Towards a Near Real-time Agricultural Drought Monitoring and Forecasting

This document discusses using machine learning techniques to forecast agricultural drought by incorporating high-resolution soil moisture data. It aims to 1) forecast soil water deficit index (SWDI) up to one week using support vector machines (SVM) improved with dual ensemble Kalman filters, and 2) evaluate satellite-derived soil moisture against in situ observations to assess its use in drought indices. The results show dual EnKF greatly improves SVM predictions of SWDI at different soil layers and SMAP satellite soil moisture captures the dynamics of root-zone soil moisture compared to in situ observations.

Environment

Towards a Near Real-time Agricultural Drought Monitoring and Forecasting
Di Liu, Ashok K. Mishra
Glenn Department of Civil Engineering, Clemson University, 202 Lowry Hall, Clemson, SC 29634, USA
Abstract
Agricultural drought, usually, refers to a period with declining soil
moisture and consequent crop failure without any reference to surface
water resources. A decline of soil moisture depends on several climate-
catchment variables; therefore by incorporating high resolution real
time soil moisture into drought monitor will improve predicting
agricultural drought at near real-time conditions. This is important as
farmers/growers require real-time information on status of soil moisture
availability to decide ‘when to irrigate and how much to irrigate. Obj1:
We applied support vector machine (SVM) to forecast the agricultural
drought using soil water deficit index (SWDI) up to one week lead time.
The dual EnKF greatly improves the performance of SVM model and
predictions of agricultural droughts. Obj2: To evaluate the performance
of the rescaled SMAP L4 data against in situ stations in order to assess
the dynamics of input soil moisture into drought indices.
Drought Index: Soil Water Deficit Index (SWDI)
Figure.2: SWDI at each layer (L1-L5) derived from observed
soil moisture with 5th and 95th percentile method for the
field capacity (FC) and wilting point (WP). Location: Edisto
Research and Education Center located at Blackville, South
Carolina
Experimental Design and Results
Figure 1. (a): Variation of soil water extraction by corn with respect to
depth and plant root development patterns (Kranz et al., 2008),
(b) Time series plot of different drought indices at different soil layers.
(a) (b)
Figure 4. Daily time series plot of SWDI based on in situ and
rescaled SMAP and precipitation for the study period at
Bodega station, CA. Black continuous line indicates in-situ soil
moisture at 100cm; Red continuous line shows the Bias
corrected SMAP L4 root-zone soil moisture.
(a) SMAP L4
root zone (30th
May 2015)
(b) Palmer
Modified Drought
Index (May 2015)
(c) Palmer Z –
Index (May 2015)
Figure.5: Comparison of CONUS drought map using SMAP L4
products and Palmer drought indices
Case Input
R RMSE DA
techniqueL1 L2 L3 L4 L5 L1 L2 L3 L4 L5
Case1 t2m_avg, SR, P, rh_avg 0.546 0.621 0.557 0.382 0.258 0.263 0.196 0.194 0.301 0.396
No
Case2
Case1 + LAI 0.538 0.506 0.574 0.396 0.284 0.265 0.222 0.183 0.307 0.402
Case1 + SM 0.506 0.619 0.537 0.381 0.305 0.272 0.198 0.195 0.303 0.375
Case1 + LAI + SM 0.647 0.642 0.544 0.374 0.281 0.229 0.193 0.201 0.304 0.382
Case3
Case1 0.707 0.710 0.707 0.531 0.399 0.218 0.182 0.159 0.270 0.337
Dual
EnKF
Case1 + LAI 0.783 0.779 0.724 0.628 0.423 0.182 0.153 0.148 0.231 0.348
Case1+SM 0.815 0.748 0.704 0.660 0.466 0.171 0.163 0.150 0.216 0.306
Case1 + LAI + SM 0.700 0.698 0.604 0.658 0.539 0.220 0.185 0.172 0.217 0.287
Abbreviation: t2m_avg: average daily air temperature (Celsius); SR: total daily solar energy (WJ/m^2); P: daily total precipitation
(mm); rh_avg: average relative humidity (%); LAI: leaf area index; L1, L2, L3, L4, L5: soil water deficit index (SWDI) at 5cm, 10cm,
20cm, 50cm and 100cm soil layer depth, respectively.
Training Length: 07/01/2009 to 11/16/2010 ; Model validation & Prediction: 11/17/2010 to 09/20/2011
Figure 3. Flowchart of SVM modeling and EnKF technique
updating schemes.

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A Water Quality Valuation Approach To Strategic Planning

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Assessing the ability of SWAT as a water quality model in the Lake Victoria b...

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This study evaluated the impact of agricultural stream restoration on hydrology, biogeochemistry, and greenhouse gas emissions. The goals were to understand how physical restoration changes influence near-stream hydrology and water quality, and the drivers of nutrient cycling in restored versus unrestored stream-riparian systems. The key findings were that denitrification rates varied with landscape position and were generally higher in riparian zones than stream sediments. Instream denitrification and nutrient uptake depended on geomorphology. Additionally, the restoration construction process caused disturbance that reduced water quality function initially and maintenance is important due to common failures.

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Biologically Based Fertilizer Recommendations to Meet Yield Expectations and ...

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Five years of nitrogen and sulfur deposition treatments significantly impacted soil microbial communities and carbon cycling in forest soils. Specifically: 1) Nitrogen and sulfur additions separately and interactively altered the composition of bacterial and fungal communities. 2) Litter decomposition rates increased under nitrogen and sulfur additions. 3) Soil carbon dioxide fluxes were highest under nitrogen additions while methane fluxes were suppressed by sulfur.

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