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!"#"$%&'"()*+(,*-#+$.+)/%(*%0*1%/$*2%/3).4"5 - - PowerPoint PPT Presentation

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slide-1
SLIDE 1

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SLIDE 2

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SLIDE 3

Drought monitoring

  • Common metrics:

– Meteorological indices – Surface water monitoring – Remotely sensed vegetation indices

  • Why not measured soil

moisture?

– Measurement challenges – Conceptual challenges

  • Objective: identify effective

agricultural drought indices based on soil moisture measurements

US Drought Monitor, May 24, 2018. Courtesy of NDMC-UNL. Oklahoma automated in situ soil moisture mapping system, 5-cm depth, May 23, 2018.

slide-4
SLIDE 4

Study region

slide-5
SLIDE 5

Methods

Data

  • Oklahoma Mesonet

– Heat dissipation sensors

  • West Texas Mesonet

– Water content reflectometers

  • National Agricultural Statistics

Service

– County non-irrigated yields

  • Crop types

– Winter wheat – Hay – Cotton

Limitations

  • No sensors in cropland!
  • Different sensor types
  • No measured soil properties

for West Texas Mesonet

  • Limited NASS crop data

availability

slide-6
SLIDE 6

Candidate soil-moisture based indices

Variables

  • Matric potential (MP):

indicator of the potential energy of the soil water; kPa

  • Soil water storage (SWS):

volumetric water content X soil depth; mm

  • Fraction of available water

capacity (FAW): volumetric water content scaled between 0 (wilting point) and 1 (field capacity); unitless

Expressions

  • Raw values
  • Anomalies: current value

minus mean value for this day

  • f year
  • Statistically standardized:

empirical pdf fit for each day

  • f year and used to estimate

cumulative probability which is transformed to a standard normal value

slide-7
SLIDE 7

Strong county- level correlations

  • Measured soil moisture

positively correlated with crop yield

  • Correlations stronger

for warm-season crops than cool-season crops

  • NE to SW trend in

correlation strength for winter wheat

Maximum correlation coefficients (r) between soil water storage anomaly and wheat, hay, or cotton yield anomaly for individual counties in Oklahoma (2000-2016) and the Texas Panhandle from (2002-2016). The day of year on which maximum correlation occurred varied by county.

slide-8
SLIDE 8

Long lead times

  • Wheat yields most

strongly correlated with soil moisture in late March

  • Hay yields most strongly

correlated with soil moisture in June and July.

  • Cotton yields most

strongly correlated with soil moisture in March and April

Average correlation (r) between SWS-anomaly and wheat, hay, or cotton yield anomaly for counties in Oklahoma (2000-2016) and the Texas Panhandle (2002-2016). The black line represents the across- county average correlation coefficient for each day of year for counties with significant soil moisture-yield anomaly relationships, and the shaded area around each line represents one standard

  • deviation. The dashed lines are the limits of significant correlation (P

< 0.05)

slide-9
SLIDE 9

Seasonality differs across sites

Time series of soil water storage (SWS), SWS-anomaly, and standardized SWS (SSWS) for the Marena Oklahoma Mesonet station near Stillwater, Oklahoma from 2000-2016 and the Reese Center West Texas Mesonet station near Lubbock, Texas from 2002-2016. The solid black lines represent mean values for each day of the year, and the shaded region is the area between 10th and 90th percentile values. For SWS, maximum and minimum values are represented by dashed lines.

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SLIDE 10

Standardization minimizes seasonality

Correlogramsfor soil water storage (SWS), SWS-anomaly, and standardized SWS (SSWS) for the Marena Oklahoma Mesonet station near Stillwater, Oklahoma from 2000-2016 and the Reese Center West Texas Mesonet station near Lubbock, Texas from 2002-2016. Dashed lines are included at ±0.2 as an estimate of the limit of practically meaningful autocorrelation.

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SLIDE 11

Standardization strengthens regional-level correlations

  • Matric potential, soil

water storage, and fraction of available water capacity similarly correlated with yields

  • Statistically-standardized

values more strongly correlated than raw values in 7 cases and than anomalies in 1 case

Correlation coefficients (r) between drought indices and wheat, hay,

  • r cotton yield anomaly. County-level data for counties with

significant soil moisture-crop yield anomaly relationships were combined into a single correlation analysis for each drought index- crop combination, and Oklahoma data were from 2000-2016 and Texas Panhandle data were from 2002-2016. Drought indices included matric potential (MP), soil water storage (SWS), and fraction of available water capacity (FAW), and r is shown for index values, anomalies, and statistically standardized indices. Error bars are 90% confidence intervals, and columns with different lowercase are significantly different at P < 0.10.

slide-12
SLIDE 12

Summary

  • Two promising indices

– Soil water storage anomaly – Standardized soil water storage

  • Soil property data essential

for some purposes, but not necessarily for drought monitoring

  • Strong potential for soil

moisture measurements in drought monitoring

  • Soil water storage

anomaly

– Depth units (mm or in) – Easier to construct – Easier to interpret

  • Standardized soil water

storage

– Unitless – More difficult to construct and interpret – Slightly stronger correlations to yield

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SLIDE 13

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