Convert experiments and docs to use LandSimulation

Improve ITime support

Modify lowres artifact warning

Convert SoilCanopy global exp to use LandSimulation

Convert integrated_timestep_test to use LandSim

fixed additive inverse gpu broadcast error

fix missing import itt

Additional ITime support

More use LandSim

bucket era5

covert global bucket function

global_bucket_temporal conversion

revert to saveat

use landsim in snowmip

Clean up LandSim time conversions

Remove unused FT arg

saturated energy hydrologyu

Fix kwarg merging

convert soil experiments

WIP

bugfixes

find prob

date bugfix

more megre fixes

rm ref_date

transfer all exps except calib and bench

fluxnet fixes from rebase

Make suggested changes

refactor conversions

bugfixes

Update docs

Add docstrings

docs bugfix

fix docs

drop unused constructor

WIP

Replace t0 with start_date when possible

t0 to start_date in docs

rebase fixes

Unify t0 vs t_start in set_ic

float -> FT

docs fixes

Revert ozark conservation and snowmip

Author: imreddyTeja

docs/Manifest.toml

@@ -1,6 +1,6 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.10.9"
+julia_version = "1.10.10"
 manifest_format = "2.0"
 project_hash = "0e313427333375504d76ddc31cd582bee13918ac"
 
@@ -2186,7 +2186,7 @@ version = "3.2.4+0"
 [[deps.OpenLibm_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
-version = "0.8.1+4"
+version = "0.8.5+0"
 
 [[deps.OpenMPI_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "Hwloc_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "MPIPreferences", "TOML", "Zlib_jll"]

docs/src/tutorials/integrated/soil_canopy_tutorial.jl

@@ -50,6 +50,7 @@ using ClimaLand.Soil
 using ClimaLand.Soil.Biogeochemistry
 using ClimaLand.Canopy
 using ClimaLand.Canopy.PlantHydraulics
+import ClimaLand.Simulations: LandSimulation, solve!
 import ClimaLand
 import ClimaLand.Parameters as LP
 using DelimitedFiles
@@ -65,7 +66,11 @@ earth_param_set = LP.LandParameters(FT);
 site_ID = "US-MOz"
 # Timezone (offset from UTC in hrs)
 time_offset = 7
-start_date = DateTime(2010) + Hour(time_offset)
+start_date = DateTime(2010) + Hour(time_offset) + Day(150) # start mid year
+# Specify the time range and dt value over which to perform the simulation.
+N_days = 100
+end_date = start_date + Day(N_days)
+dt = Float64(30)
 
 # Site latitude and longitude
 lat = FT(38.7441) # degree
@@ -74,11 +79,6 @@ long = FT(-92.2000) # degree
 # Height of the sensor at the site
 atmos_h = FT(32) # m
 
-# Specify the time range and dt value over which to perform the simulation.
-t0 = Float64(150 * 3600 * 24)# start mid year
-N_days = 100
-tf = t0 + Float64(3600 * 24 * N_days)
-dt = Float64(30)
 
 # - We will be using prescribed atmospheric and radiative drivers from the
 #   US-MOz tower, which we read in here. We are using prescribed
@@ -186,8 +186,8 @@ soilco2 = Soil.Biogeochemistry.SoilCO2Model{FT}(domain, drivers);
 # Read in prescribed LAI at the site from global MODIS data
 surface_space = domain.space.surface;
 modis_lai_ncdata_path = ClimaLand.Artifacts.modis_lai_multiyear_paths(;
-    start_date = start_date + Second(t0),
-    end_date = start_date + Second(t0) + Second(tf),
+    start_date = start_date,
+    end_date = end_date,
 );
 LAI = ClimaLand.prescribed_lai_modis(
     modis_lai_ncdata_path,
@@ -273,50 +273,40 @@ canopy = ClimaLand.Canopy.CanopyModel{FT}(
 # Now we can combine the soil and canopy models into a single combined model.
 land = SoilCanopyModel{FT}(soilco2, soil, canopy);
 
-# Now we can initialize the state vectors and model coordinates, and initialize
-# the explicit/implicit tendencies as usual. The Richard's equation time
-# stepping is done implicitly, while the canopy model may be explicitly stepped,
-# so we use an IMEX (implicit-explicit) scheme for the combined model.
-
-Y, p, coords = initialize(land);
-exp_tendency! = make_exp_tendency(land);
-imp_tendency! = make_imp_tendency(land);
-jacobian! = make_jacobian(land);
-jac_kwargs =
-    (; jac_prototype = ClimaLand.FieldMatrixWithSolver(Y), Wfact = jacobian!);
-
 # We need to provide initial conditions for the model:
-Y.soil.ϑ_l = FT(0.4)
-Y.soil.θ_i = FT(0.0)
-T_0 = FT(288.7)
-ρc_s =
-    volumetric_heat_capacity.(
-        Y.soil.ϑ_l,
-        Y.soil.θ_i,
-        land.soil.parameters.ρc_ds,
-        earth_param_set,
-    )
-Y.soil.ρe_int =
-    volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)
-
-Y.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air
-
-canopy_retention_model = canopy.hydraulics.parameters.retention_model
-ψ_stem_0 = FT(-1e5 / 9800)
-ψ_leaf_0 = FT(-2e5 / 9800)
-
-S_l_ini =
-    inverse_water_retention_curve.(
-        canopy_retention_model,
-        [ψ_stem_0, ψ_leaf_0],
-        plant_ν,
-        plant_S_s,
-    )
-for i in 1:2
-    Y.canopy.hydraulics.ϑ_l.:($i) .=
-        augmented_liquid_fraction.(plant_ν, S_l_ini[i])
+function set_ic!(Y, p, t0, model)
+    Y.soil.ϑ_l = FT(0.4)
+    Y.soil.θ_i = FT(0.0)
+    T_0 = FT(288.7)
+    ρc_s =
+        volumetric_heat_capacity.(
+            Y.soil.ϑ_l,
+            Y.soil.θ_i,
+            land.soil.parameters.ρc_ds,
+            earth_param_set,
+        )
+    Y.soil.ρe_int =
+        volumetric_internal_energy.(Y.soil.θ_i, ρc_s, T_0, earth_param_set)
+
+    Y.soilco2.C .= FT(0.000412) # set to atmospheric co2, mol co2 per mol air
+
+    canopy_retention_model = canopy.hydraulics.parameters.retention_model
+    ψ_stem_0 = FT(-1e5 / 9800)
+    ψ_leaf_0 = FT(-2e5 / 9800)
+
+    S_l_ini =
+        inverse_water_retention_curve.(
+            canopy_retention_model,
+            [ψ_stem_0, ψ_leaf_0],
+            plant_ν,
+            plant_S_s,
+        )
+    for i in 1:2
+        Y.canopy.hydraulics.ϑ_l.:($i) .=
+            augmented_liquid_fraction.(plant_ν, S_l_ini[i])
+    end
+    evaluate!(Y.canopy.energy.T, atmos.T, t0)
 end
-evaluate!(Y.canopy.energy.T, atmos.T, t0)
 
 # Choose the timestepper and solver needed for the problem.
 timestepper = CTS.ARS343()
@@ -328,55 +318,45 @@ ode_algo = CTS.IMEXAlgorithm(
     ),
 );
 
-# Now set the initial values for the cache variables for the combined soil and plant model.
-set_initial_cache! = make_set_initial_cache(land)
-set_initial_cache!(p, Y, t0);
 
-# Set the callbacks, which govern
+# Set the callbacks update times, which govern
 # how often we save output, and how often we update
 # the forcing data ("drivers")
 
 n = 120
-saveat = Array(t0:(n * dt):tf)
+saveat = Array(start_date:Second(n * dt):end_date);
 sv = (;
-    t = Array{Float64}(undef, length(saveat)),
+    t = Array{DateTime}(undef, length(saveat)),
     saveval = Array{NamedTuple}(undef, length(saveat)),
 )
 saving_cb = ClimaLand.NonInterpSavingCallback(sv, saveat)
-model_drivers = ClimaLand.get_drivers(land)
-updatefunc = ClimaLand.make_update_drivers(model_drivers)
-updateat = Array(t0:1800:tf)
-driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
-cb = SciMLBase.CallbackSet(driver_cb, saving_cb);
-
-# Carry out the simulation
-prob = SciMLBase.ODEProblem(
-    CTS.ClimaODEFunction(
-        T_exp! = exp_tendency!,
-        T_imp! = SciMLBase.ODEFunction(imp_tendency!; jac_kwargs...),
-        dss! = ClimaLand.dss!,
-    ),
-    Y,
-    (t0, tf),
-    p,
-);
-sol = SciMLBase.solve(
-    prob,
-    ode_algo;
-    dt = dt,
-    callback = cb,
-    adaptive = false,
-    saveat = saveat,
+updateat = Array(start_date:Second(1800):end_date);
+
+# Create the LandSimulation object, which will also create and initialize the state vectors,
+# the cache, the driver callbacks, and set the initial conditions.
+simulation = LandSimulation(
+    start_date,
+    end_date,
+    dt,
+    land;
+    set_ic! = set_ic!,
+    updateat = updateat,
+    solver_kwargs = (; saveat = deepcopy(saveat)),
+    timestepper = ode_algo,
+    user_callbacks = (saving_cb,),
+    diagnostics = (),
 );
 
+sol = solve!(simulation);
+
 # # Plotting
 
 # Now that we have both a soil and canopy model incorporated together, we will
 # show how to plot some model data demonstrating the time series produced from
 # each of these models. As before, we may plot the GPP of the system as well
 # as transpiration showing fluxes in the canopy.
 
-daily = sol.t ./ 3600 ./ 24
+daily = FT.(sol.t) ./ 3600 ./ 24
 model_GPP = [
     parent(sv.saveval[k].canopy.photosynthesis.GPP)[1] for
     k in 1:length(sv.saveval)