runmicro: Runs micro- or mesoclimate model

In ilyamaclean/microclima: microclimate modelling with R

Runs micro- or mesoclimate model

Description

runmicro produces a high-resolution dataset of downscaled temperatures for one time interval

Usage

runmicro(params, netrad, wind, continuous = FALSE)

Arguments

params	a data.frame of parameter estimates as produced by fitmicro()
netrad	a SpatRaster object, two-dimensional array or matrix of downscaled net radiation as produced by shortwaveveg() - longwaveveg() or shortwavetopo() - longwavetopo().
wind	a SpatRaster object, two-dimensional array or matrix of downscaled wind speed, as produced by reference wind speed x the output of windcoef().
continuous	an optional logical value indicating whether the model was fitted by treating wind speed as a continuous variable.

Details

If netrad is a SpatRaster object, a SpatRaster object is returned. If modelling mesoclimate, it is assumed that altitudinal, coastal and cold-air drainage effects have already been accounted for in the calculation of reference temperature (see example). It is assumed that the most important energy fluxes determining near-surface temperature are those due to the radiation flux and convection that occurs at the surface–atmosphere boundary. Heat fluxes into the soil and latent heat exchange are considered to be small and proportional to the net radiation flux, and the heat capacity of the vegetation is considered to be small so that, compared to the time-scale of the model, surface temperature rapidly reach equilibrium. In consequence, the difference between the near-ground temperature and the ambient temperature is a linear function of netrad. The gradient of this linear relationship is a measure of the thermal coupling of the surface to the atmosphere. If this relationship is applied to vegetation, assuming the canopy to act like a surface, while air density and the specific heat of air at constant pressure are constant, the slope varies as a function of a wind speed factor, such that different slope values are assumed under high and low wind conditions.

Value

a SpatRaster object, two-dimensional array or matrix of temperature anomolies from reference temperature, normally in ºC, but units depend on those used in fitmicro().

See Also

fitmicro()

Examples

library(terra)
# =======================================================================
# Run microclimate model for 2010-05-24 11:00 (one of the warmest hours)
# =======================================================================
params <- fitmicro(microfitdata)
netrad <- netshort1m - netlong1m
tempanom <- runmicro(params, netrad, wind1m)
reftemp <- rast(temp100[,,564])
ext(reftemp) <- ext(rast(dtm1m))
reftemp <- resample(reftemp, rast(dtm1m))
temps <- tempanom + as.matrix(reftemp, wide = TRUE)
plot(if_raster(temps, dtm1m), main =
     expression(paste("Temperature ",(~degree~C))))

# ======================================================================
# Run mesoclimate model for 2010-05-01 11:00 from first principles
# ======================================================================
# --------------------------
# Resample SpatRast:
# --------------------------
resamplerast <- function(a, ro) {
  r <- rast(a)
  ext(r) <- c(-5.40, -5.00, 49.90, 50.15)
  crs(r) <- "+init=epsg:4326"
  r <- project(r, crs(ro))
  r <- resample(r, ro)
  as.matrix(r, wide = TRUE)
}
# --------------------------
# Resample SpatRast: 24 hours
# --------------------------
get24 <- function(a) {
  ai <- rast(a[,,2881:2904])
  ext(ai) <- c(-5.40, -5.00, 49.90, 50.15)
  crs(ai) <- "+init=epsg:4326"
  ai <- project(ai, crs(rast(dtm1km)))
  ao <- resample(ai, rast(dtm1km))
  as.array(ao)
}

# ----------------------------
# Derive hourly temperatures
# ----------------------------
tmax <- tas[,,121] + dtr[,,121] / 2
tmin <- tas[,,121] - dtr[,,121] / 2
tme <- as.POSIXct(c(0:364) * 24 * 3600, origin="2010-01-01", tz = "GMT")
out <- as.POSIXct(c(0:23) * 3600, origin="2010-05-01", tz = "GMT")
h <- arrayspline(huss, tme, out = out)
p <- arrayspline(pres, tme, out = out)
n <- get24(cfc)
dni <- get24(dnirad)
dif <- get24(difrad)
jd <- julday(2010, 5 , 1)
tc <- h[,,1] * NA
lr <- h[,,1] * NA # Also calculates lapse rate
for (i in 1:19) {
  for (j in 1:22) {
    if (is.na(tmax[i,j]) == F)
    {
      ht <- hourlytemp(jd, em = NA, h[i, j, ], n[i, j, ], p[i, j, ], dni[i, j, ],
                       dif[i, j, ], tmin[i,j], tmax[i,j], 50.02, -5.20)
      tc[i, j]<- ht[12]
      lr[i, j] <- lapserate(tc[i, j], h[i, j, 12], p[i, j, 12])
    }
  }
}
# ----------------------------
# Calculate coastal effects
# ----------------------------
sst <- 10.771
dT <- if_raster(sst - tc, dtm1km)
lsw <- if_raster(landsearatios[,,28], dtm100m)  # upwind
lsa <- if_raster(apply(landsearatios, c(1, 2), mean), dtm100m) # mean, all directions
dTf <- coastalTps(dT, lsw, lsa)

# ------------------------------
# Calculate altitudinal effects
# ------------------------------
lrr <- if_raster(lr, rast(dtm1km))
lrr <- resample (lrr, rast(dtm100m))
tc <- sst - dTf + lrr * rast(dtm100m)

# ------------------------------
# Downscale radiation
# ------------------------------
dni <- resamplerast(dnirad[,,2891], rast(dtm100m))
dif <- resamplerast(difrad[,,2891], rast(dtm100m))
n <- resamplerast(cfc[,,2891], rast(dtm100m))
h <- resample(if_raster(h[,,12], dtm1km), rast(dtm100m))
p <- resample(if_raster(p[,,12], dtm1km), rast(dtm100m))
sv <- skyviewtopo(rast(dtm100m))
netshort <- shortwavetopo(dni, dif, jd, 11, dtm = rast(dtm100m), svf = sv)
netlong <- longwavetopo(h, tc, p, n, sv)
netrad <- netshort - netlong

# ------------------
# Downscale wind
# ------------------
ws <- array(windheight(wind2010$wind10m, 10, 1), dim = c(1, 1, 8760))
wh <- arrayspline(ws, as.POSIXct(wind2010$obs_time), 6, "2010-05-01 11:00")
ws <- windcoef(rast(dtm100m), 270, reso = 100) * wh

# ------------------
# Fit and run model
# ------------------
params <- fitmicro(mesofitdata, continuous = T)
anom <- runmicro(params, netrad, ws, continuous = T)
tc <- tc + anom
plot(mask(tc, rast(dtm100m)), main =
     expression(paste("Mesoclimate temperature ",(~degree~C))))

ilyamaclean/microclima documentation built on Sept. 5, 2024, 8:37 p.m.