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

technique L1 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.