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Stochastic variables. Mean. Standard deviation. 0.0212. 0.0252. (soil parameter). n (soil parameter). 1.4144. 0.0381. Emergence date (eDate). November 22. 7 days. Depth to groundwater.
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Stochastic variables Mean Standard deviation 0.0212 0.0252 (soil parameter) n (soil parameter) 1.4144 0.0381 Emergence date (eDate) November 22 7 days Depth to groundwater 435 cm 33.5 cm Irrigation scheduling (Ta /Tp) 0.72 0.28 Irrigation quality 2.4 dS m-1 0.74 dS m-1 STOCHASTIC DATA ASSIMILATION TECHNIQUE IN REGIONAL HYDROLOGY Amor V.M. Ines1, Peter Droogers2, Kyoshi Honda1 and Ashim Das Gupta1 INTRODUCTION One major problem in regional hydrological modeling is the derivation of distributed input data. The classical approach to derive these information is time consuming, labor intensive and costly to implement. Remote Sensing (RS) data … The objectives of this study were: 1. 2. 3. Actual Case Study: METHODOLOGY Data Assimilation: RESULTS AND DISCUSSIONS Numerical Case Study: CONCLUSIONS 1. 2. 3. REFERENCES Droogers P. and W.G.M. Bastiaanssen. 2002. Irrigation performance using hydrological and remote sensing modeling. Journal of Irrigation and Drainage Engineering. 128:11-18. Feddes, R.A., Menenti, M., Kabat, P. and W.G.M. Bastiaanssen. 1993. Is large-scale inverse modeling of unsaturated flow with areal average evaporation and surface soil moisture as estimated by remote sensing feasible? Journal of Hydrology. 143:125-152. Ines, A.V.M. and P. Droogers. 2002. Inverse modeling to quantify irrigation system characteristics and operational management. Irrigation and Drainage Systems. 16 (3): 233-252. Kite, G. 2001. Modeling the Mekong: hydrological simulation for environmental impact studies. Journal of Hydrology. 253: 1-13. Jan 19 Dec 22 Jan 19 Dec 22 Feb 16 Feb 16 Mar 16 Mar 16 2FutureWater, Eksterstraat 7, 6823 DH Arnhem, The Netherlands. Email: p.droogers@futurewater.nl 1Asian Institute of Technology, P.O. Box 4 Klong Luang 12120 Pathumthani, Thailand. Emails: avmines@ait.ac.th, honda@ait.ac.th, adg@ait.ac.th