Drought and Model Consensus:Reconstructing and Monitoring Drought in the US with Multiple Models

Theodore J. Bohn1, Aihui Wang2, and Dennis P. Lettenmaier1

1University of Washington, Seattle, Washington, USA; 2Institute for Atmospheric Physics, Beijing, China

UW Water Center Annual Review of Research, Seattle, WA, USA, Feb 14, 2008

Eastern US•Strong agreement•Smaller uncertainty

Western US•Poor agreement•Larger uncertainty

Models used in this study•VIC•CLM3.5•NOAH•Catchment•Sacramento/Snow-17 (SAC)•Hybrid of CLM3.5 and VIC (CLM-VIC)

Typical Large-scale Hydrological Models•Break land surface into large (1/8-degree), flat grid cells with uniform soil properties

•Continental US = 3322 1/8-degree grid cells•Most have vegetation layer, some allow multiple vegetation “tiles”•Multi-layer soil column•Input = daily or sub-daily meteorological data•Solve water, energy balances on sub-daily time step•Represent small-scale vegetation and soil dynamics with parameterizations

Retrospective Simulation: 1920-2003

Domain: Continental USExamine Average Monthly Soil Moisture

How do models compare in time?

How do models compare in space?

How do models compare in time?

Wide spread here = large uncertainty

Narrow spread here = more confidence

All models agree that drier-than-normal conditions prevailed in the West and North during the 1930s and that the South and Southeast experienced several dry periods.

Assessing Model AgreementCorrelations among modelsGive indication of model agreement

Average Model Correlation

Monitoring Current Conditions

Severity-Area-Duration (SAD) Analysis

Challenge: how to assess drought across the entire US in the face of sparse observations?

Sparse observations•Soil moisture sampling sites are sparse•Remote sensing techniques only scrape the surface (literally)•Historical records are limited in length

Large-scale models fill in the gaps•A model can transform our (relatively) dense network of meteorological observations into soil moisture estimates across the US

Problem: how to assess errors?•Without observations to constrain model estimates, how can we trust the model?

Solution: use multiple models•Mean of results tends to cancel random errors•Spread of results gives estimate of uncertainty

Large-scale Hydrological Models

Example: VIC

How to Compare/Combine Model Results?Problem: Soil column has no common definition•Depth of soil column considered by a given model is arbitrary•Soil moisture storage capacities and dynamics differ widely among models

Method 1 (Ensemble-0)•Express each model’s monthly soil moisture SM(y,m) as percentile of its distribution over entire period (1920-2003) for that month, using Weibull plotting position•Average the percentiles to get ensemble average percentile SMens(y,m)•Note: this method de-emphasizes extreme values

This Study

Response time (months)

Is this pattern realistic?•Normally we think of drier soils, such as found in the West, as having shorter “memory” due to action of evaporation (“erasing” memory) than wetter soils, such as found in the East•This seems to contradict the pattern observed in our models•However, here we are computing the “memory” of percentiles (which have a different reference point each month) rather than “memory” of absolute soil moisture. Thus, we lose seasonality; this may help explain difference•Or, models may simply assume soils that are too deep in the West

Conclusions

Simulated Average Monthly Soil Moisture (40.25N, 112.25W),1920-2003

Driest moisture level of CLM3.5 is wetter than wettest level of any other model

Solution: “normalize” the soil moistures•Express as percentile of historical distribution (% of “normal”)

Method 2 (Ensemble-1)•Normalize each model’s monthly soil moisture

SM*(y,m) = (SM(y,m) - SMmin(m)) / (SMmax(m) - SMmin(m))where y = year; m = month

•Average SM*(y,m) across all models to get ensemble average SMens*(y,m)

•Express SMens*(y,m) as percentile of distribution over entire period (1920-2003) for that month, using Weibull plotting position

Little agreement among models over Colorado Plateau

General agreement among models over Great Plains

Ensemble average only exhibits extreme values where models exhibit strong agreement•Especially true of Ensemble-0

General agreement among models over South

Example: droughts of 1930s (dust bowl) and 1950s

CLM3.5 soil moisture changes much more slowly than other models

Soil Moisture Response Time•Define soil moisture response time = lag (months) at which autocorrelation of soil moisture (percentile) falls below 1/e•Measure of soil moisture “memory”

Models have different response times to climate variations•All models except Catchment, and both ensembles, exhibit much longer “memory” of soil moisture percentiles in the West than in the East•CLM3.5 has response times of several years in much of West•Ensembles have response times that are intermediate compared to range of model response times•Result: models and ensembles may tend to make dry/wet periods last longer in the West than in the East

Correlation and Response Times•In general, long response times (West) correspond to poor model agreement•Response times may affect uncertainty

SAD Analysis•Identify regions of contiguous dry grid cells•Categorize these regions by average moisture percentile, area, and duration

Most models (and ensembles) agree that:•The 1930s drought was characterized by large areas of high severity but short duration•The 1950s drought was characterized by large areas of lower severity but longer duration•More recent droughts were characterized by smaller areas of high severity and moderate duration

VIC SACCLM3.5

NOAH Catchment ENS-0 ENS-1

CLM-VIC

Benefits in a real-time monitoring system•Ensemble captures evolution of drought in SE US•Ensemble damps out disagreement in Western US•Different response times result in robustness against “spurious” meteorological observations

•An ensemble of multiple land surface models can reconstruct historical droughts across the continental US

•Ensemble average is more trustworthy than individual model

•Cancels out disagreements among models

•Multi-model ensembles can give us insight into uncertainty

•Variation of uncertainty with location•Greater uncertainty in Western US, more confidence in Eastern US

•Sources of uncertainty•Meteorology vs. model parameters•Long response times (West) correspond to poor model agreement•Model response times may affect uncertainty

•Multi-model ensembles can be useful in real-time monitoring

•Diversity of response times causes damped response to spurious real-time meteorological observations

All models and both ensembles successfully capture the droughts of the 1930s and 1950s•Agreement with historic observations as to general areas affected•Agreement among models as to general shape and extent of affected area•Some disagreement as to details

Area-averaged soil moisture percentiles

Example: Recent drought in Southeast US

Soil Moisture Percentiles wrt (1920-2003)2007-Nov-01 2007-Dec-01 2008-Jan-01

Ensemble-0

Real-time monitoring system•Drive models with real-time meteorological observations•Express daily soil moistures as percentiles of historical monthly distributions