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Dam failure can cause extraordinary flooding. Water and wastewater treatment plants, electricity generation facilities, bridges, highways, population centers, and other dams often lie downstream of a dam. If poorly maintained, aging dams can be overtopped by extreme rainfall events, and long-term loss of critical infrastructure services for many communities and regions may result. Such events are now more likely as the climate changes. How should we prioritize dams that need immediate attention and retrofit or remove them? Losses from a dam failure could exceed the infrastructure's replacement costs, through cascading effects in multiple sectors such as electricity, transportation, water supply, and environmental services. The existing dam hazard classification frameworks in the US and many other countries do not formally characterize "hazard hotspots" considering these impacts. Given that there are over 90,000 dams at different stages of repair, maintenance, and budgetary constraints, a systematic approach is needed to rank their potential failure hazard and allocate resources for risk mitigation. In this paper, we synthesize an approach for preliminary ranking of the priority areas of concern. The approach is scalable over many regions for rapid assessment of magnitude and exposure of a dam failure. The estimation of the consequences of a dam failure, including its financial losses, as well as affected critical infrastructures and population, is explored using publicly available dam break and consequence tools as well as national infrastructure data sets. [ABSTRACT FROM AUTHOR]

We present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, Cluster-Based Climate-Conditioned Hurricane Intensity and Track Simulator (C3-HITS), extends a previous version of HITS. HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA's Best Track Data, version 2, dataset. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the Niño-3.4 index, representing the eastern equatorial Pacific Ocean sea surface temperature (SST) departure from climatology, and main development region, representing tropical North Atlantic SST departure from climatology in the North Atlantic TC main development region. A track clustering procedure is used to identify track families, and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate-conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season. [ABSTRACT FROM AUTHOR]

Depletion and pollution of groundwater, Earth's largest and most accessible freshwater stock, is a global sustainability concern. A changing climate, marked by more frequent and intense hydrologic extremes, poses threats to groundwater recharge and amplifies groundwater use. However, widespread human development and contamination of groundwater reservoirs pose an immediate threat of resource extinction with impacts in many regions with dense population or intensive agriculture. A rapid increase in global groundwater studies has emerged, but this has also highlighted the extreme paucity of data for substantive trend analyses and assessment of the state of the global resource. Noting the difficulty in seeing and measuring this typically invisible resource, we discuss factors that determine the current state of global groundwater, including the uncertainties accompanying data and modeling, with an eye to identifying emerging issues and the prospects for informing local to global resource management in critical regions. We comment on some prospective management strategies. [ABSTRACT FROM AUTHOR]

Evolving patterns of droughts and wet spells in the conterminous United States (CONUS) are examined over 555 years using a tree-ring-based paleoclimate reconstruction of the modified Palmer drought severity index (PDSI). A hidden Markov model is used as an unsupervised method of classifying climate states and quantifying the temporal evolution from one state to another. Modeling temporal variability in spatial patterns of drought and wet spells provides the ability to objectively assess and simulate historical persistence and recurrence of similar patterns. The Viterbi algorithm reveals the probable sequence of states through time, enabling an examination of temporal and spatial features and associated large-scale climate forcing. Distinct patterns of sea surface temperature that are known to enhance or inhibit rainfall are associated with some states. Using the current CONUS PDSI field the model can be used to simulate the space–time PDSI pattern over the next few years, or unconditional simulations can be used to derive estimates of spatially concurrent PDSI patterns and their persistence and intensity across the CONUS. [ABSTRACT FROM AUTHOR]

Understanding spatially correlated floods and modeling joint hazard associated with threshold exceedances across multiple locations is crucial for accurate estimation of continental‐scale portfolio risk. This work uses a non‐parametric copula‐based spatial simulator to analyze peak floods across the United States to derive the first‐of‐its‐kind continental portfolio risk estimates at the 10‐ and 100‐year return levels. We find significant interdependence in floods across the nation, revealing the recurring pattern of extreme events affecting the Northeast, Central, West, and Northwest United States in the same year. The stochastic simulator effectively manages high‐dimensional data and offers reliable uncertainty estimates for both spatially dependent floods and the aggregated flood losses at the continental level. El Niño‐Southern Oscillation and Atlantic Multidecadal Oscillation are identified as statistically significant tele‐connectors of aggregate loss. This research aims to advance the understanding of compound continental flood hazard and the potential large‐scale climate teleconnections that lead to such compound floods. Plain Language Summary: It is important to know why past floods occurred in different parts of the United States in the same year and how they were connected to each other. This knowledge helps determine how likely it is for major annual floods to occur all across the country in the future. Consequently, an understanding of what drives such total annual flood losses each year and their relation to climate conditions is of strategic importance. Large‐scale climate patterns can influence where, how much, and how often it floods in a given year. The coincidence of many such events in a year across the country is an example of a compound flood hazard, whose aggregate loss is of interest. This study uses a stochastic model for simulating multiple floods across the United States while preserving information as to their co‐occurrence. The model also calculates the likelihood of combined flood losses, along with its uncertainty. The key patterns in the ocean and the atmosphere that lead to these widespread losses are then identified. The broader impact of this study is to establish a foundation that any country or region can use to evaluate potential consequences of compound risk from joint extreme events. Key Points: A non‐parametric copula‐based stochastic framework is forwarded to assess the risk of multiple peak floods across a regionPeak floods in the US exhibit significant co‐occurrence of extreme events, leading to compound flood hazard with high aggregate lossesThe joint occurrence of extreme flood events across the country is a signature of large‐scale climate teleconnections [ABSTRACT FROM AUTHOR]

We estimate local and regional Generalized Extreme Value (GEV) distribution parameters for flood frequency analysis in a multilevel, hierarchical Bayesian framework, to explicitly model and reduce uncertainties. As prior information for the model, we assume that the GEV location and scale parameters for each site come from independent log-normal distributions, whose mean parameter scales with the drainage area. From empirical and theoretical arguments, the shape parameter for each site is shrunk towards a common mean. Non-informative prior distributions are assumed for the hyperparameters and the MCMC method is used to sample from the joint posterior distribution. The model is tested using annual maximum series from 20 streamflow gauges located in an 83,000 km 2 flood prone basin in Southeast Brazil. The results show a significant reduction of uncertainty estimates of flood quantile estimates over the traditional GEV model, particularly for sites with shorter records. For return periods within the range of the data (around 50 years), the Bayesian credible intervals for the flood quantiles tend to be narrower than the classical confidence limits based on the delta method. As the return period increases beyond the range of the data, the confidence limits from the delta method become unreliable and the Bayesian credible intervals provide a way to estimate satisfactory confidence bands for the flood quantiles considering parameter uncertainties and regional information. In order to evaluate the applicability of the proposed hierarchical Bayesian model for regional flood frequency analysis, we estimate flood quantiles for three randomly chosen out-of-sample sites and compare with classical estimates using the index flood method. The posterior distributions of the scaling law coefficients are used to define the predictive distributions of the GEV location and scale parameters for the out-of-sample sites given only their drainage areas and the posterior distribution of the average shape parameter is taken as the regional predictive distribution for this parameter. While the index flood method does not provide a straightforward way to consider the uncertainties in the index flood and in the regional parameters, the results obtained here show that the proposed Bayesian method is able to produce adequate credible intervals for flood quantiles that are in accordance with empirical estimates. [ABSTRACT FROM AUTHOR]

Tropical moisture exports (TMEs) may play an important role in extreme precipitation. An analysis of the spatiotemporal structure of precipitation associated with TMEs for the eastern United States at seasonal and daily time scales is presented. TME-based precipitation is characterized based on the change in specific humidity along TME tracks delineated in a Lagrangian analysis of the ERA-Interim dataset. The empirical orthogonal functions (EOFs) of seasonal TME-based precipitation are analyzed separately for each season to identify the dominant modes of interannual variability. Loading patterns for the first EOF show a distinct seasonal cycle in the core region of TME-based precipitation across the eastern United States, while the second EOF describes a northwest-southeast oscillation in the center of TME-induced precipitation occurrence. The EOFs for TMEs are compared against EOFs of gauged flood count data, which exhibit similar spatial structures. Correlations between TME EOFs, geopotential heights, and sea surface temperatures suggest a strong connection between TME-based precipitation, the Pacific-North American (PNA) pattern, Pacific decadal oscillation (PDO), and the Intra-Americas Sea patterns for much of the calendar year. Daily TME-based and total precipitation is projected onto the leading seasonal EOFs to examine the characteristics of upper-quantile daily events. The daily analysis suggests that the PNA can potentially provide information regarding heavy TME-based precipitation at a lead time of 1-10 days or more in most seasons and total precipitation in the winter. The potential for subseasonal, seasonal, and decadal forecasts or conditional simulations of precipitation in the study region is discussed. [ABSTRACT FROM AUTHOR]

Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered. [ABSTRACT FROM AUTHOR]

Event magnitude and area scaling relationships for rainfall over different regions of the world have been presented in the literature for relatively short durations and over relatively small areas. In this paper, we present the first ever results on a global analysis of the scaling characteristics of extreme rainfall areas for durations ranging from 1 to 30 days. Broken power law models are fit in each case. The past work has been focused largely on the time and space scales associated with local and regional convection. The work presented here suggests that power law scaling may also apply to planetary scale phenomenon, such as frontal and monsoonal systems, and their interaction with local moisture recycling. Such features may have persistence over large areas corresponding to extreme rain and regional flood events. As a result, they lead to considerable hazard exposure. A caveat is that methods used for empirical power law identification have difficulties with edge effects due to finite domains. This leads to problems with robust model identification and interpretability of the underlying relationships. We use recent algorithms that aim to address some of these issues in a principled way. Theoretical research that could explain why such results may emerge across the world, as analyzed for the first time in this paper, is needed. [ABSTRACT FROM AUTHOR]

This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using nonparametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA. [ABSTRACT FROM AUTHOR]

ABSTRACT We investigate the space-time structure of extreme precipitation in Europe over the last century, using daily rainfall data from the European Climate Assessment & Dataset ( ECA&D) archive. The database includes 267 stations with records longer than 100 years. In the winter season (October to March), for each station, two classes of daily rainfall amount values are selected that, respectively, exceed the 90th and 95th percentile of daily rainfall amount over all the 100 years. For each class, and at each location, an annual time series of the frequency of exceedance and of the total precipitation, defined respectively as the number of days the rainfall threshold (90th and 95th percentiles) is exceeded and total precipitation on days when the percentile is exceeded, are developed. Space-time structure of the frequency and total precipitation time series at the different locations are then pursued using multivariate time and frequency domain methods. The identified key trends and organized spectral modes are linked to well-known climate indices, as North Atlantic Oscillation ( NAO) and El Nino Southern Oscillation ( ENSO). The spectra of the leading principal component of frequency of exceedance and of total precipitation have a peak with a 5-year period that is significant at the 5% level. These are also significantly correlated with ENSO series with this period. The spectrum of total rainfall is significant at the 10% level with a period of ∼8 years. This appears to be significantly correlated to the NAO index at this period. Thus, a decomposition of both secular trends and quasi-periodic behaviour in extreme daily rainfall is provided. [ABSTRACT FROM AUTHOR]

Although the relationships between meteorological conditions and waterway bacterial contamination are being better understood, statistical models capable of fully leveraging these links have not been developed for highly urbanized settings. We present a hierarchical Bayesian regression model for predicting transient fecal indicator bacteria contamination episodes in urban waterways. Canals, creeks, and rivers of the New York City harbor system are used to examine the model. The model configuration facilitates the hierarchical structure of the underlying system with weekly observations nested within sampling sites, which in turn were nested inside of the harbor network. Models are compared using cross-validation and a variety of Bayesian and classical model fit statistics. The uncertainty of predicted enterococci concentration values is reflected by sampling from the posterior predictive distribution. Issuing predictions with the uncertainty reasonably reflected allows a water manager or a monitoring agency to issue warnings that better reflect the underlying risk of exposure. A model using only antecedent meteorological conditions is shown to correctly classify safe and unsafe levels of enterococci with good accuracy. The hierarchical Bayesian regression approach is most valuable where transient fecal indicator bacteria contamination is problematic and drainage network data are scarce. [ABSTRACT FROM AUTHOR]

Summary Historically, flood risk management and flood frequency modeling have been based on assumption of stationarity, i.e., flood probabilities are invariant across years. However, it is now recognized that in many places, extreme floods are associated with specific climate states which may recur with non-uniform probability across years. Conditional on knowledge of the operating climate regime, the probability of a flood of a certain magnitude can be higher or lower in a given year. Here we explore nonstationary flood risk for the streamflow series of the Negro River at the city of Manaus in Brazil by investigating climate teleconnections associated with the interannual variability of the peak flows. We evaluate attributes and the fit of a generalized extreme value (GEV) distribution with nonstationary parameters to the annual peak series of the Negro River stages. The annual peak flood occurs between May and July and its magnitude depends on the Negro River stage at the beginning of the year and on the previous December sea surface temperature (SST) of a region in the tropical Pacific Ocean. A statistically significant monotonic trend is also observed in the peak level series. The indexing of the parameters of a GEV distribution to the NINO3 index and to the observed river stage at the beginning of the year reveals a changing flood hazard for the city, with the joint occurrence of high values associated with La Niña conditions in the previous December and high river stages in January preceding the flood season. The proposed model is shown to be useful for quantifying the changing flood hazard several months in advance for Manaus, thus providing an early flood alert system for the city and may be an important tool for the dynamic flood risk management for the region. [ABSTRACT FROM AUTHOR]

Integrated atmospheric water vapor transport (IVT) is a determinant of global precipitation. In this paper, using the CERA-20C climate reanalysis dataset, we explore three questions in Northern Hemisphere precipitation for four seasons: 1) What is the covariability between the leading spatiotemporal modes of seasonal sea surface temperature (SST), the seasonal IVT, and the seasonal precipitation for the Northern Hemisphere? 2) How well can the leading spatial modes of seasonal precipitation be reconstructed from the leading modes of IVT and SST for the same season? 3) How well can the leading modes of precipitation for the next season be predicted from the leading modes of the current season's SST and IVT? Wavelet analyses identify covariation in the leading modes of seasonal precipitation and those of IVT and SST in the 2–8-yr band, with the highest amplitude in the March–May (MAM) season, and provide a firm physical explanation for the potential predictability. We find that a subset of the 10 leading principal components of the seasonal IVT and SST fields has significant trends in connections with seasonal precipitation modes, and provides an accurate statistical concurrent reconstruction and one-season-ahead forecast of the leading seasonal precipitation modes, thus providing a pathway to improving the understanding and prediction of precipitation extremes in the context of climate change attribution, seasonal and longer prediction, and climate change scenarios. The same-season reconstruction model can explain 76% of the variance, and the next-season forecast model can explain 58% variance of hemispheric precipitation on average. [ABSTRACT FROM AUTHOR]

Research on migration has become more challenging due to at least four factors: (1) more complex migration traditions; (2) the development of migration economies that engage many types of migrants from ever more social and cultural backgrounds; (3) increasing likelihood of climate change‐driven environmental migration; and (4) increasing likelihood of conflict‐based migration in some contexts. These developments have shaken economic theories of migration and have encouraged interdisciplinary, methodologically mixed, qualitative and quantitative research and analysis. From a review of the literature, we have gleaned 11 common themes about environmental, economic and conflict migration that we differentiate by process (migration behaviours that are still evolving) and patterns (migration behaviours that have become customary). We then consider how positive and negative dimensions of migration can be captured and represented with close attention to livelihood constellations (multiple economic activities combined by individuals, households and families). Finally, focusing on Somalia and agro‐pastoral peoples generally, where recent environmental and conflict migration have been added to decades of economic migration and centuries of seasonal, environmental migration associated with pastoralism, we combine historical and qualitative work to demonstrate the value of a livelihood constellation perspective. [ABSTRACT FROM AUTHOR]

We assess the potential financial benefits of rooftop rainwater harvesting (RWH) in Mexico City from the perspective of property owners and entrepreneurs. A bottom‐up approach was followed by evaluating RWH at individual buildings and aggregating the results to a borough/city level. We consider sector‐specific water demands, potable and nonpotable uses, and user‐specific water tariffs. We find that RWH is economically most beneficial for nondomestic users rather than for small domestic users, who are often the target of RWH interventions. Based on a net present value analysis, a potable RWH system is not favored for most domestic users under the current subsidized municipal water tariff structure. Our analysis only considers capital and maintenance expenses, and not other benefits related to increased access to water and reliability, or social benefits from a switch to a RWH system. If the initial capital expense for RWH is partly financed by transferring the water subsidy to an entrepreneur, then RWH becomes financially attractive for a wide range of domestic users. To improve water access in Mexico City, RWH is attractive in the most marginalized boroughs where water use is currently lower and precipitation is higher. For domestic users relying on trucked water, RWH can have great financial benefits. Our approach provides quantitative data with high spatial specificity, highlighting the places and types of users that would benefit most from RWH. Research Impact Statement: Rainwater harvesting has high financial benefits for the commercial sector in Mexico City, but municipal water subsidies need to be re‐targeted for favorable potable domestic use results. [ABSTRACT FROM AUTHOR]

This study proposes an improved nonstationary model for flood frequency analysis by investigating the relationship between flood peak and flood volume, using the Three Gorges Dam (TGD), China, for verification. First, the generalized additive model for location, scale and shape (GAMLSS) is used as the prior distribution. Then, under Bayesian theory, the prior distribution is updated using the conditional distribution, which is derived from the copula function. The results show that the improvement of the proposed model is significant compared with the GAMLSS-based prior distribution. Meanwhile, selection of a suitable prior distribution has a significant effect on the results of the improvement. For applications to the TGD, the nonstationary model can obviously increase the engineering management benefits and reduce the perceived risks of large floods. This study provides guidance for the dynamic management of hydraulic engineering under nonstationary conditions. [ABSTRACT FROM AUTHOR]

Highlights • Water loss of the Chari River discharge is evaluated using a statistical approach. • The contribution of climate variability and human activities is distinguished. • Previously the contribution of human activities has been underestimated seriously. Abstract Lake Chad was once the sixth largest lake in the world, with a surface area of about 25,000 km2 in the early 1960s. However, it has shrunk to almost only one-twentieth of its maximum area by now. As the biggest river of the Lake Chad basin, the Chari/Logone River contributes approximately 90% of Lake Chad's water input. Consequently, several previous studies have focused on the Chari/Logone River system to investigate the causes of Lake Chad's shrinkage. However, due to the lack of authoritative irrigation and natural runoff observations, it is usually difficult to explain the relative contribution of human activities and climate variability. In this paper, unlike the hydrological modeling approaches used in previous studies, a conceptual and statistical approach was presented to investigate discharge variations and the causes leading to such variations. Specifically, the runoff change caused by climate variability (Δ Q climate ) was estimated using Budyko analysis, while that caused by human activities (Δ Q human ) was estimated using regression analysis. The accuracy of estimation was evaluated through cross validation and comparison with previous studies. The comparison suggests that due to the lack of natural runoff and irrigation withdrawal observations, the runoff simulation produced by previous hydrological models suffers from large errors. The water loss of the Chari/Logone River discharge is dominated by human activities rather than climate variability. On annual average scale, the total water loss caused by climate variability and anthropogenic activities is 16.76 km3. The relative contribution of Δ Q climate and Δ Q human is 26.83% and 73.17%, respectively. The role of human activities is particularly obvious in recent ten years from 2003 to 2013. Due to the increase of rainfall and irrigation withdrawals, the relative contribution of human activities accounts for more than 80% of the total water loss. [ABSTRACT FROM AUTHOR]

Ensuring safe water supply for communities across the United States is a growing challenge in the face of aging infrastructure, impaired source water, and strained community finances. In the aftermath of the Flint lead crisis, there is an urgent need to assess the current state of US drinking water. However, no nationwide assessment has yet been conducted on trends in drinking water quality violations across several decades. Efforts to reduce violations are of national concern given that, in 2015, nearly 21 million people relied on community water systems that violated healthbased quality standards. In this paper, we evaluate spatial and temporal patterns in health-related violations of the Safe Drinking Water Act using a panel dataset of 17,900 community water systems over the period 1982-2015. We also identify vulnerability factors of communities and water systems through probit regression. Increasing time trends and violation hot spots are detected in several states, particularly in the Southwest region. Repeat violations are prevalent in locations of violation hot spots, indicating that water systems in these regions struggle with recurring issues. In terms of vulnerability factors, we find that violation incidence in rural areas is substantially higher than in urbanized areas. Meanwhile, private ownership and purchased water source are associated with compliance. These findings indicate the types of underperforming systems that might benefit from assistance in achieving consistent compliance. We discuss why certain violations might be clustered in some regions and strategies for improving national drinking water quality. [ABSTRACT FROM AUTHOR]

Abstract: Roof rainwater harvesting (RWH) has the potential to augment water supplies for urban and suburban uses throughout the United States (U.S.). Studies of the performance of RWH at the building and city scales in the U.S. are available, but a countrywide overview of the potential performance of RWH at the county scale has not been done before. Three approaches were taken: (1) assess the viability of RWH in terms of the rainfall that could be captured in relation to the water demand in each county (excluding agriculture), (2) evaluate the performance of a “typical” domestic RWH system across all counties with metrics related to its ability to supply the potable and nonpotable demand, and (3) evaluate the effect of adding a 50% rainwater reuse component to the analysis. We find RWH could be a viable supplemental water source in the U.S., particularly in counties of the Pacific Northwest, Central, and Eastern regions (percent demand covered >50%). Low population density counties have the potential to meet their annual water needs with RWH, while high‐density counties could only source a small portion (~20%) of their annual demand with RWH. Typical RWH systems in counties in the Central and Eastern U.S. performed better than in Western counties. Adding a reuse component can be a key factor in making RWH attractive in many areas of the country. This work can inform future water infrastructure investment and planning in the U.S. [ABSTRACT FROM AUTHOR]

This article assesses the impact of the tensiometer on the consumption of groundwater and electric power in paddy cultivation in Indian Punjab, and its subsequent economic benefits. We find that compared to the continuous flooding method, the tensiometer-based application of irrigation reduces water and power consumption by 13%, cutting variable costs by 7% without any yield penalty. If 30% of the paddy area is irrigated following tensiometer-based schedules, then the state could save a total of 0.67 million ha m of water and 1516 million kWh of electric power in 2010-2025, with aggregate economic benefits of US$ 459 million. [ABSTRACT FROM AUTHOR]

Abstract: Seasonal precipitation forecasts over the contiguous United States (CONUS) during the 2015–2016 El Niño exhibited significant bias over many regions, especially in the western United States where seasonal information is particularly valuable for reservoir operation. Diagnosing the origin of this bias requires understanding the empirical signal from tropical heating to midlatitude precipitation. In this paper, we find that atmospheric zonal wind indices computed over the region typically associated with the winter jet stream provide a skillful, spatially distributed, linear prediction of precipitation over CONUS, over all winters (January–March; JFM). Furthermore, we show that more (less) central (eastern) Pacific Ocean heating may have contributed to the unexpected 2016 JFM CONUS precipitation and that this was likely predictable based on antecedent (December) sea surface temperatures. The zonal wind indices act as intermediate variables in a causal chain, and our analyses provide support for the potential for empirical prediction and also a diagnostic for physics‐based models to help improve forecasts. [ABSTRACT FROM AUTHOR]

A hierarchical Bayesian model is developed for predicting monthly residential per capita electricity consumption at the state level across the USA. The summer period is selected to target cooling requirements that are generally directly associated with electricity use compared with winter heating requirements that are derived from a mix of energy sources that has changed over time. Historical monthly electricity consumption data from 1990 to 2013 are used to build a predictive model with a set of corresponding climate and non-climate covariates. A clustering analysis was performed first to identify groups of states that had similar temporal patterns for the cooling-degree-days of each state. Then, a partial pooling model is applied to each cluster to assess the sensitivity of monthly per capita residential electricity demand to each predictor (these are cooling-degree-days, gross domestic product (GDP) per capita, per capita electricity demand from the previous month and previous year, and the residential electricity price). Most of the predictors are significant for most of the states across USA. The sensitivity of residential electricity demand to cooling-degree-days has an identifiable geographic distribution with a stronger relationship in northeastern United States. This predictive model considers interactions between causal factors at large spatial scales and quantifies the model uncertainty. This study can be applied as guide in the future energy management. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate is a primary driver for extreme rainfall and flood events. In this paper, the temporally changing flood risk associated with the annual maximum 30-day rainfall (30-day AMR) and the annual maximum daily inflow ( AMDI) related to the Xidayang Reservoir catchment is analysed from a climatic context. This is the largest catchment in Daqing River Basin in North China and is highly prone to floods related to the East Asian summer monsoon. Two climate factors, the average May-June-July sea surface temperature anomalies in areas of the northern Indian Ocean and western Pacific Ocean, are identified. They show a high negative correlation with the AMDI and 30-day AMR. Bayesian nonstationary models are then developed for the AMDI and 30-day AMR using the climate predictors identified as covariates. We compared three types of models of AMDI and 30-day AMR: (1) time-invariant, (2) linear temporal trend and (3) climate informed, and found that the climate-informed models exhibit the best performance according to Deviance Information Criterion (DIC) and 90th percentile Bayesian coverage rate for both AMDI and 30-day AMR. A significant decreasing trend is identified in the AMDI and the 30-day AMR, which is found to be associated with the climate predictors. Leave one out cross validation (LOOCV) is used to demonstrate that these models have decent skill in predicting year-to-year variability in flood risk. This can help to provide flood/rainfall dynamic management measures for reservoirs in Daqing River Basin using information from before the beginning of monsoon season, thus facilitating adaptation to a changing climate. [ABSTRACT FROM AUTHOR]

China has a large economic and demographic exposure to extreme events that is increasing rapidly due to its fast development, and climate change may further aggravate the situation. This paper investigates China's socioeconomic risk from extreme events under climate change over the next few decades with a focus on sub-national heterogeneity. The empirical relationships between socioeconomic damages and their determinants are identified using a hierarchical Bayesian approach, and are used to estimate future damages as well as associated uncertainty bounds given specified climate and development scenarios. Considering projected changes in exposure, we find that the southwest and central regions and Hainan Island of China are likely to have a larger percentage of population at risk, while most of the southwest and central regions could generally have higher economic losses. Finally, the analysis suggests that increasing income can significantly decrease the number of people affected by extremes. [ABSTRACT FROM AUTHOR]

We present the first global precipitation predictability estimates corresponding to the recently discovered flavors of El Niño Southern Oscillation (ENSO) that are encoded in the hidden states of Tropical Pacific sea surface temperatures identified using a non‐homogeneous hidden Markov model. For each calendar month and for each hidden state, we assess future precipitation predictability through the conditional standardized anomaly of the average and the standard deviation of monthly precipitation, at 1, 3, 6, 9, and 12 months lead times. We find statistically significant potential predictive skill for key regions for each hidden state and calendar month, even for 12‐months in the future. We apply the algorithm sequentially over the period of record to identify regions that can be consistently predicted for different lead times and calendar months. The cross‐validated correlation skill is demonstrably superior to that of regression with an ENSO index used in the same way. Plain Language Summary: El Niño Southern Oscillation (ENSO) information is routinely used by hydrologists, agriculturalists and others as a useful prognosticator of upcoming precipitation. Since the characterization of ENSO has recently been broadened to consider different "flavors," the NINO3.4 correlations used by many people may not be the most informative. Our machine learning model identified five hidden states of the tropical Pacific sea surface temperatures that correspond to the different ENSO flavors. We explore how future monthly precipitation may be predictable knowing which hidden state is currently identified and show that this leads to a higher predictability for up to almost a year in the future for many more calendar months in more regions of the world than NINO3.4 correlations. Key Points: Predictability of monthly global rainfall using hidden states of Sea Surface Temperatures in the tropical Pacific Ocean is demonstratedHighly significant skill for many global regions and months is identified by sampling of means and deviations given hidden stateA sequential application of the method leads to much higher cross‐validated correlations than those based on regression on an El Niño Southern Oscillation index [ABSTRACT FROM AUTHOR]

Precipitation is considered one of the most important forcing data in scientific investigations involving agriculture, water management, and climate variability. Knowledge of precipitation variation during rainy seasons is the key to the understanding of the precipitation variability under the effect of climate change. This study evaluated some rainy season features (e.g., onset, retreat, and rainy-season precipitation) over the Yellow River basin (YRB), China, and the influence of the El Niño-Southern Oscillation (ENSO). The multi-scale moving t-test was applied to capture the onset and retreat of rainy season. The possible linkage between ENSO-induced precipitation and monsoon and atmospheric circulation was also explored. From the analysis, four conclusions can be drawn: (1) Rainy season began the earliest (latest) and ended the latest (earliest) in the southern (northern) YRB, with rainy-season precipitation increasing from northwest to southeast. (2) Rainy-season precipitation showed strong correlation to SST in Niño regions. Precipitation can reach up to 20% above the average precipitation for decaying central Pacific warming (CPW) and down to 35% below the average precipitation for developing eastern Pacific warming (EPW) in most areas of the YRB. (3) Developing El Niño showed the strongest dry signals among developing ENSO and ENSO Modoki phases. Decaying El Niño and El Niño Modoki indicated overall increasing precipitation, with La Niña and La Niña Modoki during the corresponding period showing the opposite tendency. (4) Different performances of ENSO-induced precipitation attributed to the combined influence of the monsoon from the India Ocean and the atmospheric circulation in the Western North Pacific (WNP). Stronger anti-cyclone and monsoon are related to increasing rainy-season precipitation. These findings can improve the predictability of rainy season features and ENSO-induced precipitation. [ABSTRACT FROM AUTHOR]

A multiscale-modeling framework for daily rainfall is considered and diagnostic results are presented for an application to the winter season in Northwest India. The daily rainfall process is considered to follow a hidden Markov model (HMM), with the hidden states assumed to be an unknown random function of slowly varying climatic modulation of the winter jet stream and moisture transport dynamics. The data used are from 14 stations over Satluj River basin in winter (December-January-February-March). The period considered is 1977/78-2005/06. The HMM identifies four discrete weather states, which are used to describe daily rainfall variability over study region. Each state was found to be associated with a distinct atmospheric circulation pattern, with the driest and drier states, State 1 and 2 respectively, characterized by a lack of synoptic wave activity. In contrast, the wetter and wettest states, States 3 and 4 respectively, are characterized by a zonally oriented wave train extending across Eurasia between 20N and 40N, identified with 'western disturbances' (WD). The occurrence of State 4 is strongly conditioned by the El Nino and Indian Ocean Dipole (IOD) phenomena in winter, which is demonstrated using large-scale correlation maps based on mean sea level pressure and sea surface temperature. This suggests that there is a tendency of higher frequency of the wet days and intense WD activities in winter during El Nino and positive IOD years. These findings, derived from daily rainfall station records, help clarify the sequence of Northern Hemisphere mid-latitude storms bringing winter rainfall over Northwest India, and their association with potentially predictable low frequency modes on seasonal time scales and longer. [ABSTRACT FROM AUTHOR]

The article offers information on a study that explores how groundwater depletion has reduced cropping intensity in India. It mentions that groundwater depletion as becoming a global threat to food security, yet the ultimate impacts of depletion on agricultural production and the efficacy of available adaptation strategies remain poorly quantified.

This narrative review paper explores the diverse applications of artificial intelligence (AI) in the field of dam engineering. Authored by research engineers specializing in civil engineering and data science, and reviewed by experienced dam engineers and AI experts, the paper aims to provide a comprehensive overview of the subject. The paper is structured into three parts. Part 1 offers a concise introduction to AI, covering its historical background, major categories, and current state of development. In Part 2, the focus shifts to the specific applications of AI in hydropower and dam engineering. This section delves into the utilization of AI for predictive modeling, real-time monitoring, optimization, and planning and design. Notable case studies and examples of machine learning techniques applied to predictive models and numerical simulations in the context of dams and levees are also discussed. Part 3 explores the current and emerging technologies being implemented in the industry, including automated decision-making systems and the use of AI-powered drones for efficient dam inspection. Additionally, the section sheds light on the challenges that must be addressed to fully integrate AI into dam engineering practices. This paper targets dam engineers, AI experts, and individuals interested in the intersection of these fields. Its primary contribution lies in providing a comprehensive and up-to-date review of the applications, challenges, and potential future developments of AI in dam engineering. Over 3300 articles on the intersection of dam engineering and AI were identified, and approximately 550 articles (about 17%) were further processed for inclusion in this study. Moreover, it emphasizes the need for robust data management, algorithmic transparency, and ethical considerations in the implementation of AI systems. [ABSTRACT FROM AUTHOR]

Ground-based evaporative fraction (EF) observations have been used widely for validation purposes in previous remote sensing-based EF models. Few studies have investigated whether such measurements can be utilized for calibration use. In this paper, an observation-driven optimization method is proposed to quantify EF over a large heterogeneous area within the surface temperature-vegetation index framework. It is designed at both daily scale and seasonal scale with MODIS products and in-situ EF observations over the Southern Great Plains in the US. The goal is to search for the optimal dry edge within the allowable range that minimizes the difference between the estimated and observed EF of a given site. Results show that the accuracy produced using only one site for calibration has reached a level comparable to those produced by traditional triangle methods. Compared with the daily-scale optimization method, the seasonal-scale optimization method has not only demonstrated its superiority in accuracy but also held distinctive advantages over the traditional triangle methods. Specifically, the dry edge produced by our optimization method holds true under both clear sky and partially cloudy conditions. This has not only bypassed the repetitive work of previous triangle methods but also made it possible to conduct a continuous monitoring of EF. Besides, the optimization method is characterized by its simplicity in algorithm, stability in accuracy and extensibility in parameterization, which makes it a suitable tool for providing a quick and reasonable estimation of EF over large heterogeneous areas from a limited number of in-situ EF observations. • An optimization method is proposed to calibrate RS-based EF models. • Accuracy produced using one site for calibration has reached an acceptable level. • The method has the capacity to provide a quick and continuous monitoring of EF. • The method is also characterized by its simplicity, stability and extensibility. [ABSTRACT FROM AUTHOR]

We develop a hybrid statistical forecasting model for the simultaneous season‐ahead forecasting of both seasonal rainfall and the 24‐hr maximum rainfall for the upcoming season, using predictors identified through the Shared Reciprocal Nearest Neighbor approach. The model uses a generalized linear regression and a four‐parameter Beta distribution model for downscaling extremes using the predictors that were identified. A cross‐validation experiment for the last four decades in both Han‐River and Geum‐River watersheds, South Korea was performed to test the efficacy of the model. The leave‐one‐out cross‐validated seasonal precipitation forecast demonstrates a correlation that ranges from 0.69–0.78 to 0.68–0.76 for the Han‐River and Geum‐River watershed, respectively. Similarly, for the 24‐hr maximum rainfalls in the upcoming season, the cross‐validated correlations between the predicted and the observed values range from 0.67–0.73 to 0.50–0.63, for the two river basins. A discussion of the potential causes of the skill is offered. Key Points: A novel method developed for a simultaneous warm season‐ahead forecasting of both seasonal rainfall and the 24‐hr maximum rainfallThe SST and SLP dipole‐like patterns identified through the SRRN are clearly related to summer precipitation over the Korean PeninsulaThe predictive skills in both seasonal rainfall and 24‐hr maximum rainfall appeared better than those of the persistence model Plain Language Summary: There have been many efforts to reliably predict summer rainfall anomalies over East Asia, including South Korea in the form of teleconnection. However, their potential predictability during the summer season has not been well demonstrated, in particular for prediction of intraseasonal extreme rainfall. The time‐lagged climate variables related to summer precipitation are derived and used as predictors in a predictive model for seasonal rainfall over the next three months. The predictive skills in both seasonal rainfall and 24‐hr maximum rainfall appeared comparable and comparatively better than those of the existing model. Especially, the obtained extreme prediction results were generally good agreement and favorable as compared with those of previous studies that have limited predictability for the intraseasonal extreme rainfall over South Korea. [ABSTRACT FROM AUTHOR]

China's water stress and its pressure on food security is a widely recognized crisis. A framework is proposed and applied at the provincial level to evaluate China's water security for food production. It is based on a water stress index that accounts for the deficit between water supply and aggregate demand of 23 major field crops over the study period (1961–2015). The water demand is estimated based on method recommended by the Food and Agriculture Organization of the United Nations. The water supply is composed of rainfall and irrigation. Consequently, the water security of both rainfed and irrigated agriculture is evaluated in our research, and the respective contribution of rainfall and irrigation is distinguished. Suggestions regarding adjustment in irrigation investment and planting area are proposed for practical application. The framework also holds great potential to investigate the impact of climate change and the virtual water flow of crops. Plain Language Summary: With 7% of the world's arable land and 6% of its water resources, China has to feed 20% of the world's population. Consequently, China's water stress and its pressure on food security is a widely recognized crisis. A framework is proposed in our research to evaluate China's water security for food production. It is mainly designed to answer the following three questions critical to China's food security but have not been systematically addressed in the literature. How much water is required under China's current agriculture planting structure? What is the respective role of rainfall and irrigation in meeting this requirement? What is the status of water security for China's rainfed and irrigated agriculture? The above three questions are answered based on a water stress index that accounts for the deficit between water supply and aggregate demand of 23 major field crops over the study period (1961–2015). We have also proposed suggestions regarding water security for food production from the framework. For future use, our proposed framework lays the groundwork to investigate the virtual water flow of crops. Climate change signals can also be incorporated into this framework to inform climate‐driven water risk for agriculture. Key Points: A new framework is proposed to evaluate water security for food productionRespective contribution of rainfall and irrigation to water security is distinguishedSpatial patterns of China's water security for food production are investigated [ABSTRACT FROM AUTHOR]

Drinking-water contaminants pose a risk to public health. When confronted with elevated levels of contaminants, individuals can take actions to reduce exposure. Yet, few studies address averting behavior due to impaired water, particularly in high-income countries. This is a problem of national interest, given that 9 million to 45 million people have been affected by water quality violations in each of the past 34 years. No national analysis has focused on the extent to which communities reduce exposure to contaminated drinking water. Here, we present an assessment that sheds light on how communities across the United States respond to violations of the Safe Drinking Water Act, using consumer purchases of bottled water. This study provides insight into how averting behavior differs across violation types and community demographics. We estimate the change in sales due to water quality violations, using a panel dataset of weekly sales and violation records in 2,151 counties from 2006 to 2015. Critical findings show that violations which pose an immediate health risk are associated with a 14% increase in bottled water sales. Generally, greater averting action is taken against contaminants that might pose a greater perceived health risk and that require more immediate public notification. Rural, low-income communities do not take significant averting action for elevated levels of nitrate, yet experience a higher prevalence of nitrate violations. Findings can inform improvements in public notification and targeting of technical assistance from state regulators and public health agencies in order to reduce community exposure to contaminants. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union - EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the journals listed here. [ABSTRACT FROM AUTHOR]

Abstract Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

Anthropogenic warming may change precipitation patterns, impacting infrastructure performance and reliability. Future precipitation statistics generated using General Circulation Models (GCM) are, however, often biased and not easily applied to problems such as runoff estimation. Stochastic weather generation is hence used as an alternative to GCMs in hydrology and hydraulic modelling. This paper explores the dependence of fine temporal precipitation characteristics on air pressure and air temperature using historic observations. The goal is to develop, based on the key causes of precipitation, a climatological basis for a stochastic precipitation generator for non-stationary precipitation under climate change conditions. The analysis focuses on precipitation in the urban Northeast United States and utilizes pooled observations from meteorological stations in New York City, Philadelphia, and Boston over 60 years. A negative correlation between hourly Probability of Precipitation (POP) and air pressure is observed. When the historical records are discretized using air Pressure Change Events (PCE), Decreasing Pressure Change Events (DePCEs) had a higher POP and a higher Precipitation Depth (PD) than Increasing Pressure Change Events (InPCEs). Temperature was more strongly associated with PD during DePCEs than InPCEs; this association was more pronounced during high magnitude PCEs and extreme events. The potential for simulating future hourly precipitation by associating historic hourly precipitation patterns with PCE’s and monthly temperature is assessed. [ABSTRACT FROM AUTHOR]

Abstract: This study develops a reconstruction of the frequency of extreme cold‐season precipitation and the occurrence of landfalling atmospheric river (AR) storm tracks across the southwestern Unites States using a network of tree ring chronologies and the Living Blended Drought Atlas (LBDA), a 500‐year tree ring based reconstruction of the summer Palmer Drought Severity Index. The first two rotated empirical orthogonal functions of the LBDA across the Southwest are shown to relate well to previously identified patterns of regional AR activity. Accordingly, the rotated empirical orthogonal functions also record patterns of extreme precipitation associated with those ARs, albeit with some uncertainty introduced by nonextreme precipitation. A network of chronologies sensitive to cold‐season precipitation is then used to reconstruct the occurrence of landfalling ARs and extreme precipitation along the southern Californian coast, demonstrating for the first time the feasibility of reconstructing AR landfalls and extreme events in the Southwest based on spatial patterns in a network of dendroclimatic proxies. [ABSTRACT FROM AUTHOR]

Joint research between economists and hydrologists increasingly contributes to optimising the economic value gained from water, while safeguarding its social and environmental values. The application of hydro-economic analysis to mining regions, however, is limited. This paper examines why this is the case and how to confront it. The paper focuses on identifying and describing features of large-scale mines and mine regions that are challenging to analyse such as: magnitude of capital involved, time-scale and remoteness of projects, inherent environmental risks, and strong negative perceptions about mining's impacts on water. These characteristics may limit the applicability of established hydro-economic concepts and methods, thus risk-based metrics are discussed as complementary tools. We also contend that further research and development in water-related ecosystem services should be a priority, in order to better quantify trade-offs between the economic benefits of water use by mining and competing users, including environmental flows. Case studies of mining regions in Chile, Madagascar and Sweden are summarised to illustrate some of the issues raised. While data limitations are an obstacle, new and extended case studies are required to explore how the challenges may be addressed. [ABSTRACT FROM AUTHOR]

A general framework for the identification of optimal strategies for mitigating the impact of regional shocks to the global food production network is introduced. The framework utilises multi-objective ant colony optimisation (ACO) as the optimisation engine and is applicable to production-, demand-, storage- and distribution-focussed mitigation options. A detailed formulation for using trade as the mitigation option is presented and applied to a shock to wheat production in North America for illustrative purposes. Different strategies for improving the performance of the ACO algorithm are also presented and tested. Results indicate that the proposed framework has the potential to identify a range of practical trade mitigation strategies for consideration by decision makers, including trade-offs between the extent to which regional shocks can be mitigated and the degree to which existing trade arrangements have to be modified, as well as the relative importance of various trade agreements and different exporting countries. [ABSTRACT FROM AUTHOR]

Summary A new drought index is introduced that explicitly considers both water supply and demand. It can be applied to aggregate demand over a geographical region, or for disaggregated demand related to a specific crop or use. Consequently, it is more directly related than existing indices, to potential drought impacts on different segments of society, and is also suitable to use as an index for drought insurance programs targeted at farmers growing specific crops. An application of the index is presented for the drought characterization at the county level for the aggregate demand of eight major field crops in the conterminous United States. Two resiliency metrics are developed and applied with the drought index time series. In addition, a clustering algorithm is applied to the onset times and severity of the worst historical droughts in each county, to identify the spatial structure of drought, relative to the cropping patterns in each county. The geographic relationship of drought severity, drought recovery relative to duration, and resilience to drought is identified, and related to attributes of precipitation and also cropping intensity, thus distinguishing the relative importance of water supply and demand in determining potential drought outcomes. [ABSTRACT FROM AUTHOR]

The evolution of El Niño can be separated into two phases-namely, growth and mature-depending on whether the regional sea surface temperature has adjusted to the tropospheric warming in the remote tropics (tropical regions away from the central and eastern tropical Pacific Ocean). The western Sahel's main rainy season (July-September) is shown to be affected by the growth phase of El Niño through (i) a lack of neighboring North Atlantic sea surface warming, (ii) an absence of an atmospheric column water vapor anomaly over the North Atlantic and western Sahel, and (iii) higher atmospheric vertical stability over the western Sahel, resulting in the suppression of mean seasonal rainfall as well as number of wet days. In contrast, the short rainy season (October-December) of tropical eastern Africa is impacted by the mature phase of El Niño through (i) neighboring Indian Ocean sea surface warming, (ii) positive column water vapor anomalies over the Indian Ocean and tropical eastern Africa, and (iii) higher atmospheric vertical instability over tropical eastern Africa, leading to an increase in the mean seasonal rainfall as well as in the number of wet days. While the modulation of the frequency of wet days and seasonal mean accumulation is statistically significant, daily rainfall intensity (for days with rainfall > 1 mm day−1), whether mean, median, or extreme, does not show a significant response in either region. Hence, the variability in seasonal mean rainfall that can be attributed to the El Niño-Southern Oscillation phenomenon in both regions is likely due to changes in the frequency of rainfall. [ABSTRACT FROM AUTHOR]

• A new annual (Nov-Oct) streamflow reconstruction of HCYRB at Tangnaihai Station of nearly two millennia was presented. • The nested principal component regression model is improved by the stepwise best tree-ring subset selection method in terms of AIC, CRSQ , VRSQ , CE, and RE. • The significant high-flow periods of HCYRB are the early 3rd century, circa 300 C.E., early 13th century, 16th century and circa 1900 C.E., while the low-flow periods are the late 5th century and late 15th century. • The reconstruction suggests that a warm climate is more likely accompanied by a high-flow period and low-flow periods are more likely to happen in cold periods. • The results provide adequate data foundation to analyze characteristics of streamflow of HCYRB and long-term optimal operation of Longyangxia over-year regulation reservoir. The headwater catchment of the Yellow River Basin (HCYRB) controls 35% of the streamflow of the Yellow River (YR) which faces increasing water shortages. To better understand streamflow variability in the region we require a better understanding of high and low flow characteristics. This study presents a new annual (Nov-Oct) streamflow reconstruction at the Tangnaihai station in the HCYRB for the last two millennia (159–2016 C.E.) using 12 tree-ring chronologies. The nested principal component regression model combined with the stepwise best subset selection method was proposed to improve the temporal length and model skill of reconstruction. The stepwise best subset selection method was presented to select the best principal components subset, instead of a confidence test, based on k-fold cross-validation error and Akaike's information criteria (AIC). The model assessment results verify that the proposed model exhibits strong reconstruction skills. Besides, the magnitude and duration of both high and low flow periods were analyzed. The results show that (1) the significant high-flow periods are the early 3rd century, circa 300 C.E., early 13th century, 16th century and circa 1900 C.E., while the low-flow periods are the late 5th century and late 15th century; (2) the durations and magnitudes of low-flow periods are longer and larger than high-flow periods and the severities of high-flow periods are greater than low-flow periods. The reconstruction also suggests that a warm climate is more likely accompanied by a high-flow period and low-flow periods are more likely to occur in cold periods associated with the Asian Summer Monsoon and solar activity. [ABSTRACT FROM AUTHOR]

• In some U.S. counties, up to 50% of unsewered homes use straight pipes. • Straight pipes have received little attention from regulators or researchers. • Lack of funding precludes quantification and discourages reporting. Roughly ¼ of U.S. residents (80 million people) lack access to sanitary sewers and are required to treat their wastewater through a permitted onsite wastewater treatment system (OWTS). The vast majority use conventional septic systems with subsurface infiltration, which work well under most conditions. However, certain geologic conditions (e.g., impermeable soil, high water table) can preclude use of septic systems, requiring investment in expensive advanced OWTS. The confluence of lack of sewer, unsuitable geology, and poverty can lead households to have no feasible option for treating wastewater. In many such communities households discharge raw sewage onto the ground through what are commonly called "straight pipes." Here, we present the first effort to synthesize available evidence documenting the scope of straight pipe use in the U.S., including estimates of close to 50% straight pipe use in some counties. Despite reports that straight pipes are widespread and troubling preliminary evidence of adverse health effects, there has been no national effort to estimate the use or impacts of straight pipes. There are various disincentives that discourage the reporting of straight pipes by both residents and government actors. We propose ways to improve quantification of straight pipes and increase knowledge of their adverse effects. We identify the characteristics of areas with large proportions of straight pipes and describe the role of new and pending government programs in encouraging reporting and providing solutions. Image, graphical abstract [ABSTRACT FROM AUTHOR]

A multi-reservoir simulation-optimization model GRAPS, Generalized Multi-Reservoir Analyses using Probabilistic Streamflow Forecasts, is developed in which reservoirs and users across the basin are represented using a node-link representation. Unlike existing reservoir modeling software, GRAPS can handle probabilistic streamflow forecasts represented as ensembles for performing multi-reservoir prognostic water allocation and evaluate the reliability of forecast-based allocation with observed streamflow. GRAPS is applied to four linked reservoirs in the Jaguaribe Metropolitan Hydro-System (JMH) in Ceará, North East Brazil. Results from the historical simulation and the zero-inflow policy over the JMH system demonstrate the model's capability to support monthly water allocation and reproduce the observed monthly releases and storages. Additional analyses using streamflow forecast ensembles illustrate GRAP's abilities in developing storage-reliability curves under inflow-forecast uncertainty. Our analyses show that GRAPS is versatile and can be applied for 1) short-term operating policy studies, 2) long-term basin-wide planning evaluations, and 3) climate-information based application studies. • GRAPS is a generalized multi-reservoir simulation-optimization model for water allocation and reservoir management. • GRAPS is intended to assist engineers and stakeholders as a decision-supporting tool to support seasonal water allocation. • GRAPS can handle streamflow forecasts as ensembles to quantify the reliability of meeting target storage and demand. [ABSTRACT FROM AUTHOR]