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Charles Sturt University (2011)

A Geoinformatics Approach for Spatial Water Accounting and Irrigation Demand Forecasting for a Gravity Irrigation System

Kaleem Ullah, Muhammad

Titre : A Geoinformatics Approach for Spatial Water Accounting and Irrigation Demand Forecasting for a Gravity Irrigation System

Auteur : Kaleem Ullah, Muhammad

Grade : Doctor of Philosophy in Applied Hydrology (PhD) 2011

Université de soutenance : Charles Sturt University

Résumé
Water scarcity is rapidly becoming a critical global issue, and water demand already exceeds area was mapped using Landsat 5 TM satellite images by applying a simplified hybrid classification approach mainly based on a supervised classification algorithm. The SEBAL model was used to map spatial daily ETa from 18 Landsat 5 TM satellite images covering various parts of the cropping season in summer 2008-09 and 2009-10. For 2008-09, the seasonal ETa values ranged from 20 mm to 1,705 mm, and for 2009-10 the seasonal ETa was 13 mm to 1,645 mm. Overall, it was found that the remote sensing based energy balance algorithm coupled with ground data can be an efficient and reliable method for estimation of evapotranspiration at different spatio-temporal scales. Water accounting and productivity analysis of three commonly used indicators shows wide variation across the 22 nodes as well as at the system level in the CIA. Results show that a large amount of water is lost through non-process depletion (80% of available water) rather than process depletion (20% of available water) in both seasons at the system level. Most of the non-process depletion is from native vegetation, dry grass, fallow fields and bare soil as a result of rainfall stored in the root zone. The performance of irrigation water at the system level was also low (49%) due to extremely low water availability, and most of the irrigation water depleted non-beneficially through seepage from channels. In terms of productivity, average rice water productivity was low in the region as compared to the past. Daily irrigation demand for a seven day period has been forecasted using remote sensing based water use efficiencies and actual crop coefficients (Kc_act), and weather forecast data at the individual node as well as the system level. Results of net irrigation demand forecast for fields (NIDFF) at the node level showed relatively high variability across the nodes, while it has good agreement with actual water delivered to fields at the system level based on system records. The net irrigation demand forecasting (NIDF) at the system level shows relatively high variability with respect to total water diversion to the CIA. This variability is mainly attributed to the use of a fixed value of conveyance system loss to maintain system operation. However, the overall demand forecast has reasonable agreement with actual water diverted to the system and can help in improving irrigation water management. The applied methodology is very simple and cost effective for a demand driven irrigation system, and daily demand can be forecasted and updated using remote sensing image analysis. This demand forecasting tool, based on a sound understanding of hydrological behaviour, novel remote sensing technology and forecasted meteorological data, is useful for improved irrigation water management ranging from node to system level in CIA as well as in other irrigation systems located in arid and semi-arid regions around the globe. The developed tool will practically help irrigation managers to overcome the risks associated with over and under irrigation application by matching the demand and supply in near real time environment.

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Page publiée le 7 novembre 2011, mise à jour le 11 juin 2017