Suitability Analyses

The overlap of water available for capture and geology suitable for recharge

Suitability Mapping

The Recharge team is examining surface and subsurface properties across the state to identify areas suitable for groundwater recharge. This includes the compilation of recharge suitability parameters including geologic and soil properties, land surface properties, land cover type, depth to water, etc., and combining these parameters with specific weighting criteria to provide an assessment of recharge potential across the state. 

This geologic data is combined with the statewide summary of hydrologic components (hydroclimate data, baseflow indices, etc.) to identify the overlap of where there is water available for capture and where surface and subsurface properties are suitable for recharging that water.  This suitability analysis tool is a flexible way to prioritize potential recharge sites.

Publications

(in Review)

E Lima, Ryan, Neha Gupta, Travis Zalesky, Temuulen Tsagaan Sankey, Abraham E Springer, Patrick D. Broxton, and Katharine Jacobs. n.d. “Mapping Landscape Suitability for Thinning to Reduce Evapotranspiration and Enhance Groundwater Recharge in Semi-Arid Ponderosa Pine Forests.” Journal of Environmental Management.

Abstract:

Literature on the relationship between forest thinning and water yield was used to develop suitability criteria to map where forest treatment is most likely to enhance groundwater recharge across the ponderosa pine (Pinus ponderosa) forests of Arizona. Recharge in ponderosa pine forests is ephemeral and focused in periods of snowmelt and locations of enhanced permeability when soil moisture exceeds threshold levels. Our approach combines thematic maps of criteria such as fraction of precipitation as snow, slope, aspect, landscape morphology, forest basal area, canopy cover, lineament density, lithology, and hydrologic soil type into a GIS-Multi-Criteria Decision Analysis. Thematic layers were grouped into indices and suitability was determined through a weighted sum model of variables and indices. Approximately 10% (182,000 hectares) of the 1.8 million ha of ponderosa pine forests across western and northern Arizona were found to be highly suitable for groundwater recharge enhancement from forest thinning. This study finds that much of the area where thinning is already planned for the purpose of restoring other ecosystem services may also improve groundwater management in a state facing serious groundwater declines. This GIS-based approach enables hydrologically informed and spatially targeted forest management in semi-arid landscapes from catchment to regional scales.

Suitability Analysis Layers

Arizona Topographic Moisture Index

The Topographic Relative Moisture Index (TRMI) is a terrain-based index that estimates how wet or dry a location is likely to be based on its topographic attributes. TRMI incorporates several topographic parameters that influence moisture dynamics, including slope gradient, aspect, relative elevation (or topographic position), and landscape convexity or concavity.

Arizona Soil Moisture Infiltration Index

The Soil Moisture Infiltration Index is a custom index created to discern where on the landscape water is likely to infiltrate into the soil, it is a weighted linear combination of Topographic Relative Moisture Index (TRMI) and Soil Saturated Hydraulic Conductivity (sKsat).

Applications and Tools

Arizona Well Data Explorer

This application is an interactive groundwater data explorer for Arizona. It utilizes data from the 2024 GWSI well database and allows the user to:

Visualize groundwater well depths and water level elevations.
Explore well characteristics by region, aquifer, and geology.
Overlay hydrologic features like Subbasins, AMAs, and Major Aquifers.
Use the dropdowns to filter wells, and view 3D depth profiles and geographic maps.

Additional information and data for this application are available on Ryan Lima's GitHub page.

Analytical Hierarchy Process Tool

This application allows the user to calculate a weights matrix for multi-criterion decision making (MCDM) using the analytical hierarchy process (AHP).

Input two or more component variables influencing your decision making. Adjust the pair wise comparison sliders in the direction of the more important variable, using Saaty's absolute numbers as a reference. Based on user input a balanced weights matrix will be generated, quantifying the absolute importance of all component variables. These weights can be incorporated into a mathematical formula for decision making, especially for GIS based suitability analysis, or other matrix-based equations.

Additional information for this application is available on Travis Zalesky's GitHub page.