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MAPP Webinar


Date/Time Title
February 12, 2013
1:00 PM – 2:30 PM ET
Understanding and Monitoring Drought
  Speakers and Topics: Christopher Hain (NOAA National Environmental Satellite, Data, and Information Services)
Development of a Thermal Infrared Based Framework for Mapping Drought: Regional Applications and Progress towards a Global-scale Implementation

Christa Peters-Lidard (NASA Goddard Space Flight Center)
The Impact of Soil Moisture and Snow Assimilation on North American Land Data Assimilation System (NLDAS) Drought Metrics

Amir AghaKouchak (University of California at Irvine)
Multi-Index Drought Monitoring: A Global Drought GeoServer

Andy Wood (NOAA Northwest River Forecast Center)
A framework of metrics to evaluate new approaches for the monitoring and prediction of drought

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Christopher Hain — Development of a Thermal Infrared Based Framework for Mapping Drought: Regional Applications and Progress towards a Global-scale Implementation — The presentation will address the development of a multi-scale drought monitoring tool based on remotely sensed estimates of evapotranspiration. The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET (fPET), generated with the thermal remote sensing based Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model and associated disaggregation algorithm, DisALEXI demonstrated that ESI maps over the continental US (CONUS) show good correspondence with standard drought metrics and with patterns of antecedent precipitation, but can be generated at significantly higher spatial resolution due to a limited reliance on ground observations. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall, for example in areas where drought impacts are being mitigated by intense irrigation or shallow water tables. As such, the ESI is a measure of actual stress rather than potential for stress, and has physical relevance to projected crop development.  Because precipitation is not used in construction of the ESI, this index provides an independent assessment of drought conditions and will have particular utility for real-time monitoring in regions with sparse rainfall data or significant delays in meteorological reporting. ESI results will be shown over North America, Europe, and Africa.

One additional ongoing application of ESI is the development of a real-time system for optimal assimilation of thermal infrared (TIR) and microwave (MV) soil moisture (SM) and insertion of near real-time vegetation fraction (GVF) into the NLDAS Noah LSM towards the improvement of LSM-based drought monitoring. It has been demonstrated that diagnostic information about SM and evapotranspiration (ET) from MW and TIR remote sensing can reduce SM drifts in LSMs such as Noah. The two retrievals have been shown to be quite complementary: TIR provides relatively high spatial (down to 100 m) and low temporal resolution (due to cloud cover) retrievals over a wide range of GVF, while MW provides relatively low spatial (25-60 km) and high temporal resolution (can retrieve through cloud cover), but only over areas with low GVF. Furthermore, MW retrievals are sensitive to SM only in the first few centimeters of the soil profile, while TIR provides information about SM conditions integrated over the full root-zone, reflected in the observed canopy temperature.

Finally, a brief overview will be provided highlighting the pathway to a global implementation of the ALEXI system and the operationalization of ALEXI over North America at NOAA/NESDIS.

Christa Peters-Lidard — The Impact of Soil Moisture and Snow Assimilation on North American Land Data Assimilation System (NLDAS) Drought Metrics — We will show results from NASA’s Land Information System (LIS) configured using the North American Land Data Assimilation System (NLDAS) inputs, to demonstrate the impacts of soil moisture and snow product assimilation on drought assessment.  NLDAS Phase 2 has produced over 34 years (Jan 1979 to present) of hourly land-surface meteorology (produced by best available observations and reanalyses) and surface states and fluxes (produced by land-surface models, LSMs).  For our soil moisture assimilation experiments, we utilize surface soil moisture retrievals from the European Space Agency’s Essential Climate Variable (ESA ECV) product.  ESA ECV uses C-band scatterometers and multi-frequency radiometers to produce soil moisture from 1978 to 2011.  For the snow assimilation experiments, we utilize the snow depth products from SMMR and SSM/I and bias-corrected snow depth from AMSR-E.  Together, these snow products also span from 1978 to 2011.  Simulations were made using the LIS framework both with and without assimilating these products in the Noah LSM.

The Land surface Verification Toolkit (LVT), also developed at NASA, will be used to show improvements as a result of data assimilation to simulated soil moisture, snowpack, and streamflow.  To quantify the impact of assimilation on drought metrics, we will also show changes to standard NLDAS drought monitoring products, such as soil moisture percentiles, as well as traditional drought metrics such as the Standardized Runoff Index (SRI) and the Standardized Soil Wetness Index (SSWI).  Comparisons are also made to the area drought extent product produced by the U.S. Drought Monitor.

Amir AghaKouchak — Multi-Index Drought Monitoring: A Global Drought GeoServer — Development of reliable monitoring and prediction indices are fundamental to drought monitoring and prediction. Numerous indices have been developed for drought monitoring based on various indicator variables (e.g., precipitation, soil moisture, water storage). Defining droughts based on a single variable (e.g., precipitation, soil moisture or runoff) may not be sufficient for reliable risk assessment and decision making. In this presentation, a multivariate multi-index drought monitoring framework is suggested using the concept of joint empirical probability. The suggested Multivariate Standardized Drought Index (MSDI) combines Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) probabilistically for drought characterization. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of droughts. MSDI is compared with SPI and SSI for characterizing drought condition across the globe using NASA MERRA-Land data. The results revealed that MSDI indicated drought onsite and termination based on the combination of all two indices with onsite time being dominated by SPI and drought enduring being more similar to SSI behavior. Overall, MSDI seems to be a reasonable model for combining multiple indices probabilistically. This paper presents an online drought portal (GeoServer), designed to provide access to global drought data based on the MSDI. The objective of the drought GeoServer is to provide interactive access to a composite multi-index drought data.

Andy Wood — A framework of metrics to evaluate new approaches for the monitoring and prediction of drought — In recent decades, NIDIS and other initiatives have supported a wide range of efforts to improve drought monitoring and prediction.  In the earth sciences, researchers have demonstrated and operationalized improved climate and land surface modeling, remote sensing applied to the Earth’s water and energy balance, the depiction of contemporary moisture anomalies via new statistical indices, and other capabilities.  Yet published assessments of these capabilities do not typically highlight their performance in assessing and forecasting drought specifically, as opposed to their performance across varied hydro-climate states, making it difficult to ascertain the value of these advances from a drought perspective.  The NOAA MAPP Drought Task Force has tackled this challenge by developing a framework of metrics for evaluating drought monitoring and prediction science and highlighting recent US drought case studies.  This presentation describes the Drought Task Force and the challenges of such an assessment, and provides metrics framework examples related to land surface monitoring and operational streamflow prediction.



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