GIS in Hydrology and Water Management

These are four possible applications of a GIS-hydrological model relationship:

1) Hydrological assessment to represent hazard/vulnerability through weighted/summed influences of significant factors rather than through physical laws

2) Hydrological parameter determination as the GIS provides input to the model in terms of parameters such as surface slope/channel length/land use/soil characteristics

3) Hydrological modeling within the GIS, which some say is feasible with time snapshots/temporal averages and not time-series

4) Linking GIS and hydrological models to utilize the GIS as an input/display device, including real-time process monitoring if the necessary [remotely sensed?] observations are available.  The primary challenge here is the disparity between GIS and hydrological data models.

Whereas scientific GIS was developed in a modeling context, commercial water utilities first used GIS as a core integrating technology which led to failed expectations.  The database is now the center of corporate information handling, with network computing capability expected to replace the database’s central role in the future. 

In water utilities 70-80% of all corporate information is geographic, but there are several problems with increasing data resolution:

1) Necessitated increase in storage/processing speed – technological advances tend to annul this

2) Pixelated raster models tend to speckle at high resolution.  This requires zooming out or smoothing, which remove the benefits of higher resolution.  If resolution increases to a magnitude where zooming in does not create speckling, this issue would be solved (in theory).  Is this feasible?

3) Many features represented are physically “fuzzy” or temporally time-bound transitions

4) Higher resolution may create specificity that decreases unknowns/errors in estimating risk probability which, due to professional implications, may render it a net detriment

High Resolution in GIS & Flood Insurance:

-Higher resolution allows underwriter to remove extreme high risk individuals, depending on the location this could be a benefit or loss for the underwriter depending on whether the high risk individuals capitalized in the past or whether future-oriented process models are more suited, the % of the customer base for the firms is important here

-raises premium for high risk individuals to the point that they can’t afford insurance, this % is important

            -social justice concerns arise here

-low risk individuals opt out of insurance, how many % would this be?

-based on these prior numbers the company may or may not be able to lower mid-range premiums to bring in a potentially wider customer base and continue operating

-depending on the % individuals lost on the high-risk end [a benefit for the company] and the % individuals lost on the low-risk end [a loss for the company], the firm may or may not be able to continue operating

-this situation is significant because GIS models are often off by as much as 3.75 m in estimating risk and insurance is a pivotal component of national/international infrastructure through which advanced societies manage/mitigate risk

-a discussion concerning the role of how corporate structure is related to technological innovation [e.g. is large-scale information analysis predicated upon a corporate structure, or can business change this? – or at least I’d like more information on this], modeling, natural systems change that can’t be predicted based on past experience, the use of technology to stop technology is all warranted here

Clark, M. J. (1998). Putting water in its place: a perspective on GIS in hydrology and water management. Hydrological Processes, 12, 823-834.

Retrieved from https://lms.southwestern.edu/file.php/3722/Literature/Clark-1998-Putting_Water.pdf