METHODOLOGIES FOR ANALYZING IMPACT OF URBANIZATION ON IRRIGATION DISTRICTS

Transcription

1 METHODOLOGIES FOR ANALYZING IMPACT OF URBANIZATION ON IRRIGATION DISTRICTS USCID Water Management Conference Irrigated Agriculture Responds to Water Use Challenges Strategies for Success Austin, Texas April 3-6, 2012

2 Gabriele Bonaiti Extension Associate Guy Fipps Professor and Director Texas AgriLife Extension Service Department of Biological and Agricultural Engineering Texas A&M University, College Station Project based upon work supported by the Cooperative State Research, Education, and Extension Service, U.S. Department of Agriculture, under Agreement No For program information, see

3 AREA OF STUDY

4 Hidalgo, Willacy, and Cameron Irrigation districts (ID): 29 ID area: 760,000 acres Delivery network: 3,200 miles

5 Water rights Individual irrigators and irrigation districts hold more than 80% of total water rights along the Texas Rio Grande, when hydroelectric is excluded Irrigation districts hold 70% of agricultural water right

6 Urban growth Texas is predicted to have the fastest population growth in the USA between 2010 and 2060 The Rio Grande Regional Water Planning Area (Region M), which is one of the 16 Texas Water Planning Regions established by Senate Bill 1 in 1997, is predicted to have the highest growth in Texas, with +182% (Texas Water Development Board, 2012) Within Region M, Hidalgo and Cameron are the most populated counties, with an expected growth of +103 and +164%, respectively between 2010 and 2060 (Rio Grande Regional Water Planning Group, 2010) The main growing sectors are industrial, commercial and retirement community development

7 Potential impacts on districts Access to network and structures Transfer of water rights from agricultural to other uses Increasing liability for canal breaks and flooding Fragmentation and shrinking of irrigation area

8 PREVIOUS ANALYSIS

9 Urban area identification

10 1996

11 2006

12 Urbanization growth in irrigation Irrigation districts districts

13 Overlap of urban area with water distribution network

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15 Overlap with water distribution network By county

16 Calculation of a Network Fragmentation Index

17 Overlap with water distribution network in 1996 and 2006, in the McAllen area

18 Network Fragmentation Index (NFI) Cell size: 0.2 mi Search radius: 1.5 mi (Search area: 7.1 mi 2 ) 17 fragments Normalization (NFI): 2.4/7.3 = 0.34

19 NFI and District Fragmentation Index (DFI) in 2006, in the Cameron, Hidalgo, and Willacy Counties

20 NEW METHODOLOGIES ANALYSIS

21 ANALYSIS - 1 Urban area identification with automatic procedures The most utilized automatic approaches in literature are Pixel-Oriented (PO) and Object- Oriented (OO) analysis In the last decade, several studies demonstrated that the OO method can give more accurate results We used the ecognition software

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24 Network Fragmentation Index (NFI) using Manual Urbanization maps (MUM) Network Fragmentation Index (NFIa) using Automatic Urbanization maps (AUM)

25 ANALYSIS - 2 Urban area categorization with the Morphological Segmentation Method The categories that are defined by the procedure are: Core, Edge, Perforation, Bridge, Loop, Branch, Islet We used the GUIDOS 1.3 software (Vogt, 2010)

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27 Categorization map Adding weights to categories Network Fragmentation Index (NFIc) using Categorization maps

28 Network Fragmentation Index (NFIc) using Categorized Manual Urbanization Maps Network Fragmentation Index (NFI) using Manual Urbanization Maps

29 ANALYSIS - 3 Calculation of a NFI with a simplified procedure based on a probable number of Network Fragments (NF) We created an Urban Fragments Density Map by calculating the density of urban fragments We created a Network Density Map by calculating the density of canals and pipelines We combined the results to obtain the Network Potential Fragmentation Index and the District Potential Fragmentation Index

30 Urban Fragments Density Map using MUM Network Density Map Network Potential Fragmentation Index (NPFI)

31 Network Fragmentation Index (NFI) using Manual Urbanization Maps Network Potential Fragmentation Index (NPFI)

32 NPFI for different elements of the water distribution network in the year Circles show examples of differences Open canals and pipelines Only open canals

33 CONCLUSIONS

34 The two methodologies proposed for urban areas identification, manual and automatic, accurately identify urbanization. While the manual procedure required weeks of processing, the automatic one took only few days Interpretation of urban fragmentation dynamics was improved by using categories defining the type of urbanization. By assigning weights to such categories, we obtained a Network Fragmentation Index comparable to the one obtained from manual or automatic urbanization maps The set up of a simplified procedure to calculate impact of urbanization showed potential for application, even if analysis was based on probability of fragmentation rather than observations

35 RECOMMENDATIONS FOR FUTURE WORK Identify correlation between analysis results and observed impact on district operation, especially when applying weights to urbanization categories Identify which elements of the distribution network have more impact on district operation when fragmented (i.e. open canals, pipelines) Further evaluate the advantages in term of computation of automatic analysis Statistically evaluate goodness of predictions on urbanized area and network fragmentation