Modelling soil water dynamics considering measurement uncertainty

Kisekka, Isaya; Migliaccio, Kati W.; Muñoz‐Carpena, Rafael; Schaffer, Bruce; Khare, Yogesh

doi:10.1002/hyp.10173

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Soil water content (SWC) is a key variable for driving plant growth, precision irrigation management [ 1 , 2 ], and coupled hydrological, environmental, climatological, and ecohydrological processes [ 3 ]. SWC plays a significant role in hydrology to understand the hydrological cycle [ 4 ], rainfall patterns [ 5 ], interflow [ 6 ], and groundwater recharge [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor

Peddinti

Hopmans

Najm

et al. 2020

Sensors

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Low-cost, accurate soil water sensors combined with wireless communication in an internet of things (IoT) framework can be harnessed to enhance the benefits of precision irrigation. However, the accuracy of low-cost sensors (e.g., based on resistivity or capacitance) can be affected by many factors, including salinity, temperature, and soil structure. Recent developments in wireless sensor networks offer new possibilities for field-scale monitoring of soil water content (SWC) at high spatiotemporal scales, but to install many sensors in the network, the cost of the sensors must be low, and the mechanism of operation needs to be robust, simple, and consume low energy for the technology to be practically relevant. This study evaluated the performance of a resistivity–capacitance-based wireless sensor (Sensoterra BV, 1018LE Amsterdam, Netherlands) under different salinity levels, temperature, and soil types in a laboratory. The sensors were evaluated in glass beads, Oso Flaco sand, Columbia loam, and Yolo clay loam soils. A nonlinear relationship was exhibited between the sensor measured resistance (Ω) and volumetric soil water content (θ). The Ω–θ relationship differed by soil type and was affected by soil solution salinity. The sensor was extremely sensitive at higher water contents with high uncertainty, and insensitive at low soil water content accompanied by low uncertainty. The soil solution salinity effects on the Ω–θ relationship were found to be reduced from sand to sandy loam to clay loam. In clay soils, surface electrical conductivity (ECs) of soil particles had a more dominant effect on sensor performance compared to the effect of solution electrical conductivity (ECw). The effect of temperature on sensor performance was minimal, but sensor-to-sensor variability was substantial. The relationship between bulk electrical conductivity (ECb) and volumetric soil water content was also characterized in this study. The results of this study reveal that if the sensor is properly calibrated, this low-cost wireless soil water sensor has the potential of improving soil water monitoring for precision irrigation and other applications at high spatiotemporal scales, due to the ease of integration into IoT frameworks.

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Section: Introductionmentioning

confidence: 99%

Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor

Peddinti

Hopmans

Najm

et al. 2020

Sensors

Self Cite

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“…The identification of the multi-source SWC data quality or measurement error is not an easy task. This limitation instead can create extra uncertainties in DA systems (Kisekka et al, 2015) . Third, it is the diversity of scenarios contained in prior data rather than its volume that is more decisive for the generalization ability of machine learning methods (Wang et al, 75 2020) .…”

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confidence: 99%

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Wang¹,

Wang

et al. 2023

Preprint

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Abstract. Conventional data-worth (DW) analysis for soil water problems depends on physical dynamic models. The widespread occurrence of model structural errors and the strong nonlinearity of soil water flow may lead to biased or wrong worth assessment. By introducing the nonparametric data-worth analysis (NP-DWA) framework coupled with ensemble Kalman filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations regarding the reconstruction of fully data-driven soil water flow models prior to data gathering. The DW of real-time soil moisture observations after Gaussian process training and Kalman update was quantified with three representative information metrics, including the trace, Shannon entropy difference, and relative entropy. The sequential NP-DWA framework was examined by a number of cases in terms of the variable of interest, spatial location, observation error, and prior data content. Our results indicated that the overall increasing trend of the DW from the sequential augmentation of additional observations was susceptible to interruptions by localized surges due to never-experienced atmospheric conditions (i.e., rainfall events) within the NP-DWA framework. Fortunately, this performance degradation can be effectively alleviated by enriching training scenarios or the appropriate amplification of observational noise under extreme meteorological conditions. Nevertheless, a substantial expansion of the prior data content may cause an unexpected increase in DW of future potential observations due to the possible introduction of ensuing observation noises. Hence, high-quality and representative “small” data may be a better choice than unfiltered “big” data. Compared with the observations in the surface layer with the strongest time-variability, the soil water content in the middle layer robustly exhibited remarkable superiority in the construction of model-free soil moisture models. An alternative monitoring strategy with a larger data-worth was prone to a higher DW assessment accuracy within the proposed NP-DWA framework. We also demonstrated that the DW assessment performance was jointly determined by ‘3C’, i.e., capacity of potential observation realizations to “capture” actual observations, correlation of potential observations with the variables of interest, and choice of DW indicators. Direct mapping from regular meteorological data to soil water content within the NP-DWA mitigated the adverse effects of nonlinearity-related interference, which thus facilitated the identification of the soil moisture covariance matrix, especially the cross-covariance.

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“…The identification of the multi-source SWC data quality or measurement error is not an easy task. This limitation can instead create extra uncertainties in DA systems (Kisekka et al, 2015). Third, it is the diversity of scenarios contained in prior data rather than its volume that is more decisive for the generalization ability of machine learning methods (Wang et al, 2020).…”

mentioning

confidence: 99%

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Wang¹,

Wang

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Conventional data worth (DW) analysis for soil water problems depends on physical dynamic models. The widespread occurrence of model structural errors and the strong nonlinearity of soil water flow may lead to biased or wrong worth assessment. By introducing the nonparametric data worth analysis (NP-DWA) framework coupled with the ensemble Kalman filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations regarding the reconstruction of fully data-driven soil water flow models prior to data gathering. The DW of real-time soil moisture observations after Gaussian process training and Kalman update was quantified with three representative information metrics, including the trace, Shannon entropy difference and relative entropy. The sequential NP-DWA framework was examined by a number of cases in terms of the variable of interest, spatial location, observation error, and prior data content. Our results indicated that, similarly to the traditional DW analysis based on physical models, the overall increasing trend of the DW from the sequential augmentation of additional observations within the NP-DWA framework was also susceptible to interruptions by localized surges due to never-experienced atmospheric conditions (i.e., rainfall events). The difference is that this biased DW in the former is caused by model structural errors triggered by contrasting scenarios, which is difficult to be compensated for by assimilating more prior data, while this performance degradation in the NP-DWA can be effectively alleviated by enriching training scenarios or the appropriate amplification of observational noise under extreme meteorological conditions. Nevertheless, a substantial expansion of the prior data content may cause an unexpected increase in the DW of future potential observations due to the possible introduction of ensuing observation noises. Hence, high-quality and representative small data may be a better choice than unfiltered big data. Compared with the observations in the surface layer with the strongest time variability, the soil water content in the middle layer robustly exhibited remarkable superiority in the construction of model-free soil moisture models. We also demonstrated that the DW assessment performance was jointly determined by 3C, i.e., the capacity of potential observation realizations to capture actual observations, the correlation of potential observations with the variables of interest and the choice of DW indicators. Direct mapping from regular meteorological data to soil water content within the NP-DWA mitigated the adverse effects of nonlinearity-related interference, which thus facilitated the identification of the soil moisture covariance matrix, especially the cross-covariance.

show abstract

Modelling soil water dynamics considering measurement uncertainty

Cited by 6 publications

References 32 publications

Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor

Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Data worth analysis within a model-free data assimilation framework for soil moisture flow

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