a field experiment was carried out to assess the applicability of the CRNP for measuring soil moisture in rocky areas of Taihang Mountains of North China. The results showed that the Pearson correlation coefficient and the root mean square error between the soil moisture from CRNP and the drying method are 0.911 and 0.025 m 3 m −3 , respectively, indicating that the CRNP can estimate the average soil moisture well in the study area. Compared with the capacitive sensor, the CRNP overestimated soil moisture when small rainfall events occurred, which was caused by the interception of canopy and litter. The nonlinear weighting method performed better than the linear weighting method in representing average soil moisture within the CRNP footprint. The high gravel content that contained high lattice water content reduced the penetration depth of CRNP. Biomass reduces the accuracy of the CRNP by affecting the neutron intensity. In summary, CRNP can measure soil moisture accurately in rocky areas of the Taihang Mountains, especially in dry environments with low biomass.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"39 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elisabeth Nissler, Samuel Scherrer, Holger Class, Tanja Müller, Mark Hermannspan, Esad Osmancevic, Claus Haslauer
Abstract Drinking‐water quality in supply pipe networks can be negatively affected by high temperatures during hot summer months due to detrimental bacteria encountering ideal conditions for growth. Thus, water suppliers are interested in estimating the temperature in their distribution networks. We investigate both experimentally and by numerical simulation the heat and water transport from ground surface into the subsurface, (i.e., above drinking‐water pipes). We consider the meteorological forcing functions by a sophisticated approach to model the boundary conditions for the heat balance at the soil–atmosphere interface. From August to December 2020, soil temperatures and soil moisture were measured dependent on soil type, land‐use cover, and weather data at a pilot site, constructed specifically for this purpose at the University of Stuttgart with polyethylene and cast‐iron pipes installed under typical in situ conditions. We included this interface condition at the atmosphere–subsurface boundary into an integrated non‐isothermal, variably saturated (Richards') the numerical simulator DuMu x 3. This allowed, after calibration, to match measured soil temperatures with ±2°C accuracy. The land‐use cover influenced the soil temperature in 1.5 m more than the soil material used for back‐filling the trench above the pipe.
在炎热的夏季，由于有害细菌遇到理想的生长条件，供水管网中的饮用水质量可能受到高温的负面影响。因此，供水商对估计其分配网络中的温度很感兴趣。我们通过实验和数值模拟研究了从地表到地下(即饮用水管道上方)的热量和水分输送。我们考虑了气象强迫函数，用一种复杂的方法来模拟土壤-大气界面热平衡的边界条件。从2020年8月到12月，根据土壤类型、土地利用覆盖和天气数据，在斯图加特大学专门为此目的建造的一个试验点测量了土壤温度和土壤湿度，在典型的原位条件下安装了聚乙烯和铸铁管。我们将大气-地下边界的这一界面条件纳入了一个集成的非等温、变饱和(Richards’)数值模拟器DuMu x 3。这允许，校准后，以±2°C的精度匹配测量的土壤温度。土地利用覆盖对1.5 m范围内土壤温度的影响大于管道上方回填沟所使用的土壤材料。
{"title":"Heat transport from atmosphere through the subsurface to drinking‐water supply pipes","authors":"Elisabeth Nissler, Samuel Scherrer, Holger Class, Tanja Müller, Mark Hermannspan, Esad Osmancevic, Claus Haslauer","doi":"10.1002/vzj2.20286","DOIUrl":"https://doi.org/10.1002/vzj2.20286","url":null,"abstract":"Abstract Drinking‐water quality in supply pipe networks can be negatively affected by high temperatures during hot summer months due to detrimental bacteria encountering ideal conditions for growth. Thus, water suppliers are interested in estimating the temperature in their distribution networks. We investigate both experimentally and by numerical simulation the heat and water transport from ground surface into the subsurface, (i.e., above drinking‐water pipes). We consider the meteorological forcing functions by a sophisticated approach to model the boundary conditions for the heat balance at the soil–atmosphere interface. From August to December 2020, soil temperatures and soil moisture were measured dependent on soil type, land‐use cover, and weather data at a pilot site, constructed specifically for this purpose at the University of Stuttgart with polyethylene and cast‐iron pipes installed under typical in situ conditions. We included this interface condition at the atmosphere–subsurface boundary into an integrated non‐isothermal, variably saturated (Richards') the numerical simulator DuMu x 3. This allowed, after calibration, to match measured soil temperatures with ±2°C accuracy. The land‐use cover influenced the soil temperature in 1.5 m more than the soil material used for back‐filling the trench above the pipe.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":" 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anne‐Martine Doucet, Frances Jones, Katherine E. Raymond, Gregory Dipple, Thomas Andrew Black, Bethany Ladd, Klaus Ulrich Mayer
Abstract The alkaline playas at Atlin, BC, provide a unique opportunity for studying the carbonate–bicarbonate system and carbonate mineral stability at the Earth's surface. In this study, dynamic closed chambers (DCCs) and pore‐gas sampling were used to directly quantify carbon dioxide (CO 2 ) emission rates and characterize processes governing the CO 2 exchange across the playa‐atmosphere interface. Data were collected at the Atlin site continuously over 27 days in 2020 and 14 days in 2021. Results indicate minimal net exchange of CO 2 across the playa‐atmosphere interface during the monitoring periods, with average fluxes over the two periods of −0.03 and 0.09 µmol m −2 s −1 in 2020 and 2021, respectively. However, distinct diurnal oscillations of CO 2 fluxes were measured with average daytime fluxes of 0.15 ± 0.34 µmol m −2 s −1 (2020) and 0.15 ± 0.19 µmol m −2 s −1 (2021) and nighttime fluxes of −0.24 ± 0.31 µmol m −2 s −1 (2020) and 0.04 ± 0.18 µmol m −2 s −1 (2021). These observations, supported by reactive transport modeling, indicate that CO 2 exchange is predominantly governed by changes in CO 2 solubility in alkaline porewater related to diurnal temperature fluctuations and variations in CO 2 concentrations in ambient air above the ground surface. Even though CO 2 concentrations exceed 8000 ppmv at 1‐m depth, CO 2 emissions to the atmosphere were found to be minimal, likely due to high moisture contents, low connectivity, and tortuosity, limiting upward CO 2 migration. These findings provide insights into CO 2 flux dynamics in alkaline arid regions and show promise for the application of the DCC method for monitoring ex situ carbon mineralization at sites with enhanced mineral weathering.
{"title":"Quantitative analysis of diurnal CO<sub>2</sub> flux variations above an alkaline playa","authors":"Anne‐Martine Doucet, Frances Jones, Katherine E. Raymond, Gregory Dipple, Thomas Andrew Black, Bethany Ladd, Klaus Ulrich Mayer","doi":"10.1002/vzj2.20292","DOIUrl":"https://doi.org/10.1002/vzj2.20292","url":null,"abstract":"Abstract The alkaline playas at Atlin, BC, provide a unique opportunity for studying the carbonate–bicarbonate system and carbonate mineral stability at the Earth's surface. In this study, dynamic closed chambers (DCCs) and pore‐gas sampling were used to directly quantify carbon dioxide (CO 2 ) emission rates and characterize processes governing the CO 2 exchange across the playa‐atmosphere interface. Data were collected at the Atlin site continuously over 27 days in 2020 and 14 days in 2021. Results indicate minimal net exchange of CO 2 across the playa‐atmosphere interface during the monitoring periods, with average fluxes over the two periods of −0.03 and 0.09 µmol m −2 s −1 in 2020 and 2021, respectively. However, distinct diurnal oscillations of CO 2 fluxes were measured with average daytime fluxes of 0.15 ± 0.34 µmol m −2 s −1 (2020) and 0.15 ± 0.19 µmol m −2 s −1 (2021) and nighttime fluxes of −0.24 ± 0.31 µmol m −2 s −1 (2020) and 0.04 ± 0.18 µmol m −2 s −1 (2021). These observations, supported by reactive transport modeling, indicate that CO 2 exchange is predominantly governed by changes in CO 2 solubility in alkaline porewater related to diurnal temperature fluctuations and variations in CO 2 concentrations in ambient air above the ground surface. Even though CO 2 concentrations exceed 8000 ppmv at 1‐m depth, CO 2 emissions to the atmosphere were found to be minimal, likely due to high moisture contents, low connectivity, and tortuosity, limiting upward CO 2 migration. These findings provide insights into CO 2 flux dynamics in alkaline arid regions and show promise for the application of the DCC method for monitoring ex situ carbon mineralization at sites with enhanced mineral weathering.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":" 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Accurate large‐scale soil moisture (SM) maps are crucial for catchment‐scale hydrological models used for water resource management and warning systems for droughts, floods, and wildfires. SM can be mapped by mobile cosmic‐ray neutron (CRN) systems of moderated detectors at homogeneous landscapes of similar soil and vegetation. In this study, we present a new approach for mobile CRN detection to perform to its full potential, where CRN measurements can also be converted to SM at heterogeneous landscapes. The approach is based solely on thermal and epithermal neutron datasets obtained from mobile dual‐spectra CRN detection, combined with theoretical developments using a particle transport model. For each measurement point, the land cover type is identified using the thermal‐to‐epithermal (T/E) ratio, and the relevant neutron‐count‐to‐soil‐moisture conversion function is estimated from CRN stations located at the main land cover types in the catchment. With this approach, the requirement of collecting 100+ soil samples for each point along the survey route is omitted. We use this T/E‐dependent approach to obtain SM maps from 12 CRN surveys and compare it with a simple approach where only the conversion function from the agricultural site is used. SM by the simple approach is comparable to the estimates of the agricultural stations of a capacitance sensor network, while the estimates of the T/E‐dependent approach also compare well with the heathland and forest stations. With accurate SM estimates for all landcover types, the average error is reduced from 0.089 to 0.038 when comparing CRN SM with space‐borne Soil Moisture Active Passive Mission estimates.
{"title":"Mapping spatiotemporal soil moisture in highly heterogeneous agricultural landscapes using mobile dual‐spectra cosmic‐ray neutron sensing","authors":"Mie Andreasen, Søren Julsgaard Kragh, Rena Meyer, Karsten Høgh Jensen, Majken C. Looms","doi":"10.1002/vzj2.20287","DOIUrl":"https://doi.org/10.1002/vzj2.20287","url":null,"abstract":"Abstract Accurate large‐scale soil moisture (SM) maps are crucial for catchment‐scale hydrological models used for water resource management and warning systems for droughts, floods, and wildfires. SM can be mapped by mobile cosmic‐ray neutron (CRN) systems of moderated detectors at homogeneous landscapes of similar soil and vegetation. In this study, we present a new approach for mobile CRN detection to perform to its full potential, where CRN measurements can also be converted to SM at heterogeneous landscapes. The approach is based solely on thermal and epithermal neutron datasets obtained from mobile dual‐spectra CRN detection, combined with theoretical developments using a particle transport model. For each measurement point, the land cover type is identified using the thermal‐to‐epithermal (T/E) ratio, and the relevant neutron‐count‐to‐soil‐moisture conversion function is estimated from CRN stations located at the main land cover types in the catchment. With this approach, the requirement of collecting 100+ soil samples for each point along the survey route is omitted. We use this T/E‐dependent approach to obtain SM maps from 12 CRN surveys and compare it with a simple approach where only the conversion function from the agricultural site is used. SM by the simple approach is comparable to the estimates of the agricultural stations of a capacitance sensor network, while the estimates of the T/E‐dependent approach also compare well with the heathland and forest stations. With accurate SM estimates for all landcover types, the average error is reduced from 0.089 to 0.038 when comparing CRN SM with space‐borne Soil Moisture Active Passive Mission estimates.","PeriodicalId":23594,"journal":{"name":"Vadose Zone Journal","volume":"68 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135809958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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