An Empirical Topoclimate Model for Regional and Landscape Scale Assessments of
Water Balance and Related Ecologic/Hydrologic Processes in Complex Topography
ID#
GC31A-1017
Jared W. Oyler*, Steven W. Running |University of Montana, Numerical Terradynamic Simulation Group | *jared.oyler@ntsg.umt.edu
OVERVIEW
MODEL PARAMETER OPTIMIZATION AND VALIDATION
While several high resolution conterminous United States (CONUS) spatial climate datasets are currently available
(e.g.—PRISM1, Daymet2, WordClim3) they often do not contain all the necessary climate variables needed to drive
process-based ecologic/hydrologic models for assessing regional effects of climatic water deficit/balance, do not
have the required temporal extent or resolution, or do not entirely account for critical topographic influences.
Additionally, many of the models used to build these datasets are closed source, making it difficult for end users
to assess limitations and uncertainty in regions of interest. Therefore, an empirical topoclimate model, TopoMet,
is developed with the following objectives:
1) to use existing weather station observations of temperature and precipitation to create a topographicallyinformed, daily 1948-2011 spatial climatology (30-arcsec resolution) for the CONUS with the required
forcing variables for ecologic/hydrologic process-based models (daily TMIN, TMAX, PRCP, SRAD, VPD);
2) to provide reliable and accessible quantification of model uncertainty and limitations;
3) to provide an open platform whereby end users can collaborate and contribute to ongoing development.
TMIN, TMAX, PRCP
Observation
Overall, TopoMet consists of 4 main components:
Topoclimate Modeling
Quality Assurance
and Interpolation
The main weather stations data sources are:
Missing/Flagged
• GHCN-Daily; 37,674 potential input stations
SRAD and VPD
Value Infilling and
• SNOTEL; 778 potential input stations
Estimation via MtClim4
Data Record Extension
• Model parameterization and validation were performed separately for each of the
National Ecological Observatory Network’s (NEON) eco-climatic CONUS domains.
• Model error was assessed via cross-validation by comparing a station’s values with
those interpolated when the respective station was withheld from the dataset.
• Numerical optimization was used to separately select model parameter values for
each NEON domain via minimization of cross-validation error.
RESULTS: NORTHERN ROCKIES
OBSERVATION QUALITY ASSURANCE
• Consisting of various checks for duplicate data, outliers, and numerous internal, temporal, and spatial
inconsistencies, the automated GHCN-Daily TMIN, TMAX, and PRCP QA procedures5 were recoded and
applied to the SNOTEL data.
• Subtracting missing and QA-flagged values, only stations with at least 3 years of data for TMIN, TMAX, or
PRCP were selected for input to the infilling and data record extension component.
Variable
TMIN
Input Stations Post-QA
12,837
TMAX
12,838
Optimized Model Parameters for Each NEON Domain
𝑵𝒎𝒊𝒏 The min number of neighboring stations to use for an interpolation point.
𝑾𝒇𝒅 Importance factor by which distance weights are modified.
𝑾𝒇𝒛 Importance factor by which elevation weights are modified.
𝑾𝒇𝒕𝒅 Importance factor by which topographic dissection weights are modified.
𝑺𝒛 Radius (km) by which the input DEM grid is smoothed.
𝑺𝒕𝒅 Range of grid window sizes over which to calculate topographic dissection.
𝑷𝒄𝒓𝒊𝒕 Threshold at which PRCP occurrence probability indicates a day with PRCP.
Variable
PRCP
OVERALL (unique stations)
Input Stations Post-QA
20,930
The N. Rockies domain (#11 above) has a relatively sparse station
network within a topographically-complex landscape. It provides a
good test case example for assessing TopoMet’s performance. To
facilitate comparison to existing PRISM products, TopoMet’s 19712000 TMIN, TMAX, and PRCP normals are shown with their relationship to PRISM values. 1948-2011 MAE by station is also presented.
NEON ECO-CLIMATIC DOMAINS
Pacific
NW
15
16
N Rockies
N Plains
11
8
Great
Basin
Pacific
SW
Great Lakes
14
S Rockies/
CO Plateau
13
12
Desert SW
Central
Plains
9
Prairie 5
Peninsula
Aplcns /
2
Cumber- Mid
land Plt. Atlantic
7
6
Ozarks Complex
3 SE
10
Atlantic
Neotropical
Source:
http://www.neoninc.org/science/domains
(merged with SE)
TopoMet: 1948-2011 Normals
Cross Validation MAE by Station
TopoMet Minus PRISM:
1971-2000 Normals
TMAX
°C
difference
< -4
TopoMet: 1971-2000 Annual Normals
-4 - -3
°C
0.0-0.5
-3 - -2
0.5-1.0
-2 - -1
1.0-1.5
1.5-2.0
> 2.0
-1 - 1
20,998
4
S Plains
TMAX
1-2
TMAX
NE 1
Mean absolute cross-validation
Error (MAE) by NEON eco-climatic
domain. Temperature MAE is in
relation to observed mean value
while PRCP MAE is % error in
relation to the sum of total PRCP
1948-2011. Overall, western
domains with sparser stations
and more complex topography
have larger error.
2-3
3-4
>4
MISSING/FLAGGED VALUE INFILLING AND DATA RECORD EXTENSION
• All station records were made serially complete from 1948-2011 by modeling a target station’s relationship
with neighboring observations. TMIN and TMAX records were infilled/extended with a spatial regression
approach5. PRCP records were infilled/extended by modeling both daily occurrence and amount. PRCP
occurrence was estimated by calculating a PRCP occurrence probability while PRCP amount was estimated
using a normal-ratio method.
TMIN
TOPOCLIMATE MODELING AND INTERPOLATION
-Low: -0.48 °C
(3)
residual for station 𝑖.
𝑷𝑶𝑷(𝒍𝒐𝒏, 𝒍𝒂𝒕) =
𝒏
𝒊=𝟎 𝑾𝒊 𝒑𝒐𝒊
𝒏 𝑾
𝒊=𝟎 𝒊
-4 - -3
0.5-1.0
-3 - -2
1.0-1.5
1.5-2.0
> 2.0
2-3
PRCP
TMIN
3-4
>4
PRCP
Mean = 1.03 °C
PRCP
%
difference
< -50
%
-50 – -25
0-10
-25 – -10
-10 – 10
10-25
25-50
10 – 25
50-75
25 – 50
-Low: 17 cm
-High: 241 cm -Low: -10.55 °C
-High: 4.99 °C
50 – 75
75 – 100
PRISM vs TopoMet Scatterplots: 1971-2000 Normals
> 100
Mean = 12%
𝒑𝒅𝒊
𝒏
𝑾
𝒊=𝟎 𝒊 𝒑𝒎
𝒊
𝒏 𝑾
𝒊=𝟎 𝒊
, 𝑃𝑅𝐶𝑃 is the estimated PRCP amount at point 𝑙𝑜𝑛, 𝑙𝑎𝑡 , 𝑝𝑑𝑖 is the PRCP
amount at station 𝑖 and 𝑝𝑚𝑖 is the total PRCP amount at station 𝑖 for the given month.
SUMMARY, DATA AVAILABILITY, FUTURE WORK
• TopoMet is an empirical topoclimate model for generating daily
spatial climate data in complex terrain similar to PRISM and Daymet,
but seeking to address their limitations.
• TopoMet is not meant as a replacement for other models, but can be
used in tandem with datasets like PRISM for climate sensitivity/
uncertainty analyses of various ecologic/hydrologic models within a
region and to disaggregate monthly PRISM data to a daily timestep.
• TopoMet is built within the open NASA Earth Exchange (NEX)
platform where end users will have access to a TopoMet project blog,
wiki, performance analyses, and algorithm codes, which will enable
extensive collaboration and evaluation of the TopoMet system.
• Future work will concentrate on improved TMIN and PRCP
estimations via the incorporation of reanalysis datasets, short-term
high-density temperature logger data, and more advanced empiricalstatistical downscaling techniques.
• Information on data availability can be obtained on the TopoMet NEX
website (https://c3.nasa.gov/nex/projects/1277/) or by contacting
the authors.
REFERENCES
, 𝑃𝑂𝑃 is the estimated PRCP occurrence at point (𝑙𝑜𝑛, 𝑙𝑎𝑡), 𝑝𝑜𝑖 is the PRCP
occurrence (1=PRCP, 0= no PRCP) at station 𝑖.
(4) 𝑷𝑹𝑪𝑷(𝒍𝒐𝒏, 𝒍𝒂𝒕) =
°C
0.0-0.5
1-2
TMAX
TMIN
PRCP
cm
(2)
, 𝑛 is the number of surrounding stations, 𝑊𝑖 is the weight for station 𝑖, 𝑒𝑖 is the
°C
difference
< -4
-1 - 1
-High: 19.0 °C
Main Equations
𝒕 𝒍𝒐𝒏, 𝒍𝒂𝒕 = 𝜷𝟎 𝒍𝒐𝒏, 𝒍𝒂𝒕 + 𝜷𝟏 𝒍𝒐𝒏, 𝒍𝒂𝒕 𝒙 + 𝜷𝟐 𝒍𝒐𝒏, 𝒍𝒂𝒕 𝒚 + 𝜷𝟑 𝒍𝒐𝒏, 𝒍𝒂𝒕 𝒛 + 𝒆 𝒍𝒐𝒏, 𝒍𝒂𝒕 ,
𝑡 is the estimated daily temperature for the point at (𝑙𝑜𝑛, 𝑙𝑎𝑡), 𝛽0 is the intercept, 𝛽1 is the coefficient for
(1)
the 𝑥 direction (longitude), 𝛽2 is the coefficient for the 𝑦 direction (latitude), 𝛽3 is the coefficient for the 𝑧
direction (elevation), 𝑒 is the residual.
𝒆 𝒍𝒐𝒏, 𝒍𝒂𝒕 =
TMIN
-2 - -1
• The overall topoclimate model is a daily geographically weighted regression (GWR)6 in the form of
equation 1. The GWR residual in equation 1 is estimated with equation 2.
• The surrounding station observations of an estimation point 𝑙𝑜𝑛, 𝑙𝑎𝑡 are used to build the daily GWR and
are weighted by distance (SYMAP weighting function7), similarity in elevation, and similarity in
topographic dissection (𝑇𝐷; a measure of a point’s position in relation to surrounding terrain). It has been
shown that 𝑇𝐷 can be effective in capturing the influences of cold air drainage8 (TMIN inversions).
• For PRCP, the GMR procedure is applied at a monthly time step to estimate the total PRCP for the month.
• Daily PRCP occurrence probability (POP)2 is then estimated by equation 3.
• If POP is > a specified threshold for a day, daily PRCP amount is then estimated by equation 4.
𝒏
𝒊=𝟎 𝑾𝒊 𝒆𝒊
𝒏 𝑾
𝒊=𝟎 𝒊
Mean = 0.52 °C
cm
Given the easier predictability of TMAX lapse rates, agreement between TopoMet
and PRISM is very high and station MAE is relatively low. The different TMIN
spatial patterns and greater error are likely the result of inversions common in
complex terrain. During optimization, topographic dissection was shown to be an
important parameter for predicting TMIN, but not TMAX.
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