Canadian Drought Research and its Contributions to Sustainable Development in Western Canada
The widespread multi-year drought that North America experienced during the 1999-2004 period led to losses
of $6 million in Gross Domestic Product and 41,000 jobs in western Canada. Furthermore, these impacts
occurred in key sectors such as forestry, agriculture and water resources that are critical for western Canada's
development. The processes that initiated and maintained the drought were related to large-scale atmospheric
circulation patterns and were moderated by landscape processes and land-atmosphere interactions. The
prolonged dry conditions had serious regional hydrological effects impacting soil moisture, then runoff and
wetlands, and finally groundwater. The Canadian Foundation for Climate and Atmospheric Sciences (CFCAS)
has been supporting the Drought Research Initiative (DRI) to study this event. Through its network of 15 funded
investigators from six Canadian universities, and collaborators in other universities and government research
laboratories and programs, DRI has characterized the drought's development, examined the critical processes
that initiated, maintained and terminated the drought, and assessed and improved the ability to predict
hydrological drought and its impacts. This presentation provides an overview of the research results obtained
to date from DRI with a special emphasis on those results that relate to economic growth and sustainable
Drought Impact Characterization for the Canadian Prairie Using Remote Sensing and Ecosystem Models
Drought can cause diverse impacts on terrestrial ecosystems and land surface including plant physiology, surface albedo, hydrology, carbon sequestration, etc. Accurate characterization of drought impact is not only required for drought and drought-related studies, it is also an important component in assessing the social- economical impact and in decision making. In this presentation we will discuss the spatial-temporal distributions of the impact of the drought occurred around 2001-2003 over the Canadian prairies. Our method is based on the integration of satellite observations and the ecosystem model EALCO. Parameters used to illustrate the drought impacts include canopy stomatal conductance, vegetation indices (e.g., NDVI), land surface albedo, fraction of absorbed photosynthetically active radiation (fAPAR), evapotranspiration, and plant productivities.
The Association between the Standardized Precipitation Index and Interannual and Interdecadal Oscillations Over Canada
NCEP/NLDAS Drought Monitoring and Prediction
The NCEP Environmental Modeling Center (EMC) collaborated with its CPPA (Climate Prediction Program of the Americas) partners to develop a North American Land Data Assimilation System (NLDAS, http://www.emc.ncep.noaa.gov/mmb/nldas) to monitor and predict the drought over the Continental United States (CONUS). The realtime NLDAS drought monitor, executed daily at NCEP/EMC, including daily, weekly and monthly anomaly and percentile of six fields (soil moisture, snow water equivalent, total runoff, streamflow, evaporation, precipitation) outputted from four land surface models (Noah, Mosaic, SAC, and VIC) on a common 1/8th degree grid using common hourly land surface forcing. The non-precipitation surface forcing is derived from NCEP's retrospective and realtime North American Regional Reanalysis System (NARR). The precipitation forcing is anchored to a daily gauge-only precipitation analysis over CONUS that applies a Parameter-elevation Regressions on Independent Slopes Model (PRISM) correction. This daily precipitation analysis is then temporally disaggregated to hourly precipitation amounts using radar and satellite precipitation. The NARR- based surface downward solar radiation is bias-corrected using seven years (1997-2004) of GOES satellite- derived solar radiation retrievals. The uncoupled ensemble seasonal drought prediction utilizes the following three independent approaches for generating downscaled ensemble seasonal forecasts of surface forcing: (1) Ensemble Streamflow Prediction, (2) CPC Official Seasonal Climate Outlook, and (3) NCEP CFS ensemble dynamical model prediction. For each of these three approaches, twenty ensemble members of forcing realizations are generated using a Bayesian merging algorithm developed by Princeton University. The three forcing methods are then used to drive the VIC model in seasonal prediction mode over thirteen large river basins that together span the CONUS domain. One to nine month ensemble seasonal prediction products such as air temperature, precipitation, soil moisture, snowpack, total runoff, evaporation and streamflow are derived for each forcing approach. The anomalies and percentiles of the predicted products for each approach may be used for CONUS drought prediction. This system is executed at the beginning of each month and distributes its products by the 10th of each month. The prediction products are evaluated using corresponding monitoring products for the VIC model and are compared with the prediction products from other research groups (e.g., University of Washington at Seattle, NASA Goddard) in the CONUS.
Spatial Variability of Actual Evapotranspiration Across the Canadian Prairies During Drought
The spatial variability of actual evapotranspiration was examined over the Canadian Prairies during a recent drought. This was done by integrating physically-based modeling, remote sensing observations, and near surface climate data. A critical consideration is the lack of spatially distributed observations of plant available soil moisture. As such, a combination ET model based on the complementary relationship theory between actual to potential evaporation which does not require soil moisture information (e.g. Granger and Gray) is necessary. Two dates in a recent severe drought were analysed; July 6, 2001, and July 2, 2002. These dates had good available data and show differing spatial pattern to the drought. MODIS visible and thermal wavelengths and AVHRR (visible) were used for deriving albedo, surface temperature, and aerodynamic roughness. Environment Canada climate stations and gridded North American Regional Reanalysis model output were used for hourly meteorological variables. Radiation is problematic as it is rarely measured in the Prairies. Estimates of mid-day net radiation were indexed to a reference location (AmeriFlux short grass prairie site at Lethbridge, Alberta) from the mid-day incoming short and longwave radiation components, and one- time-of-day remote sensing images of albedo and surface temperature. A measured value of mean daily net radiation from the reference site was then distributed over the entire study area as a function of the mid-day radiation index. The resulting spatial distributions of ET for drought and non-drought areas are instructive as the variance of ET changes as the landscape dries out or wets up.
Utilizing a Multi-sensor Satellite Time Series in Real-time Drought Monitoring Across the United States
Drought events frequently occur in the United States and result in billions of dollars of damage, often exceeding
the costs of other weather-related hazards. Monitoring drought conditions is a necessary function of
government agencies at State, Federal, and local levels as part of decision support for planning, risk
management, and hazard mitigation activities.
In partnership with the National Drought Mitigation Center, the National Aeronautics and Space Administration,
the U.S. Department of Agriculture Risk Management Agency, and the High Plains Regional Climate Center,
the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center is developing an
operational drought decision support tool with relatively higher spatial resolution (1 km2) than traditional
drought monitoring maps. The Vegetation Drought Response Index (VegDRI) is a geospatial model that
integrates in-situ climate, satellite, and biophysical data, providing an indicator of canopy vegetation condition
The satellite data ingested into VegDRI are collected from daily polar-orbiting earth observing systems
including the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging
Spectroradiometer (MODIS). These instruments provide regular synoptic measurements of land surface
conditions in near-real time. In VegDRI, remote sensing data provide proxy information about the vegetation
status (or health) related to climate-induced changes and are integrated with traditional drought indices based
on in-situ climate observations. When merged, the two complementary sources of drought-related data
provide a comprehensive and detailed picture of drought impacts across the landscape.
A 20-year history of AVHRR time-series data produced over the U.S. at a 1 km2 resolution provides a historical
context for monitoring drought conditions. However, the MODIS instrument has improved sensor
characteristics designed for land surface monitoring. To seamlessly extend the multi-sensor NDVI data
record, inter-sensor NDVI continuity/compatibility has been examined and appropriate adjustments, or multi-
sensor translations, have been made to datasets by means of cross-calibration. The data translation
equations were derived from an overlapping period of observations with geometric mean regressions to treat
variations in both AVHRR and MODIS datasets equally.
Since standard MODIS products are often not delivered quickly enough to aid operational decisions, USGS has
designed a system based on a direct broadcast model called eMODIS. The eMODIS system at EROS
provides the near-real time MODIS vegetation index data needed to supply VegDRI products on a schedule that
meets the needs of the U.S. drought monitoring community, largely driven by the U.S. Drought Monitor and the
National Integrated Drought Information System. The requirements of the community include providing synoptic
indicators in a timely fashion and in an easy-to-interpret format for incorporation into the weekly U.S. Drought
Monitor map process.
Estimating probabilistic rainfall and food security outcomes for eastern and southern Africa
Since 1980, the number of undernourished people in eastern and southern Africa has more than doubled. Rural development stalled and rural poverty expanded during the 1990s. Population growth remains high, and declining per-capita agricultural capacity retards development. In September of 2008, Ethiopia, Kenya, Djibouti, and Somalia faced high or extreme conditions of food insecurity caused by repeated droughts and rapid food price inflation. In this talk we present research, performed for the US Agency for International Development on probabilistic projections of rainfall and food security trends for eastern and southern Africa. Analyses of station data and satellite-based estimates of precipitation have identified another problematic trend: main growing- season rainfall has diminished by ~15% in food-insecure countries clustered along the western rim of the Indian Ocean. Occurring during the main growing seasons in poor countries dependent on rain-fed agriculture, these declines constitute a long term danger to subsistence agricultural and pastoral livelihoods. Tracing moisture deficits upstream to an anthropogenically-induced warming Indian Ocean leads us to conclude that further rainfall declines are likely. We present analyses suggesting that warming in the central Indian Ocean disrupts onshore moisture transports, reducing continental rainfall. Thus, late 20th century Indian Ocean warming has probably already produced societally dangerous climate change by creating drought and social disruption in some of the world's most fragile food economies. We quantify the potential impacts of the observed precipitation and agricultural capacity trends by modeling millions of undernourished people as a function of rainfall, population, cultivated area, and seed and fertilizer use. Persistence of current trends may result in a 50% increase in undernourished people. On the other hand, modest increases in per-capita agricultural productivity could more than offset the observed precipitation declines. Increased investment in agricultural development would help mitigate climate change while decreasing rural poverty and vulnerability.
Impacts of Regional Scale Climate Variability on the Occurrence and Severity of Drought Events
Drought is an extreme event which is related to soil moisture patterns, precipitation and runoff. The severity and frequency of drought occurrence is related to regional climate variability. One common indicator used to express climate variability it the El Niño/Southern Oscillation (ENSO), which is related to anomalies in surface temperature and precipitation. In the present work, the effect of ENSO on drought occurrence and its severity in the Midwestern United States is examined using a reconstruction of long term soil moisture, precipitation and daily temperature extremes. The following objectives are addressed: (i) reconstruct soil moisture data using gridded daily climatological forcings for the period of 1916-2007 using the Variable Infiltration Capacity (VIC) model, (ii) estimate changes and trends associated with climate and water balance variables, (iii) identify regional scale historic (1916-2007) extreme drought events and their correlation with ENSO events and (iv) to characterize and classify extreme drought events on the basis of their correlation with ENSO and variations of water balance components. The VIC model was calibrated for monthly stream flow at five streamflow gauging stations and was then evaluated for soil moisture and its persistence, soil temperature and soil heat fluxes. After calibration and evaluation the VIC model was implemented for the full historic (1916-2007) period across the study domain. The non parametric Mann-Kendall method was used to estimate trends using the gridded climatology of precipitation and air temperature variables. Trends were also estimated for annual anomalies of soil moisture variables, snow water equivalent and total runoff. Historic agricultural drought, meteorological drought and hydrological drought events were identified using soil moisture percentiles; the Standard Precipitation Index (SPI) and the Standard Runoff Index (SRI), respectively. ENSO phase (warm event, cold event and base event) was estimated using an index based on tropical Pacific sea surface temperature (SST) anomaly. Results indicate that precipitation, minimum air temperature, total column soil moisture and runoff have experienced upward trends while maximum air temperature, frozen soil total moisture and snow water equivalent experienced downward trends. Furthermore the decreasing trends were significant for frozen soil moisture in the study domain. Results also demonstrated that historic drought events were successfully reconstructed using the VIC model. Finally, the effects of climate variability, as defined by ENSO state, on the frequency of drought occurrence and on its magnitude were evaluated.