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[1] We address the growing need for accurate water temperature predictions in regulated rivers to inform decision support systems and protect aquatic habitats. Although many suitable river temperature models exist, few simultaneously model water temperature dynamics while considering uncertainty of predictions and assimilating observations. Here, we employ a stochastic dynamics approach to water temperature modeling that estimates both the water temperature state and its uncertainty by propagating error through a physically based dynamical system. This method involves converting the governing hydrodynamic and heat transport equations into a state space form and assimilating observations via the Kalman Filter. This model, called the River Assessment for Forecasting Temperature (RAFT), closes the heat budget by tracking heat movement using a robust semi-Lagrangian numerical scheme. RAFT considers key thermodynamic processes, including advection, longitudinal dispersion, atmospheric heat fluxes, lateral inflows, streambed heat exchange, and unsteady nonuniform flow. Inputs include gridded meteorological forecasts from a numerical weather prediction model, bathymetric cross-sectional geometry, and temperature and flow measurements at the upstream boundary and tributaries. We applied RAFT to an ∼100 km portion of the Sacramento River in California, downstream of Keswick Dam (a regulatory dam below Shasta Dam), at a spatial resolution of 2 km and a temporal resolution of 15 min. Model prediction error over a 6 month calibration period was on the order of 0.5°C. When temperature and flow gage data were assimilated, the mean prediction error was significantly less (0.25°C). The model accurately predicts the magnitude and timing of diel temperature fluctuations and can provide 72 h water temperature forecasts when linked with meteorological forecasts and real-time flow/temperature monitoring networks. RAFT is potentially scalable to model and forecast fine-grained one-dimensional temperature dynamics covering a broad extent in a variety of regulated rivers provided that adequate input data are available.

[1] A new framework for simulating the El Nino/Southern Oscillation (ENSO) using a generalized linear model (GLM) is provided. The GLM provides a versatile and computationally inexpensive method for investigating ENSO dynamics, by conditioning an ENSO index on an arbitrary set of input variables. Here the system state (El Nino/neutral/La Nina) at previous times is combined with the first few principal components of sea surface temperature (SST) and thermocline depth. Despite having relatively few degrees of freedom, the model accurately reproduces 20th century SST time series, seasonal variance, power spectra, and autocorrelation functions for both the eastern and western Pacific. The GLM also has good overall forecast skill, especially at subyearly lead times; performance is competitive with models currently used for operational ENSO forecasting. The model is then used to examine changes to El Nino/La Nina statistics under CO2 increases, by using the GLM to represent simulations run with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM), versions 3.5 and 4. GLM simulations of 21st century CCSM4 changes to El Nino/La Nina magnitudes show insignificant results, despite a slight increase in El Nino persistence. GLM fits conditioned on millennial stabilized CCSM3.5 simulations with varying CO2 levels, however, show a weakening and shortening of El Nino events as CO2 concentration increases, whereas La Nina events become markedly stronger and do not change significantly in length. The reduction in El Nino persistence in CCSM3.5 is consistent with previous results showing that at higher CO2 levels, a stronger seasonal cycle creates a Southern Hemisphere “seasonal footprint” leading to more efficient El Nino termination.

[1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

[1] Observations of 408 monsoon low-pressure systems (MLPSs) including 196 monsoon depressions (MDs) that formed in the Bay of Bengal during the 1951–2007 period, and the gridded analysis of daily rainfall fields for the same period, were used to identify the association of antecedent rainfall (1 week average rainfall prior to the genesis of MLPS) with the genesis of MLPS and length of inland penetration by MDs. Prestorm rainfall is treated as a surrogate to prestorm ground wetness conditions due to unavailability of historical soil-moisture data over the monsoon region. These observations were analyzed using self-organizing maps (SOMs) to group nine different prestorm monsoon rainfall patterns into different transition states like active, active-to-break, break-to-active, break, etc. The analysis indicates that MLPS are four times more likely to form on a day during active monsoon state compared to break state. Analysis of MLPSs linked to each monsoon state represented by SOM nodes shows that MDs with higher inland penetration were associated with higher antecedent rainfall. On the other hand, there was no significant difference in low-level atmospheric circulation for MDs with shortest and longest inland penetration.

[1] Despite recent challenges, conventional wisdom has held that heating over the Tibetan Plateau leads to increased Indian summer monsoon rainfall via enhancement of cross-equatorial circulation aloft, and a concurrent strengthening of both the Somali Jet and westerly winds that bring moisture to southern India. We show that such heating, quantified by monthly estimates of moist static energy in the atmosphere just above the surface, correlates with summer monsoon rainfall, but only in the early (20 May to 15 June) and late (September 1 to 15 October) monsoon season. Correlations during the main monsoon season (15 June to 31 August) are small and insignificant. The positive correlations with early and late monsoon season, however, allow for heating over Tibet to modulate as much as ~30% of the total rainfall. Furthermore, we demonstrate that heating over Tibet is independent of the El Nino Southern Oscillation, so that together they explain a substantial portion of variability in the early and late season rainfall, providing potential predictability. These links may also explain the wet conditions over India during early Holocene time and provide a quantitative link between a rise of Tibet and stronger Somali Jet.

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non-sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non-sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high-resolution LiDAR snow depth observations at six separate 1.17-km2 sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross-validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R2. Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR-measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross-validation bias at the spatial scale of inference. We model at the 30-m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R2) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R2 (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R2 scores obtained by LiDAR-measured predictors. Under cross validation, the RMSE and R2 skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30-m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross-validation bias but also maximize true R2. The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.

Humans have substantially altered the thermal regimes of freshwater habitats worldwide, with significant environmental consequences. There is a critical need for a comprehensive modeling framework for forecasting the downstream impacts of two of the most common anthropogenic structures that alter river water temperatures: 1) dams that selectively release water from thermally stratified reservoirs, and 2) power generating stations and industrial plants that use river water for once-through cooling. These facilities change the thermal dynamics of the downstream waters through a complex interaction of water release volume and temperature and the subsequent exchange with the environment downstream. In order to stay within the downstream temperature limits imposed by regulatory agencies, managers must monitor not just release volumes and temperatures, but also need to be able to forecast the thermal impacts of their day-to-day operations on habitat which may be hundreds of kilometers downstream. Here we describe a coupled modeling framework that links mesoscale weather and ecological models to generate inputs for a physically-based water temperature model for monitoring and forecasting river temperatures downstream from these facilities at fine spatiotemporal scales. We provide an example of how this modeling framework is being applied to a water allocation decision support system (DSS) for the management of Endangered Species Act (ESA) listed salmon species in the Sacramento River in California.

We develop a multiproxy, reduced-dimension methodology to blend magnesium-calcium (Mg/Ca) and alkenone (U 37k) paleo sea surface temperature (SST) records from the eastern and western equatorial Pacific, to recreate snapshots of full field SSTs and zonal winds from 10 to 2ka B.P. in 2000year increments. Single-proxy reconstructions (Mg/Ca only versus U 37K only) reveal differences in the timing and duration of maximum cooling across the east-central equatorial Pacific. The largest zonal temperature differences (average west Pacific SST minus average east Pacific SST) occur at 6ka B.P. for the Mg/Ca-only reconstruction (0.61 degrees C) and at 10 and 4ka for the U 37K-only reconstruction (0.55 degrees C and 0.47 degrees C, respectively). Disagreements between SST trends suggested by each proxy call for methods that can resolve the common patterns between each and have motivated the work presented in this study. In combining inferences from these proxies, we treat both Mg/Ca and U 37K reconstructions of SST as annual average values, but we recognize that they may be sensitive to different seasons. In the multiproxy reconstruction, the zonal SST difference is largest at 10ka (0.26 degrees C), with coldest SST anomalies of approximate to-0.9 degrees C in the eastern equatorial Pacific and concurrent easterly maximum zonal wind anomalies of 7ms(-1) throughout the central Pacific. From 10 to 2ka, the entire equatorial Pacific warms, but at a faster rate in the east than the west, and the average central Pacific easterly winds weaken gradually to approximately 2ms(-1). These patterns are broadly consistent with previous inferences of reduced El Nino-Southern Oscillation variability associated with a La Nina-like state during the early to middle Holocene.

By building on a recently introduced genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel methodology to compute construction univariate and bivariate construction safety risk at a situational level. Our fully data-driven approach provides construction practitioners and academicians with an easy and automated way of extracting valuable empirical insights from databases of unstructured textual injury reports. By applying our methodology on an attribute and outcome dataset directly obtained from 814 injury reports, we show that the frequency-magnitude distribution of construction safety risk is very similar to that of natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we introduce univariate and bivariate nonparametric stochastic safety risk generators, based on Kernel Density Estimators and Copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithfully to the original data, allowing safetyrelated decision-making under uncertainty to be grounded on extensive empirical evidence. Just like the accurate modeling and simulation of natural phenomena such as wind or streamflow is indispensable to successful structure dimensioning or water reservoir management, we posit that improving construction safety calls for the accurate modeling, simulation, and assessment of safety risk. The underlying assumption is that like natural phenomena, construction safety may benefit from being studied in an empirical and quantitative way rather than qualitatively which is the current industry standard. Finally, a side but interesting finding is that attributes related to high energy levels and to human error emerge as strong risk shapers on the dataset we used to illustrate our methodology.

Though new water supply source development has always required planning under many forms of uncertainty, climate change presents an added dimension that may exacerbate supply and water quality challenges. Hence, new decision-support tools are needed, and in this paper an approach that incorporates the uncertainty of climate variability and change into a cost assessment framework for a municipal drinking water provider in Colorado is developed and applied. The water utility provider is developing a new source of water supply, but treatment costs are relatively high because of the advanced processes needed to treat the water to desired standards. Furthermore, the new water source has variable salinity concentrations that are not removed by any of the implemented treatment processes, requiring blending with an existing lower salinity water source. This results in an increase in the finished water salinity (as compared with the existing water source), which can have negative impacts on customer satisf...

Over the past three decades, hydrologists and water resources specialists have been concerned with the issue of nonstationarity arising from several factors. First is the effect of human intervention on the landscape that may cause changes in the precipitation–runoff relationships at various temporal and spatial scales. Second is the occurrence of natural events such as volcanic explosions or forest fires that may cause changes in the composition of the air, the soil surface, and geomorphology. Third is the low-frequency component of oceanic–atmospheric phenomena that may have significant effects on the variability of hydrological processes such as annual runoff, peak flows, and droughts. Fourth is global warming, which may cause changes to oceanic and atmospheric processes, thereby affecting the hydrological cycle at various temporal and spatial scales. There has been a significant amount of literature on the subject and thousands of research and project articles and books published in recent decades. Examples of human intrusion on the landscape are the changes in land use resulting from agricultural developments in semiarid and arid lands (e.g., Pielke et al. 2007, 2011), changes caused by large-scale deforestation (e.g., Gash and Nobre 1997), changes resulting from open-pit mining operations (e.g., Salas et al. 2008), and changes from increasing urbanization in watersheds (e.g., Konrad and Booth 2002, Villarini et al. 2009). These intrusions change hydrologic response characteristics such as the magnitude and timing of floods. In many situations, current systems and management practices will be ill equipped to cope with such changes unless adjustments are made. Large-scale landscape changes such as deforestation in the tropical regions can potentially alter atmospheric circulation patterns, and consequently affect global weather and climate (e.g., Lee et al. 2008, 2009). Major natural events, such as the volcanic explosion of Mount St. Helens in 1980 or the El Chichon volcanic explosion of 1982 induce a shock to the climate system in the form of global cooling that continues for several years. These events can also affect global circulation. Low-frequency climate drivers of the oceanic– atmospheric system such as the El Nino/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) modulate global climate at interannual and multidecadal time scales. These drivers are the main sources of nonstationarity in global climate and hydrology. Large numbers of papers documenting the effect of these drivers on global hydroclimatology continue to emerge (e.g., Dilley and Heyman 1995; Mantua et al. 1997; Enfield et al. 2001; Akintug and Rasmussen 2005; Hamlet et al. 2005). In addition to climate variability and change due to the previously mentioned factors, anthropogenic warming of the oceans and atmosphere because of increased greenhouse gas concentrations and the ensuing changes to the hydrologic cycle are topics of serious pursuit. The international scientific community is making strides in understanding the potential warming and its effects on all aspects of climate variability [Intergovernmental Panel on Climate Change (IPCC) 2007], but the impacts on the hydrologic cycle remain debatable and inconclusive (e.g., Cohn and Lins 2005; Legates et al. 2005; Hirsch and Ryberg 2011). Based on analyses of the global mean CO2 (GMCO2) and annual flood records in the United States, no strong statistical evidence for flood magnitudes increasing with GMCO2 increases were found (Hirsch and Ryberg 2011). Although general circulation models have had success in the attribution of warming global temperatures to anthropogenic causes, their credibility and utility in reproducing variables that are relevant to hydrology and water resources applications is less clear. For example, the IPCC Report for Latin America acknowledges that “the current GCMs do not produce projections of changes in the hydrological cycle at regional scales with confidence. In particular the uncertainty of projections of precipitation remain high : : :That is a great limiting factor to the practical use of such projections for guiding active adaptation or mitigation policies” (Magrin et al. 2007; Boulanger et al. 2007). A variety of methods exist that address the concern of nonstationarity in hydrological processes and the topic remains an active research area. For example, in watersheds in which increasing urbanization has been documented causing significant effects in the flood response and magnitude, watershed modeling has been utilized to estimate the possible changes in the flood frequency and magnitude. Frequency analysis methods also have been applied when the parameters (or the moments such as the mean and variance) of a given model (e.g., the Gumbel model) may vary with time (e.g., Strupczewski et al. 2001; Clarke 2002). In addition, the role that low-frequency components of the oceanic– atmospheric system (represented, for example, by large-scale oscillations such as ENSO, PDO, and AMO) have on extreme events such as floods has been recognized. These large-scale forcing factors have been shown to exert in-phase and out-of-phase oscillations in the magnitude of floods, mean flows, and droughts

An analysis of annual hydroclimatic variability in the Upper Colorado River basin (UCRB) for the period of 1906–2006 was performed to understand the dominant modes of multidecadal variability. First, wavelet-based spectral analysis was employed for streamflow at Lees Ferry, Arizona (aggregate location for UCRB flow), which identified two significant modes: a “low frequency” (~64-yr period) mode and a strong “decadal” (~15-yr period) component active only in recent decades. Subsequent investigation of temperature and precipitation data for the UCRB indicated that the low-frequency variability is associated with temperature via modulation of runoff efficiency while the decadal is strongly tied to moisture delivery. Simple hydrology and climate model experiments are also provided to support the aforementioned findings. Correlation of UCRB precipitation with global sea surface temperature (SST) anomalies showed a strong link with the equatorial and northern Pacific during periods of heightened variability of the decadal mode. The correlation of UCRB temperature with global SST anomalies showed strongest values in the Atlantic consistent with the Atlantic multidecadal oscillation mode. Wavelet spectral analysis of paleo-reconstructed streamflow at Lees Ferry shows both the low-frequency and decadal flow variability features. Furthermore, the strength of the decadal mode is modulated at an ~75-yr time scale, and these are consistent with epochal variations of overall streamflow variance.

Stochastic weather generators are commonly used to simulate time series of daily weather, especially minimum (Tmin) and maximum (Tmax) temperature and amount of precipitation. Recently, generalized linear models (GLM) have been proposed as a convenient approach to fit- ting weather generators. One limitation of weather generators is a marked tendency to underesti- mate the observed interannual variance in monthly, seasonal, or annual total precipitation and mean temperature, termed the 'overdispersion' phenomenon. In this study, aggregated statistics, consisting of seasonal total precipitation and mean Tmin and Tmax, are introduced as additional covariates into the GLM weather generator. With an appropriate degree of smoothing of these aggregated statistics, this approach is shown to virtually eliminate overdispersion when applied to 2 sites, Pergamino and Pilar, in the Argentine Pampas. The addition of these covariates does not distort the performance of the weather generator in other respects, such as annual cycles in the probability of precipitation and in the mean Tmin and Tmax. For seasonal total precipitation, the reduction in overdispersion is partially attributable to a corresponding reduction in the overdisper- sion of the frequency of precipitation occurrence, as well as to apparent temporal trends or 'regime' shifts. For seasonal mean Tmin and Tmax, the reduction in overdispersion is largely due to temporal trends on an interannual time scale.

Rule extraction is a promising technique for developing or fine-tuning supervisory control strategies in buildings. Three data mining techniques are examined that extract rules from offline model predictive control (MPC) results for a mixed mode building operated during the cooling season: generalized linear models (GLM), classification and regression trees (CART), and adaptive boosting. All rules were able to recover approximately 90% of the original optimizer energy savings under open loop tests, but the GLM-based rules saw significant performance degradation under simulated tests. CART and boost rules only degraded in performance by a few percentage points, still retaining the vast majority of optimizer savings (84% and 93% for the CART and boost rules, respectively). The results demonstrate that the proposed rule extraction techniques may allow building automation systems to achieve near-optimal supervisory control strategies without online MPC systems, although further research is required to broadly...

Open-source software development is the next stage in the evolution of product development, particularly software products. Compared with the prevailing proprietary approaches, open-source software products are developed by co-opting external developers and prospective users. Although a core group of developers might still play a key role in the initial design and development, a notable aspect of the open-source software paradigm is the role of peripheral developers in the enhancement and popularization of the product. Peripheral developers are not formal members of the core development team. They voluntarily contribute their time and creative talent in improving the quality of the product or in popularizing the product through word-of-mouth advocacy. As volunteers, they are not subject to the traditional hierarchical controls, nor are they contractually obligated. Peripheral developers represent a novel and unique aspect of open-source software development, and there is a greater interest in tapping their potential. However, there has been limited evidence about how and when their participation has beneficial impacts. We examine how peripheral developers contribute to product quality and diffusion by utilizing longitudinal data on 147 open-source software products. Hierarchical linear modeling analysis indicates that peripheral developers make significant contributions to product quality and diffusion, especially on projects that are in the more mature stages of product development.

Traditional stochastic simulation methods that are crafted to capture measures such as mean, variance and skew fail to reproduce significant spectral properties of the observed data. A growing body of literature indicates that many geo-physical data, especially streamflow, exhibit quasi-periodic and non-stationary variability driven by large scale climate features. Thus, methods which accurately model this behavior, in particular, the time evolution of variability, frequency of wet/dry epochs, etc. are crucial for risk assessment and management of water resources. In this paper, a Wavelet based Auto Regression Modeling (WARM) framework is proposed for data with significant non-stationary spectral features. This approach has four broad steps – (i) the wavelet transform of a time series is reconstructed as several periodic components based on dominant variability frequencies, (ii) scale averaged wavelet power (SAWP) is computed for each band to capture the time varying power and the components are scaled by this, (iii) Auto Regressive (AR) models fit to the scaled components and, (iv) simulations are performed from the AR models, rescaled and combined to obtain simulations of the original time series. Step (ii) is a new and unique departure from the WARM proposed by Kwon et al. (2007). We demonstrate this approach on annual streamflow at the Lee’s Ferry gauge on the Colorado River. Furthermore, this is coupled with a spatial disaggregation method to generate streamflow ensembles at multiple locations upstream. We also show that this combination captures the spectral properties at several locations in a parsimonious manner.

For central India and its west coast, rainfall in the early (15 May–20 June) and late (15 September–20 October) monsoon season correlates with Pacific Ocean sea-surface temperature (SST) anomalies in the preceding month (April and August, respectively) sufficiently well, that those SST anomalies can be used to predict such rainfall. The patterns of SST anomalies that correlate best include the equatorial region near the dateline, and for the early monsoon season (especially since ~1980), a band of opposite correlation stretching from near the equator at 120°E to ~25°N at the dateline. Such correlations for both early and late monsoon rainfall and for both regions approach, if not exceed, 0.5. Although correlations between All India Summer Monsoon Rainfall and typical indices for the El Nino-Southern Oscillation (ENSO) commonly are stronger for the period before than since 1980, these correlations with early and late monsoon seasons suggest that ENSO continues to affect the monsoon in these seasons. We exploit these patterns to assess predictability, and we find that SSTs averages in specified regions of the Pacific Ocean in April (August) offer predictors that can forecast rainfall amounts in the early (late) monsoon season period with a ~25% improvement in skill relative to climatology. The same predictors offer somewhat less skill (~20% better than climatology) for predicting the number of days in these periods with rainfall greater than 2.5 mm. These results demonstrate that although the correlation of ENSO indices with All India Rainfall has decreased during the past few decades, the connections with ENSO in the early and late parts have not declined; that for the early monsoon season, in fact, has grown stronger in recent decades.

Numerous studies have developed new methods for skillful long-lead seasonal streamflow forecasts, especially in the western United States, and most assume that the streamflow forecast skills translate into improved water resources decision making, but there has been little comprehensive demonstration of this. This translation is not straightforward because the decision system is nonlinear. This paper develops a prototype decision support system (DSS) to systematically evaluate the translation of streamflow forecast skills to water resource operational variables and decision variables in the Gunnison River basin (GRB). The DSS consists of two main modules: (1) a multimodel ensemble streamflow forecast module that uses large-scale climate information and issues forecast ensembles, and (2) a decision support module (developed in the generalized decision tool, RiverWare) that captures the seasonal operations and management of the GRB water resources system. Ensembles of streamflow forecast scenarios drive the...

There is increasing interest in decentralization of wastewater collection and treatment systems. However, there have been no systematic studies of the performance of small treatment facilities compared with larger plants. A statistical analysis of 4 years of discharge monthly report (DMR) data from 210 operating wastewater treatment facilities was conducted to determine the effect of average flow rate and capacity utilization on effluent biochemical oxygen demand (BOD), total suspended solids (TSS), ammonia, and fecal coliforms relative to permitted values. Relationships were quantified using generalized linear models (GLMs). Small facilities (40 m 3 /d) had violation rates greater than 10 times that of the largest facilities (400,000 m 3 /d) for BOD, TSS, and ammonia. For facilities with average flows less than 40,000 m 3 /d, increasing capacity utilization was correlated with increased effluent levels of BOD and TSS. Larger facilities tended to operate at flows closer to their design capacity while maintaining treatment suggesting greater efficiency.

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.

Climatic fluctuations have profound effects on water resources variability in the western United States. The research reported herein centers on streamflow predictability at the medium- and long-range scales in rivers that originate in Colorado. Specifically, we want to improve forecasting seasonal and yearly streamflows based on atmospheric-oceanic forcing factors, such as geopotential height, wind, and sea surface temperature, as well as hydrologic factors, such as snow water equivalent. The approach followed in the study involves searching for potential predictors, applying principal component analysis (PCA) and multiple linear regression (MLR) for forecasting at individual sites, canonical correlation analysis (CCA) for forecasting at multiple sites, and testing the forecasts using various performance measures. The analysis includes comparisons of forecasts by using various combinations of possible predictors, such as hydrologic, atmospheric, and oce- anic variables. The study brought into relevance the significant benefits of using atmospheric, oceanic, and hydrological predictors for long- range streamflow forecasting. It has been shown that forecasts based on PCA applied to individual sites give very good results for both seasonal and yearly timescales. We also found that although PCA has been applied on a site-by-site basis, the forecasts approximated well the historical cross correlations, although some underestimation was noted for two sites. Furthermore, the forecasts based on CCAwere less efficient than those based on PCA. DOI: 10.1061/(ASCE)HE.1943-5584.0000343. © 2011 American Society of Civil Engineers. CE Database subject headings: Forecasting; Streamflow; Stochastic processes; Colorado; Hydrologic data. Author keywords: Forecasting streamflows; Atmospheric/oceanic predictors; Flow prediction; Stochastic analysis; PCA; CCA.

Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested. The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method.

A series of model-predictive control (MPC) techniques have been explored for optimizing control sequences for window operation in mixed-mode (MM) buildings using EnergyPlus, and results for a simplified MM office building have been presented. Initial results for a small office in Boulder, Colorado show the ability to save upwards of 40% of cooling energy through near-optimal night cooling strategies, even in existing facilities. Strategies can be tuned to avoid overcooling the space by introducing heating energy into the objective function used in the MPC process. A complementary statistical technique has been introduced that allows for the “extraction” of logistic decision models from the optimal control results. The process works best when some time-lagged information is present as a predictor variable to ensure that some process memory is preserved. A generalized linear model (GLM) in the form of a multi-logistic regression was able to mimic the general characteristics of the optimizer results, achieving 70–90% of optimizer energy savings, but at a small fraction of the computational expense. Given the simple mathematical formulation of the logistic regression, it would be possible to implement this sort of decision model into modern direct digital control systems to control MM buildings in a near-optimal manner in real time.

Motivating people to contribute knowledge has become an important research topic and a major challenge for organizations. In order to promote knowledge sharing, managers need to under-stand the mechanism that drives individuals to contribute their valuable knowledge. Several theories have been applied to study knowledge-sharing behavior. However, the research set-tings and findings are often inconsistent. In this study, we use the social exchange theory as the basis to develop an extended model to explain knowledge-sharing behavior and include IT con-text as a moderator. A meta-analysis on 52 reported studies was conducted to examine how different factors in the social exchange theory affect knowledge-sharing behavior. The findings indicate that the social exchange theory plays an important role underlying individuals' knowledge-sharing behavior. The results also demonstrate that social interaction and trust can enhance individual's knowledge-sharing behavior. Furthermore, IT facilitation can moderate the effect of social interaction.

Constant exposure to the environment makes highway construction highly dependent on weather. However, highway construction contracts are often unclear about the potential influence of weather-related delays on highway construction project schedules. There is a need to discourage litigation arising from weather-related delays by including in contracts a reasonable number of nonwork days as a consequence of adverse weather and providing an equitable criteria for the course of action when the predictions in the contracts turn out to be inaccurate. To address this need, an integrated framework consisting of the following two key components is proposed: (1) identification of attributes of weather that cause construction delays and (2) generation of synthetic weather sequences using a stochastic weather generator to quantify and provide probabilistic forecasts of weather threshold values. The utility of this framework is demonstrated through its application to construction work on a project in Texas. The use of probabilistic forecast of construction delay attributes provided by a semiparametric weather generator in this research is an example of interdisciplinary study to help address this problem. The result of the research is better decision support for agencies who wish to author contracts that more equitably allow for the influence of weather during construction.

A new method to quantify changes in El Niño–Southern Oscillation (ENSO) variability is presented, using the overlap between probability distributions of the wavelet spectrum as measured by the wavelet probability index (WPI). Examples are provided using long integrations of three coupled climate models. When subsets of Niño-3.4 time series are compared, the width of the confidence interval on WPI has an exponential dependence on the length of the subset used, with a statistically identical slope for all three models. This exponential relationship describes the rate at which the system converges toward equilibrium and may be used to determine the necessary simulation length for robust statistics. For the three models tested, a minimum of 250 model years is required to obtain 90% convergence for Niño-3.4, longer than typical Intergovernmental Panel on Climate Change (IPCC) simulations. Applying the same decay relationship to observational data indicates that measuring ENSO variability with 90% confidence requires approximately 240 years of observations, which is substantially longer than the modern SST record. Applying hypothesis testing techniques to the WPI distributions from model subsets and from comparisons of model subsets to the historical Niño-3.4 index then allows statistically robust comparisons of relative model agreement with appropriate confidence levels given the length of the data record and model simulation.

Floods are often generated by more than one physical mechanism, e.g., rainfall and snowmelt. Consequently, traditional flood frequency methods that use a single distribution may not adequately describe the observed flood variability. Mixed distribution models have been proposed but they have two major drawbacks when applied to observed data: (1) determining the appropriate number of components or flood mechanisms and (2) identifying the probability distribution to be used for each component. Further, available flood data are often not sufficient for detecting mixture populations. As a result, mixed-distribution models can be difficult to apply in practice. In this paper we present a nonparametric approach based on local polynomial regression for estimating a flood quantile function that is data driven, flexible, and can capture any arbitrary features present in the data, alleviating the drawbacks of the traditional methods. We applied the proposed method to a suite of synthetic data from mixture of conven...

Sea-level rise is not globally uniform. A combination of observations and climate-model simulations reveals a pattern of sea-level changes in the Indian Ocean, with a decrease in the southern tropical Indian Ocean and a rise elsewhere, that can be attributed to changes in the atmospheric overturning circulation. Global sea level has risen during the past decades as a result of thermal expansion of the warming ocean and freshwater addition from melting continental ice1. However, sea-level rise is not globally uniform1,2,3,4,5. Regional sea levels can be affected by changes in atmospheric or oceanic circulation. As long-term observational records are scarce, regional changes in sea level in the Indian Ocean are poorly constrained. Yet estimates of future sea-level changes are essential for effective risk assessment2. Here we combine in situ and satellite observations of Indian Ocean sea level with climate-model simulations, to identify a distinct spatial pattern of sea-level rise since the 1960s. We find that sea level has decreased substantially in the south tropical Indian Ocean whereas it has increased elsewhere. This pattern is driven by changing surface winds associated with a combined invigoration of the Indian Ocean Hadley and Walker cells, patterns of atmospheric overturning circulation in the north–south and east–west direction, respectively, which is partly attributable to rising levels of atmospheric greenhouse gases. We conclude that—if ongoing anthropogenic warming dominates natural variability—the pattern we detected is likely to persist and to increase the environmental stress on some coasts and islands in the Indian Ocean.

Predicting the behavior of natural organic matter (NOM), alkalinity, and pH during drinking water coagulation is difficult because of the heterogeneous chemical nature of NOM and the complexity of carbonate chemistry. Parametric and nonparametric statistical regression methods were implemented to model the removal of NOM, as measured by total organic carbon (TOC), from raw water by conventional surface water treatment and to track the behavior of pH and alkalinity. The United States Environmental Protection Agency (U.S. EPA) Information Collection Rule (ICR) database was sampled for raw water and postsedimentation data from conventional surface water plants. All models were evaluated in terms of their fit and predictive capability, and for all variables explored, the nonparametric local polynomial models outperformed their parametric linear least-squares counterparts. This was most pronounced with the pH model, and was attributed to the nonlinear relationship found between pH and one of the predi...

We examined the effects of land cover change over the Indian subcontinent during pre-monsoon season (March, April, and May—MAM) on early Indian summer monsoon (ISM) rainfall using observed Normalized Difference Vegetation Index (NDVI) and July precipitation for the period of 1982–2003. MAM NDVI anomalies have increased in the Indian subcontinent and the increases are significantly correlated with increases in the irrigated area, not preceding rainfall. July rainfall significantly decreased in central and southern India, and the decrease is statistically related to the increase in the preceding MAM NDVI anomalies. Decreased July surface temperature in the Indian subcontinent (an expected result of increased evapotranspiration due to irrigation and increased vegetation) leads to a reduced land–sea thermal contrast, which is one of the factors driving the monsoon, and therefore weakens the monsoon circulation. A weak early ISM appears to be at least partially a result of irrigation and the resultant increased vegetation and crop activity prior to the monsoon. Copyright © 2008 Royal Meteorological Society

The upper White Nile Basin above Malakal, Sudan, is considered to be one of the most complicated and diverse hydrologic settings on Earth. Accurately depicting and predicting the streamflow at Malakal is essential for water managers considering Nile Basin-wide initiatives and potential large-scale projects. Dynamical, statistical, and combination models are assessed for their ability to predict monthly streamflow at Malakal. The dynamical model represents a lumped parameter, average-monthly water balance, whereas the statistical model incorporates a nonparametric approach based on local polynomial regression, utilizing principal components of precipi- tation and temperature. The combination of dynamical and statistical models through linear regression produces model weights of 0.44 and 0.59, respectively, implying a relatively balanced influence. Evaluation of the combination model demonstrates significant overall skill correlation coefficients equal to 0.83, outperforming either individual model for the validation periods selected. Peak streamflow analyses of timing and quantity also exhibit superior performance by the combination model. An ensemble approach, practical for planning and management from a probabilistic standpoint, is additionally demonstrated.

This study investigates dominant patterns of snow variability and their relationship to large-scale climate circulations over the eastern half of the United States. Two snowfall variables—total seasonal snowfall (TSF) and number of snow days (NSD)—are examined. A principal components (PC) analysis is conducted on data from 124 snowfall stations. The leading mode of variability for both TSF and NSD is driven by the North Atlantic Oscillation (NAO). The secondary mode of variability for TSF is driven by the Pacific/North American pattern (PNA), while the secondary mode of variability for NSD is driven by a dipole pattern and is attributable to regional influences and noise. These patterns exhibit persistence, which provides prospects for seasonal predictions of snowfall variables. This research compliments and extends the work of Serreze et al(1998), who performed a PC analysis of geopotential heights during the winter season and correlated the spatial patterns of the leading modes of variability with seasonal snowfall values. Copyright © 2007 Royal Meteorological Society

We use the upper-level divergence zone at 150 hPa to define the areas of study for the East Asian summer monsoon (EASM) and to show the advances and retreats of the EASM. We find that the EASM can be subdivided into a northern and southern component with distinctly different driving mechanisms. The northern EASM (NEASM) is affected by heat sources in the tropical oceans related to El Nino events while the southern EASM (SEASM) is affected by the subtropical oceans related to a North Pacific sea surface temperature (SST) dipole mode. A stronger NEASM is related to above-normal western North Pacific (WNP) anticyclonic anomalies, while a stronger SEASM is related to below-normal WNP anticyclonic anomalies. These WNP anticyclonic anomalies are connected to SST anomalies in the tropical and subtropical Pacific during the pre-monsoon season (December∼May). We also find that NEASM precipitation can be predicted from regional oceanic heat sources, i.e. SST and ocean heat content, in the tropical Pacific and Indian Oceans during the pre-monsoon season using a linear regression model. SEASM precipitation can be predicted from pre-monsoon SST in the eastern North Pacific. The NEASM forecast model is more skillful than that for the SEASM. Copyright © 2007 Royal Meteorological Society

A nonparametric, nonlinear regression technique—which is introduced to the environmental engineering community in this article—was used to develop models to predict total organic carbon (TOC) breakthrough time for a wide assortment of granular activated carbon (GAC) adsorbers. The models were developed using breakthrough data extracted from the U.S. EPA's Information Collection Rule (ICR) treatment studies database and included breakthrough results from 221 small-scale tests performed using 35 different source waters. Model development included an evaluation of twelve independent (and possibly correlated) variables, of which only four proved to be of statistical importance: influent TOC concentration, pH, empty bed contact time (EBCT), and field-scale GAC size. The predictive capability and overall robustness of the models were evaluated using three methods. First, the models were externally validated using smalland field-scale breakthrough data. Second, a sensitivity analysis was performed to ensure that...

Summary In the western United States many rivers experience high salinity resulting from natural and anthropogenic sources. This impacts the water quality and hence, is closely monitored. The salinity is closely linked with streamflow quantity in that, a higher flow brings with it more salt but also provides substantial dilution to reduce the salt concentration and vice-versa during low flow regimes. Decision makers typically plan strategies for salinity mitigation and evaluate impacts of water management policy options on salinity in the basin using decision support models. These models require statistically consistent basin wide scenarios of streamflow and salinity. Recognizing this need, we develop a basin wide stochastic generator of salinity that is consistent with the flows. The framework integrates a nonparametric space–time disaggregation model, applied to both flow and salt, and a nonparametric regression model, which simulates natural salt magnitude for a given natural flow. The nonparametric techniques do not require prior assumptions about the underlying functional relationship unlike traditional parametric methods, are data driven, and robust at capturing any functional form present in the data. They are also easily portable across sites. Within this integrated framework we propose two representative approaches, from several possible variations, for generating salt scenarios, each capturing different aspects of the flow and salt statistics. We demonstrate the performance of these approaches by applying them to flow and salt data from four gauges in the Upper Colorado River Basin. The spatial and temporal statistics of salt and their relationship with flows are well captured. This framework has utility in a variety of applications such as, short term salinity forecast conditioned on streamflow forecast, long term salinity scenarios conditioned on climate change projections, and also in reconstructing paleo salinity from paleo streamflows.

Multifaceted demands on western water supply, such as irrigation and mandated biological flows, coupled with climate variability are increasing the importance of supply forecasting to water managers. In this study, we develop and examine the accuracy of a seasonal ensemble streamflow forecasting model for the Yakima River Basin. The model incorporates large-scale climate information, related to the Pacific North American (PNA) pattern, with the objective of increasing the skill of the forecasts for water managers and stakeholders in the basin. Our study has found that spring runoff in the Yakima Basin is strongly correlated (correlation significant at the 5% significance level) with two of the large-scale circulation patterns associated with the PNA pattern from the preceding fall and winter seasons. Incorporating such climate information into our forecasts allowed a longer lead-time (a season in advance) planning period for water managers and stakeholders from the current practice of an April 1st forecast. The ensemble nature of our streamflow forecasts provides continuous probability distributions that will help the decision maker to objectively quantify the associated risk with selected streamflow values.

Water managers in the western United States and throughout the world are facing the increasing challenge of supplying a variety of water demands under the growing stresses of climate variability and of population and economic growth. Accurate streamflow forecasts are key to the water resources planning and decision-making process. There is growing evidence that variability in western streamflow is modulated by large-scale ocean-atmospheric features. Traditional forecasting techniques, however, do not systematically utilize large-scale climate information. In this study we present a framework for incorporating climate information into water resources decision-making and demonstrate the method on the semiarid Truckee-Carson Basin in Nevada where environmental, agricultural, and municipal demands compete for a limited supply of water. In this basin, water managers must plan carefully to meet the demands of timing and required flowrates. The framework presented in this paper consists of a streamflow forecast ...

Warm summer stream temperatures due to low flows and high air temperatures are a critical water quality problem in many western United States river basins because they impact threatened fish species’habitat. One way to alleviate this problem is for local and federal organizations to purchase water rights to be used to increase flows, hence decrease temperatures. Presented is a Decision Support System (DSS) that can be used in an operations mode to effectively use water acquired to mitigate warm stream temperatures. The DSS uses a statistical model for predicting daily stream temperatures and a rule-based module to compute reservoir releases. Water releases are calculated to meet fish habitat temperature targets based on the predicted stream temperature and a user specified confidence of the temperature predictions. Strategies that enable effective use of a limited amount of water throughout the season have also been incorporated in the DSS. The utility of the DSS is demonstrated by an example application to the Truckee River near Reno, Nevada, using hypothetical operating policy and 1988 through 1994 inflows. Results indicate that the DSS could substantially reduce the number of target temperature violations (i.e., stream temperatures exceeding the target temperature levels detrimental to fish habitat).

Ethiopian agriculture and Nile River flows are heavily dependent upon the Kiremt season (June–September) precipitation in the upper Blue Nile basin, as a means of rain-fed irrigation and streamflow contribution, respectively. Climate diagnostics suggest that the El Niño–Southern Oscillation phenomenon is a main driver of interannual variability of seasonal precipitation in the basin. One-season (March–May) lead predictors of the seasonal precipitation are identified from the large-scale ocean–atmosphere–land system, including sea level pressures, sea surface temperatures, geopotential height, air temperature, and the Palmer Drought Severity Index. A nonparametric approach based on local polynomial regression is proposed for generating ensemble forecasts. The method is data driven, easy to implement, and provides a flexible framework able to capture any arbitrary features (linear or nonlinear) present in the data, as compared to traditional linear regression. The best subset of predictors, as determined by the generalized cross-validation (GCV) criteria, is selected from the suite of potential large-scale predictors. A simple technique for disaggregating the seasonal precipitation forecasts into monthly forecasts is also provided. Cross-validated forecasts indicate significant skill in comparison to climatological forecasts, as currently utilized by the Ethiopian National Meteorological Services Agency. This ensemble forecasting framework can serve as a useful tool for water resources planning and management within the basin.

Analysis is performed on the spatiotemporal attributes of North American monsoon system (NAMS) rainfall in the southwestern United States. Trends in the timing and amount of monsoon rainfall for the period 1948–2004 are examined. The timing of the monsoon cycle is tracked by identifying the Julian day when the 10th, 25th, 50th, 75th, and 90th percentiles of the seasonal rainfall total have accumulated. Trends are assessed using the robust Spearman rank correlation analysis and the Kendall–Theil slope estimator. Principal component analysis is used to extract the dominant spatial patterns and these are correlated with antecedent land–ocean–atmosphere variables. Results show a significant delay in the beginning, peak, and closing stages of the monsoon in recent decades. The results also show a decrease in rainfall during July and a corresponding increase in rainfall during August and September. Relating these attributes of the summer rainfall to antecedent winter–spring land and ocean conditions leads to the proposal of the following hypothesis: warmer tropical Pacific sea surface temperatures (SSTs) and cooler northern Pacific SSTs in the antecedent winter–spring leads to wetter than normal conditions over the desert Southwest (and drier than normal conditions over the Pacific Northwest). This enhanced antecedent wetness delays the seasonal heating of the North American continent that is necessary to establish the monsoonal land–ocean temperature gradient. The delay in seasonal warming in turn delays the monsoon initiation, thus reducing rainfall during the typical early monsoon period (July) and increasing rainfall during the later months of the monsoon season (August and September). While the rainfall during the early monsoon appears to be most modulated by antecedent winter–spring Pacific SST patterns, the rainfall in the later part of the monsoon seems to be driven largely by the near-term SST conditions surrounding the monsoon region along the coast of California and the Gulf of California. The role of antecedent land and ocean conditions in modulating the following summer monsoon appears to be quite significant. This enhances the prospects for long-lead forecasts of monsoon rainfall over the southwestern United States, which could have significant implications for water resources planning and management in this water-scarce region.

Decadal trends in cloudiness are shown to affect incoming solar radiation (SWSFC) in the Krishna River basin (13–20°N, 72–82°E), southern India, from 1952 to 1997. Annual average cloudiness at 14 meteorological stations across the basin decreased by 0.09% of the sky per year over 1952–1997. The decreased cloudiness partly balanced the effects of aerosols on incoming solar radiation (SWSFC), resulting in a small net increase in SWSFC in monsoon months (0.1–2.9 W m−2 per decade). During the non-monsoon, aerosol forcing dominated over trends in cloud forcing, resulting in a net decrease in SWSFC (−2.8 to − 5.5 W m−2 per decade). Monthly satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) covering 1983–1995 were used to screen the visual cloudiness measurements at 26 meteorological stations, which reduced the data set to 14 stations and extended the cloudiness record back to 1952. SWSFC measurements were available at only two stations, so the SWSFC record was extended in time and to the other stations using a combination of the Angstrom and Hargreaves-Supit equations. The Hargreaves-Supit estimates of SWSFC were then corrected for trends in aerosols using the literature values of aerosol forcing over India. Monthly values and trends in satellite measurements of SWSFC from National Aeronautics and Space Administration's (NASA's) surface radiation budget (SRB) matched the aerosol-corrected Hargreaves-Supit estimates over 1984–1994 (RMSE = 11.9 W m−2, 5.2%). We conclude that meteorological station measurements of cloudiness, quality checked with satellite imagery and calibrated to local measurements of incoming radiation, provide an opportunity to extend radiation measurements in space and time. Reports of decreased cloudiness in other parts of continental Asia suggest that the cloud-aerosol trade-off observed in the Krishna basin may be widespread, particularly during the rainy seasons when changes in clouds have large effects on incoming radiation compared with aerosol forcing. Copyright © 2007 Royal Meteorological Society

One of the recent advancements in preconstruction safety management is the identification and quantification of risks associated with fundamental attributes of construction work environments that cause injuries. The goal of this paper is to test the validity of using these fundamental risk attributes to predict safety outcomes. The modeling approach required two steps, as follows: (1) a principal component analysis was performed on the safety attributes to reduce dimension of the data and remove collinearity among attributes (the principle component analysis provided insights into the relative importance of the various attributes and provided an orthogonal decomposition of the data), and (2) the leading principal components (which are orthogonal by definition) were used as potential predictors in a generalized linear model with a logit link function to model the probability of different accident categories. The predictive power was then assessed using a rank probability skill score, which quantifi...

Quantifying safety risks and performing comparative analyses is an emerging research field. Unfortunately, current risk assessment strategies are problematic because they require every new infrastructure feature and construction method to be individually evaluated using laborious research processes. To enhance the current construction safety management methods, an attribute-based risk identification and analysis method is presented that helps designers and preconstruction planners identify and model safety risk independently of specific activities or building components. The inspiration for this new risk management technique was derived from the Human Genome Project, which implies that while there are billions of people around the world, their vulnerability towards specific kinds of disease can be explained by a limited number of genes. This concept for attribute-based risk assessment was adapted by testing the hypothesis that injuries and fatalities in construction result from a finite number of ...

The Sacramento River is the largest river in California, and an important source of water for agricultural, municipal, and industrial users. Input to the Sacramento River comes from Shasta Lake and is controlled by operators of Shasta Dam, who are challenged with meeting the competing needs of these users while also maintaining a cold water habitat for Endangered Species Act (ESA) listed winter-run Chinook salmon. The cold water habitat goals are constrained by the volume of cold water storage in the lake, which operators attempt to selectively deploy throughout the critical late summer/fall window. To make informed decisions about the release of this limited cold water resource, skillful forecasts of downstream water temperature attributes at the seasonal time scale are crucial. To this end, we offer a generalized linear modeling (GLM) framework with a local polynomial method for function estimation, to provide predictions of a range of daily water temperature attributes (maximum daily water temp...

A nonparametric stochastic model is developed and tested for the simulation of tropical cyclone tracks. Tropical cyclone tracks demonstrate continuity and memory over many time and space steps. Clusters of tracks can be coherent, and the separation between clusters may be marked by geographical locations where groups of tracks diverge as a result of the physics of the underlying process. Consequently, their evolution may be non-Markovian. Markovian simulation models, as are often used, may produce tracks that potentially diverge or lose memory quicker than happens in nature. This is addressed here through a model that simulates tracks by randomly sampling track segments of varying length, selected from historical tracks. For performance evaluation, a spatial grid is imposed on the domain of interest. For each grid box, long-term tropical cyclone risk is assessed through the annual probability distributions of the number of storm hours, landfalls, winds, and other statistics. Total storm length is determined at birth by local distribution, and movement to other tropical cyclone segments by distance to neighbor tracks, comparative vector, and age of track. The model is also applied to the conditional simulation of hurricane tracks from specific positions for hurricanes that were not included in the model fitting so as to see whether the probabilistic coverage intervals properly cover the subsequent track. Consequently, tests of both the long-term probability distributions of hurricane landfall and of event simulations from the model are provided.

The 132-year historical rainfall record reveals that severe droughts in India have always been accompanied by El Niño events. Yet El Niño events have not always produced severe droughts. We show that El Niño events with the warmest sea surface temperature (SST) anomalies in the central equatorial Pacific are more effective in focusing drought-producing subsidence over India than events with the warmest SSTs in the eastern equatorial Pacific. The physical basis for such different impacts is established using atmospheric general circulation model experiments forced with idealized tropical Pacific warmings. These findings have important implications for Indian monsoon forecasting.