Patricia K. Smith

Glacier mass balance simulation using SWAT distributed snow algorithm

ABSTRACT Application of a temperature-index melt model incorporated into the Soil and Water Assessment Tool (SWAT) is presented to simulate mass balance (MB) and equilibrium line altitude (ELA) of three glaciers. The snow accumulation/melt parameters were adjusted to glacierized and free glacier areas, respectively. The SWAT snow algorithm enabled us to consider spatial variation of snow parameters by elevation bands across the sub-basins, while in the previous studies using SWAT, the related parameters were constant for an entire basin. The results show slight improvement in runoff simulation and significant improvement in simulated MB when considering ELA in model calibration. The results showed that SWAT can be applied to simulate MB, vertical MB distribution and annual ELA, with light calibration efforts for data-scarce catchments. The accuracy of the results depends on the modelled area of ablation zone from which most of the meltwater is released.

MATLAB Tool for Calculating and Plotting Goodness-of-Fit Indicators Considering Measurement Uncertainty and/or Model Uncertainty

Measurement uncertainty and model uncertainty should be accounted for in model application and evaluation. By modifying the error term in pair-wise comparisons of measured and predicted values for goodness-of-fit indicators, methods have been proposed to include measurement uncertainty and model uncertainty in these calculations. This paper presents a process-oriented MATLAB® program to calculate goodness-of-fit indicators that includes these uncertainty estimates. The program requires minimum input files and user specified parameters. Nine types of common probability distribution combinations for hydrologic and water quality data can be selected. The program outputs include input data statistical properties, input data plots, and goodness-of-fit indicator values. The developed MATLAB® tool, Goodness of Fit Indicator Tool (GOFIT), will be available for users as a stand-alone application or may also be integrated into other tools used in model optimization.

Determination of Uncertainty in Measured Streamflow and Water Quality Data and Application to Model Evaluation

In spite of the importance and even scientific responsibility to address uncertainty related to hydrologic and water quality measurement, uncertainty estimates corresponding to measured data are rarely made. The lack of uncertainty estimates can be attributed to the previous lack of a straightforward method to realistically quantify uncertainty. The recent development of fundamental methods to quantify the uncertainty inherent in measured hydrologic and water quality data, however, should increase the application of uncertainty estimates to measured data. If uncertainty estimates are included with measured data sets and adequately communicated to scientists, public interests, and decision makers, then optimal monitoring project design, enhanced model-based decision making, and improved stakeholder understanding will result. The primary objectives of this presentation are: 1) to describe a method for realistic estimation of uncertainty in measured streamflow and water quality data and 2) to illustrate its application in several case studies. The discussion and results presented focus on uncertainty related to discharge measurement, sample collection, sample preservation/storage, and laboratory analysis procedures for measurement of streamflow, nitrogen (N), phosphorus (P), and total suspended sediment (TSS) data from small watersheds. It is hoped that this method (with supporting data and field form templates) will assist monitoring project personnel in making uncertainty estimates for their measured data. The case study results will provide uncertainty estimates associated with individual procedural steps and for the resulting data under a range of monitoring conditions. A secondary objective is to introduce modified goodness-of-fit indicators that consider measurement uncertainty in hydrologic and water quality model evaluation.