Assessing and managing design storm variability and projection uncertainty in a changing coastal environment.

Coastal urban infrastructure and water management programs are vulnerable to the impacts of long-term hydroclimatic changes and to the flooding and physical destruction of disruptive hurricanes and storm surge. Water resilience or, inversely, vulnerability depends on design specifications of the storm and inundation, against which water infrastructure and environmental assets are planned and operated. These design attributes are commonly derived from statistical modeling of historical measurements. Here we argue for the need to carefully examine the approach and associated design vulnerability in coastal areas because of the future hydroclimatic changes and large variability at local coastal watersheds. This study first shows significant spatiotemporal variations of design storm in the Chesapeake Bay of the eastern U.S. Atlantic coast, where the low-frequency high-intensity precipitations vary differently to the tropical cyclones and local orographic effects. Average and gust wind speed exhibited much greater spatial but far less temporal variability than the precipitation. It is noteworthy that these local variabilities are not fully described by the regional gridded precipitation used in CMIP5 climate downscaling and by NOAA's regional design guide Atlas-14. Up to 46.4% error in the gridded precipitation for the calibration period 1950-1999 is further exacerbated in the future design values by the ensemble of 132 CMIP5 projections. The total model projection error (δM) up to -61.8% primarily comes from the precipitation regionalization (δ1), climate downscaling (δ2), and a fraction from empirical data modeling (δE). Thus, a post-bias correction technique is necessary. The bias-corrected design wind speed for 10-yr to 30-yr storms has small changes <20% by the year 2100, but contains large spatial variations even for stations of close proximity. Bias-corrected design precipitations are characteristic of large spatial variability and a notable increase of 2-5 year precipitation in the future along western shores of the Lower and Middle Chesapeake Bay. All these accounts point to the potential vulnerability of water infrastructure and water program in coastal areas, when the hydrological design basis using regional values fails to account for significant spatiotemporal precipitation variations in local coastal watersheds.

Reconstruction of sea-land interactions between terrestrial vegetation cover and water quality constituents in the Mattapoisett Harbor area during the 1991 Hurricane Bob event.

Hurricane Bob passed over the New England region in August 1991, causing significant damage to life, property, and the environment, making it one of the costliest hurricanes in New England history. The environmental impact of a hurricane of this magnitude warrants careful assessment to devise preventive measures and mitigation strategies to bolster water resources management programs against future events. In this paper, we show the reconstructed simultaneous impacts of Hurricane Bob on the vegetative cover of the Mattapoisett river watershed and the water quality of the Mattapoisett Harbor with the aid of remote sensing for earth observations. The water quality impacts, especially in terms of Total Organic Carbon (TOC) and Sea Surface Salinity (SSS), can be identified from variations of SSS and TOC near coastal estuaries due to the influx of freshwater from the coastal Mattapoisett River to the continent-ocean transition between natural tides and bay waves. Using the Landsat satellite images, the Normalized Difference Vegetation Index (NDVI) and water quality constitutes (TOC and SSS) were reconstructed and retrieved for the assessment of the sea-land interactions during the Hurricane Bob event in 1991. Results indicate phenomenal interactive patterns between the harbor and the coastal watershed, as well as the riverine system. TOC and NDVI, especially in the upper watershed region, can be strongly correlated with hurricane impacts according to the singular value decomposition analysis.

A Greedy Sampling Design Algorithm for the Modal Calibration of Nodal Demand in Water Distribution Systems.

This paper presents a greedy optimization algorithm for sampling design to calibrate WDS hydraulic model. The proposed approach starts from the existing sensors and sequentially adds one new sensor at each optimization simulation step. In each step, the algorithm tries to minimize the calibration prediction uncertainty. The new sensor is installed in the location where the uncertainty is greatest but also sensitive to other nodes. The robustness of the proposed approach is tested under different spatial and temporal demand distribution. We found that both the number of sensors and the perturbation ratio affect the calibration accuracy as defined by the average nodal pressure deviation itself and its variability. The plot of the calibration accuracy versus the number of sensors can reasonably guide the trade-off between model calibration accuracy and number of sensors placed or the cost. This proposed approach is superior in calibration accuracy and modeling efficiency when compared to the standard genetic algorithm (SGA) and Monte Carlo Sampling algorithm (MCS).

Deriving suitability factors for CA-Markov land use simulation model based on local historical data.

Multiple Criteria Evaluation (MCE) is a multi-attributes decision making tool often used in land suitability analysis and land use simulation using Cellular Automata (CA)-Markov model. The goal of this research is to explore the feasibility of using historical data of a study area to select, score, and weight factors quantitatively in the MCE. We have developed logistic regression models fitted by the historical land use changes to select and score each potential factor, and used the Entropy method to determine weights for the selected factors. The MCE output is then used as the input of CA-Markov model to simulate land use changes from 2001 to 2011. The land use simulation result was compared against observed 2011 land use in order to examine the performance of the updated MCE method. The result shows that the use of MCE factors derived from historical data produces reasonable goodness of fit, based on current literature. The major advantage of the updated MCE method is that the factor selection, scores, and weights are all derived from local data reflecting the actual historical trend. This quantitative approach also allows one to efficiently calibrate CA-Markov model and develop different land use planning scenarios by adjusting scores and weights for different factors with the knowledge of historical change.

Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models.

Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intra-annual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment.

Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers' behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority).

Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead.

Adjustment of the water treatment process to changes in water quality is a focus area for engineers and managers of water treatment plants. The desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada, in the water-stressed western U.S. Long-term monthly averaged results showed no simultaneous impact from forest fire events on accelerating the rise of TSS concentration. However, the results showed a probable impact of a decade of drought on increasing TSS concentration in the Colorado River Arm and Overton Arm. Results of the forecasting model highlight the reservoir water level as a significant parameter in predicting TSS in Lake Mead. In addition, the R-squared value of 0.98 and the root mean square error of 0.5 between the observed and predicted TSS values demonstrates the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake.

Water chemistry impacts on arsenic mobilization from arsenopyrite dissolution and secondary mineral precipitation: implications for managed aquifer recharge.

Managed aquifer recharge (MAR) is a water reuse technique with the potential to meet growing water demands. However, MAR sites have encountered arsenic mobilization resulting from recharge operations. To combat this challenge, it is imperative to identify the mechanisms of arsenic mobilization during MAR. In this bench-scale study, arsenic mobilization from arsenopyrite (FeAsS) was characterized for conditions relevant to MAR operations. Experimentally determined activation energies for arsenic mobilization from FeAsS under aerobic conditions were 36.9 ± 2.3 kJ/mol for 10 mM sodium chloride, 40.8 ± 3.5 kJ/mol for 10 mM sodium nitrate, and 43.6 ± 5.0 kJ/mol for secondary effluent from a wastewater treatment plant. Interestingly, the sodium chloride system showed higher arsenic mobilization under aerobic conditions. In addition, secondary mineral precipitation varied among systems and further affected arsenic mobilization. For example, the wastewater system inhibited precipitation, while in the sodium chloride system, faster phase transformation of iron(III) (hydr)oxide precipitates was observed, resulting in hematite formation after 7 days. The phase transformation to hematite will result in less available surface area for arsenic attenuation. These new observations and activation energies can be useful to develop improved reactive transport models for the fate of arsenic during MAR, and develop strategies to minimize arsenic release.

Low-wind and other microclimatic factors in near-road black carbon variability: A case study and assessment implications.

Airborne black carbon from urban traffic is a climate forcing agent and has been associated with health risks to near-road populations. In this paper, we describe a case study of black carbon concentration and compositional variability at and near a traffic-laden multi-lane highway in Cincinnati, Ohio, using an onsite aethalometer and filter-based NIOSH Method 5040 measurements; the former measured 1-min average black carbon concentrations and the latter determined the levels of organic and elemental carbon (OC and EC) averaged over an approximately 2-h time interval. The results show significant wind and temperature effects on black carbon concentration and composition in a way more complex than predicted by Gaussian dispersion models. Under oblique low winds, namely ux [= u × sin(g=q)]~ (0,-0.5 m s-1), which mostly occurred during morning hours, black carbon concentrations per unit traffic flow were highest and had large variation. The variability did not always follow Gaussian dispersion but was characteristic of a uniform distribution at a near-road distance. Under all other wind conditions, the near-road black carbon variation met Gaussian dispersion characteristics. Significant differences in roadside dispersion are observed between OC and EC fractions, between PM2.5 and PM10-2.5, and between the morning period and rest of the day. In a general case, the overall black carbon variability at the multi-lane highway can be stated as bimodal consisting of Gaussian dispersion and non-Gaussian uniform distribution. Transition between the two types depends on wind velocity and wind angle to the traffic flow. In the order of decreasing importance, the microclimatic controlling factors over the black carbon variability are: 1) wind velocity and the angle with traffic; 2) diurnal temperature variations due to thermal buoyancy; and 3) downwind Gaussian dispersion. Combinations of these factors may have created various traffic-microclimate interactions that have significant impact on near-road black carbon transport.

Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example.

The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.

Arsenic mobilization and attenuation by mineral-water interactions: implications for managed aquifer recharge.

Managed aquifer recharge (MAR) has potential for addressing deficits in water supplies worldwide. It is also widely used for preventing saltwater intrusion, maintaining the groundwater table, and augmenting ecological stream flows, among many other beneficial environmental applications. However, field MAR sites have experienced arsenic mobilization from aquifer formation minerals due to induced changes in groundwater chemistry. To address this environmental concern, it is crucial to understand the potential sources and sinks impacting arsenic mobilization. This paper outlines important mineral-water interactions that can occur at MAR sites. Detailed information on minerals of concern, physiochemical processes for arsenic mobilization or attenuation, and the potential impact of microbial activity and hydrology on these processes is provided. Based on these mineral-water interactions, guidelines for predicting arsenic mobility are presented, and recommendations are made concerning MAR site monitoring. The review emphasizes important aspects in correlating interfacial reactions to reactive transport modeling and elucidating future challenges, a first step toward developing safer and more sustainable MAR operations.

Second-order chlorine decay and trihalomethanes formation in a pilot-scale water distribution systems.

It is well known that model-building of chlorine decay in real water distribution systems is difficult because chlorine decay is influenced by many factors (e.g., bulk water demand, pipe-wall demand, piping material, flow velocity, and residence time). In this paper, experiments were run to investigate the kinetic model of chlorine decay and the formation model of trihalomethanes (THMs) in pilot-scale water distribution systems. Experimental results show that the rate constants of chlorine decay, including wall decay and bulk decay, increasing with temperature. Moreover, the kinetic model of chlorine decay and the formation model of THMs describe experiment data of pilot-scale water distribution systems. The effect of different piping material on chlorine decay and THMs formation were also investigated. The rate constants of chlorine decay are ranked in order: stainless steel pipe, ductile iron pipe, and last, polyethelene pipe because wall decay is the largest in stainless steel pipe than that in other piping material. Correspondingly, the rate of THMs formation follows the order of stainless steel pipe, ductile iron pipe, and last, polyethelene pipe because of less chlorine in bulk water reacting with the trihalomethane formation potential (THMFP).

Optimal expansion of a drinking water infrastructure system with respect to carbon footprint, cost-effectiveness and water demand.

Urban water infrastructure expansion requires careful long-term planning to reduce the risk from climate change during periods of both economic boom and recession. As part of the adaptation management strategies, capacity expansion in concert with other management alternatives responding to the population dynamics, ecological conservation, and water management policies should be systematically examined to balance the water supply and demand temporally and spatially with different scales. To mitigate the climate change impact, this practical implementation often requires a multiobjective decision analysis that introduces economic efficiencies and carbon-footprint matrices simultaneously. The optimal expansion strategies for a typical water infrastructure system in South Florida demonstrate the essence of the new philosophy. Within our case study, the multiobjective modeling framework uniquely features an integrated evaluation of transboundary surface and groundwater resources and quantitatively assesses the interdependencies among drinking water supply, wastewater reuse, and irrigation water permit transfer as the management options expand throughout varying dimensions. With the aid of a multistage planning methodology over the partitioned time horizon, such a systems analysis has resulted in a full-scale screening and sequencing of multiple competing objectives across a suite of management strategies. These strategies that prioritize 20 options provide a possible expansion schedule over the next 20 years that improve water infrastructure resilience and at low life-cycle costs. The proposed method is transformative to other applications of similar water infrastructure systems elsewhere in the world.

Development of the Metropolitan Water Availability Index (MWAI) and short-term assessment with multi-scale remote sensing technologies.

Global climate change will influence environmental conditions including temperature, surface radiation, soil moisture, and sea level, and it will also significantly impact regional-scale hydrologic processes such as evapotranspiration (ET), precipitation, runoff, and snowmelt. The quantity and quality of water available for drinking and other domestic usage is also likely to be affected by changes in these processes. Consequently, it is necessary to assess and reflect upon the challenges ahead for water infrastructure and the general public in metropolitan regions. One approach to the problem is to use index-based assessment, forecasting and planning. The drought indices previously developed were not developed for domestic water supplies, and thus are insufficient for the purpose of such an assessment. This paper aims to propose and develop a "Metropolitan Water Availability Index (MWAI)" to assess the status of both the quantity and quality of available potable water sources diverted from the hydrologic cycle in a metropolitan region. In this approach, the accessible water may be expressed as volume per month or week (i.e., m(3)/month or m(3)/week) relative to a prescribed historical record, and such a trend analysis may result in final MWAI values ranging from -1 to +1 for regional water management decision making. The MWAI computation uses data and information from both historical point measurements and spatial remote-sensing based monitoring. Variables such as precipitation, river discharge, and water quality changes at drinking water plant intakes at specific locations are past "point" measurements in MWAI calculations. On the other hand, remote sensing provides information on both spatial and temporal distributions of key variables. Examples of remote-sensing images and sensor network technologies are in-situ sensor networks, ground-based radar, air-borne aircraft, and even space-borne satellites. A case study in Tampa Bay, Florida is described to demonstrate the short-term assessment of the MWAI concept at a practical level. It is anticipated that such a forecasting methodology may be extended for middle-term and long-term water supply assessment.

Real-Time Water Distribution System Hydraulic Modeling Using Prior Demand Information by Formal Bayesian Approach.

Real-time water distribution system (WDS) hydraulic models are used in water utilities to facilitate the planning and operation of the water distribution system. As a critical model input, spatiotemporally varying nodal water demands significantly affect the performance and applicability of such WDS models. Thus, real-time nodal demands must be calibrated for reliability before their use. The main difficulty for real-time calibration is the lack of observed data sufficient to determine thousands of nodal demands accurately in a network. To address the difficulty, this study proposes a formal Bayesian approach to determine nodal demands in WDS hydraulic modeling by explicitly taking prior water demand information into account and coupling more information to constrain the nodal water demand modeling. Application of the approach on a simple hypothetical network and a field network in a city of eastern Zhejiang Province, China demonstrates that by adding prior information, the nodal demand can be uniquely determined in real time. The approach limits uncertainty propagation and improves the robustness of the real-time model calibration and analysis.

Trihalomethane precursor reactivity changes in drinking water treatment unit processes during a storm event.

Source water quality can significantly impact the efficacy of water treatment unit processes and the formation of chlorinated and brominated trihalomethanes (THMs). Current water treatment plant performance models may not accurately capture how source water quality variations, such as organic matter variability, can impact treatment unit processes. To investigate these impacts, a field study was conducted wherein water samples were collected along the treatment train for 72 hours during a storm event. Systematic sampling and detailed analyses of water quality parameters, including non-purgeable organic carbon (NPOC), UV absorbance, and THM concentrations, as well as chlorine spiking experiments, reveal how the THM formation potential changes in response to treatment unit processes. Results show that the NPOC remaining after treatment has an increased reactivity towards forming THMs, and that brominated THMs form more readily than chlorinated counterparts in a competitive reaction. Thus both the reactivity and quantity of THM precursors must be considered to maintain compliance with drinking water standards, a finding that should be incorporated into the development of model-assisted treatment operation and optimization. Advanced granular activated carbon (GAC) treatment beyond conventional coagulation-flocculation-sedimentation processes may also be necessary to remove the surge loading of THM-formation precursors during a storm event.

Treatment performance comparison between regular O3-BAC and O3-BAC with rear sand filtration: verification in a full-scale study.

BACKGROUND: To improve the microbial safety of drinking water, an arrangement of O3-BAC with rear sand filtration (O3-BAC-sand) has been proposed. In this study, efforts were devoted to evaluate the benefits and drawbacks of O3-BAC-sand in a full-scale water treatment plant. The performance of the two configurations was compared in terms of particles, turbidity, CODMn and typical odorants and pesticides. RESULTS: The O3-BAC-sand yielded lower turbidity but higher CODMn (by approximately 7%) in the finished water than regular O3-BAC (sand-O3-BAC). Both systems removed odors in raw water; however, sand-O3-BAC was more effective on septic and musty odorants. The total pesticide removals by sand-O3-BAC and O3-BAC-sand were 78% and 72%, respectively; though the latter had shorter activated carbon durable years. CONCLUSION: The re-location of the sand filter would sacrifice the BAC efficiency in removals of organic matter and micropollutants. This tradeoff is a result of the loss of the particulate organic matter removal by sand filters, because locating the sand filter behind BAC causes particle load increase on BAC; some measures of enhanced coagulation should be suggested to improve the turbidity and particle removal. The study will be helpful for improvement of the O3-BAC process in drinking water treatment.

Demand-Driven Spatiotemporal Variations of Flow Hydraulics and Water Age by Comparative Modeling Analysis of Distribution Network.

Distribution network modeling is often used to investigate and manage water quality variations in a distribution network. It relies on pipe network simplification through skeletonization and uses water demand patterns that are often generalized or derived historical monthly water usage records. As automatic water meter reading and supervisory control and data acquisition (SCADA) technologies are widely used, it is possible now to explore the hydraulic complexity in the network. Processes such as stochastic and pulse water demand on solute transport characteristics can be investigated. Fidelity and appropriateness of network modeling by network simplification can be quantified. In this paper, these two questions are assessed by using real-time water demand measurements and comparative network simulations for an independent segment of a large water utility in the U.S. An all-pipe all-demand (APAD) model and an hourly demand variation curves (HDVC) demand model are simulated for the same network operations. The results show the prevalence of intermittent and pulse water demand particularly in network perimeters and dead-end branches. The results also highlight different node hydraulic properties such as Re, water age, and flow oscillation when water demand in APAD model is replaced by HDVC-based time-continuous generalized demand patterns. The degrees of such difference varies specific to the distribution network configurations such as H-loop, branches and dead-ends. These additional insights provide further understanding of the varying flow properties and their impacts on the movement of water parcels in pipe configurations. It is suggested that APAD network simulation be used for accuracy-demanding water quality simulation.

The dependence of chlorine decay and DBP formation kinetics on pipe flow properties in drinking water distribution.

Simultaneous chlorine decay and disinfection byproduct (DBP) formation have been discussed extensively because of their regulatory and operational significance. This study further examines chemical reaction variability in the water quality changes under various hydrodynamic conditions in drinking water distribution. The variations of kinetic constant for overall chlorine decay (kE) and trihalomethane (THM) formation were determined under stagnant to turbulent flows using three devices of different wall demand and two types of natural organic matters (NOM) in water. The results from the comparative experiments and modeling analyses show the relative importance of wall demand (kw), DBP-forming chlorine decay (kD), and other bulk demand (kb') for pipe flows of Re = 0-52500. It is found that chlorine reactivity of virgin NOM is the overriding factor. Secondly, for tap water NOM of lower reactivity, pipe flow properties (Re or u) can significantly affect kE, the THM yield (T), formation potential (Y), and the time to reach the maximum THM concentration (tmax) through their influence on kinetic ratio kD(kb'+kw). These observations, corroborating with turbidity variations during experiments, cannot be explained alone by chlorine dispersion to and from the pipe wall. Mass exchanges through deposition and scale detachment, most likely being flow-dependent, may have contributed to the overall chlorine decay and DBP formation rates. Thus for the simultaneous occurrence of chlorine decay and DBP formation, model considerations of NOM reactivity, pipe types (wall demand), flow hydraulics, and their interactions are essential.

An approach to measure parameter sensitivity in watershed hydrological modelling.

Hydrological responses vary spatially and temporally according to watershed characteristics. In this study, the hydrological models that we developed earlier for the Little Miami River (LMR) and Las Vegas Wash (LVW) watersheds in the USA were used for detailed sensitivity analyses. To compare the relative sensitivities of the hydrological parameters of these two models, we used normalized root mean square error (NRMSE). By combining the NRMSE index with the flow duration curve analysis, we derived an approach to measure parameter sensitivities under different flow regimes. Results show that the parameters related to groundwater are highly sensitive in the LMR watershed, whereas the LVW watershed is primarily sensitive to near-surface and impervious parameters. The high and medium flows are more impacted by most of the parameters. The low flow regime was highly sensitive to groundwater-related parameters. Moreover, our approach is found to be useful in facilitating model development and calibration.