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The performance of uncertainty estimation methods, namely bootstrapping and Markov chain Monte Carlo (MCMC), in univariate frequency analysis of hydrometeorological extremes has been well tested in the literature. However, the two methods have not been thoroughly compared for multivariate frequency analysis of such events. In this study, we compare the performance of bootstrapping and MCMC in estimating the uncertainty of bivariate quantiles of extremes as defined by the return period quantiles of hydrologic drought duration and severity, and concurrent meteorological drought and heat wave. Using a copula framework, we analyse the accuracy and size of confidence intervals of the bivariate quantiles, and bias in point estimates of them. We also investigate the performance of the two methods in estimating the uncertainty of univariate quantiles of the marginal distributions of the resulting bivariate copulas. This is to evaluate if any advantage of one method over the other is consistent, whether in estimating the univariate or bivariate quantiles. We conduct this study with synthetic datasets of various sample sizes and predefined distributions derived from a set of empirical data. The results show MCMC to be superior when estimating the uncertainty of bivariate quantiles where the sample size is small (~50). Where the sample size is large (~100 and ~200), the results show bootstrapping to be the better option for estimating uncertainties of bivariate quantiles. For estimating uncertainties of univariate quantiles, bootstrapping is performing better under all investigated sample sizes. Results and conclusions in this study will be beneficial for hydrometeorological risk assessment, hydrologic infrastructure design, and water resources assessment. © 2021 Royal Meteorological Society

The performance of uncertainty estimation methods, namely bootstrapping and Markov chain Monte Carlo (MCMC), in univariate frequency analysis of hydrometeorological extremes has been well tested in the literature. However, the two methods have not been thoroughly compared for multivariate frequency analysis of such events. In this study, we compare the performance of bootstrapping and MCMC in estimating the uncertainty of bivariate quantiles of extremes as defined by the return period quantiles of hydrologic drought duration and severity, and concurrent meteorological drought and heat wave. Using a copula framework, we analyse the accuracy and size of confidence intervals of the bivariate quantiles, and bias in point estimates of them. We also investigate the performance of the two methods in estimating the uncertainty of univariate quantiles of the marginal distributions of the resulting bivariate copulas. This is to evaluate if any advantage of one method over the other is consistent, whether in estimating the univariate or bivariate quantiles. We conduct this study with synthetic datasets of various sample sizes and predefined distributions derived from a set of empirical data. The results show MCMC to be superior when estimating the uncertainty of bivariate quantiles where the sample size is small (~50). Where the sample size is large (~100 and ~200), the results show bootstrapping to be the better option for estimating uncertainties of bivariate quantiles. For estimating uncertainties of univariate quantiles, bootstrapping is performing better under all investigated sample sizes. Results and conclusions in this study will be beneficial for hydrometeorological risk assessment, hydrologic infrastructure design, and water resources assessment. © 2021 Royal Meteorological Society

Earlier researches have proposed algorithms to quantify the measurement uncertainty in rating curves and found that the magnitude of the uncertainty can be significant enough to impact hydrologic modeling. Therefore, they suggested frameworks to include measurement uncertainty in the rating curve to make it robust. Despite their efforts, a robust rating curve is often ignored in traditional practices, considering the investment of time and money as well as the resulting benefit from it. In the current research, we are interested in understanding the role of the measurement error variance in real-time streamflow forecasting. Our objectives are (i) to employ a state-of-the-art statistical forecasting model that can handle measurement uncertainty in daily streamflow and (ii) to understand the trade-off in forecasting performance when substantial knowledge regarding the measurement uncertainty is provided to the modeler. We apply the Bayesian dynamic hierarchical model (BDHM) on four gauging sites in the United States. Results show that the BDHM performs better than the daily climatology and local linear regression model. Also, the forecast variance changes proportionally with the change in the error variance as an input in the observation equation. Following this, we design a simulation-based study, which assigns the measurement error in the reported streamflow to obtain multiple realizations of the true streamflow. The inclusion of substantial knowledge about the true error improves the BDHM's performance by lowering the CRPS (continuous rank probability score) values. However, the inclusion increases the forecast variance to bring the true streamflow within the sampling variability of the forecasted streamflow. Overall, an improved trade-off between the success rate of forecasts and the forecast variance can be achieved by including the measurement error in the BDHM for rivers that witness less dispersed streamflow data. ©2020. American Geophysical Union. All Rights

A broader understanding of the behavioral aspect of energy consumption is increasingly critical for the formulation of policies that are effective in encouraging households to save energy. Such understanding is currently hindered, as the two counterparts of energy-saving behavior, namely efficiency and conservation, are not examined holistically; it is also hindered by the limited exploration of the underlying factors that may shape energy consumers' perceptions of environmental, economic and comfort objectives. To address these research gaps, this paper is examining air-conditioning energy use patterns and attitudes in an urban setting, and provides evidence that consumer perceptions are affected by the choice of behavior: for instance, the importance scores assigned to environmental and economic objectives for efficiency behavior are by 33% higher compared to those for conservation behavior. On the other hand, comfort is the most important objective for conservation. Moreover, these perceptions are found to be affected more by the consumers' psycho-cognitive characteristics, and less by socio-demographics. These findings introduce the

Crowdsourcing of rainfall measurements incorporating common citizens as a rich source of data is an emerging concept with huge potential to provide valuable high spatiotemporal resolution rainfall observations. Here we investigate the merging of crowdsourced rainfall data with traditional radar and rain gauge data to maximize their utility. For this purpose, we develop a tailored fast Bayesian regression kriging (FBRK) method combining regression kriging and Laplace approximation in a Bayesian framework. A strength of the FBRK method lies in its ability to capture the differences between rain gauge and crowdsourced measurement errors. Another lies in its fast yet reasonably accurate approximation of the Bayesian posterior, making it suitable to use in real time. We conduct synthetic computer simulations to evaluate the FBRK method alongside three other merging methods. In the simulations, we compare the accuracies of their resulting rainfall estimates, as well as the skill of those estimates as input to a storm water flow forecasting model. In both aspects, we observe the FBRK method to lead to more accurate results and truer representations of the associated uncertainties. However, we also observe the performance of the FBRK method to be sensitive to the choice of the Bayesian prior under certain conditions. Finally, from the synthetic simulations, we find merging crowdsourced data with traditional data to lead to more accurate estimation of the ground truth rainfall field and, subsequently, more accurate flow forecasts (though only when an adequate merging method, e.g., the FBRK method, is used), and the results to be fairly robust to bias in the input crowdsourced data. © 2019. American Geophysical Union. All Rights Reserved.

Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. In this paper, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. Specifically, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision-making process of a central manager, an agent-based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The results suggest the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we find that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade-off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward-based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops. ©2019. American Geophysical Union. All Rights Reserved.

Although a number of adaptation strategies for coping with the ever-growing problem of inflow nonstationarity have been proposed in the reservoir operation literature, the robustness of reservoir operating rules to inflow nonstationarity has largely been ignored. This present study fills this gap. Fuzzy inference system based reservoir operating rules (FIS-ORs) are developed by optimization-simulation and compared with linear, nonlinear, and artificial neural network rules under simulated stationary and nonstationary inflow conditions. Results are obtained for two case studies assuming, in each case, a single reservoir with water supply, flood control, and environmental flow allocation functions. The FIS-ORs are found to be most robust to inflow nonstationarity. Applying the FIS-ORs to 30 years of projected future inflows show their advantage to be enhanced when they are recalibrated every 10 years or so, as compared to when there is no recalibration. It is also observed the advantage of the FIS-ORs to be more significant when the weighting of the problem objectives is such that the overall objective function is more difficult to satisfy.

We investigate the potential of integrating desalination to existing reservoir systems to mitigate supply uncertainty. Desalinated seawater and wastewater are relatively reliable but expensive. Water from natural resources like reservoirs is generally cheaper but climate sensitive. We propose combining the operation of a reservoir and seawater and wastewater desalination plants for an overall system that is less vulnerable to scarcity and uncertainty, while constraining total cost. The joint system is modeled as a multiobjective optimization problem with the double objectives of minimizing risk and vulnerability, subject to a minimum limit on resilience. The joint model is applied to two cases, one based on the climate and demands of a location in India and the other of a location in California. The results for the Indian case indicate that it is possible for the joint system to reduce risk and vulnerability to zero given a budget increase of 20–120% under current climate conditions and 30–150% under projected future conditions. For the Californian case, this would require budget increases of 20–80% and 30–140% under current and future conditions, respectively. Further, our analysis shows a two-way interaction between the reservoir and desalination plants where the optimal operation of the former is just as much affected by the latter as the latter by the former. This highlights the importance of an integrated management approach. This study contributes to a greater quantitative understanding of desalination as a redundancy measure for adapting water supply infrastructures for a future of greater scarcity and uncertainty. © 2016. American Geophysical Union. All Rights Reserved.

We investigate the potential of integrating desalination to existing reservoir systems to mitigate supply uncertainty. Desalinated seawater and wastewater are relatively reliable but expensive. Water from natural resources like reservoirs is generally cheaper but climate sensitive. We propose combining the operation of a reservoir and seawater and wastewater desalination plants for an overall system that is less vulnerable to scarcity and uncertainty, while constraining total cost. The joint system is modeled as a multiobjective optimization problem with the double objectives of minimizing risk and vulnerability, subject to a minimum limit on resilience. The joint model is applied to two cases, one based on the climate and demands of a location in India and the other of a location in California. The results for the Indian case indicate that it is possible for the joint system to reduce risk and vulnerability to zero given a budget increase of 20–120% under current climate conditions and 30–150% under projected future conditions. For the Californian case, this would require budget increases of 20–80% and 30–140% under current and future conditions, respectively. Further, our analysis shows a two-way interaction between the reservoir and desalination plants where the optimal operation of the former is just as much affected by the latter as the latter by the former. This highlights the importance of an integrated management approach. This study contributes to a greater quantitative understanding of desalination as a redundancy measure for adapting water supply infrastructures for a future of greater scarcity and uncertainty.

Freshwater resources around the world are increasing in scarcity due to population growth, industrialization and climate change. This is a serious concern for water stressed countries, including those in Asia and North Africa where future food production is expected to be negatively affected by this. To address this problem, we investigate the potential of combining freshwater reservoir and wastewater reclamation operations. Reservoir water is the cheaper source of irrigation, but is often limited and climate sensitive. Treated wastewater is a more reliable alternative for irrigation, but often requires extensive further treatment which can be expensive. We propose combining the operations of a reservoir and a wastewater reclamation plant (WWRP) to augment the supply from the reservoir with reclaimed water for increasing crop yields in water stressed regions. The joint system of reservoir and WWRP is modeled as a multi-objective optimization problem with the double objective of maximizing the crop yield and minimizing total cost, subject to constraints on reservoir storage, spill and release, and capacity of the WWRP. We use the crop growth model Aquacrop, supported by The Food and Agriculture Organization of the United Nations (FAO), to model crop growth in response to water use. Aquacrop considers the effects of water deficit on crop growth stages, and from there estimates crop yield. We generate results comparing total crop yield under irrigation with water from just the reservoir (which is limited and often interrupted), and yield with water from the joint system (which has the potential of higher supply and greater reliability). We will present results for locations in India and Africa to evaluate the potential of the joint operations for improving food security in those areas for different budgets.

As freshwater resources are becoming increasingly scarce, unconventional sources of water should be given new consideration. In coastal cities, seawater, with minimal treatment, can be used for toilet flushing, reducing the demand for freshwater. Currently, it is practised on a large scale only in Hong Kong. This study estimates the cost of seawater flushing and compares it to the cost of wastewater recycling for 15 major coastal cities around the world: Buenos Aires, Chennai, Hong Kong, Jakarta, Karachi, Los Angeles, Miami, Mumbai, New York City, Osaka, San Francisco, Shanghai, Singapore, Sydney and Tokyo. While seawater flushing requires a separate network of mains and, therefore, a greater capital cost, wastewater recycling has a higher ongoing treatment cost. Wastewater recycling, depending on the potability of the recycled water, may also require a separate network of mains, but one with a lower maintenance cost due to its lower vulnerability to corrosion compared to seawater mains. This study finds Chennai, Mumbai and Shanghai to have strong potentials for seawater flushing. That these cities have among the highest population densities in the world and are in the developing world explains their relatively lower unit costs for seawater mains.

This paper presents a system of systems (SoS) Biofuel model considering the interdependency among the systems involved in biofuel development, including biofuel refinery location, transportation infrastructure, agricultural production and markets, environment, and social communities. The model provides the optimal infrastructure development and land-use allocation for biofuel production in a region considering socio-economic and water quality and quantity effects. The optimal development plan quantifies economic and hydrologic outputs and specifies biofuel refinery locations and capacities, refinery operations, land allocation between biofuel and food crops, optimal shipments of products and feedstock, and transportation infrastructure. The model is formulated as a mixed integer linear program (MILP) and is solved by an algorithm developed specifically to cope with the large size of the optimization problem. In addition to the development of the SoS-Biofuel model, this paper demonstrates the functionality of the model and its ability to analyze the effects of interdependency among subsystems by applying it to a watershed in Illinois. The SoS-Biofuel model is used to investigate the effects of different biofuel polices on infrastructure needs and related environmental consequences, highlighting the interdependencies inherent in the optimal development of the entire system.

Reservoir management is a key component in effective water supply management. In real systems, reservoir operation is typically governed by a decision rule, an input-output relationship that prescribes reservoir release as a function of inflows, storage and other inputs. In this study, fuzzy inference decision rules (FIDRs) for reservoir operation are developed. Due to their highly flexible nature that enables their capturing of highly nonlinear interactions, the FIDRs are expected to perform better than conventional rules. The parameters of the FIDRs are optimized by applying differential evolution to a reservoir simulation model. A comparison of the FIDRs against linear rules, nonlinear rules and artificial neural networks, applied to two scenarios of a hypothetical reservoir on the Ganges River, show the FIDRs to perform best.

Accurate stream flow forecasts are critical for reservoir operations for water supply planning. As the world urban population increases, the demand for water in cities is also increasing, making accurate forecasts even more important. However, accurate forecasting of stream flows is difficult owing to short- and long-term weather variations. We propose to reduce this need for accurate stream flow forecasts by augmenting reservoir operations with seawater desalination and wastewater recycling. We develop a robust operating policy for the joint operation of the three sources. With the joint model, we tap into the unlimited reserve of seawater through desalination, and make use of local supplies of wastewater through recycling. However, both seawater desalination and recycling are energy intensive and relatively expensive. Reservoir water on the other hand, is generally cheaper but is limited and variable in its availability, increasing the risk of water shortage during extreme climate events. We operate the joint system by optimizing it using a genetic algorithm to maximize water supply reliability and resilience while minimizing vulnerability subject to a budget constraint and for a given stream flow forecast. To compute the total cost of the system, we take into account the pumping cost of transporting reservoir water to its final destination, and the capital and operating costs of desalinating seawater and recycling wastewater. We produce results for different hydro climatic regions based on artificial stream flows we generate using a simple hydrological model and an autoregressive time series model. The artificial flows are generated from precipitation and temperature data from the Canadian Regional Climate model for present and future scenarios. We observe that the joint operation is able to effectively minimize the negative effects of stream flow forecast uncertainty on system performance at an overall cost that is not significantly greater than the cost of a st