Multiparameter Regression Modeling for Improving Quality of Measured Rainfall and Runoff Data in Densely Instrumented Watersheds

Abstract

The Walnut Gulch Experimental Watershed is a semi-arid experimental watershed and long-term agro-ecosystem research (LTAR) site managed by the USDA-Agricultural Research Services (ARS) Southwest Watershed Research Center for which highresolution, long-term hydroclimatic data are available across its 149-km 2 drainage area. Quality control and quality assurance of the massive data set are a major challenge. We present the analysis of 50 years of data sets to develop a strategy to identify errors and inconsistencies in historical rainfall and runoff databases. A multiple regression model was developed to relate rainfall, watershed properties, and the antecedent conditions to runoff characteristics in 12 subwatersheds ranging in area from 0.002-94 km 2. A regression model was developed based on 18 predictor variables, which produced predicted runoff with correlation coefficients ranging from 0.4-0.94 and Nash efficiency coefficients up to 0.76. The model predicted 92% of runoff events and 86% of no-runoff events. The modeling approach is a complement to existing quality assurance and quality control (QAQC) procedures and provides a specific method for ensuring that rainfall and runoff data in the USDA-ARS Walnut Gulch Experimental Watershed database are consistent and contain minimal error. The model has the potential for making runoff predictions in similar hydroclimatic environments with available high-resolution observations.

Curating 62 years of Walnut Gulch Experimental Watershed dataset: Improving QAQC methods of rainfall and runoff measurements.

“…The third condition identified those rainfall events with observed runoff, but no predicted flow. For details of the development and performance of the regression model in QAQC application in the WGEW case, see Bitew et al (2019) .…”

“…Errors might have occurred due to the changes in the equipment used for data reading, storage, and digitizing recorded charts. The current challenge is to find new ways to improve data quality by identifying and correcting errors within the extensive database (Bitew, et al, 2019 ). In addition to the potential errors listed, there were other problems such as decommissioning a large number of rain gauges between 1982 to 1989 during the winter season (DAP provides complete metadata for these dates), when frontal rainfall dominates and is more spatially uniform, due to budgetary constraints.…”

“…and the watershed properties, including ephemeral channels, ground cover, soil, slope shape, etc. This principle was applied in two forms: To evaluate the temporal relationship between runoff and precipitation and watershed properties and antecedent condition (Regression Method; Bitew et al, 2019 ); and, To calculate an index that accounts for the ratio of runoff to rainfall amount (Runoff Coefficient Method).…”

“…The main hydrograph of the event in Figure 8b (7/28/1966 on watershed WS11) does not seem to have a problem, but the fact that the runoff baseflow component in the recession side continued over 14 hours with the runoff rate value close to 0.2 cfs which appeared as a sequence of runoff events with no associated rainfall. The runoff coefficient method identified the events in a, c, and d, and the events in e and f were flagged by the regression method (see more on Bitew et al, 2019 ).…”

Runoff forecasting model based on CEEMD and combination model: a case study in the Manasi River, China

“…In general, it is necessary to consider the common influence of multiple forecasting factors on the forecasting object. Therefore, multiple regression model is often used in runoff time series prediction (Niu et al 2016;Bitew et al 2019). However, in practical engineering application, many variables or parameters affecting runoff are difficult to obtain, which will limit the application of regression analysis model.…”