Comment on hess-2023-131

Published: Sept. 15, 2023

Abstract. Backward probabilities such as backward travel time probability density function for pollutants in natural aquifers/rivers had been used by hydrologists decades water-quality related applications. Reliable calculation of probabilities, however, has challenged non-Fickian pollutant transport dynamics and variability the resolution velocity at study sites. To address these two issues, we built an adjoint model deriving a backward-in-time fractional-derivative equation subordinated to regional flow, developed Lagrangian solver, applied model/solver backtrack various flow systems. The applies subordination reversed field, converts forward-in-time boundaries either absorbing or reflective boundaries, reverses tempered stable define mechanical dispersion. corresponding solver is computationally efficient projecting super-diffusive dispersion along streamlines. Field applications demonstrate that can successfully recover release history, dated groundwater age, spatial location(s) source(s) systems with upscaled constant velocity, non-uniform divergent fine-resolution velocities non-stationary, regional-scale aquifer, where significantly affects characteristics. Caution needed when identifying phase-sensitive (aqueous versus absorbed) source media. Possible extensions are also discussed tested quantifying more complex media, discrete fracture networks.

Language: Английский

---

Comment on hess-2023-131

Published: Sept. 17, 2023

Abstract. Backward probabilities such as backward travel time probability density function for pollutants in natural aquifers/rivers had been used by hydrologists decades water-quality related applications. Reliable calculation of probabilities, however, has challenged non-Fickian pollutant transport dynamics and variability the resolution velocity at study sites. To address these two issues, we built an adjoint model deriving a backward-in-time fractional-derivative equation subordinated to regional flow, developed Lagrangian solver, applied model/solver backtrack various flow systems. The applies subordination reversed field, converts forward-in-time boundaries either absorbing or reflective boundaries, reverses tempered stable define mechanical dispersion. corresponding solver is computationally efficient projecting super-diffusive dispersion along streamlines. Field applications demonstrate that can successfully recover release history, dated groundwater age, spatial location(s) source(s) systems with upscaled constant velocity, non-uniform divergent fine-resolution velocities non-stationary, regional-scale aquifer, where significantly affects characteristics. Caution needed when identifying phase-sensitive (aqueous versus absorbed) source media. Possible extensions are also discussed tested quantifying more complex media, discrete fracture networks.

Language: Английский

---

Reply on RC2

Published: Sept. 22, 2023

Abstract. Backward probabilities such as backward travel time probability density function for pollutants in natural aquifers/rivers had been used by hydrologists decades water-quality related applications. Reliable calculation of probabilities, however, has challenged non-Fickian pollutant transport dynamics and variability the resolution velocity at study sites. To address these two issues, we built an adjoint model deriving a backward-in-time fractional-derivative equation subordinated to regional flow, developed Lagrangian solver, applied model/solver backtrack various flow systems. The applies subordination reversed field, converts forward-in-time boundaries either absorbing or reflective boundaries, reverses tempered stable define mechanical dispersion. corresponding solver is computationally efficient projecting super-diffusive dispersion along streamlines. Field applications demonstrate that can successfully recover release history, dated groundwater age, spatial location(s) source(s) systems with upscaled constant velocity, non-uniform divergent fine-resolution velocities non-stationary, regional-scale aquifer, where significantly affects characteristics. Caution needed when identifying phase-sensitive (aqueous versus absorbed) source media. Possible extensions are also discussed tested quantifying more complex media, discrete fracture networks.

Language: Английский

---

Reply on RC1

Published: Sept. 22, 2023

Abstract. Backward probabilities such as backward travel time probability density function for pollutants in natural aquifers/rivers had been used by hydrologists decades water-quality related applications. Reliable calculation of probabilities, however, has challenged non-Fickian pollutant transport dynamics and variability the resolution velocity at study sites. To address these two issues, we built an adjoint model deriving a backward-in-time fractional-derivative equation subordinated to regional flow, developed Lagrangian solver, applied model/solver backtrack various flow systems. The applies subordination reversed field, converts forward-in-time boundaries either absorbing or reflective boundaries, reverses tempered stable define mechanical dispersion. corresponding solver is computationally efficient projecting super-diffusive dispersion along streamlines. Field applications demonstrate that can successfully recover release history, dated groundwater age, spatial location(s) source(s) systems with upscaled constant velocity, non-uniform divergent fine-resolution velocities non-stationary, regional-scale aquifer, where significantly affects characteristics. Caution needed when identifying phase-sensitive (aqueous versus absorbed) source media. Possible extensions are also discussed tested quantifying more complex media, discrete fracture networks.

Language: Английский

---

Groundwater contamination source identification based on Sobol Sequences-based Sparrow Search Algorithm with a BiLSTM surrogate model

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: Dec. 22, 2022

Abstract In the traditional linked simulation-optimization method, solving optimization model requires massive invoking of groundwater numerical simulation model, which causes a huge computational load. present study, surrogate origin was developed using Bidirectional Long and Short-term Memory neural network method (BiLSTM). Compared with models built by shallow learning methods (BP network) LSTM methods, BiLSTM has higher accuracy better generalization performance while reducing The to solved Sparrow Search Algorithm based on Sobol sequences (SSAS). SSAS enhances diversity initial population sparrows introducing introduces nonlinear inertia weights control search range efficiency. SSA, stronger global ability faster And identifies contamination source location release intensity stably reliably. This study also applied Cholesky decomposition establish Gaussian field for hydraulic conductivity evaluate feasibility method.

Language: Английский

---