OVERVIEW 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 topographically-

informed, 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. Overall, TopoMet consists of 4 main components: The main weather stations data sources are: • GHCN-Daily; 37,674 potential input stations • SNOTEL; 778 potential input stations

Main Equations

(1)

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

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.

(2) 𝒆 𝒍𝒐𝒏, 𝒍𝒂𝒕 =

𝑾𝒊𝒆𝒊𝒏𝒊=𝟎

𝑾𝒊𝒏𝒊=𝟎

, 𝑛 is the number of surrounding stations, 𝑊𝑖 is the weight for station 𝑖, 𝑒𝑖 is the

residual for station 𝑖.

(3) 𝑷𝑶𝑷(𝒍𝒐𝒏, 𝒍𝒂𝒕) =

𝑾𝒊𝒑𝒐𝒊𝒏𝒊=𝟎

𝑾𝒊𝒏𝒊=𝟎

, 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) = 𝑾𝒊

𝒑𝒅𝒊𝒑𝒎𝒊

𝒏𝒊=𝟎

𝑾𝒊𝒏𝒊=𝟎

, 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.

MODEL PARAMETER OPTIMIZATION AND VALIDATION • 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.

ID# GC31A-1017

An Empirical Topoclimate Model for Regional and Landscape Scale Assessments of Water Balance and Related Ecologic/Hydrologic Processes in Complex Topography Jared W. Oyler*, Steven W. Running |University of Montana, Numerical Terradynamic Simulation Group | *jared.oyler@ntsg.umt.edu

Missing/Flagged Value Infilling and

Data Record Extension

Observation Quality Assurance

SRAD and VPD Estimation via MtClim4

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 Input Stations Post-QA Variable Input Stations Post-QA

TMIN 12,837 PRCP 20,930

TMAX 12,838 OVERALL (unique stations) 20,998

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.

TOPOCLIMATE MODELING AND INTERPOLATION • 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 Rockies Pacific NW

Great Basin

Pacific SW

Desert SW

S Rockies/ CO Plateau

N Plains

Central Plains

S Plains

Great Lakes

Prairie Peninsula Aplcns /

Cumber- land Plt.

Ozarks Complex

NE

SE

Mid Atlantic

Atlantic Neotropical (merged with SE)

1

2

3

4 5

6 7

8

9

10

11

12 13

14

15

16

Source: http://www.neoninc.org/science/domains

NEON ECO-CLIMATIC DOMAINS

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.

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.

RESULTS: NORTHERN ROCKIES

-High: 19.0 °C -Low: -0.48 °C

TMAX

-High: 4.99 °C -Low: -10.55 °C

TMIN

TopoMet: 1971-2000 Annual Normals

-High: 241 cm -Low: 17 cm

PRCP

< -4

-4 - -3

-3 - -2

-2 - -1

-1 - 1

1 - 2

2 - 3

3 - 4

> 4

°C difference

> 100

75 – 100

50 – 75

25 – 50

-10 – 10

10 – 25

-25 – -10

-50 – -25

< -50

% difference

TopoMet Minus PRISM: 1971-2000 Normals 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 1971- 2000 TMIN, TMAX, and PRCP normals are shown with their relation-ship to PRISM values. 1948-2011 MAE by station is also presented. 0.5-1.0

1.0-1.5

1.5-2.0

> 2.0

0.0-0.5 °C

0-10

10-25

50-75

25-50

%

TopoMet: 1948-2011 Normals Cross Validation MAE by Station

TMAX

TMIN

PRCP

0.5-1.0

1.0-1.5

1.5-2.0

> 2.0

0.0-0.5 °C

TMAX

TMIN

PRCP

PRISM vs TopoMet Scatterplots: 1971-2000 Normals

TMIN, TMAX, PRCP Topoclimate Modeling

and Interpolation

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 empirical-statistical 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.

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.

REFERENCES [1] Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., Pasteris, P.P., 2008. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031-2064. [2] Thornton, P.E., Running, S.W., White, M.A., 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology 190, 214–251. [3] Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965-1978. [4] Thornton, P.E., Hasenauer, H., White, M.A., 2000. Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria. Agricultural and Forest Meteorology 104, 255-271. [5] Durre, I., Menne, M.J., Gleason, B.E., Houston, T.G., Vose, R.S., 2010. Comprehensive Automated Quality Assurance of Daily Surface Observations. J. Appl. Meteor. Climatol. 49, 1615-1633. [6] Fotheringham, A.S., Brunsdon, C., Charlton, M., 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, 1st ed. Wiley. [7] Frei, C., Schär, C., 1998. A precipitation climatology of the Alps from high-resolution rain-gauge observations. International Journal of Climatology 18, 873–900. [8] Holden, Z.A., Abatzoglou, J.T., Luce, C.H., Baggett, L.S., 2011. Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain. Agricultural and Forest Meteorology 151, 1066-1073.

Mean = 1.03 °C

Mean = 0.52 °C

Mean = 12%

TMAX TMIN PRCP

< -4

-4 - -3

-3 - -2

-2 - -1

-1 - 1

1 - 2

2 - 3

3 - 4

> 4

°C difference

cm

cm