Using similarity of soil texture and hydroclimate to enhance soil moisture estimation

Coopersmith, E. J.; Minsker, Barbara; Sivapalan, Murugesu

doi:10.5194/hess-18-3095-2014

Cited by 14 publications

(15 citation statements)

References 23 publications

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“…[] and subsequently updated by Pan [], is the diagnostic soil moisture equation. As a simple, lumped‐bucket model, this equation convolutes the antecedent precipitation series and, via six parameters that can be calibrated via a genetic algorithm [ Coopersmith et al ., ], returns a soil moisture estimate as shown in equations and .

θ_{normale normals normalt} = θ_{normalr normale} + ((), ϕ_{e} - θ_{normalr normale}) ((), 1 - e^{prefix- c_{4} β})

β = true {true\sum}_{i = 2}^{i = n - 1} ([], \frac{P_{i}}{η_{i}} ((), 1 - e^{prefix- \frac{η_{i}}{z}}) e^{prefix- true {true\sum}_{j = 1}^{j = i - 1} ((), \frac{η_{j}}{z})}) + \frac{P_{1}}{η_{1}} ((), 1 - e^{prefix- \frac{η_{1}}{z}}) .

…”

Section: Methodsmentioning

confidence: 99%

“…For further information regarding the calibration of these models and their implementation, please review the original literature [ Pan et al ., ; Pan, ] or the literature describing their more recent, machine learning‐based updates in Coopersmith et al . [].…”

Section: Methodsmentioning

confidence: 99%

“…One such model, developed by Pan et al [2003] and subsequently updated by Pan [2012], is the diagnostic soil moisture equation. As a simple, lumped-bucket model, this equation convolutes the antecedent precipitation series and, via six parameters that can be calibrated via a genetic algorithm [Coopersmith et al, 2014b], returns a soil moisture estimate as shown in equations (4) and (5).…”

Section: 1002/2016gh000033mentioning

confidence: 99%

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Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions

et al. 2017

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Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp. , in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long‐term, in situ soil moisture data sets, it has not been feasible to perform a direct analysis of the relationships between soil moisture levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ soil moisture gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower soil moisture in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to soil moisture levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location.

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θ_{normale normals normalt} = θ_{normalr normale} + ((), ϕ_{e} - θ_{normalr normale}) ((), 1 - e^{prefix- c_{4} β})

β = true {true\sum}_{i = 2}^{i = n - 1} ([], \frac{P_{i}}{η_{i}} ((), 1 - e^{prefix- \frac{η_{i}}{z}}) e^{prefix- true {true\sum}_{j = 1}^{j = i - 1} ((), \frac{η_{j}}{z})}) + \frac{P_{1}}{η_{1}} ((), 1 - e^{prefix- \frac{η_{1}}{z}}) .

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: 1002/2016gh000033mentioning

confidence: 99%

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Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions

et al. 2017

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“…It is worth noting that precipitation and soil moisture are not independent variables in studying past conditions if the soil moisture data are derived from the precipitation data -this must be acknowledged if the extended soil moisture record is deployed for subsequent analysis. Though the original research calibrated the diagnostic soil moisture equation as a daily model, our approach outfits the model as an hourly estimator, using genetic algorithms for calibration [7] rather than the Monte Carlo approach favored by Pan [21]. By calibrating the diagnostic soil moisture equation and validating those algorithms during previous years, this work demonstrates the feasibility of such an approach at USCRN stations, thereby extending the soil moisture record.…”

Section: Introductionmentioning

confidence: 91%

“…[27]). This has been achieved by applying the parameters of soil moisture models calibrated by in situ instruments with co-located precipitation gauges to hydro-climatically and edaphically similar locations that lack such sensors [7] or by interpolating between the sensors of sparse networks maintained by the http://dx.doi.org/10.1016/j.advwatres.2015.02.006 0309-1708/Published by Elsevier Ltd.…”

Section: Introductionmentioning

confidence: 99%

Extending the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

Coopersmith

Bell

Cosh

2015

Advances in Water Resources

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a b s t r a c tSoil moisture estimates are valuable for hydrologic modeling, drought prediction and management, climate change analysis, and agricultural decision support. However, in situ measurements of soil moisture have only become available within the past few decades with additional sensors being installed each year. Comparing newer in situ resources with older resources, previously required a period of crosscalibration, often requiring several years of data collection. One new technique to improve this issue is to develop a methodology to extend the in situ record backwards in time using a soil moisture model and ancillary available data sets. This study will extend the soil moisture record of the U.S. Climate Reference Network (USCRN) by calibrating a precipitation-driven model during the most recent few years when soil moisture data are available and applying that model backwards temporally in years where precipitation data are available and soil moisture data are not. This approach is validated by applying the technique to the Soil Climate Analysis Network (SCAN) where the same model is calibrated in recent years and validated during preceding years at locations with a sufficiently long soil moisture record. Results suggest that if two or three years of concurrent precipitation and soil moisture time series data are available, the calibrated model's parameters can be applied historically to produce RMSE values less than 0.033 m 3 /m 3 . With this approach, in locations characterized by in situ sensors with short or intermittent data records, a model can now be used to fill the relevant gaps and improve the historical record as well.Published by Elsevier Ltd.

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A hydrometeorological approach for probabilistic simulation of monthly soil moisture under bare and crop land conditions

Das

Maity

2015

Water Resources Research

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This study focuses on the probabilistic estimation of monthly soil moisture variation by considering (a) the influence of hydrometeorological forcing to model the temporal variation and (b) the information of Hydrological Soil Groups (HSGs) and Agro-Climatic Zones (ACZs) to capture the spatial variation. The innovative contributions of this study are: (i) development of a Combined Hydro-Meteorological (CHM) index to extract the information of different influencing hydrometeorological variables, (ii) consideration of soil-hydrologic characteristics (through HSGs) and climate regime-based zoning for agriculture (through ACZs), and (iii) quantification of uncertainty range of the estimated soil moisture. Usage of Supervised Principal Component Analysis (SPCA) in the development of the CHM index helps to eliminate the ''curse of dimensionality,'' typically arises in the multivariate analysis. The usage of SPCA also ensures the maximum possible association between the developed CHM index and soil moisture variation. The association between these variables is modeled through their joint distribution which is obtained by using the theory of copula. The proposed approach is also spatially transferable, since the information on HSGs and ACZs is considered. The ''leave-one-out'' cross-validation (LOO-CV) approach is adopted for stations belong to a particular HSG to examine the spatial transferability. The simulated soil moisture values are also compared with a few existing soil moisture data sets, derived from different Land Surface Models (LSMs) or retrieved from different satellite-based missions. The potential of the proposed approach is found to be promising and even applicable to crop land also, though with a lesser degree of efficiency as compared to bare land conditions.

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Using similarity of soil texture and hydroclimate to enhance soil moisture estimation

Cited by 14 publications

References 23 publications

Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions

Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions

Extending the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

A hydrometeorological approach for probabilistic simulation of monthly soil moisture under bare and crop land conditions

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