Uncertainty quantification of surface-water/groundwater exchange estimates in large wetland systems using Python

Friday, 19 December 2014
Joseph Davis Hughes and Patricia A Metz, USGS Florida Water Science Center Lutz, Lutz, FL, United States
Most watershed studies include observation-based water budget analyses to develop first-order estimates of significant flow terms. Surface-water/groundwater (SWGW) exchange is typically assumed to be equal to the residual of the sum of inflows and outflows in a watershed. These estimates of SWGW exchange, however, are highly uncertain as a result of the propagation of uncertainty inherent in the calculation or processing of the other terms of the water budget, such as stage-area-volume relations, and uncertainties associated with land-cover based evapotranspiration (ET) rate estimates. Furthermore, the uncertainty of estimated SWGW exchanges can be magnified in large wetland systems that transition from dry to wet during wet periods. Although it is well understood that observation-based estimates of SWGW exchange are uncertain it is uncommon for the uncertainty of these estimates to be directly quantified. High-level programming languages like Python can greatly reduce the effort required to (1) quantify the uncertainty of estimated SWGW exchange in large wetland systems and (2) evaluate how different approaches for partitioning land-cover data in a watershed may affect the water-budget uncertainty.

We have used Python with the Numpy, Scipy.stats, and pyDOE packages to implement an unconstrained Monte Carlo approach with Latin Hypercube sampling to quantify the uncertainty of monthly estimates of SWGW exchange in the Floral City watershed of the Tsala Apopka wetland system in west-central Florida, USA. Possible sources of uncertainty in the water budget analysis include rainfall, ET, canal discharge, and land/bathymetric surface elevations. Each of these input variables was assigned a probability distribution based on observation error or spanning the range of probable values. The Monte Carlo integration process exposes the uncertainties in land-cover based ET rate estimates as the dominant contributor to the uncertainty in SWGW exchange estimates. We will discuss the uncertainty of SWGW exchange estimates using an ET model that partitions the watershed into open water and wetland land-cover types. We will also discuss the uncertainty of SWGW exchange estimates calculated using ET models partitioned into additional land-cover types.