Regional water cycle studies:Current activities and future plans

Water System Retreat, NCAR14 January 2015

Martyn Clark, Naoki Mizukami, Andy Newman, Pablo Mendoza, Andy Wood (NCAR)

Luis Samaniego (UFZ)

Bart Nijssen (UW)

Outline• Motivation

▫ Large inter-model differences in representation of the land component of the water cycle

▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty

• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing

• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex

process-based models make adequate use of the data on meteorology, vegetation, soils and topography?

▫ Use of simple models (statistical, bucket) as benchmarks

• Summary

Basins of interest for this study

The Colorado Headwaters Region offers a major renewable water supply in the southwestern United States, with approximately 85 % of the streamflow coming from snowmelt. Hence, we conduct this research over three basins located in this area:

- Yampa at Steamboat Springs

- East at Almont

- Animas at Durango

Simulations in the Colorado Headwaters

How does hydrologic model choice affect the magnitude and direction of climate change signal?

4

Uncalibrated model simulations Calibrated model simulations

Uncalibrated models: Climate change signal in Noah (↑ET and ↑Runoff) differs from the rest of models (↑ET and ↓Runoff).

After calibration, signal direction from Noah-LSM switches to ↑ET and ↓Runoff.

Inter-model agreement does not necessarily improve in terms of magnitude and direction.

Results: hydrology

CONUS-scale simulationsinterplay between downscaling methodology and hydrology simulations

UCO

MR

AR

GBCA

LCO RIO

NLDAS 12km domain- 205 x 462 grid boxes

Understanding different sources of uncertainty

GCM initial conditions

EmissionsScenario(s)

Global ClimateModel(s)

Downscalingmethod (s)

HydrologicModel

Structure(s)

HydrologicModel

Parameter(s)projection

PD

F

projection

PD

F

projection

PD

F

projection

PD

F

projection

PD

F

projection

PD

F

Combined uncertainty

projection

PD

F

66

Inter-model difference in canopy evaporation Submitted to JHM

Impact on Annual water balance – statistical downscaling methods and models

8Extreme runoff – inter-forcing differenceHigh flow

20yr Daily Max. flow [mm/day]

Low flow 7Q10 [mm/day]

9Extreme runoff – Inter-model difference

Low flow estimate is more dependent on models

High flow 20yr Daily Max. flow [mm/day]

Low flow 7Q10 [mm/day]

10SWE – Inter-model difference vs. inter-forcing difference

Inter-model differences are larger than inter-forcing

Inter-model comparison in peak SWE [mm]

Inter-forcing comparison in peak SWE [mm]

Outline• Motivation

▫ Large inter-model differences in representation of the land component of the water cycle

▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty

• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing

• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex

process-based models make adequate use of the data on meteorology, vegetation, soils and topography?

▫ Use of simple models (statistical, bucket) as benchmarks

• Summary

What are the key issues that constrain progress in model development?• Unsatisfactory process representation

▫ Missing processes (e.g., spatial heterogeneity, groundwater)▫ Dated/simplistic representation of some processes

• Limited capabilities to isolate and evaluate competing model hypotheses▫ The failure of MIPs and the need for a controlled approach to model

evaluation

• Insufficient recognition of the interplay between different modeling decisions▫ The interplay between model parameters and process

parameterizations▫ Interactions among different model components

• Inadequate attention to model implementation▫ Impact of operator-splitting approximations in complex models▫ Bad behavior of conceptual hydrology models

• Ignorance of uncertainty in models and data▫ To what extent does data uncertainty constrain our capabilities to

effectively discriminate among competing modeling approaches?▫ Are we so “over-confident” in some parts of our model that we may

reject modeling advances in another part of the model?

Modeling approachPropositions: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

Clark et al. (WRR 2011); Clark et al. (under review)

soil soil

aquiferaquifer

soilsoil

aquifer

soil

c) Column organization

a) GRUs b) HRUs

i) lump ii) grid

iii) polygon

The Structure for Unifying Multiple Modeling Alternatives (SUMMA)

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

LouisObukhov

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

Supercooled liquid water

K-theory

L-theory

Vertical redistribution

Example simulationsImpact of model parameters, process parameterizations and model architecture on simulations of transpiration

Stomatal resistance parameterizations

Rooting profilesSubsurface flow

among soil columns

Outline• Motivation

▫ Large inter-model differences in representation of the land component of the water cycle

▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty

• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing

• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex

process-based models make adequate use of the data on meteorology, vegetation, soils and topography?

▫ Use of simple models (statistical, bucket) as benchmarks

• Summary

The parameter estimation problem…• Many CONUS-scale applications based on

very uncertain a-priori model parameters

• Basin-by-basin calibration efforts provide patchwork-quilt of model parameters (no physical realism)

• Traditional model calibration leads to the “right answers for the wrong reasons” (compensatory effects)

Solutions?• One of the “unsolved” problems of large-

scale hydrology

• Need systematic evaluation of existing methods (currently limited understanding of what works)

• Need to reduce dimensionality of the parameter estimation problem (multiple signatures)

• Need to represent multi-scale behavior

Initial computational infrastructure

Continental-scale parameter estimation

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Soil Data (e.g., STATSGO space)

βi

Adjust TF coefficients

Model Layers (e.g., 3 Layers)

𝑃 𝑖

Horisontal upscaling

Model Params (e.g., 3 Layers)

𝑃 𝑖  

Vertical upscaling

Model Params (e.g., STATSGO space)

Pi

(Pedo-) transfer function

simulations

Individual basins; donor catchments

21

A priori parameter NLDAS Calibrated parameters single basin

Max Soil Moisture Storage in bottom layer

Calibrated parameters – region

Nearest Neighbor

Calibrated multiplier• Cρbulk(basini)• Cd1(basini)• Cd2(basini)• Cztot(basini)i = 1,…15

Estimation with default TF coefficients Estimation with calibrated TF coefficients

Max Soil Moisture Storage in bottom layer

Calibrated TF coef. • aρbulk

• ad1

• ad2

• aztot

Transfer function calibration

Outline• Motivation

▫ Large inter-model differences in representation of the land component of the water cycle

▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty

• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing

• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex

process-based models make adequate use of the data on meteorology, vegetation, soils and topography?

▫ Use of simple models (statistical, bucket) as benchmarks

• Summary

Uncertainties in model forcing data

• N-LDAS vs. Maurer▫ Gridded meteorological forcing

fields (12-km grid) across the CONUS, 1979-present

• Opportunities to improve these products▫ 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)

CLM simulations over the Upper Colorado River basin for three elevation bands, using two different meteorological forcing datasetsMizukami et al. (JHM, 2013)

See Andy Newman’s presentationon ensemble forcing (next)

RoutingCLM simulations coupled with network-based routing model configured for the USGS geospatial fabric

Outline• Motivation

▫ Large inter-model differences in representation of the land component of the water cycle

▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty

• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing

• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex

process-based models make adequate use of the data on meteorology, vegetation, soils and topography?

▫ Use of simple models (statistical, bucket) as benchmarks

• Summary

Simple models as benchmarks•The NERD approach (statistical models as

benchmarks)•Bucket-style models as a statistical model

Can more complex models extract the same information content from the available data on meteorology, vegetation, soils and topography?

If not, why not?

What work do we need to do in order to ensure that physically realistic models perform better than models with inadequate process representations?

Newman et al., HESS (in press)

Model constraints?

Hard coded parameters are the most sensitive ones

Outline• Motivation

▫ Large inter-model differences in representation of the land component of the water cycle

▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty

• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing

• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex

process-based models make adequate use of the data on meteorology, vegetation, soils and topography?

▫ Use of simple models (statistical, bucket) as benchmarks

• Summary

Summary and future plans • Work underway for a continental-scale implementation of the

flexible hydrologic modeling approach, improving continental-scale parameter estimates and improving characterization of forcing uncertainty▫ Applications: Improved representation of hydrologic processes in

climate risk assessments and in streamflow prediction systems

• Work started on improving representation of hydrologic processes in CLM▫ Collaboration with CUAHSI

• Collaboration with the Canadians (University of Saskaskewan)▫ Cold season hydrologic processes; interest in WRF-Hydro

• New focal areas: Alaska and Hawaii▫ Extend methods developed in the CONUS to “more challenging”

modeling environments

• Funding from Bureau of Reclamation, US Army Corps of Engineers, NASA, NOAA, NSF, and (hopefully) DOE