Comprehensive study on parameter sensitivity for flow and nutrient modeling in the Hydrological Simulation Program Fortran model

Numerous parameters are used to construct the HSPF (Hydrological Simulation Program Fortran) model, which results in significant difficulty in calibrating the model. Parameter sensitivity analysis is an efficient method to identify important model parameters. Through this method, a model’s calibration process can be simplified on the basis of understanding the model’s structure. This study investigated the sensitivity of the flow and nutrient parameters of HSPF using the DSA (differential sensitivity analysis) method in the Xitiaoxi watershed, China. The results showed that flow was mostly affected by parameters related to groundwater and evapotranspiration, including DEEPFR (fraction of groundwater inflow to deep recharge), LZETP (lower-zone evapotranspiration parameter), and AGWRC (base groundwater recession), and most of the sensitive parameters had negative and nonlinear effects on flow. Additionally, nutrient components were commonly affected by parameters from land processes, including MON-SQOLIM (monthly values limiting storage of water quality in overland flow), MON-ACCUM (monthly values of accumulation), MON-IFLW-CONC (monthly concentration of water quality in interflow), and MON-GRND-CONC (monthly concentration of water quality in active groundwater). Besides, parameters from river systems, KATM20 (unit oxidation rate of total ammonia at 20 °C) had a negative and almost linear effect on ammonia concentration and MALGR (maximal unit algal growth rate for phytoplankton) had a negative and nonlinear effect on ammonia and orthophosphate concentrations. After calibrating these sensitive parameters, our model performed well for simulating flow and nutrient outputs, with R ² and E_NS (Nash–Sutcliffe efficiency) both greater than 0.75 for flow and greater than 0.5 for nutrient components. This study is expected to serve as a valuable complement to the documentation of the HSPF model to help users identify key parameters and provide a reference for performing sensitivity analyses on other models.     

Salting our landscape: an integrated catchment model using readily accessible data to assess emerging road salt contamination to streams

A new integrated catchment model for salinity has been developed to assess the transport of road salt from upland areas in watersheds to streams using readily accessible landscape, hydrologic, and meteorological data together with reported salt applications. We used Fishkill Creek (NY) as a representative watershed to test the model. Results showed good agreement between modeled and measured stream water chloride concentrations. These results suggest that a dominant mode of catchment simulation that does not entail complex deterministic modeling is an appropriate method to model salinization and to assess effects of future applications of road salt to streams. We heuristically increased and decreased salt applications by 100% and results showed that stream chloride concentrations increased by 13% and decreased by 7%, respectively. The model suggests that future management of salt application can reduce environmental concentrations, albeit over some time.     

Directional biases in back trajectories caused by model and input data

Back trajectory analyses are often used for source attribution estimates in visibility and other air quality studies. Several models and gridded meteorological datasets are readily available for generation of trajectories. The Big Bend Regional Aerosol and Visibility Observational (BRAVO) tracer study of July to October 1999 provided an opportunity to evaluate trajectory methods and input data against tracer concentrations, particulate data, and other source attribution techniques. Results showed evidence of systematic biases between the results of different back trajectory model and meteorological input data combinations at Big Bend National Park during the BRAVO. Most of the differences were because of the choice of meteorological data used as input to the trajectory models. Different back trajectories also resulted from the choice of trajectory model, primarily because of the different mechanisms used for vertical placement of the trajectories. No single model or single meteorological data set was found to be superior to the others, although rawinsonde data alone are too sparse in this region to be used as the only input data, and some combinations of model and input data could not be used to reproduce known attributions of tracers and simulated sulfate.     

Estimating receptor sensitivity to spatial proximity of emissions sources

Spatial proximity of emissions sources to receptors may affect sensitivity to potential adverse human health effects. This research investigates whether receptor sensitivity to the location of emission sources can be utilized efficiently to minimize health risk in selecting sites for industrial enterprises, thermal electric stations, etc. A sensitivity function that is independent of the location of pre-existing emission sources is derived and applied to Minsk, Belarus. The function estimates exposures based on weather and climatic conditions as well as the distribution of population density at a given locality. Arraying prospective sites based on their sensitivity function magnitude provides a technique for minimizing health risk based on receptor sensitivity to the spatial proximity of atmospheric emissions sources.     

The sensitivity of surface waters of Great Britain to acidification predicted from catchment characteristics

Using a combination of soil, land use and geological information, a map of Great Britain has been derived which indicates the sensitivity of surface waters to acidification. For the geological information, a slightly modified version of an available map was used which indicated the sensitivity of groundwaters to acidification. For soils, 1-km databases of soil information for England and Wales and for Scotland were employed to map the soil sensitivity as determined by buffering capacity. The derived soils map was modified to take account of agricultural liming in arable and managed grassland areas using the ITE Land Classification. The final map of surface water sensitivity was obtained by using a geographic information system overlay procedure which enabled each combination of soil and geology sensitivity to be uniquely defined. The final sensitivity classification was based upon expert knowledge and the experience of a similar sensitivity mapping exercise for Wales.     

Employing sensitivity analysis to catchments having scanty data

Environmental Science and Pollution Research - Sensitivity analysis (SA) is generally desirable for parameter optimization, mapping, and calibration in hydrological models, yet the implementation...     

Predicting ozone-induced soybean losses: sensitivity to model form and inputs

In this study, alternative dose-response equations for assessing the effects of O3 on soybeans (Glycine max (L.) Merr.) were established. For each of three soybean cultivars, three models (linear, quadratic, and Weibull) were fitted to relate different measures of O3 dose, during the soybean flowering maturity period, to the soybean yield. The dose measures were 7-h (9:00 a.m. to 4:00 p.m.) and 12-h (7:00 a.m. to 7:00 p.m.) means, 7-h and 12-h total doses, and the 90th and 75th percentile O3 concentrations. Using data for primarily rural and small city O3 monitoring sites, county-level O3 doses were calculated, and soybean losses due to O3 were predicted for Illinois, Kentucky, and Virginia. The sensitivity of O3-induced soybean loss predictions to model forms and inputs was determined with regard to: (1) inter-year differences in ambient O3, (2) differences among the six dose measures, (3) differences among the three different model forms, (4) the impact of the agricultural practice of double-cropped soybean production, and (5) variance in response to O3 among three different soybean cultivars.     

Integrated catchment modeling for nutrient reduction: scenarios showing impacts, potential, and cost of measures

A hydrological-based model (HBV-NP) was applied to a catchment (1900 km2) in the southern part of Sweden. Careful characterization of the present load situation and the potential for improved treatment or reduced soil leaching were analyzed. Several scenarios were modeled to find strategies to reach the Swedish environmental goals of reducing anthropogenic nitrogen load by 30% and phosphorus load by 20%. It was stated that the goals could be reached by different approaches that would affect different polluters and social sectors. However, no single measure was enough by itself. Instead, a combination of measures was necessary to achieve the goals. The nitrogen goal was the most difficult to attain. In order to be cost-effective, these measures should be applied to areas contributing the most to the net loading of the sea. This strategy could reduce the costs by 70%-80% when compared with implementing the measures in the entire catchment. Integrated catchment models may thus be helpful tools for reducing costs in environmental control programs.     

Individual sensitivity distribution evaluation from survival data using a mechanistic model: implications for ecotoxicological risk assessment

Two main alternatives are typically used to model mechanistically dose-survival relationship in ecotoxicity tests. Effects are related to a concentration of concern, for instance body concentration, and, to account for their differences relative to time-to-death, individuals have either different concentration thresholds for death (“individual tolerance approach”), or equal probability to die, with death occurring randomly (“stochastic death approach”). A general framework to unify both approaches has recently been proposed. We derived a model from this framework to analyse five datasets (daphnids exposed to selenium, guppies exposed to dieldrin and second, third and fourth instars chironomids exposed to copper), by extending the standard stochastic death approach. We showed the possibility to estimate properly the toxicity parameters together with inter-organisms differences of sensitivity for at least one of these parameters (here the threshold for effect). For the daphnids, there was no improvement of using the extended model, which confirms the expected low variability among genetically identical individuals. For all the other datasets, our model outperformed the standard approach without accounting for differences of sensitivity. We estimated coefficients of variations in the distribution of the logarithm of the threshold from 44% to 4% and showed, for chironomids, a decrease of inter-individual differences of sensitivity with the age of the larvae. All standard threshold estimates were close but above the medium value of the distribution in the new approach, which means that a concentration equal to the standard threshold would ultimately result in the death of more than half of the exposed organisms. A more relevant parameter, such as the concentration protecting 95% of the population, would be 2-4 times inferior to the standard threshold.     

Sensitivity of the DERMA long-range Gaussian dispersion model to meteorological input and diffusion parameters

The Danish Emergency Response Model of the Atmosphere (DERMA) is described and applied to the first ETEX experiment. By using analysed low-resolution numerical weather-prediction data from the global model of the European Centre for Medium-range Weather Forecast (ECMWF) as well as higher-resolution data from two versions of the High Resolution Limited Area Model (HIRLAM), which are operational at the Danish Meteorological Institute (DMI), the sensitivity of DERMA to the resolution of meteorological data is analysed by comparing DERMA results with concentration measurements. Furthermore, the sensitivity to boundary-layer height and diffusion parameters is studied. These parameters include the critical bulk Richardson number, which is used to estimate the atmospheric boundary-layer height, the horizontal eddy diffusivity and the Lagrangian turbulence time scale. The parameters, which provide the best performance of DERMA, are 0.25 for the critical bulk Richardson number, 6×10³ m² s^-1 for the horizontal eddy diffusivity, and 3 h for the Lagrangian time scale. DERMA is much more sensitive to boundary-layer parameters when using high-resolution DMI-HIRLAM data than when using data of lower resolution from the ECMWF. Finally, the bulk Richardson number method of boundary-layer height calculation applied to DMI-HIRLAM data is verified directly against routine radiosondes released under the tracer gas plume. The boundary-layer height estimates based on analysed NWP model data agree well with observations, and the agreement deteriorates as a function of forecast length.     

A model to predict the pollution potential with the use of Airways Surface Observation data

An atmospheric diffusion model of the Lettau-box type is developed, tested and applied. The principle attribute of the model is that readily available Airways Surface Observation data is used to estimate the mixing depth. In addition, the model does not assume that specific emitter-receptor location information be available. The test of the model yields added support to the validity of several physical relationships found in the literature. An application of the model emphasizes the value of Holzworth's “pollution potential” in its use of making comparisons between different cities.     

Comparison among model estimates of critical loads of acidic deposition using different sources and scales of input data

The critical load (CL) of acidic atmospheric deposition represents the load of acidity deposited from the atmosphere to the earth’s surface at which harmful acidification effects on sensitive biological receptors are thought to occur. In this study, the CL for forest soils was estimated for 27 watersheds throughout the United States using a steady-state mass balance approach based on both national and site-specific data and using different approaches for estimating base cation weathering. Results suggested that the scale and source of input data can have large effects on the calculated CL and that the most important parameter in the steady-state model used to estimate CL is base cation weathering. These results suggest that the data and approach used to estimate weathering must be robust if the calculated CL is to be useful for its intended purpose.     

Evaluating the sensitivity of a subsurface multicomponent reactive transport model with respect to transport and reaction parameters.

The input variables for a numerical model of reactive solute transport in groundwater include both transport parameters, such as hydraulic conductivity and infiltration, and reaction parameters that describe the important chemical and biological processes in the system. These parameters are subject to uncertainty due to measurement error and due to the spatial variability of properties in the subsurface environment. This paper compares the relative effects of uncertainty in the transport and reaction parameters on the results of a solute transport model. This question is addressed by comparing the magnitudes of the local sensitivity coefficients for transport and reaction parameters. General sensitivity equations are presented for transport parameters, reaction parameters, and the initial (background) concentrations in the problem domain. Parameter sensitivity coefficients are then calculated for an example problem in which uranium(VI) hydrolysis species are transported through a two-dimensional domain with a spatially variable pattern of surface complexation sites. In this example, the reaction model includes equilibrium speciation reactions and mass transfer-limited non-electrostatic surface complexation reactions. The set of parameters to which the model is most sensitive includes the initial concentration of one of the surface sites, the formation constant (Kf) of one of the surface complexes and the hydraulic conductivity within the reactive zone. For this example problem, the sensitivity analysis demonstrates that transport and reaction parameters are equally important in terms of how their variability affects the model results.     

Attempt to model laboratory-scale diffusion and retardation data

Different approaches for measuring the interaction between radionuclides and rock matrix are needed to test the compatibility of experimental retardation parameters and transport models used in assessing the safety of the underground repositories for the spent nuclear fuel. In this work, the retardation of sodium, calcium and strontium was studied on mica gneiss, unaltered, moderately altered and strongly altered tonalite using dynamic fracture column method. In-diffusion of calcium into rock cubes was determined to predict retardation in columns. In-diffusion of calcium into moderately and strongly altered tonalite was interpreted using a numerical code FTRANS. The code was able to interprete in-diffusion of weakly sorbing calcium into the saturated porous matrix. Elution curves of calcium for the moderately and strongly altered tonalite fracture columns were explained adequately using FTRANS code and parameters obtained from in-diffusion calculations. In this paper, mass distribution ratio values of sodium, calcium and strontium for intact rock are compared to values, previously obtained for crushed rock from batch and crushed rock column experiments. Kd values obtained from fracture column experiments were one order of magnitude lower than Kd values from batch experiments.     

Acid-sensitive waters of the English Lake District: a steady-state model of streamwater chemistry in the upper Duddon catchment

Data on deposition and streamwater chemistry, obtained for the upper catchment of the River Duddon in the 1970s and 1980s, are reviewed. These data, together with soil chemical data, are used to deduce key processes in the deposition-catchment interaction, the analysis being based on current concepts of acidification. The processes are incorporated into a steady-state model that allows streamwater compositions to be calculated. The large baseflow pH range (5-7) of Duddon streams is accounted for in the model by a range of base cation weathering rates. Other processes invoked are evapotranspiration, the uptake of nitrogen by plants, dissolution of Al(OH)(3) in the mineral soil, precipitation of Al(OH)(3) in the baserock zone and in streamwater, Al(3+) hydrolysis, and reactions of the carbonate system. Both cation exchange and sulphate adsorption are ignored, because they are assumed to influence rates-of-change between steady states, but not steady-state water compositions per se. The model can be used to estimate variations in streamwater composition with flow. Model calculations suggest that a 50% decrease in depositional acidifying components (sulphur oxides and NH(4)(+)) would result in increases of up to 1 pH unit in streamwaters with present-day baseflow pH values of 5 or less. It appears that water quality in the upper Duddon is currently more sensitive to inputs of NH(4)(+) than of H(2)SO(4). To improve the reliability of model predictions, more information is required on (a) the pH dependence of base cation weathering, (b) transformations involving nitrogen, (c) aluminium chemistry and (d) partial pressures of CO(2) in soil and baserock.     

Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain

The photochemical grid model, UAM-V, has been used by regulatory agencies to make decisions concerning emissions controls, based on studies of the July 1995 ozone episode in the eastern US. The current research concerns the effect of the uncertainties in UAM-V input variables (emissions, initial and boundary conditions, meteorological variables, and chemical reactions) on the uncertainties in UAM-V ozone predictions. Uncertainties of 128 input variables have been estimated and most range from about 20% to a factor of two. 100 Monte Carlo runs, each with new resampled values of each of the 128 input variables, have been made for given sets of median emissions assumptions. Emphasis is on the maximum hourly-averaged ozone concentration during the 12–14 July 1995 period. The distribution function of the 100 Monte Carlo predicted domain-wide maximum ozone concentrations is consistently close to log-normal with a 95% uncertainty range extending over plus and minus a factor of about 1.6 from the median. Uncertainties in ozone predictions are found to be most strongly correlated with uncertainties in the NO₂ photolysis rate. Also important are wind speed and direction, relative humidity, cloud cover, and biogenic VOC emissions. Differences in median predicted maximum ozone concentrations for three alternate emissions control assumptions were investigated, with the result that (1) the suggested year-2007 emissions changes would likely be effective in reducing concentrations from those for the year-1995 actual emissions, that (2) an additional 50% NO_x emissions reductions would likely be effective in further reducing concentrations, and that (3) an additional 50% VOC emission reductions may not be effective in further reducing concentrations.     

Development of the hybrid cells in series model to simulate ammonia nutrient pollutant transport along the Umgeni River

Discharge of organic waste results in high nutrient pollution of the water bodies which is a major menace to the environment. A high quantity of nutrients such as ammonia causes a reduction in the dissolved oxygen level and induces algal growth in the water bodies. Water quality models have been the tools to evaluate the rate at which streams can disperse the pollutants they receive. Many water quality models are flawed either because of their inadequacy to completely simulate the advection component of the pollutant transport, or because of the limited application of the models, due to inaccurate estimation of model parameters. The hybrid cell in series (HCIS) developed by Ghosh et al. (2004) has been able to overcome such difficulties associated with the mixing cell-based models. Thus, the current study focuses on developing an analytical solution for the pollutant transport of the ammonia concentration through the plug flow, the first and second well-mixed cells of the HCIS model. The HCIS model coupled with the first order kinetic equation for ammonia nutrient was developed to simulate the ammonia pollutant concentration in the water column. The ammonia concentration at various points along the river system was assessed by considering the effects of the transformation of ammonia to nitrite, the uptake of ammonia by the algae, the respiration rate of the algae and the input of benthic source to the ammonia concentration in the water column. The proposed model was tested using synthetic data, and the HCIS-NH₃ model simulations for spatial and temporal variation of ammonia pollutant transport were analysed. The simulated results of the HCIS-NH₃ model agreed with the Fickian-based advection-dispersion equation (ADE) for simulating ammonia concentration solved using an explicit finite difference scheme. The HCIS-NH₃ model also showed a good agreement with the observed data from the Umgeni River, except during rainy periods.     

Estimating CO2 emissions using a fractional grey Bernoulli model with time power term

Environmental Science and Pollution Research - Global warming caused by CO2 emissions will directly harm the health and quality of life of people. Accurate prediction of CO2 emissions is highly...