RZ-TRADEOFF: A New Model to Estimate Riparian Water and Air Quality Functions

Abstract

Riparian zones are often used as best management practices due to their ability to remove nitrate (NO3−) from subsurface flow. Research suggests that beyond local biogeochemical controls, the impact of riparian zones on nitrogen removal and other functions, such as phosphorus dynamics and greenhouse gas emissions, largely depends on land-use/land-cover, hydrogeomorphology, and weather. In this study, we therefore present RZ-TRADEOFF, a novel and easily applicable model that connects multiple riparian functions and characteristics (NO3− and phosphate (PO43−), concentration and removal in subsurface flow, total phosphorus (TP) removal in overland flow, nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2) emissions, water table) to landscape hydrogeomorphic characteristics, weather, and land-cover/land-use. RZ-TRADEOFF was developed with data from past studies and digital databases, and validated with data collected from the literature. Three functions (water table, PO43− and CO2) were observed to be significantly influenced by climate/weather, while the others were primarily influenced by hydrogeomorphology and land use. The percent bias and normalized root mean square error respectively were −3.35% and 0.28 for water table, 16.00% and 0.34 for NO3− concentration, −7.83% and 20.82 for NO3− removal, 6.64% and 0.35 for PO43− concentration, 2.55% and 0.17 for TP removal, 40.33% and 0.23 for N2O, 72.68% and 0.18 for CH4, and −34.98% and 0.91 for CO2. From a management standpoint, RZ-TRADEOFF significantly advances our ability to predict multiple water and air quality riparian functions using easily accessible data over large areas of the landscape due to its scalability.

Deriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zones

“…They concluded that more factors need to be considered. In accordance, Hassanzadeh et al [16] achieved even higher r 2 values by considering various RBZ and external variables in their regression model. Nonetheless, such regression models have limitations which can be addressed by Bayesian networks (BN, also termed Bayesian belief networks, [26]).…”

“…[74,76,83,84], paired n = 30), (b) preferential flow (left: data from references [78,85], right: data from [86], without switchgrass barrier, paired n = 6). As a side effect, these two BN nodes can generally integrate the effects of hydrology [46,87], land use, and land management [16,21]. These variables affect the concentration [47,88], timing, amount, and composition of nutrient and sediment entering RBZ and have been used in other empirical RBZ models (e.g., [16,46]).…”

“…Therefore, modelling approaches to predict RBZ efficacy in large-scale applications tend to be (too) data-demanding, e.g., process-based models like VFSMOD [14] and REMM [15], or (too) simplistic [16]. For instance, Weissteiner et al [17] used RBZ width as single variable to estimate the efficacy of N and P retention in their model for the EU.…”

Riparian Land Use and Hydrological Connectivity Influence Nutrient Retention in Tropical Rivers Receiving Wastewater Treatment Plant Discharge

“…Furthermore, understanding the importance of riparian areas for river nutrient concentrations can be equally relevant to global nutrient exportation models. The inclusion of habitat-specific biochemical reactions such as those occurring in riparian systems may enhance the reliability and sensitivity of estimates, incorporating actual process-based data to predict the fate and transport of solutes affecting overall water quality (e.g., Hassanzadeh et al, 2019;Pissarra et al, 2019).…”

Editorial: Riparian Buffer Nutrient Dynamics and Water Quality

“…While riparian buffers (permanently vegetated areas between cropland and streams) are considered an important nutrient mitigation best management practice, nitrogen (N) and phosphorus (P) removal varies widely and can be difficult to predict (Hill, 1996;Hoffmann et al, 2009). More research is needed on site-specific factors and interactions affecting N and P cycling and transport to help refine/validate riparian water quality models (Vidon et al, 2018;Hassanzadeh et al, 2019). Specifically, studies focusing on interactions among hydrology, soils, redox conditions, and riparian vegetation are needed to better predict mitigation potential at field, watershed and inter-biome scales.…”