The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach.

Biofiltration systems can help mitigate the impact of urban runoff as they can treat, retain and attenuate stormwater. It is important to select the optimal design characteristics of biofilters (e.g., vegetation, filter media depth) to ensure high treatment performance. Operational conditions (e.g., infiltration rate) can also lead to significant changes in biofilter treatment performance over time. The impact of specific operational conditions on water quality treatment performance of stormwater biofilters is still not well understood. Furthermore, despite the importance of design characteristics and operational conditions on biofilter treatment performance, there is a lack of models that can be used to determine the optimal design and operation. In this paper, we developed a series of statistical models to predict the Total Phosphorus (TP) and Total Nitrogen (TN) removal performance of stormwater biofilters using various numbers of design characteristics and operational conditions. These statistical models were tested using data collected from four extensive laboratory-scale biofilter column studies. It was found that all models performed relatively well with a Nash-Sutcliffe Efficiency (NSE) of 0.42 - 0.61 for TP and 0.37 - 0.63 for TN. The most important design characteristics were filter media type and depth for TP treatment, and vegetation type and submerged zone depth for TN treatment. In addition, infiltration rate and inflow concentrations were the operational conditions that greatly influence outflow TP and TN concentrations from stormwater biofilters. As such, these variables need to be carefully considered when designing and operating stormwater biofilters. Sensitivity analysis results indicate that the model was quite sensitive to all regression coefficients and intercepts. Additional modelling exercises show that the model could be further simplified by reducing the number of cross-correlated parameters. These models can be used by practitioners for not just optimising the design, but also operating biofilters using real-time monitoring and control to achieve optimum performance.

Modeling hydrological impacts of afforestation on intermittent streams.

Although the majority of river networks across the world are intermittent or ephemeral, afforestation management of these catchments is mostly founded on studies in perennial catchments. The hydrological model CATHY (CATchment HYdrology) was used here to simulate the effects that different degrees of progressive conversion from pasture to plantation have on the streamflow generation in intermittent streams. The model was applied to two rural catchments with different size and topographic features in southwest Victoria, Australia. Simulated scenarios included different levels of plantation establishment in pasture areas planting gradually from downslope to upslope and vice versa. Different models for root water uptake were compared to account for water stress, oxygen stress, and root water compensation. A function of root growth over time was also explored to see how it affected model results. The model results show that complex interactions between topographic features and afforestation patterns are crucial in controlling catchments hydrological behavior. In particular, results show that planting in the prone-saturation areas has the largest effects on streamflow. Oxygen stress has a more significant impact than root water compensation on streamflow changes. A time dependent root growth results in smaller streamflow reduction on average, although with different impacts on the two catchments, also due to the interplay between topography and plantation patterns. Overall, our results show that there are multiple factors affecting the water balance when a catchment is partially or completely afforested and those must be taken into account when implementing forestry management strategies.

A review of ion and metal pollutants in urban green water infrastructures.

In urban environments, the breakdown of chemicals and pollutants, especially ions and metal compounds, can be favoured by green water infrastructures (GWIs). The overall aim of this review is to set the basis to model GWIs using deterministic approaches in contrast to empirical ones. If a better picture of chemicals and pollutant input and an improved understanding of hydrological and biogeochemical processes affecting these pollutants were known, GWIs could be designed to efficiently retain these pollutants for site-specific meteorological patterns and pollutant load. To this end, we surveyed the existing literature to retrieve a comprehensive dataset of anions and cations, and alkaline and transition metal pollutants incoming to urban environments. Based on this survey, we assessed the pollution load and ecological risk indexes for metals. The existing literature was then surveyed to review the metal retention efficiency of GWIs, and possible biogeochemical processes related to inorganic metal compounds were proposed that could be integrated in biogeochemical models of GWIs.

Decomposition pathways and rates of human urine in soils.

This study proposes a comprehensive reaction network of the soil microbial breakdown of the main compounds in human urine to the end products NH3 and NH4⁺. A reactive model was developed and parameters were determined against experimental data. The model was used to assess the amount and release rate of NH3 and NH4⁺ in a soil control volume flushed with (i) pulses of urine at various dilutions and (ii) a continuous flow of urine at various dilutions and flow rates. In scenario i, 90% of incoming organic nitrogen was converted to NH3 and NH4⁺ between 5 and 20 days from application at rates strongly dependent on the initial microbial soil content. Urea and hippuric acid were largely correlated to NH3 and NH4⁺ release, whereas microbial functional groups in the same scenarios were poorly correlated with NH3 and NH4⁺ release. In scenario ii, 90% conversion was generally reached for low flow rates and was highly nonlinear with the dilution. Finally, a stochastic analysis showed that urine decomposition was more sensitive to uncertainty in microbial growth rate parameters than half-saturation concentrations.

Occasional large emissions of nitrous oxide and methane observed in stormwater biofiltration systems.

Designed, green infrastructures are becoming a customary feature of the urban landscape. Sustainable technologies for stormwater management, and biofilters in particular, are increasingly used to reduce stormwater runoff volumes and peaks as well as improve the water quality of runoff discharged into urban water bodies. Although a lot of research has been devoted to these technologies, their effect in terms of greenhouse gas fluxes in urban areas has not been yet investigated. We present the first study aimed at quantifying greenhouse gas fluxes between the soil of stormwater biofilters and the atmosphere. N2O, CH4, and CO2 were measured periodically over a year in two operational vegetated biofiltration cells at Monash University in Melbourne, Australia. One cell had a saturated zone at the bottom, and compost and hardwood mulch added to the sandy loam filter media. The other cell had no saturated zone and was composed of sandy loam. Similar sedges were planted in both cells. The biofilter soil was a small N2O source and a sink for CH4 for most measurement events, with occasional large emissions of both N2O and CH4 under very wet conditions. Average N2O fluxes from the cell with the saturated zone were almost five-fold greater (65.6 µg N2O-N m(-2) h(-1)) than from the other cell (13.7 µg N2O-N m(-2) h(-1)), with peaks up to 1100 µg N2O-N m(-2) h(-1). These N2O fluxes are of similar magnitude to those measured in other urban soils, but with larger peak emissions. The CH4 sink strength of the cell with the saturated zone (-3.8 µg CH4-C m(-2) h(-1)) was lower than the other cell (-18.3 µg CH4-C m(-2) h(-1)). Both cells of the biofilter appeared to take up CH4 at similar rates to other urban lawn systems; however, the biofilter cells displayed occasional large CH4 emissions following inflow events, which were not seen in other urban systems. CO2 fluxes increased with soil temperature in both cells, and in the cell without the saturated zone CO2 fluxes decreased as soil moisture increased. Other studies of CO2 fluxes from urban soils have found both similar and larger CO2 emissions than those measured in the biofilter. The results of this study suggest that the greenhouse gas footprint of stormwater treatment warrant consideration in the planning and implementation of engineered green infrastructures.

Effect of different jump distributions on the dynamics of jump processes.

The paper investigates stochastic processes forced by independent and identically distributed jumps occurring according to a Poisson process. The impact of different distributions of the jump amplitudes are analyzed for processes with linear drift. Exact expressions of the probability density functions are derived when jump amplitudes are distributed as exponential, gamma, and mixture of exponential distributions for both natural and reflecting boundary conditions. The mean level-crossing properties are studied in relation to the different jump amplitudes. As an example of application of the previous theoretical derivations, the role of different rainfall-depth distributions on an existing stochastic soil water balance model is analyzed. It is shown how the shape of distribution of daily rainfall depths plays a more relevant role on the soil moisture probability distribution as the rainfall frequency decreases, as predicted by future climatic scenarios.

Intertime jump statistics of state-dependent Poisson processes.

A method to obtain the probability distribution of the interarrival times of jump occurrences in systems driven by state-dependent Poisson noise is proposed. Such a method uses the survivor function obtained by a modified version of the master equation associated to the stochastic process under analysis. A model for the timing of human activities shows the capability of state-dependent Poisson noise to generate power-law distributions. The application of the method to a model for neuron dynamics and to a hydrological model accounting for land-atmosphere interaction elucidates the origin of characteristic recurrence intervals and possible persistence in state-dependent Poisson models.

State-dependent fire models and related renewal processes.

We introduce a general class of stochastic processes forced by instantaneous random fires (i.e., jumps) that reset the state variable x to a given value. Since in many physical systems the fire activity is often dependent on the actual value of the state variable, as in the case of natural fires in ecosystems and firing dynamics in neuronal activity, the frequency of fire occurrence is assumed to be state dependent. Such dynamics leads to independent interfire statistics--i.e., to renewal point processes. Various functions relating the frequency of fire occurrence to x(t) are analyzed and compared. The relation between the probabilistic dynamics of x(t) and the interfire statistics is derived and some exact probability distribution of both x(t) and the interfire times are obtained for systems with different degrees of complexity. After studying processes in which the fire activity is coupled only to a deterministic drift, we also analyze processes forced by either additive or multiplicative Gaussian white noise.

Probabilistic dynamics of some jump-diffusion systems.

Some exact solutions to the forward Chapman-Kolmogorov equation are derived for processes driven by both Gaussian and compound Poisson (shot) noise. The combined action of these two forms of white noise is analyzed in transient and equilibrium conditions for different jump distributions and additive Gaussian noise. Steady-state distributions with power-law tails are obtained for exponentially distributed jumps and multiplicative linear Gaussian noise. Two applications are discussed: namely, the virtual waiting-time or Takàcs process including Gaussian oscillations and a simplified model of soil moisture dynamics, in which rainfall is modeled as a compound Poisson process and fluctuations in potential evapotranspiration are Gaussian.

Similarity solutions of nonlinear diffusion problems related to mathematical hydraulics and the Fokker-Planck equation.

Similarity solutions of the shallow-water equation with a generalized resistance term are studied for open channel flows when both inertial and gravity forces are negligible. The resulting model encompasses various particular cases that appear, in addition to mathematical hydraulics, in diverse physical phenomena, such as gravity currents, creeping flows of Newtonian and non-Newtonian fluids, thin films, and nonlinear Fokker-Planck equations. Solutions of both source-type and dam-break problems are analyzed. Closed-form solutions are discussed, when possible, along with a qualitative study of two phase-plane formulations based on two different variable transformations.

Soil water balance and ecosystem response to climate change.

Some essential features of the terrestrial hydrologic cycle and ecosystem response are singled out by confronting empirical observations of the soil water balance of different ecosystems with the results of a stochastic model of soil moisture dynamics. The simplified framework analytically describes how hydroclimatic variability (especially the frequency and amount of rainfall events) concurs with soil and plant characteristics in producing the soil moisture dynamics that in turn impact vegetation conditions. The results of the model extend and help interpret the classical curve of Budyko, which relates evapotranspiration losses to a dryness index, describing the partitioning of precipitation into evapotranspiration, runoff, and deep infiltration. They also provide a general classification of soil water balance of the world ecosystems based on two governing dimensionless groups summarizing the climate, soil, and vegetation conditions. The subsequent analysis of the links among soil moisture dynamics, plant water stress, and carbon assimilation offers an interpretation of recent manipulative field experiments on ecosystem response to shifts in the rainfall regime, showing that plant carbon assimilation crucially depends not only on the total rainfall during the growing season but also on the intermittency and magnitude of the rainfall events.