Development, growth, and biomass simulations of two common wetland tree species in Texas.

Monitoring the health and condition of wetlands using biological assessments can serve as an effective tool for environmental managers to better evaluate and monitor the status and trends of their wetland ecosystems. Woody species can be used as conspicuous biological assessment tools due to their direct response to environmental change, such as hydrologic alteration. The purpose of this study is to use field-measured morphological measurement indices to develop and optimize tree growth parameters and growth curves using multi-model combination approach to improve tree biomass estimations. Field morphological investigations were conducted for two common wetland tree species in Texas. A range of morphological characteristics including leaf area index, height, and biomass was measured for black willow (Salix nigra Marsh) and green ash (Fraxinus pennsylvanica) sampled from 15 sites in a wetland near Cameron, Texas. The measured morphological parameters were used to optimize tree growth and development with the ALMANAC model. The developed tree growth parameters and growth curves were subsequently used in the APEX model to simulate tree biomass at the catchment scale. Both models accurately simulated biomass of trees growing in the wetland. This accurate biomass prediction will be useful to advance science to better monitor and assess wetland health on a large scale (e.g. national or global).

Modeling the Effects of Onsite Wastewater Treatment Systems on Nitrate Loads Using SWAT in an Urban Watershed of Metropolitan Atlanta.

Onsite wastewater treatment systems (OWTSs) can be a source of nitrogen (N) pollution in both surface and ground waters. In metropolitan Atlanta, GA, >26% of homes are on OWTSs. In a previous article, we used the Soil Water Assessment Tool to model the effect of OWTSs on stream flow in the Big Haynes Creek Watershed in metropolitan Atlanta. The objective of this study was to estimate the effect of OWTSs, including failing systems, on nitrate as N (NO-N) load in the same watershed. Big Haynes Creek has a drainage area of 44 km with mainly urban land use (67%), and most of the homes use OWTSs. A USGS gauge station where stream flow was measured daily and NO-N concentrations were measured monthly was used as the outlet. The model was simulated for 12 yr. Overall, the model showed satisfactory daily stream flow and NO-N loads with Nash-Sutcliffe coefficients of 0.62 and 0.58 for the calibration period and 0.67 and 0.33 for the validation period at the outlet of the Big Haynes Watershed. Onsite wastewater treatment systems caused an average increase in NO-N load of 23% at the watershed scale and 29% at the outlet of a subbasin with the highest density of OWTSs. Failing OWTSs were estimated to be 1% of the total systems and did not have a large impact on stream flow or NO-N load. The NO-N load was 74% of the total N load in the watershed, indicating the important effect of OWTSs on stream loads in this urban watershed.

Evaluation of new farming technologies in Ethiopia using the Integrated Decision Support System (IDSS).

This study investigates multi-dimensional impacts of adopting new technology in agriculture at the farm/village and watershed scale in sub-Saharan Africa using the Integrated Decision Support System (IDSS). Application of IDSS as an integrated modeling tool helps solve complex issues in agricultural systems by simultaneously assessing production, environmental, economic, and nutritional consequences of adopting agricultural technologies for sustainable increases in food production and use of scarce natural resources. The IDSS approach was applied to the Amhara region of Ethiopia, where the scarcity of resources and agro-environmental consequences are critical to agricultural productivity of small farm, to analyze the impacts of alternative agricultural technology interventions. Results show significant improvements in family income and nutrition, achieved through the adoption of irrigation technologies, proper use of fertilizer, and improved seed varieties while preserving environmental indicators in terms of soil erosion and sediment loadings. These pilot studies demonstrate the usefulness of the IDSS approach as a tool that can be used to predict and evaluate the economic and environmental consequences of adopting new agricultural technologies that aim to improve the livelihoods of subsistence farmers.

Survival of Manure-borne and Fecal Coliforms in Soil: Temperature Dependence as Affected by Site-Specific Factors.

Understanding pathogenic and indicator bacteria survival in soils is essential for assessing the potential of microbial contamination of water and produce. The objective of this work was to evaluate the effects of soil properties, animal source, experimental conditions, and the application method on temperature dependencies of manure-borne generic , O157:H7, and fecal coliforms survival in soils. A literature search yielded 151 survival datasets from 70 publications. Either one-stage or two-stage kinetics was observed in the survival datasets. We used duration and rate of the logarithm of concentration change as parameters of the first stage in the two-stage kinetics data. The second stage of the two-stage kinetics and the one-stage kinetics were simulated with the model to find the dependence of the inactivation rate on temperature. Classification and regression trees and linear regressions were applied to parameterize the kinetics. Presence or absence of two-stage kinetics was controlled by temperature, soil texture, soil water content, and for fine-textured soils by setting experiments in the field or in the laboratory. The duration of the first stage was predominantly affected by soil water content and temperature. In the model dependencies of inactivation rates on temperature, parameter estimates were significantly affected by the laboratory versus field conditions and by the application method, whereas inactivation rates at 20°C were significantly affected by all survival and management factors. Results of this work can provide estimates of coliform survival parameters for models of microbial water quality.

Effects of urbanization and climate change on stream health in north-central Texas.

Estimation of stream health involves the analysis of changes in aquatic species, riparian vegetation, microinvertebrates, and channel degradation due to hydrologic changes occurring from anthropogenic activities. In this study, we quantified stream health changes arising from urbanization and climate change using a combination of the widely accepted Indicators of Hydrologic Alteration (IHA) and Dundee Hydrologic Regime Assessment Method (DHRAM) on a rapidly urbanized watershed in the Dallas-Fort Worth metropolitan area in Texas. Historical flow data were split into pre-alteration and post-alteration periods. The influence of climate change on stream health was analyzed by dividing the precipitation data into three groups of dry, average, and wet conditions based on recorded annual precipitation. Hydrologic indicators were evaluated for all three of the climate scenarios to estimate the stream health changes brought about by climate change. The effect of urbanization on stream health was analyzed for a specific subwatershed where urbanization occurred dramatically but no stream flow data were available using the widely used watershed-scale Soil and Water Assessment Tool (SWAT) model. The results of this study identify negative impacts to stream health with increasing urbanization and indicate that dry weather has more impact on stream health than wet weather. The IHA-DHRAM approach and SWAT model prove to be useful tools to estimate stream health at the watershed scale.

Transferability of SWAT Models between SWAT2009 and SWAT2012.

In recent years, the Soil and Water Assessment Tool (SWAT) has experienced upgrades with enhanced functionalities and modeling capacities as it gets to the current version, SWAT2012. Changes in the SWAT code on a specific process may result in propagating influences in the output of other related processes. In this study, the characteristic significance of the enhancements in SWAT code was investigated using the two recent versions, SWAT2009 and SWAT2012. Using a global optimization technique, each model was calibrated for flow, sediment, and nutrient and then tested for transferability of parameters between the models. Results indicate that flow and water quality output were well calibrated with both models. However, the calibrated parameters determined by SWAT2009 and SWAT2012 were noticeably different, due mostly to the enhancements made in SWAT2012. Our results indicate that only the stream flow result was reliable when the models were upgraded or downgraded between the two versions after calibration. Sediment prediction was marginally reliable. SWAT parameters were nontransferrable if nutrient was the main output. The differences are due to various reasons, such as disparities in algorithms at the process level and propagation of the resulting uncertainty into higher-order processes.

Quantifying climate change impacts on future water resources and salinity transport in a high semi-arid watershed.

High salinity mobilization and movement from salt-laden deposits in semi-arid landscapes impair soils and water resources worldwide. Semi-arid regions worldwide are expected to experience rising temperatures and lower precipitation, impacting water supply and spatio-temporal patterns of salinity loads and affecting downstream water quality. This study quantifies the impact of future climate on hydrologic fluxes and salt loads in the Gunnison River Watershed (GRW) (14,608 km2), Colorado, using the APEX-MODFLOW-Salt hydro-chemical watershed model and three different CMIP5 climate models projection downscaled by Multivariate Adaptive Constructed Analogs (MACA) for the period 2020-2099. The APEX-MODFLOW-Salt model accounts for the reactive transport of major salt ions (SO42-, Cl-, CO32-, HCO3-, Ca2+, Na+, Mg2+, and K+) to streams via surface runoff, rainfall erosional runoff, soil lateral flow, quick return flow and groundwater-stream exchange. Model results are analyzed for spatial and temporal trends in water yield and salt loading pathways. Although streamflow is primarily derived from surface runoff (65%), the predominant source of salt loads is the aquifer (73%) due to elevated concentrations of groundwater salt. Annual salt loading from the watershed is 582 Mkg, approximately 10% of the salt load in the Colorado River measured at Lee's Ferry, AZ. For future climate scenarios, annual salt loads from the watershed increased between 4.1% and 9.6% from the historical period due to increased salt loading from groundwater and quick return flow. From the results, applying the APEX-MODFLOW-Salt model with downscaled future climate forcings can be a helpful modeling framework for investigating hydrology and salt mobilization, transport, and export in historical and predictive settings for salt-affected watersheds.

Integrating machine learning for enhanced wildfire severity prediction: A study in the Upper Colorado River basin.

Wildfires pose significant threats worldwide, requiring accurate prediction for mitigation. This study uses machine learning techniques to forecast wildfire severity in the Upper Colorado River basin. Datasets from 1984 to 2019 and key indicators like weather conditions and land use were employed. Random Forest outperformed Artificial Neural Network, achieving 72 % accuracy. Influential predictors include air temperature, vapor pressure deficit, NDVI, and fuel moisture. Solar radiation, SPEI, precipitation, and evapotranspiration also contribute significantly. Validation against actual severities from 2016 to 2019 showed mean prediction errors of 11.2 %, affirming the model's reliability. These results highlight the efficacy of machine learning in understanding wildfire severity, especially in vulnerable regions.

Cardiovascular hardware simulator and artificial aorta-generated central blood pressure waveform database according to various vascular ages for cardiovascular health monitoring applications.

This study presents a database of central blood pressure waveforms according to cardiovascular health conditions, to supplement the lack of clinical data in cardiovascular health research, constructed by a cardiovascular simulator. Blood pressure (BP) is the most frequently measured biomarker, and in addition to systolic and diastolic pressure, its waveform represents the various conditions of cardiovascular health. A BP waveform is formed by overlapping the forward and reflected waves, which are affected by the pulse wave velocity (PWV). The increase in vascular stiffness with aging increases PWV, and the PWV-age distribution curve is called vascular age. For cardiovascular health research, extensive data of central BP waveform is essential, but the clinical data published so far are insufficient and imbalanced in quantity and quality. This study reproduces the central BP waveform using a cardiovascular hardware simulator and artificial aortas, which mimic the physiological structure and properties of the human. The simulator can adjust cardiovascular health conditions to the same level as humans, such as heart rate of 40-100 BPM, stroke volume of 40-100 mL, and peripheral resistance of 12 steps. Also, 6 artificial aortas with vascular ages in the 20-70 were fabricated to reproduce the increase in vascular stiffness due to aging. Vascular age calculated from measured stiffness of artificial aorta and central BP waveform showed an error of less than 3 years from the clinical value. Through this, a total of 636 waveforms were created to construct a central BP waveform database according to controlled various cardiovascular health conditions.

Reproduction of human blood pressure waveform using physiology-based cardiovascular simulator.

This study presents a cardiovascular simulator that mimics the human cardiovascular system's physiological structure and properties to reproduce the human blood pressure waveform. Systolic, diastolic blood pressures, and its waveform are key indicators of cardiovascular health. The blood pressure waveform is closely related to the pulse wave velocity and the overlap of the forward and reflected pressure waves. The presented cardiovascular simulator includes an artificial aorta made of biomimetic silicone. The artificial aorta has the same shape and stiffness as the human standard and is encased with a compliance chamber. The compliance chamber prevents distortion of the blood pressure waveform from strain-softening by applying extravascular pressure. The blood pressure waveform reproduced by the simulator has a pressure range of 80-120 mmHg, a pulse wave velocity of 6.58 m/s, and an augmentation index of 13.3%. These values are in the middle of the human standard range, and the reproduced blood pressure waveform is similar to that of humans. The errors from the human standard values are less than 1 mmHg for blood pressure, 0.05 m/s for pulse wave velocity, and 3% for augmentation index. The changes in blood pressure waveform according to cardiovascular parameters, including heart rate, stroke volume, and peripheral resistance, were evaluated. The same pressure ranges and trends as in humans were observed for systolic and diastolic blood pressures according to cardiovascular parameters.

Correlation analysis of human upper arm parameters to oscillometric signal in automatic blood pressure measurement.

Cardiovascular diseases are the leading cause of global deaths, making cardiovascular health monitoring important. Measuring blood pressure using an automatic sphygmomanometer is the most widely used method to monitor cardiovascular health due to its accessibility, convenience, and strong correlation with cardiovascular diseases. In this work, in order to estimate brachial artery diameter, stiffness, or thickness using an automatic sphygmomanometer, the correlation between upper arm parameters and the oscillometric signal was intensively investigated through analytical, numerical, and experimental approaches. The parametric studies commonly revealed that the inner radius of the brachial artery is the most influential parameter in determining the amplitude of the oscillometric signal. The experimental results of using a cardiovascular simulator (a virtual patient) combined with upper arm phantoms with various inner radii of the brachial artery showed a 6.5% change in the oscillometric signal amplitude with a 10% artery radius variation. It was concluded that the oscillometric signal can be used to evaluate brachial artery diameter. Based on the clinical relationship between brachial artery diameter and cardiovascular risk factors such as hypertension, diabetes, and obesity, this study showed and verified a novel method to monitor brachial artery diameter and hence, cardiovascular risks while measuring blood pressure.

The impact of rainfall distribution methods on streamflow throughout multiple elevations in the Rocky Mountains using the APEX model-Price River watershed, Utah.

The hydrology of mountainous watersheds in the western United States is significantly influenced by snow year-round. It is widely known that topography affects precipitation; however, the knowledge of how watershed rainfall designation methods affect streamflow is not well understood for high-relief areas. The objectives of this study were to assess the predictive capability of the Agricultural Policy/Environmental eXtender (APEX) model to simulate streamflow in a snowmelt-dominated watershed with high spatial rainfall variability through (a) allocating weather stations to sub-basins based on a conventional Thiessen polygon method (CM) or a rainfall-elevation-based input (RE) and using an areal average Parameter-Elevation Regression on Independent Slopes Model (PRISM) rainfall designation and (b) improving the snowmelt processes in the Price River watershed, Utah. The updated APEX model with snowmelt parameters significantly improved spring flood simulation. The RE was the most robust method in snowmelt and seasonal streamflow simulations compared with the CM and PRISM rainfall designations. Adapting the APEX model to simulate snow-dominant complex terrains will provide crucial water quantity and quality predictions for reliable environmental and watershed management assessment.

Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions.

The quantification of uncertainty in the ensemble-based predictions of climate change and the corresponding hydrological impact is necessary for the development of robust climate adaptation plans. Although the equifinality of hydrological modeling has been discussed for a long time, its influence on the hydrological analysis of climate change has not been studied enough to provide a definite idea about the relative contributions of uncertainty contained in both multiple general circulation models (GCMs) and multi-parameter ensembles to hydrological projections. This study demonstrated that the impact of multi-GCM ensemble uncertainty on direct runoff projections for headwater watersheds could be an order of magnitude larger than that of multi-parameter ensemble uncertainty. The finding suggests that the selection of appropriate GCMs should be much more emphasized than that of a parameter set among behavioral ones. When projecting soil moisture and groundwater, on the other hand, the hydrological modeling equifinality was more influential than the multi-GCM ensemble uncertainty. Overall, the uncertainty of GCM projections was dominant for relatively rapid hydrological components while the uncertainty of hydrological model parameterization was more significant for slow components. In addition, uncertainty in hydrological projections was much more closely associated with uncertainty in the ensemble projections of precipitation than temperature, indicating a need to pay closer attention to precipitation data for improved modeling reliability. Uncertainty in hydrological component ensemble projections showed unique responses to uncertainty in the precipitation and temperature ensembles.

Linking watershed modeling and bacterial source tracking to better assess E. coli sources.

Terrestrial fate and transport processes of E. coli can be complicated by human activities like urbanization or livestock grazing. There is a critical need to address contributing sources of bacterial contamination, properly assess the management of critical sources, and ultimately reduce E. coli concentrations in impaired water bodies. In particular, characterization of wildlife animal contributions and other "background" input sources of microbial pollution are highly uncertain and data are scarce. This study attempts to identify critical sources of E. coli and the efficacy of conservation practices for mitigating E. coli concentrations in the Arroyo Colorado watershed, Texas, using a process-based hydrologic and water quality model. We propose to incorporate a bacterial source tracking assessment into the modeling framework to fill the gap in data on wildlife and human contribution. In addition, other sources identified through a GIS survey, national census, and local expert knowledge were incorporated into the model as E. coli sources. Results suggest that simulated distribution of E. coli sources significantly improved after incorporating this enhanced data on E. coli sources into the model (R2 = 0.90) compared to the SWAT result without BST (R2 = 0.59). Scenario assessments indicate that wildlife contributions may remain significant despite land use change and urbanization, expected to mostly occur in agricultural and range lands. A combination of nonpoint source management measures, voluntary implementation of advanced treatment by wastewater plants where possible, and installation of aerators in the zone of impairment were demonstrated to be effective measures for restoring the recreation and aquatic life uses of the Arroyo Colorado.

Development and evaluation of the bacterial fate and transport module for the Agricultural Policy/Environmental eXtender (APEX) model.

The Agricultural Policy/Environmental eXtender (APEX) is a watershed-scale water quality model that includes detailed representation of agricultural management. The objective of this work was to develop a process-based model for simulating the fate and transport of manure-borne bacteria on land and in streams with the APEX model. The bacteria model utilizes manure erosion rates to estimate the amount of edge-of-field bacteria export. Bacteria survival in manure is simulated as a two-stage process separately for each manure application event. In-stream microbial fate and transport processes include bacteria release from streambeds due to sediment resuspension during high flow events, active release from the streambed sediment during low flow periods, bacteria settling with sediment, and survival. Default parameter values were selected from published databases and evaluated based on field observations. The APEX model with the newly developed microbial fate and transport module was applied to simulate fate and transport of the fecal indicator bacterium Escherichia coli in the Toenepi watershed, New Zealand that was monitored for seven years. The stream network of the watershed ran through grazing lands with daily bovine waste deposition. Results show that the APEX with the bacteria module reproduced well the monitored pattern of E. coli concentrations at the watershed outlet. The APEX with the microbial fate and transport module will be utilized for predicting microbial quality of water as affected by various agricultural practices, evaluating monitoring protocols, and supporting the selection of management practices based on regulations that rely on fecal indicator bacteria concentrations.

An analytical model for predicting LNAPL distribution and recovery from multi-layered soils.

An analytical model was developed for estimating the distribution and recovery of light nonaqueous phase liquids (LNAPL) in heterogeneous aquifers. Various scenarios of LNAPL recovery may be simulated using LDRM for LNAPL recovery systems such as skimmer wells, water-enhanced wells, air-enhanced wells, and trenches from heterogeneous aquifers. LDRM uses multiple horizontal soil layers to model a heterogeneous aquifer. Up to three soil layers may be configured with unique soil properties for each layer. Simulation results suggest that LNAPL distribution and its recovery volume are highly affected by soil properties. In sandy soils LNAPL can be highly mobile and the recovery efficiency can be high. In contrast, even at high LNAPL saturations, LNAPL mobility is typically low in fine-grained soils. This characteristic of LNAPL with respect to soil texture has to be carefully accounted for in the model to better predict the recovery of LNAPL from heterogeneous soils. The impact of vertical hydraulic gradient in fine grain zone was assessed. A sensitivity analysis suggests that the formation LNAPL volume can be significantly affected by a downward vertical hydraulic gradient if the magnitude is near a critical amount (=ρr-1). Sensitivity of input parameters with respect to LNAPL formation in soils and LNAPL recovery volume were identified through a sensitivity analysis. The performance of LDRM on predicting the distribution and recovery of LNAP was reasonably accurate for a short-term analysis as demonstrated in a case study. However, further validation is needed to ascertain the model's performance in long-term simulations.