Verification of Mid-/Long-term Forecasted Soil Moisture Dynamics Using TIGGE/S2S

TIGGE/S2S 기반 중장기 토양수분 예측 및 검증

Abstract

Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study, a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was also integrated to derive the soil hydraulic properties(${\alpha}$, n, ${\Theta}_r$, ${\Theta}_s$, $K_s$) as the input variables to SWAP based on the soil information(Sand, Silt and Clay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) and long-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites of RDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statistics of Root Mean Square Error(RMSE: 0.034~0.069) and Temporal Correlation Coefficient(TCC: 0.735~0.869) for validation. When we predicted the mid-/long-term soil moisture values using the TIGGE(0~15 days)/S2S(16~46 days) weather forecasts, the soil moisture estimates showed less variations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based on the increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluating agricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.

Fig. 1 Locations of the six Rural Development Administration (RDA) monitoring sites

Fig. 2 Classification of soil characteristics for the AWSmonitoring sites of Rural Development Administration (RDA) (6 sites)

Fig. 3 Comparison of the observed and estimated soil moisture dynamics at the AWS monitoring sites of Rural Development Administration (RDA) in 2017

Fig. 4 Comparison of observation and model simulation results of soil water content from the AWS sites of Rural Development Administration (RDA) in 2017

Fig. 5 Comparison of observation and model simulation results of soil water content fromthe AWS sites of Rural Development Administration (RDA) in 2017

Fig. 6 Comparison of observation and model simulation results of soil water content from the AWS sites of Rural Development Administration(RDA) in 2017

Table 1 The soil texture and ROSETTA-drived soil hydraulic properties at the AWS monitoring sites of Rural Development Administration(RDA)

Table 2 Statistics of the predicted soil water content, temperature and precipitation by TIGGE and S2S