Environmental_transport

Kantha-Clayson & Garratt boundary-layer turbulence parameterization for Lagrangian particle dispersion, as described in NOAA ARL-224 and the HYSPLIT documentation. Computes turbulent velocity standard deviations (sigmaw, sigma_u, sigma_v), Lagrangian vertical time scale (TL_v), Monin-Obukhov length (Lmo), and convective velocity scale (wstar) from surface-layer similarity theory; integrates stochastic Langevin equations for the turbulent velocity components (wprime, uprime_x, uprime_y) and the resulting Lagrangian particle position (lon, lat, lev). The upstream Julia source uses a single @brownians Bw term on all three velocity ODEs (gt-kuxo gap); this ESM migrates the deterministic drift equations only. Migrated from EnvironmentalTransport.jl@a48559c4114eb9f06668ecf9f06235aad20f63f0:src/BoundaryLayerMixingKC.jl:15.

Lagrangian Gaussian puff horizontal dispersion model based on turbulent velocity fluctuation standard deviations (σu). Integrates the growth of horizontal dispersion standard deviations sigma_x and sigma_y from the Kolmogorov-type closure d(sigma)/dt = sqrt(2)*sigma_u, and computes the ground-level centerline concentration per unit mass assuming a top-hat vertical distribution within the lowest grid layer (height dz). Migrated from EnvironmentalTransport.jl@a48559c4 src/GaussianDispersion.jl:378 (GaussianKC @component). When coupled, sigma_u_x and sigma_u_y are provided by BoundaryLayerMixingKC and z_agl is provided by Puff; in standalone mode all three are treated as constant parameters.

Classic Gaussian plume dispersion model parameterized with Pasquill-Gifford-Briggs (PGB) stability classification and dispersion coefficients. Given the current puff position (lon, lat) and height above ground (z_agl), classifies atmospheric stability from near-surface meteorology (wind speed, solar radiation, cloud fraction, temperature lapse), selects Briggs dispersion-curve coefficients, computes the haversine downwind distance from the source, and evaluates horizontal (sigma_h) and vertical (sigma_z) dispersion widths and the ground-level Gaussian concentration (C_gl). Migrated from EnvironmentalTransport.jl@a48559c4 src/GaussianDispersion.jl:83. When coupled with Puff, lon/lat/z_agl are supplied by the Puff component; in standalone mode they are treated as constant parameters.

Smoothed Heaviside surface boundary condition for coupling surface and subsurface water flow, from Wang et al. (2020) (Advances in Water Resources 137, 103499), Eqs. 3 and 5. The component switches a single surface node continuously between a ponded (saturated) boundary and an unsaturated boundary as the pressure head h crosses zero, replacing a hard step with the smooth approximations eta_omega(h) = (1/pi)*atan(h/omega) + 1/2 (smoothed Heaviside, Eq. 5a) and delta_omega(h) = (1/pi)*omega/(omega^2 + h^2) (smoothed Dirac delta, Eq. 5b; delta_omega = d eta_omega/dh). The head obeys the boundary condition d/dt[eta_omega(h)*h] = P - I_infil (Eq. 3), stated as the explicit ODE D(h) = (P - I_infil)/(delta_omega*h + eta_omega). h is the single integrated state; eta_omega and delta_omega are algebraic (observed). Migrated from EnvironmentalTransport.jl@a48559c4 src/surface_runoff.jl:232 (`HeavisideBoundaryCondition` @component). Because the head equation conserves eta_omega(h)*h - (P - I_infil)*t, every ODE-integration test below is checked against the exact conservation law, not merely against a prior solver run.

Lagrangian puff transport model that advects a parcel of matter within a 3-D fluid velocity field defined by a DomainInfo coordinate system. The puff position in three coordinates (lev, x, y) evolves under prescribed velocity components (v_lev, v_x, v_y) scaled by coordinate transforms. Vertical boundaries (glo, ghi) are enforced via ifelse clamping of the vertical velocity with a small offset buffer: upward motion is blocked when lev > ghi−offset and downward motion when lev < glo+offset. Lateral domain boundaries trigger simulation termination (FunctionalAffect gap other:P2-C); the effective lateral boundary distance is set via grid_spacing×buffer_cells offsets from the domain edge (parameter gap other:P3-B). Migrated from EnvironmentalTransport.jl@a48559c4114eb9f06668ecf9f06235aad20f63f0 src/puff.jl:24.

Full 1D Saint-Venant PDE system for surface overland flow: mass conservation ∂h̃/∂t = −∂q/∂l + (P − I) and momentum conservation ∂q/∂t = −∂F/∂l + g·h̃·(S₀ − Sᶠ), where the momentum flux F = q²/h̃ + g·h̃²/2 is an auxiliary (observed) PDE field. Migrated from EnvironmentalTransport.jl@a48559c4 src/surface_runoff.jl:117 (SaintVenantPDE builder). This is a DISCRETIZATION-AGNOSTIC leaf (esm-spec §9.7.10): the spatial fluxes are written with the rewrite-target spatial derivative op `D(·, wrt: l)` and the component imports NO discretization of its own, so it can be composed under different schemes. Its inline tests and examples inject a discretization (form C) — here the sibling adapter `saint_venant_upwind1_inflow.esm` (first-order upwind, per-variable inflow BC) — for that run only; the persisted leaf keeps its spatial `D` intact. The 1D spatial variable l is the along-slope coordinate; the sibling SurfaceRunoff component (surface_runoff_component.esm) is the single-node ODE reduction of this same PDE system with the spatial-divergence terms exposed as input parameters.

Wildfire plume-rise model based on Sofiev et al. (2012). Computes plume top height H_p (m) as a function of atmospheric boundary-layer height H_abl (m), free-troposphere Brunt-Väisälä frequency N_ft (s⁻¹), and fire radiative power P_fr (W) via one empirical algebraic equation: H_p = α·H_abl + β·(P_fr/P_f0)^γ · exp(−δ·N_ft²/N_0²). The five empirical constants (α=0.24, β=170 m, γ=0.35, δ=0.6, N_0=√(2.5×10⁻⁴) s⁻¹) are from Sofiev et al. (2012). In a coupled model H_abl and N_ft are supplied by GEOSFP reanalysis; the additional variable lev_p (plume-top pressure level index) is computed from H_p by the GEOSFP coupler via a Newton-iteration height-to-level mapping. Migrated from EnvironmentalTransport.jl@a48559c4114eb9f06668ecf9f06235aad20f63f0 src/plume_rise/sofiev_2012.jl:14.

Single-node surface-runoff model based on the Saint-Venant overland-flow equations, from Wang et al. (2020), Eq. 1. The component tracks two ODE states at one surface computing node — the ponded flow depth h_tilde and the surface runoff flux per unit width q — plus one algebraic (observed) state, the Manning friction slope S_f. Mass conservation (Eq. 1a) reads dh_tilde/dt = -dqdl + (P - I_infil): the flow depth rises with the net of precipitation P over infiltration I_infil and falls with the spatial divergence of runoff flux dqdl. Momentum conservation (Eq. 1b) reads dq/dt = -dFdl + g*max(h_tilde,h_tilde_0)*(S_0 - S_f): the runoff flux is driven by the imbalance between the surface slope S_0 and the friction slope S_f, minus the spatial divergence of momentum flux dFdl. The friction slope closes the system via Manning's equation S_f = (n_mann*q)^2 / h_tilde^(10/3), written here in non-dimensionalized form so the fractional powers act on dimensionless bases. Because this is a single-node reduction of a spatial PDE, the two spatial-divergence terms dqdl and dFdl are exposed as input parameters to be supplied by a discretization scheme or a coupled model; with both at zero the component reduces to the local mass/momentum response. Migrated from EnvironmentalTransport.jl@a48559c4 src/surface_runoff.jl:35 (`SurfaceRunoff` @component).