Reconstructed eight-century streamflow in the Tibetan Plateau reveals contrasting regional variability and strong nonstationarity.

Short instrumental streamflow records in the South and East Tibetan Plateau (SETP) limit understanding of the full range and long-term variability in streamflow, which could greatly impact freshwater resources for about one billion people downstream. Here we reconstruct eight centuries (1200-2012 C.E.) of annual streamflow from the Monsoon Asia Drought Atlas in five headwater regions across the SETP. We find two regional patterns, including northern (Yellow, Yangtze, and Lancang-Mekong) and southern (Nu-Salween and Yarlung Zangbo-Brahmaputra) SETP regions showing ten contrasting wet and dry periods, with a dividing line of regional moisture regimes at ~32°-33°N identified. We demonstrate strong temporal nonstationarity in streamflow variability, and reveal much greater high/low mean flow periods in terms of duration and magnitude: mostly pre-instrumental wetter conditions in the Yarlung Zangbo-Brahmaputra and drier conditions in other rivers. By contrast, the frequency of extreme flows during the instrumental periods for the Yangtze, Nu-Salween, and Yarlung Zangbo-Brahmaputra has increased by ~18% relative to the pre-instrumental periods.

Solving groundwater depletion in India while achieving food security.

Significant groundwater depletion in regions where grains are procured for public distribution is a primary sustainability challenge in India. We identify specific changes in the Indian Government's Procurement & Distribution System as a primary solution lever. Irrigation, using groundwater, facilitated by subsidized electricity, is seen as vital for meeting India's food security goals. Using over a century of daily climate data and recent spatially detailed economic, crop yield, and related parameters, we use an optimization model to show that by shifting the geographies where crops are procured from and grown, the government's procurement targets could be met on average even without irrigation, while increasing net farm income and arresting groundwater depletion. Allowing irrigation increases the average net farm income by 30%. The associated reduction in electricity subsidies in areas with significant groundwater depletion can help offset the needed spatial re-distribution of farm income, a key political obstacle to changes in the procurement system.

A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling.

We develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind and solar) and can simulate synthetic realizations of arbitrary length. We illustrate how this statistical simulation tool can be used in the context of regional power system planning under a scenario of high reliance on wind and solar generation and when long historical records of wind and solar power generation potential are not available. We show how our simulation model can be used to assess the probability distribution of the severity and duration of energy "droughts" at the network scale that need to be managed by long-duration storage or alternate energy sources. We present this estimation of supply-side shortages for the Texas Interconnection.

Groundwater depletion will reduce cropping intensity in India.

Groundwater depletion is 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. We use high-resolution satellite and census data from India, the world's largest consumer of groundwater, to quantify the impacts of groundwater depletion on cropping intensity, a crucial driver of agricultural production. Our results suggest that, given current depletion trends, cropping intensity may decrease by 20% nationwide and by 68% in groundwater-depleted regions. Even if surface irrigation delivery is increased as a supply-side adaptation strategy, which is being widely promoted by the Indian government, cropping intensity will decrease, become more vulnerable to interannual rainfall variability, and become more spatially uneven. We find that groundwater and canal irrigation are not substitutable and that additional adaptation strategies will be necessary to maintain current levels of production in the face of groundwater depletion.

Making waves: Right in our backyard- surface discharge of untreated wastewater from homes in the United States.

Roughly » 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.

Seven centuries of reconstructed Brahmaputra River discharge demonstrate underestimated high discharge and flood hazard frequency.

The lower Brahmaputra River in Bangladesh and Northeast India often floods during the monsoon season, with catastrophic consequences for people throughout the region. While most climate models predict an intensified monsoon and increase in flood risk with warming, robust baseline estimates of natural climate variability in the basin are limited by the short observational record. Here we use a new seven-century (1309-2004 C.E) tree-ring reconstruction of monsoon season Brahmaputra discharge to demonstrate that the early instrumental period (1956-1986 C.E.) ranks amongst the driest of the past seven centuries (13th percentile). Further, flood hazard inferred from the recurrence frequency of high discharge years is severely underestimated by 24-38% in the instrumental record compared to previous centuries and climate model projections. A focus on only recent observations will therefore be insufficient to accurately characterise flood hazard risk in the region, both in the context of natural variability and climate change.

Detecting community response to water quality violations using bottled water sales.

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.

Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning.

The massive socioeconomic impacts engendered by extreme floods provides a clear motivation for improved understanding of flood drivers. We use self-organizing maps, a type of artificial neural network, to perform unsupervised clustering of climate reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the United States. We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specific to each circulation pattern. To supplement this analysis, we have developed an interactive website with detailed information for every flood of record. We identify four primary categories of circulation patterns: tropical moisture exports, tropical cyclones, atmospheric lows or troughs, and melting snow. We find that large flood events are generally caused by tropical moisture exports (tropical cyclones) in the western and central (eastern) United States. We identify regions where extreme floods regularly occur outside the normal flood season (e.g., the Sierra Nevada Mountains due to tropical moisture exports) and regions where multiple extreme flood events can occur within a single year (e.g., the Atlantic seaboard due to tropical cyclones and atmospheric lows or troughs). These results provide the first machine-learning based near-continental scale identification of atmospheric circulation patterns associated with extreme floods with valuable insights for flood risk management.

National trends in drinking water quality violations.

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 health-based 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.

Six centuries of Upper Indus Basin streamflow variability and its climatic drivers.

Our understanding of the full range of natural variability in streamflow, including how modern flow compares to the past, is poorly understood for the Upper Indus Basin (UIB) because of short instrumental gauge records. To help address this challenge, we use Hierarchical Bayesian Regression (HBR) with partial pooling to develop six centuries long (1394-2008 C.E.) streamflow reconstructions at three UIB gauges (Doyian, Gilgit, and Kachora), concurrently demonstrating that HBR can be used to reconstruct short records with interspersed missing data. At one gauge (Partab Bridge), with a longer instrumental record (47 years), we develop reconstructions using both Bayesian Regression (BR) and the more conventionally used Principal Components Regression (PCR). The reconstructions produced by PCR and BR at Partab Bridge are nearly identical and yield comparable reconstruction skill statistics, highlighting that the resulting tree-ring reconstruction of streamflow is not dependent on the choice of statistical method. Reconstructions at all four reconstructions indicate flow levels in the 1990s were higher than mean flow for the past six centuries. While streamflow appears most sensitive to accumulated winter (January-March) precipitation and summer (MJJAS) temperature, with warm summers contributing to high flow through increased melt of snow and glaciers, shifts in winter precipitation and summer temperatures cannot explain the anomalously high flow during the 1990s. Regardless, the sensitivity of streamflow to summer temperatures suggests that projected warming may increase streamflow in coming decades, though long-term water risk will additionally depend on changes in snowfall and glacial mass balance.

An Empirical, Nonparametric Simulator for Multivariate Random Variables with Differing Marginal Densities and Nonlinear Dependence with Hydroclimatic Applications.

Multivariate simulations of a set of random variables are often needed for risk analysis. Given a historical data set, the goal is to develop simulations that reproduce the dependence structure in that data set so that the risk of potentially correlated factors can be evaluated. A nonparametric, copula-based simulation approach is developed and exemplified. It can be applied to multiple variables or to spatial fields with arbitrary dependence structures and marginal densities. The nonparametric simulator uses logspline density estimation in the univariate setting, together with a sampling strategy to reproduce dependence across variables or spatial instances, through a nonparametric numerical approximation of the underlying copula function. The multivariate data vectors are assumed to be independent and identically distributed. A synthetic example is provided to illustrate the method, followed by an application to the risk of livestock losses in Mongolia.

Scaling of extreme rainfall areas at a planetary scale.

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.

Predictive statistical models linking antecedent meteorological conditions and waterway bacterial contamination in urban waterways.

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.

The effects of land use change and precipitation change on direct runoff in Wei River watershed, China.

The principles and degrees to which land use change and climate change affect direct runoff generation are distinctive. In this paper, based on the MODIS data of land use in 1992 and 2003, the impacts of land use and climate change are explored using the Soil Conservation Service Curve Number (SCS-CN) method under two defined scenarios. In the first scenario, the precipitation is assumed to be constant, and thus the consequence of land use change could be evaluated. In the second scenario, the condition of land use is assumed to be constant, so the influence only induced by climate change could be assessed. Combining the conclusions of two scenarios, the effects of land use and climate change on direct runoff volume can be separated. At last, it is concluded: for the study basin, the land use types which have the greatest effect on direct runoff generation are agricultural land and water body. For the big sub basins, the effect of land use change is generally larger than that of climate change; for middle and small sub basins, most of them suffer more from land use change than from climate change.

Modeling the effect of algal dynamics on arsenic speciation in Lake Biwa.

Algae reduce and methylate arsenate and the end product of the reaction is correlated to their growth rate. At slow growth rates, dimethylarsinate (DMA) is produced, and at fast growth rates arsenite (As(III)) is produced. Previous work has linked this phenomenon to the phosphorus luxury uptake mechanism of algae, and a model was developed for the process (Hellweger et al. Limnol. Oceanogr. 2003, 48, 2275). This paper presents the integration of that process model for arsenic transformation by algae into a full ecological model and application to Lake Biwa, Japan. The model application allows for a quantitative analysis of the field data, consistent with the process model and the ecological dynamics of the lake. The newly developed ecological model includes a variable phytoplankton composition, which is needed to simulate luxury uptake. This is in contrast to most existing ecological models, which typically assume a fixed "Redfield" composition. The model adequately reproduces the observed ecology of Lake Biwa, including the rapid uptake of phosphate by phytoplankton without immediate growth (luxury uptake) following lake overturn. The model also reproduces the observed arsenic speciation, including the gradual appearance of DMA during the summer and peaks in As(III) concentration at the onset of spring and fall algal blooms.