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Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region

Nguyen, Hoang Hai,Kim, Hyunglok,Choi, Minha C.M.L. Publications 2017 ADVANCES IN WATER RESOURCES Vol.108 No.-
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Abstract This study was conducted to evaluate the performance of a preliminary soil moisture product estimated from the cosmic-ray neutron probe (CRNP) installed at a densely vegetated and monsoon climate area, namely the Soil Moisture - FDR and Cosmic-ray (SM-FC) site in South Korea. In this study, different calibration approaches, considering soil wetness conditions, were evaluated to select the most appropriate calibration method for deriving the best cosmic-ray soil moisture at the SM-FC site. We tested the potential application of two horizontal-vertical weighting methods, including the linear and non-linear approaches, with regard to the specific characteristics of the SM-FC site. The comparison of the two weighting approaches for in-situ soil moisture measurement suggested that the linear approach provided better performance compared to the non-linear in term of representing field-average soil moisture within the CRNP footprint. Our calibration results revealed that dry condition-based calibration outperformed wet condition-based calibration. The comparison of the cosmic-ray soil moisture utilizing dry condition-based calibration showed reasonable agreement with the linear weighted average soil moisture estimated from the FDR sensor network, with RMSE = 0.035 m3 m−3, and bias = −0.003 m3 m−3; while the worst calibration solution with the wettest conditions had RMSE and bias values of 0.077 m3 m−3 and 0.063 m3 m−3, respectively. The application of a biomass correction significantly improved the cosmic-ray soil moisture product at the SM-FC site, resulting in the reduction of RMSE from 0.035 to 0.013 m3 m−3. A temporal stability analysis was conducted to demonstrate the feasibility of cosmic-ray soil moisture in representing soil moisture for a large heterogeneous SM-FC site. Our temporal stability analysis results indicated the representativeness of cosmic-ray soil moisture over an area with a high degree of heterogeneity, compared to single measurements from FDR stations.
2
Streamflow, stomata, and soil pits: Sources of inference for complex models with fast, robust uncertainty quantification

Dwelle, M. Chase,Kim, Jongho,Sargsyan, Khachik,Ivanov, Valeriy Y. C.M.L. Publications 2019 ADVANCES IN WATER RESOURCES Vol. No.
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Abstract The scale and complexity of environmental and earth systems introduce an array of uncertainties that need to be systematically addressed. In numerical modeling, the ever-increasing complexity of representation of these systems confounds our ability to resolve relevant uncertainties. Specifically, the numerical representation of the governing processes involve many inputs and parameters that have been traditionally treated as deterministic. Considering them as uncertain introduces a large computational burden, stemming from the requirement of a prohibitive number of model simulations. Furthermore, within hydrology, most catchments are sparsely monitored, and there are limited, heterogeneous types of data available to confirm the model’s behavior. Here we present a blueprint of a general approach to uncertainty quantification for complex hydrologic models, taking advantage of recent methodological developments. We rely on polynomial chaos machinery to construct accurate surrogates that can be efficiently sampled for the ecohydrologic model tRIBS-VEGGIE to mimic its behavior with respect to a selected set of quantities of interest. The use of the Bayesian compressive sensing technique allows for fewer evaluations of the computationally expensive tRIBS-VEGGIE. The approach enables inference of model parameters using a set of observed hydrologic quantities including stream discharge, water table depth, evapotranspiration, and soil moisture from the Asu experimental catchment near Manaus, Brazil. The results demonstrate the flexibility of the framework for hydrologic inference in watersheds with sparse, irregular observations of varying accuracy. Significant computational savings imply that problems of greater computational complexity and dimension can be addressed using accurate, computationally cheap surrogates for complex hydrologic models. This will ultimately yield probabilistic representation of model behavior, robust parameter inference, and sensitivity analysis without the need for greater investment in computational resources. Highlights <UL> <LI> A general approach to uncertainty quantification with a complex, process-rich model. </LI> <LI> Construction of efficient surrogate models with Bayesian compressive sensing. </LI> <LI> Robust parametric inference using heterogeneous sources of process-scale data. </LI> <LI> Simultaneous characterization of sensitivity of hydrologic outputs to uncertain variables. </LI> </UL>
3
Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

Kang, Peter K.,Dentz, Marco,Le Borgne, Tanguy,Lee, Seunghak,Juanes, Ruben C.M.L. Publications 2017 ADVANCES IN WATER RESOURCES Vol.106 No.-
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Abstract We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that—even if the Eulerian fluid velocity is steady—the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes. Highlights <UL> <LI> Injection modes have major impact on anomalous transport in DFNs. </LI> <LI> Evolution of the Lagrangian velocity distribution is governed by injection modes. </LI> <LI> Spatial velocity Markov model for variable injection modes. </LI> <LI> Equivalence between spatial Markov model and Boltzmann equation. </LI> </UL>

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