Hydrological modelling research at NCAR

CCRN Modelling Workshop, Saskatoon Canada15 September 2014

Martyn Clark (NCAR/RAL)

• Topics▫ Hydrologic model development

WRF-Hydro SUMMA

▫ Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed

▫ Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

Outline

Example modeling framework: WRF-Hydro

WRF-Hydro is a community-based, supported coupling architecture designed to couple multi-scale process models of the atmosphere and terrestrial hydrology

Seek to provide:

1. An ‘Earth Systems-oriented’ capability to perform coupled and uncoupled multi-physics, multi-scale, spatially-continuoushydrometeorological simulations and predictions

2. Fully utilize high-performance computing platforms

3. Leverage existing and emerging standards in data formats and pre-/post-processing workflows

4. An consistent extensible, portable and scalable environment for hydrometeorological prediction, hypothesis testing, sensitivity analysis, data assimilation and observation impact research

Motivation for WRF-Hydro:

• Scientific Needs:▫ Based on community support requests it was evident that

there was a need integrated modeling capabilities for conservative prediction for complete predictions of the water cycle…climate impacts

▫ Need multi-scale framework…bridge atmosphere-hydro application scales….

▫ Need extensible, multi-physics framework…foster experimentation and expose process uncertainty…

1-10’s km 100’s m - 1’s km 1-10’s m

Motivation for WRF-Hydro:

• Prediction System Needs:▫ Need rapid pathway to operational deployment…Seamless

hydrometeorological modeling tools for continuum prediction:

▫ Linkage to ensemble forecasting methodologies…

▫ Utilization of HPC (on both local and distributed/cloud architectures…)

WRF-Hydro Architecture Description:

Basic Concepts• Modes of operation..1-way

vs. 2-way

• Model forcing and feedback components:

Forcings: T, Press, Precip., wind, radiation, humidity, BGC-scalars

Feedbacks: Sensible, latent, momentum, radiation, BGC-scalars

One-way (‘uncoupled’) →

Two-way (‘coupled’) ↔

Output products: Forecasts of water

cycle components

Clouds & WeatherPrecipitationSnowpack : SWESoil MoistureEvapotranspiration

Channel Flows at spatial resolutions of

10s to 100s of meters

Output products: Forecasts of water

cycle components

WRF-Hydro System: Summary

• Open source, community-contributed code

• Readily extensible for multiple physics options

• Multi-scale/multi-resolution

• Supported, documented, multiple test-cases

• Portable/scalable across multiple computing platforms

• Standards based I/O

• Pre-/Post-processing Support

• Topics▫ Hydrologic model development

WRF-Hydro SUMMA

▫ Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed

▫ Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

Outline

The method of multiple working hypotheses

• Scientists often develop “parental affection” for their theories

T.C. Chamberlain

• Chamberlin’s method of

multiple working hypotheses• “…the effort is to bring up into view every

rational explanation of new phenomena…

the investigator then becomes parent of a

family of hypotheses: and, by his parental

relation to all, he is forbidden to fasten his

affections unduly upon any one”

• Chamberlin (1890)

The need for a unified approach to

hydrologic modeling

• Poor understanding of differences among models▫ Model inter-comparison experiments flawed because too many differences

among participating models to meaningfully attribute differences in model behavior to differences in model equations

• Poor understanding of model limitations▫ Most models not constructed to enable a controlled and systematic

approach to model development and improvement

• Disparate (disciplinary) modeling efforts▫ Poor representation of biophysical processes in hydrologic models▫ Community cannot effectively work together, learn from each other, and

accelerate model development

Possibilities for a unified modeling framework

Propositions:1. Most hydrologic modelers share a common understanding

of how the dominant fluxes of water and energy affect the time evolution of thermodynamic and hydrologic states

▫ The collective understanding of the connectivity of state variables and fluxes allows us to formulate general governing model equations in different sub-domains

▫ The governing equations are scale-invariant

2. Differences among models relate toa) the spatial discretization of the model domain;b) the approaches used to parameterize individual

fluxes (including model parameter values); and

c) the methods used to solve the governing model equations.

General schematic of the terrestrial water cycle,

showing dominant fluxes of water and energy

Given these propositions, it is possible to develop a unifying model framework

For example, by defining a single set of governing equations, with the capability to use different

spatial discretizations (e.g., multi-scale grids, HRUs; connected or disconnected), different flux

parameterizations and model parameters, and different time stepping schemes

The unified approach to hydrologic modeling

Governing equations

Hydrology

Thermodynamics

Physical processes

XXX Model options

Evapo-transpiration

Infiltration

Surface runoff

SolverCanopy storage

Aquifer storage

Snow temperature

Snow Unloading

Canopy interception

Canopy evaporation

Water table (TOPMODEL)

Xinanjiang (VIC)

Rooting profile

Green-AmptDarcy

Frozen ground

Richards’Gravity drainage

Multi-domain

Boussinesq

Conceptual aquifer

Instant outflow

Gravity drainage

Capacity limited

Wetted area

Soil water characteristics

Explicit overland flow

Atmospheric stability

Canopy radiation

Net energy fluxes

Beer’s Law

2-stream vis+nir

2-stream broadband

Kinematic

Liquid drainage

Linear above threshold

Soil Stress function

Ball-Berry

Snow drifting

Louis

Obukhov

Melt drip Linear reservoir

Topographic drift factors

Blowing snowmodel

Snowstorage

Soil water content

Canopy temperature

Soil temperature

Phase change

Horizontal redistribution

Water flow through snow

Canopy turbulence

Supercooledliquid water

K-theory

L-theory

Vertical redistribution

16

(1) Model architecture

soil soil

aquifer

(e.g., Noah) (e.g., VIC)

aquifer

soil

soil

(e.g., PRMS) (e.g., DHSVM)

aquifer

soil

- spatial variability and hydrologic connectivity

a) GRU configuration b) HRU configuration

(2) Process parameterizations (and parameters)

Data from research basins: Interception of snow on

the vegetation canopy

• Weighing tree experiments at Umpqua

Different interception formulations

Simulations of canopy interception (Umpqua)

Data from research basins: Interplay

between model representations of

biogeophysics and hydrology

Data from Reynolds Creek, Idaho

Interplay between model parameters

and model parameterizations

Rooting

depth

Hydrologic

connectivity

Soil stress

function

• Topics▫ Hydrologic model development

WRF-Hydro SUMMA

▫ Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed

▫ Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

Outline

Data to enable community modelingMeteorological forcing data for hydrologic models

• Example community activity:

CONUS-wide forcing data▫ e.g., different gridded meteorological

forcing fields (12-km grid) across the

CONUS, 1979-present

• Advantages▫ Integrates data from stations, radar, NWP

models, and satellites

• Opportunities▫ Make more extensive use of data from

stations (additional networks) and NWP

models (finer spatial resolution) in a

formal data fusion framework

▫ Provide quantitative estimates of data

uncertainty (ensemble forcing)

▫ Undertake detailed watershed-scale

evaluation

CLM simulations over the Upper Colorado River basin for

three elevation bands, using two different meteorological

forcing datasets

Ensemble forcing data for the CONUS

• Ensemble of gauge-based forcing fields

• Should consider data uncertainty when evaluating the suitability of different model structures and model parameter sets

CONUS-wide watershed Dataset• Basin Selection

▫ Used GAGES-II, Hydro-climatic data network (HCDN)-2009

671 primarily headwater basins (median size ~330 km2)

▫ Forcing data derived from Daymet(http://daymet.ornl.gov/)

▫ Span a wide range of hydro-climatic conditions

Dryness ratios (PET/P) from 0.2 to 4

• Three spatial configurations of forcing data

▫ Basin mean

▫ Elevation bands

▫ HRUs

Benchmark Simulations

• Calibrated Sacramento/SNOW-17 model

Data to enable community modeling #1Spatial data on network topology and geophysical attributes

• Example community activity: The USGS geospatial fabric▫ Aggregation of NHD-Plus basins into

Hydrologic Response Units (HRUs) and associated stream segments

▫ Parameter values for the USGS PRMS model on each HRU

• Advantages▫ HRU-stream topology can support

multiple hydrologic models

• Opportunities▫ Currently a single set of HRUs: Desire

alternative spatial configurations HRUs based hydrologic similarity Nested HRUs/grids that can explicitly

represent lateral flow within a basin

▫ Currently PRMS-specific parameters: Desire general geophysical attributes Enables different modeling groups to

test alternative approaches to relate attributes to model parameters

Stream segments across the contiguous USA:▫ NHD: 3,000,000 flow lines; 2,600,000 catchments

▫ Geospatial fabric: 56,000 stream segments, 110,000 HRUs

CLM simulations coupled with network-based routing

model configured for the USGS geospatial fabric

• Topics▫ Hydrologic model development

WRF-Hydro SUMMA

▫ Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed

▫ Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

Outline

Motivation

• The land surface is a major

source of hydrologic (hence

drought) predictability.

• In some locations and

seasons, land-surface skill

almost entirely determines

runoff prediction skill.

Skill of Mean 6mo Runoff Forecast

SC

F U

nce

rta

inty

(F

raction

of

Clim

o V

ari

an

ce

)

IHC Uncertainty (Fraction of Climo Variance)

Crystal River Ab Avalance Crk Nr Redstone CO

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

R2

0.0

0.2

0.4

0.6

0.8

1.0

Oct 1

P

CE

rE

Nov 1 Dec 1

0.0

0.2

0.4

0.6

0.8

1.0

Jan 1 Feb 1 Mar 1

0.0

0.2

0.4

0.6

0.8

1.0

Apr 1 May 1 Jun 1

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Jul 1

0.0 0.2 0.4 0.6 0.8 1.0

Aug 1

0.0 0.2 0.4 0.6 0.8 1.0

Sep 1

Wood et al, VESPA, 2014…

Motivation

• Watershed-scale assessment across CONUS shows importance of watershed conditions in forecasting

• skill elasticities > 1

in many locations

•elasticity = unit change flow

forecast skill / unit change

predictor skill

Wood et al, VESPA, 2014…

Diurnal temp. range comparison (WY1980-08)DJF JJAMAM SON

Wet-day fraction comparison (WY1980-08)DJF JJAMAM SON

Biases in model forcing variables

°C

32CLM VIC

Analysis: Water balance

• Hydrologic model development▫ WRF-Hydro▫ SUMMA

• Supporting datasets/models and evaluation framework▫ Probabilistic QPE▫ CONUS-wide testbed

• Hydrologic model applications▫ Impacts of climate change on water resources▫ Streamflow forecasting

Summary: opportunities for collaboration?