Facilitating the Use of Drought Early Warning Information through Interactions with Agricultural Stakeholders

Otkin, Jason A.; Shafer, Mark; Svoboda, Mark D.; Wardlow, Brian D.; Anderson, Martha C.; Hain, Christopher; Basara, Jeffrey B.

doi:10.1175/bams-d-14-00219.1

Cited by 71 publications

(49 citation statements)

References 5 publications

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“…All of the three BDIs can well capture the western Russian drought of 2010 that was very long and intensive, and caused serious damage to the environment and economy (Kogan et al, ; Mu et al, ) with BDI_s showing a relatively weak signal. And both 2011 Texas drought and the US‐Great Plains drought in summer 2012 (Hoerling et al, ; Otkin et al, ) are reasonably represented by the three BDIs, while major differences are noted in 2012 with BDI_s and BDI_w missing drought signals in the Eastern and Southern U.S.…”

Section: Evaluation Of Drought Events Using Bdismentioning

confidence: 99%

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

Yin

Zhan

Hain

et al. 2018

Water Resources Research

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With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The data sets used in this paper include the Noah land surface model‐based SM estimations, Atmosphere‐Land‐Exchange‐Inverse model‐based Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEM‐derived root‐mean‐square‐errors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSE‐weighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010‐Russia, 2011‐USA, 2013‐New Zealand droughts and other reported agricultural drought occurrences over the 2009–2014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts.

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Section: Evaluation Of Drought Events Using Bdismentioning

confidence: 99%

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

Yin

Zhan

Hain

et al. 2018

Water Resources Research

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“…Given the drought monitoring capabilities of the ESI and the desire within the agricultural and natural resources communities for sub-seasonal drought intensification forecasts during the growing season (Otkin et al, 2015b), it is prudent to explore adaptation of the statistical method developed by Lorenz et al (2017a, b) so that it can be used to predict changes in the ESI rather than the US Drought Monitor because the ESI is a more direct measure of vegetation health. Such efforts would align with the increasing interest within the forecasting community to produce sub-seasonal forecasts that can fill the gap between medium-range weather forecasts and seasonal forecasts (Vitart et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States

Otkin

Zhong

Lorenz

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. This study uses correlation analyses to explore relationships between the satellite-derived Evaporative Stress Index (ESI) – which depicts standardized anomalies in an actual to reference evapotranspiration (ET) fraction – and various land and atmospheric variables that impact ET. Correlations between the ESI and forcing variable anomalies calculated over sub-seasonal timescales were computed at weekly and monthly intervals during the growing season. Overall, the results revealed that the ESI is most strongly correlated to anomalies in soil moisture and 2 m dew point depression. Correlations between the ESI and precipitation were also large across most of the US; however, they were typically smaller than those associated with soil moisture and vapor pressure deficit. In contrast, correlations were much weaker for air temperature, wind speed, and radiation across most of the US, with the exception of the south-central US where correlations were large for all variables at some point during the growing season. Together, these results indicate that changes in soil moisture and near-surface atmospheric vapor pressure deficit are better predictors of the ESI than precipitation and air temperature anomalies are by themselves. Large regional and seasonal dependencies were also observed for each forcing variable. Each of the regional and seasonal correlation patterns were similar for ESI anomalies computed over 2-, 4-, and 8-week time periods; however, the maximum correlations increased as the ESI anomalies were computed over longer time periods and also shifted toward longer averaging periods for the forcing variables.

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“…This threshold value is between the D 2 and D 3 drought intensity classes and can be inferred in terms of DNLSWI, which is approximately 60-62 days. Many agencies have used USDM drought intensity class thresholds to guide measures in a variety of assistance programs such as Livestock Forage Disaster Program (LFP), Emergency Haying and Grazing, Livestock Indemnity Program, Noninsured Crop Disaster Assistance Program (NAP), and Crop Insurance Basics (Mallya et al 2013;Mizzell and Lakshmi 2003;Otkin et al 2015). Such assistance programs can alternatively input DNLSWI thresholds for simple and easy operations as well as for a better precision in terms of spatial resolution (500 m).…”

Section: Discussionmentioning

confidence: 99%

Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS

et al. 2016

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Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D class, moderate drought) to 77 % (0 and D class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to>80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.

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Facilitating the Use of Drought Early Warning Information through Interactions with Agricultural Stakeholders

Cited by 71 publications

References 5 publications

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States

Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS

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