Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation | |
| Author | Dashrath, Kamble Baburao |
| Call Number | AIT Thesis no.RS-06-26 |
| Subject(s) | Evapotranspiration Irrigation Remote sensing |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Abstract | Monitoring irrigation system from field to regional scale has benefited a lot over the last 20 years, from the advances in remote sensing technique and developing agrohydrological models. Nowadays, operational algorithms are available to calculate evapotranspiration (ET) calculation at pixel level, which is an important state variable for water management studies. Land Surface Data assimilation is an invaluable tool in hydrological modelling as it allows to efficiently combine scarce data with a numerical model to obtain improved model predictions. In addition, data assimilation also provides an uncertainty analysis of the predictions made by the hydrological model. In this thesis, the Genetic Algorithm is used for data assimilation with a focus on agro-hydrological modeling with SWAP model. A real-coded genetic algorithm is coupled with a SoilWater-Atmosphere-Plant model (SWAP) to estimate pixel-based soil-plant-water parameters controlling the pixel evapotranspiration derived from the satellite images. The objective of SWAP-GA data assimilation scheme is to provide physically consistent estimates of spatially distributed environmental variables. It does so in an automated way via a methodology that can be viewed as a mathematical state estimation problem. Parameter values are selected on the basis of a model's posterior adherence to observational data and state estimation is usually, considered of little value in long term predictive simulations. This research work describes the methodology of SWAP-GA model and result for Sirsa irrigation Circle (Haryana-India) monitoring by mode. For remotely sensed evapotranspiration data calculated by the SEBAL (Surface Energy BA lance Algorithm). Terra-MODIS Satellite imageries used to generate temporal data. Irrigated wheat and cotton are considered of particular interest to the water Managers, since these fields produce the main cash crops of the irrigation circle. In the discussion that follows, it is shown that the remote sensing has advantage on monitoring irrigation system and provides detailed results with data assimilation scheme. This research emphasis will be on the discussion of specific advantage in applying SWAP-GA data assimilation algorithms in irrigation demand modeling, where on-demand irrigation systems which is regulated by two key factors: The depletion of water in the soil root zone; The farmer's water application criteria and preferences of irrigation water applications. In this study attempt are made to retrieve the soil moisture from the SWAP-GA data assimilation. Soil moisture readings are useful to determine how much water is available for the crop, when to start irrigating, and how much water to apply. In this study further discussion is extended to potential benefits that can be gained from assimilation of remotely sensed data, and the role of meteorological boundary conditions in land surface data assimilation, water productivity analysis |
| Year | 2006 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | HONDA, Kiyoshi; |
| Examination Committee(s) | Tripathi, Nitin Kumar;Surat Lertlum;Gupta, Asim Das ; |
| Scholarship Donor(s) | AIT Fellowship; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2006 |