1. Progress in drought monitoring and prediction
Dennis P. Lettenmaier
Department of Geography
University of California, Los Angeles
International Top-Level Forum on Engineering Science
and Technology Development Strategy
Nanjing Hydraulic Research Institute
May 29, 2015
Droughts as a natural hazard

2. Economic losses ($B) and loss of life in recent U.S. droughts
Source: NOAA Billion-dollar weather/climate disasters

3. Drought characteristics
“creeping disaster” – most evolve slowly over time
Typically cover large areas
Hence losses can be large
Definitions vary (meteorological, agricultural, hydrological)

4. Outline of this talk Characterizing droughts Drought predictability
Reconstructing drought
Flash Drought
Concluding thoughts

5. 1. Characterizing droughts

6. Severity-Area-Duration Analysis
Depth-Area-Duration technique, widely used in probable maximum precipitation analysis
Replace depth with measure of drought severity
S : severity, ΣP : cumulative percentile of soil moisture (runoff), t : event duration
from Andreadis et al., 2005

7. Soil Moisture S-A-D Curves
(a) 3 Month Duration
(b) 12 Month Duration
(c) 24 Month Duration
(d) 48 Month Duration 1
0.65 7
Severity
Severity
Area (million sq. km)
Area (million sq. km)
Severity
Severity
Area (million sq. km)
Area (million sq. km)

8. http://www. hydro. washington

14. 2. Drought predictability

15. Long-term climatology
Simulations
now
Monitoring
Long-term climatology
long-year spinup
model climatology
recent
future
model spin-up
Real-time data
forecast
Forecast Products
streamflow
soil moisture
runoff
snowpack
important point(s):
after bias correcting and downscaling the climate model forecasts, the procedure for producing hydrologic forecasts is as follows:
we spin up the hydrologic model to the start of the forecast using observed met. data (from 2 sources: NCDC cooperator stations through 3-4 months before the start of the forecasts, then LDAS 1/8 degree gridded forcings thereafter).
The GSM forecasts comprise 2 sets of ensembles, one for climatology and one for the forecast. The climatology ensemble yields a distribution of the conditions we’ve seen over the period , while the forecast ensemble yields the distribution of the conditions we might see for the next 6 months. Although the climatology ensemble is nominally unbiased against a simulated climatology based on observed met. data (rather than bias-corrected, downscaled GSM met. forcings), we compare the forecast and GSM climatology so that any unforeseen biases (resulting, perhaps, from the downscaling method) occur in both climatology and forecast. Eventually this cautionary step may be eliminated, and we’ll compare directly to the simulated observed climatology.
at the end of the spin-up period and one month before month 1 (out of 6) of the forecasts, we save the hydrologic model state. The state is then used for initializing the forecast runs. Through the first month, the model runs on observed data to the last date possible, then switches to the forecast data. Usually, we process the observed forcings up through the 15th to 25th of this initialization month, then the forecast forcing data carries the run forward for the remaining days in the month, and throughout the following 6 month forecast period. Note, the state files used for the climatology runs correspond to the spin-up associated with the particular year (out of ) from which the climatology ensemble member is drawn.
the spin-up period captures the antecedent land surface hydrologic conditions for the forecast period: in the Columbia basin, the primary field of interest is snow water equivalent.
forecast products are spatial (distributed soil moisture, runoff, snowpack (swe), etc.), and spatial runoff + baseflow is routed to produce streamflow at specific points, the inflow nodes for a management model, perhaps. A

16. Experiment 1: Ensemble Streamflow Experiment 2: Reverse-ESP
Approach: Experimental setup for partitioning the role of IHCs and FS in seasonal hydrologic forecasts
Experiment 1: Ensemble Streamflow
Prediction (ESP)
Experiment 2: Reverse-ESP
(revESP)
Known IHC, Climatological forcings.
Derives skill solely from knowledge of the IHCs.
Known forcings (i.e. perfect climate forecasts skill) and climatological IHCs.
Derives skill solely from climate forecasts.
Wood and Lettenmaier, 2008

17. Experiment 1: Ensemble Streamflow Experiment 2: Reverse-ESP
Approach: Experimental setup for partitioning the role of IHCs and FS in seasonal hydrologic forecasts
Experiment 1: Ensemble Streamflow
Prediction (ESP)
Experiment 2: Reverse-ESP
(revESP)
Known IHC, Climatological forcings.
Derives skill solely from knowledge of the IHCs.
Known forcings (i.e. perfect climate forecasts skill) and climatological IHCs.
Derives skill solely from climate forecasts.
Wood and Lettenmaier, 2008

18. Variation of RMSE ratio for cumulative runoff forecast
Climate forecast skill
dominates
IHCs dominate
Shukla and Lettenmaier, 2011, HESS

19. Shukla and Lettenmaier, 2011, HESS.1 2 3 5 4 6
Plot of the maximum lead (in months) at which RMSE Ratio [RMSE(ESP)/RMSE(revESP)] is less than 1, for cumulative runoff forecasts, initialized on the beginning of each month.

20. Soil moisture forecast
(a) Initialization: 01 January
(b) Initialization: 01 July
If you prefer showing a spatial map like this: feel free to use this. I have similar figures for SWE and CR predictability as well.
Climate FS dominates
IHCs dominate
RMSE ratio of mean monthly soil moisture forecast at lead-1, -3 and -6 months for the forecast initialized on (a) 01 January and (b) 01 July. (masked areas had RMSE resp and esp =0)

21. ESP (Ensemble streamflow prediction) vs NMME_VIC Fcsts
Starting date
ESP- P T inputs taken from randomly selected observations
Run VIC with observed P and Tsurf
Jan 1,1915 from UW
Jan 1, 1979
IC s
Fcst forward
Feb 5
Feb 6---
NMME_VIC :forcings were taken from error corrected T P from CGCM
Both ESP and NMME_VIC have the same initial conditions, but ESP has no climate forecast information of P and Tsurf

22. Fcst skill for SM Lead-1 : correlation >0.8 (WOW!!!)
Lead-3: Over the western interior dry region, the fcsts are still skillful for all seasons and the North Central for January (high skill regions)
Low skill regions are circled

23. Differences btw NMME-ESP for Lead-3
No significant differences for Lead-1 and Lead-2
Only October and January forecasts pass the Livezey Chen test
Differences are in the areas that the skill is low and dynamically active areas
Oct fcsts are helped by skillful P forecasts

24. 3. Reconstructing drought

25. China Meteorology Administration (Wang 2005)

26. Motivation
Frequency agricultural drought occurrence in China during past decades.
Widely used, but link to direct observations (e.g., of soil moisture) is weak – hence reliance on indirect methods, such as PDSI.
Need for reproducible basis for identifying drought-affected regions.
Land surface model representations of soil moisture offer an alternative means for estimating severity, frequency, duration, and variability of current droughts, and linking them to the climatology of observed droughts.
An ensemble approach to help reduce the uncertainty due to the bias of single model for reproducing soil moisture.

27. Models VIC: Variable Infiltration Capacity Model (Liang et al. 1994)
CLM3.5: Community Land Model version (Oleson et al. 2007)
NOAH LSM: NCEP, OSU, Air Force, Hydrol.
research lab (Mitchell et al. 1994, Chen and Mitchell 1996)
CLM-VIC: a hybrid of CLM 3.5 with the VIC soil hydrology scheme (Wang et al. 2008).

28. 13-month moving averages of normalized soil moisture indices from the ensemble average
Top panel: Location of regions (“China” refers to the entire domain).
Right: 13-month moving averages of normalized soil moisture indices from the ensemble average (dark lines) and range of individual models (gray shading). Inter-model variations are generally greatest in arid areas, and least in humid areas.

29. Up:22%
Dn:37%
Up:2.5%
Dn:2.3%
Up:6.1%
Up:6.5%
Dn:1.1%
Annual trends in a) soil moisture percentile; b) drought severity; c) drought duration; and d) drought frequency for The construction of time series of drought duration, severity and frequency are described in section 4e). The blue “+” indicates increasing trends, and the red “-” indicates decreasing trends.

30. Can we do longer reconstructions (to ~1870; including major Chinese famines of late 19th and early 20th centuries) using 20 C Reanalysis?
Visual courtesy Aihai Wang, CAS/IAP

31. 4. Flash drought

32. Two types of flash droughts
1. Heat wave flash drought—
Initialized by heat waves
High Tair => High positive ET anomalies=> Low SM%
P plays important but indirect role
2. P deficit flash drought
Initialized by P deficits
P deficits => Low SM% => Negative ET anomalies=> high sensible heat=> High Tair
P plays a direct role

33. Frequency of occurrence – heat wave flash drought
1. FOC is only 4-6% of the total record
2. Maxima over the North Central and the Ohio Valley and the PNW
3. SAC has the least events

34. Downward trends on the occurrence of the heat wave flash drought

35. P deficit flash drought-example
P deficits increase
ET<0 and increase
Tair increases

36. Frequency for the P deficit flash drought
Larger % than the heat wave flash drought
A band of maxima are located over the southern U.S with the center at the southern Plains
Less events over the maxima of the FOC for the heat wave flash drought but nor mutually exclusive

37. 5. Concluding thoughts
Drought monitoring systems based on land surface models are well developed, and serve as reasonable surrogates for hydrologic and agricultural droughts
Drought prediction capabilities are limited by weak predictive capability of global atmospheric models – most of the predictive skill is in hydrologic initial conditions
Drought monitoring systems have demonstrated ability to reconstruct extreme historic droughts over the period of instrumental record. For the pre-instrumental period, capability has yet to be confirmed.
Flash drought identification and predictability is currently evolving. Given the rapid evolution of these events (over time periods ~1 week or so) for which there may be precipitation forecast skill, some predictability may be possible.