Detecting seasonal and cyclical trends in agricultural runoff water quality—hypothesis tests and block bootstrap power analysis

Abstract

Seasonal and cyclic trends in nutrient concentrations at four agricultural drainage ditches were assessed using a dataset generated from a multivariate, multiscale, multiyear water quality monitoring effort in the agriculturally dominant Lower Rio Grande Valley (LRGV) River Watershed in South Texas. An innovative bootstrap sampling-based power analysis procedure was developed to evaluate the ability of Mann-Whitney and Noether tests to discern trends and to guide future monitoring efforts. The Mann-Whitney U test was able to detect significant changes between summer and winter nutrient concentrations at sites with lower depths and unimpeded flows. Pollutant dilution, non-agricultural loadings, and in-channel flow structures (weirs) masked the effects of seasonality. The detection of cyclical trends using the Noether test was highest in the presence of vegetation mainly for total phosphorus and oxidized nitrogen (nitrite + nitrate) compared to dissolved phosphorus and reduced nitrogen (total Kjeldahl nitrogen-TKN). Prospective power analysis indicated that while increased monitoring can lead to higher statistical power, the effect size (i.e., the total number of trend sequences within a time-series) had a greater influence on the Noether test. Both Mann-Whitney and Noether tests provide complementary information on seasonal and cyclic behavior of pollutant concentrations and are affected by different processes. The results from these statistical tests when evaluated in the context of flow, vegetation, and in-channel hydraulic alterations can help guide future data collection and monitoring efforts. The study highlights the need for long-term monitoring of agricultural drainage ditches to properly discern seasonal and cyclical trends.

Development of a Cyberinfrastructure for Assessment of the Lower Rio Grande Valley North and Central Watersheds Characteristics

“…High levels of total nitrogen are caused by the decomposition of detritus and any anthropogenic loadings [63]. High levels of total nitrogen are caused by the decomposition of detritus and any anthropogenic loadings [63]. TP levels were lower than the screening level of 0.7.…”

“…TKN levels have shown to be relatively higher than the screening level with the highest of 2 mg/L as N in 2018. High levels of total nitrogen are caused by the decomposition of detritus and any anthropogenic loadings [63]. High levels of total nitrogen are caused by the decomposition of detritus and any anthropogenic loadings [63].…”

Uncertainty analysis of 100-year flood maps under climate change scenarios

“…Several studies thus far have implemented bootstrap technique in hydrology and environmental modeling practices for a variety of purposes including frequency analysis (e.g., , 2003, Engeland et al, 2004, Overeem et al, 2008, model and parameter uncertainty analyses (e.g., , Li et al, 2010, Zhang et al, 2018, Gopalan et al, 2019, uncertainty assessment of water quality trends (e.g., Srinivas and Srinivasan, 2005,Hirsch et al, 2015., Uddameri et al, 2018 and uncertainty analyses in flood forecasting models in combination with artificial neural networks (ANN) (e.g., Han et al, 2007, Tiwari andChatterjee, 2010).…”