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The efficient operation of a multipurpose reservoir requires information on high and low flows. However, analyses of inflows for high flows and for low flows are typically done independently. In this paper, we considered the joint dependence of the low flow on the preceding high flow volume and duration for the wet season in the Three Gorges region of the Yangtze River Basin in China. High flow volume and duration were found to have a strong association with the annual minimum 7‐day flow in Cuntan, Wanxian, and Yichang stations. Furthermore, we identified the Arctic Oscillation, Pacific Decadal Oscillation, and snow cover in the Tibetan Plateau to have statistically significant teleconnections with the annual minimum 7‐day flow. Bayesian models that consider a different level of pooling of the site by site regressions were then developed for the annual minimum 7‐day flow conditional on the climate indices and high flow volume (or duration). The full pooling model performed best, suggesting that a homogeneous regional response is best identified given the global climate predictors. Statistics such as the deviance information criterion and reduction of error, coefficient of efficiency, and coverage rate under cross validation indicate the good performance of the model. Snow cover in the western Tibetan Plateau and high flow volume were identified as the most influential factors of the annual minimum 7‐day flow through their impact on water storage in the basin. Recent simulations since June 2003, when the Three Gorges Dam operation started, were used to analyse the effect of dam operation on the annual minimum 7‐day flow. A comparison of observations and predictions during the post‐dam period demonstrated that the dam operation effectively modifies the annual minimum 7‐day flow period to have higher flows.

We introduce a hierarchical Bayesian model for the spatial distribution of rainfall corresponding to an extreme event of a specified duration that could be used with regional hydrologic models to perform a regional hydrologic risk analysis. An extreme event is defined if any gaging site in the watershed experiences an annual maximum rainfall event and the spatial field of rainfall at all sites corresponding to that occurrence is modeled. Applications to data from New York City demonstrate the effectiveness of the model for providing spatial scenarios that could be used for simulating loadings into the urban drainage system. Insights as to the homogeneity in spatial rainfall and its implications for modeling are provided by considering partial pooling in the hierarchical Bayesian framework.

National Science FoundationNational Science Foundation (NSF); Paleo Perspective on Climate Change (P2C2) [1401698, 1404188]; National Science Foundation, Water Sustainability and Climate (WSC)National Science Foundation (NSF) [1360446]; U.S. Department of EnergyUnited States Department of Energy (DOE) [DE-SC0018124]; U.S. Bureau of Reclamation WaterSMART Program (Sustain and Manage America's Resources for Tomorrow); state of Montana Department of Natural Resources and Conservation; U.S. Geological Survey Land Resources Mission Area; North Central Climate Adaptation Science Center; Lamont-Doherty Earth Observatory [8349]

This New York City Panel on Climate Change (NPCC3) chapter builds on the projections developed by the second New York City Panel on Climate Change (NPCC2) (Horton et al., 2015). It confirms NPCC2 projections as those of record for the City of New York, presents new methodology related to climate extremes, and describes new methods for developing the next generation of climate projections for the New York metropolitan region. These may be used by the City of New York as it continues to develop flexible adaptation pathways to cope with climate change. The main topics of the climate science chapter are: (1) Comparison of observed temperature and precipitation trends to NPCC2 2015 projections. (2) New methodology for analysis of historical and future projections of heatwaves, humidity, and cold snaps. (3) Improved characterization of observed heavy downpours. (4) Characterization of observed drought using paleoclimate data. (5) Suggested methods for next generation climate risk information.

The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones.

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.

The annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

A key strategy for agriculture to adapt to climate change is by switching crops and relocating crop production. We develop an approach to estimate the economic potential of crop reallocation using a Bayesian hierarchical model of yields. We apply the model to six crops in the United States, and show that it outperforms traditional empirical models under cross-validation. The fitted model parameters provide evidence of considerable existing climate adaptation across counties. If crop locations are held constant in the future, total agriculture profits for the six crops will drop by 31% for the temperature patterns of 2070 under RCP 8.5. When crop lands are reallocated to avoid yield decreases and take advantage of yield increases, half of these losses are avoided (16% loss), but 57% of counties are allocated crops different from those currently planted. Our results provide a framework for identifying crop adaptation opportunities, but suggest limits to their potential., Switching and relocating crops could be a key pathway for agricultural adaptation to climate change. Here, Rising and Devineni use data-driven Bayesian modelling to estimate the potential for crop switching to mitigate climate impacts on US crop production under a high-emission scenario, showing considerable opportunities but also limitations.

This study presents an assessment of the spatial and temporal characteristics of large tornado outbreak (LTOs) days, in which several counties were impacted by tornadoes rated F2(EF2) or greater on the Fujita (Enhanced Fujita) scale in one day. A statistical evaluation of changes in the LTO clusters for two periods, 1950–1980 and 1989–2019, has been performed. There is a geographical shift of the nucleus (central impact location) towards the southeast United States. This spatial shift is also accompanied by reduced spatial variance, suggesting LTOs have become less dispersed (or more localized) in the recent period. The overall inter-arrival rate of LTOs, and how it changed during successive 31-year climatological blocks between 1950–2019 was investigated using an exponential probability model. The arrival rate has changed from 124 days during 1950–1980 to 164 days during 1977–2007 and remained relatively constant during later periods, indicating that LTOs are becoming less frequent.

Water risk management is perhaps the most ubiquitous challenge a stakeholder in the water or agricultural sector faces. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest Rank Probability Skill Score and lowest Mean Squared Error in a leave-one-out cross validation mode. Adaptive forecasts were made over the years 2001 through 2013 using the identified predictors and a semi-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct nine out of thirteen times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress/risk indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.

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.

Physical/semi-empirical models that do not require any calibration are of paramount need for estimating hydrological fluxes for ungauged sites. We develop semi-empirical models for estimating the mean and variance of the monthly streamflow based on Taylor Series approximation of a lumped physically based water balance model. The proposed models require mean and variance of monthly precipitation and potential evapotranspiration, co-variability of precipitation and potential evapotranspiration and regionally calibrated catchment retention sensitivity, atmospheric moisture uptake sensitivity, groundwater-partitioning factor, and the maximum soil moisture holding capacity parameters. Estimates of mean and variance of monthly streamflow using the semi-empirical equations are compared with the observed estimates for 1373 catchments in the continental United States. Analyses show that the proposed models explain the spatial variability in monthly moments for basins in lower elevations. A regionalization of parameters for each water resources region show good agreement between observed moments and model estimated moments during January, February, March and April for mean and all months except May and June for variance. Thus, the proposed relationships could be employed for understanding and estimating the monthly hydroclimatology of ungauged basins using regional parameters.

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Nino-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

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.

This study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions.

Natural streams are characterized by variation in cross-section geometry, bed-slope, bed roughness, hydraulic slope, etc., along their channels resulting from several interacting features of the riverine system including the effects of discharge changes, geologic context, sediment load, etc. Quantitative and qualitative assessment of river flow dynamics requires sufficient knowledge of hydraulics and these geophysical variables. Average flow condition theory expressed as “At-A-Station” hydraulic geometry (AHG) relations are site-specific power-functions, relating the mean stream channel forms (i.e. water depth, top-width, flow velocity, and flow area) to discharge, have been studied since 50s. Establishing robust AHG relations requires pre-assessment of data quality by means of uncertainty analysis. Our paper introduces a recursive data filtering procedure to find both random and systematic errors in streamflow and river-survey data that can be used to produce robust and informative AHG relations. The method is first verified on synthetic data and then by experiments over: (1) real discharge-stage ratings provided by daily streamflow records of U.S. Geological Survey/National Water Information System dataset (USGS/NWIS), and (2) field river survey measurement data from USGS/NWIS. This produces robust AHG relations at 4472 monitoring stations across the U.S.

Extreme rainfall events, specifically in urban areas, have dramatic impacts on society and can lead to loss of life and property. Despite these hazards, little is known about the city-scale variability of heavy rainfall events. In the current study, gridded stage IV radar data from 2002 to 2015 are employed to investigate the clustering and the spatial variability of simultaneous rainfall exceedances in the greater New York area. Multivariate clustering based on partitioning around medoids is applied to the extreme rainfall events’ average intensity and areal extent for the 1- and 24-h accumulated rainfall during winter (December–February) and summer (June–August) seasons. The atmospheric teleconnections of the daily extreme event for winter and summer are investigated using compositing of ERA-Interim. For both 1- and 24-h durations, the winter season extreme rainfall events have larger areal extent than the summer season extreme rainfall events. Winter extreme events are associated with deep and organized circulation patterns that lead to more areal extent, and the summer events are associated with localized frontal systems that lead to smaller areal extents. The average intensities of the 1-h extreme rainfall events in summer are much higher than the average intensities of the 1-h extreme rainfall events in winter. A clear spatial demarcation exists within the five boroughs in New York City for winter extreme events. Resultant georeferenced cluster maps can be extremely useful in risk analysis and green infrastructures planning as well as sewer systems’ management at the city scale.

Storage and controlled distribution of water have been key elements of a human strategy to overcome the space and time variability of water, which have been marked by catastrophic droughts and floods throughout the course of civilization. In the United States, the peak of dam building occurred in the mid-20th century with knowledge limited to the scientific understanding and hydrologic records of the time. Ecological impacts were considered differently than current legislative and regulatory controls would potentially dictate. Additionally, future costs such as maintenance or removal beyond the economic design life were not fully considered. The converging risks associated with aging water storage infrastructure and uncertainty in climate in addition to the continuing need for water storage, flood protection, and hydropower result in a pressing need to address the state of dam infrastructure across the nation. Decisions regarding the future of dams in the United States may, in turn, influence regional water futures through groundwater outcomes, economic productivity, migration, and urban growth. We advocate for a comprehensive national water assessment and a formal analysis of the role dams play in our water future. We emphasize the urgent need for environmentally and economically sound strategies to integrate surface and groundwater storage infrastructure in local, regional, and national water planning considerations. A research agenda is proposed to assess dam failure impacts and the design, operation, and need for dams considering both paleo and future climate, utilization of groundwater resources, and the changing societal values toward the environment.

A comprehensive framework is developed to assess the flood types, their spatiotemporal characteristics and causes based on the rainfall statistics, antecedent flow conditions, and atmospheric teleconnections. The Missouri River Basin (MRB) is used as a case study for the application of the framework. Floods are defined using the multivariate characteristics of annual peak, volume, duration, and timing. The temporal clustering of flood durations is assessed using a hierarchical clustering analysis, and low-frequency modes are identified using wavelet decomposition. This is followed by an identification of the synoptic scale atmospheric processes and an analysis of storm tracks that entered the basin and their moisture releases. Atmospheric teleconnections are distinctively persistent and well developed for long duration flood events. Long duration floods are triggered by high antecedent flow conditions which are in turn caused by high moisture release from the tracks. For short duration floods, these are insignificant and appear to occur random across the MRB in the recent half-century. The relative importance of hydroclimatic drivers (rainfall duration, rainfall intensity and antecedent flow conditions) in explaining the variance in flood duration and volume is discussed using an empirical log-linear regression model. The implication of analyzing the duration and volume of the floods in the context of flood frequency analysis for dams is also presented. The results demonstrate that the existing notion of the flood risk assessment and consequent reservoir operations based on the instantaneous peak flow rate at a stream gage needs to be revisited, especially for those flood events caused by persistent rainfall events, high antecedent flow conditions and synoptic scale atmospheric teleconnections.

The vegetation of the Galapagos Islands (Ecuador) is strongly influenced by climate. El Niño events, seasonality, isolation, volcanism, and increasing human activity define the ecosystems of the archipelago. Given their socio-cultural and economic importance, it is critical to monitor the response of Galapagos vegetation to changes in climate and assess its vulnerability. This study explores the potential to use Normalized Difference Vegetation Index (NDVI) as a proxy to describe trends in primary productivity in the Galapagos (2000–2019) and models the relationship between NDVI and climate variables including evaporation and atmospheric carbon dioxide concentration. From numerous possible co-variates compiled from reanalysis and satellites, we identify the independent variables that most strongly influence NDVI using the least absolute shrinkage and selection operator (LASSO) method. Significant variables, including carbon dioxide concentration, evaporation, and autocorrelation (1-month and 12-months lagged NDVI) are then used to model NDVI in a generalized linear model (GLM) framework. The model predicts NDVI more effectively where values for NDVI are high (high elevation, lush vegetation), and clearly reflects seasonality. Validation of the model across pixels produces R 2 values ranging from 0.05 to 0.94, and the mean R 2 is 0.57 (0.65 for elevation >20 m). This methodology has the potential to continuously and non-intrusively monitor vegetation changes in sensitive ecological regions, such as the Galapagos.

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.

Since 1954, the Delaware River has been managed under the framework of a Supreme Court decree and the subsequent concomitant intergovernmental collaboration between New York State, New Jersey, Pennsylvania, Delaware, New York City (NYC) and the US federal government. Taking an environmental perspective, we review the evolution of water release policies for three NYC reservoirs from the issuance of the 1954 decree through the implementation of the Flexible Flow Management Program (FFMP) of 2007–2015 and examine the policies' impact on the upper Delaware River. We describe governmental and institutional constraints on the development of Delaware water policy and show how modifications of release policies have enhanced aquatic habitat and ecological health in the upper Delaware while reliably delivering water to NYC and the Delaware's other principal stakeholders. We describe the development of the FFMP in 2006, its subsequent modification, and its augmentation by NYC's Operations Support Tool in 2012. Finally, we discuss the negative ecological consequences of the 2010–2016 stalemate on Delaware water policy resulting from conflicts between the decree parties about current and future water rights, and how the stalemate derives partially from the decision structure imposed by the 1954 decree and the Good Faith Agreement of 1983.

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.

This paper presents a new framework for quantitatively identifying, characterizing and analyzing systematic hydrological cycles resulting from streamflow variability in a way that integrates water supply and water demand. The hydrological cycles in question are measures of the most severe drought and pluvial events in a historical record of streamflow, along with their respective durations. The metrics developed here to quantify such episodes are based on an extended sequent peak algorithm that tracks the dynamic shifts in hydrological behavior of streamflow by accounting for water supply and demand with respect to streamflow. In the interest of being able to analyze the largest possible scope of hydrological cycles and behavior, we apply the quantitative methods developed in this paper to streamflow reconstructions in the Upper Missouri River Basin (UMRB) as a case study. We find that the duration of dry periods increase conspicuously as a function of increasing demand levels, the duration of pluvial events decrease as a function of increasing demand levels, and that the general tendency is for streamflow gauges on or near the main stem of the river to have shorter dry spell durations and typically lower drought severity. On the other hand, being on or near the main stem tends to result in longer-duration pluvial events, though these pluvials are typically less severe than those off the main stem. Persistence and spatial variability of streamflow reconstructions were also analyzed to shed further light on the spatial patterns identified earlier, and to see if this variability and persistence may have an influence on the behavior of the streamflow as quantified by the metrics defined in this paper. Finally, it was found that there is a stochastic dependence between the length of a drought and the time to recovery from that drought, and this dependence is used to create simple conditional probability curves to help water managers prepare for future extreme events.

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.

A new indicator of drought-induced water stress is introduced and applied at the county level in the USA. Unlike most existing drought metrics, we directly consider current daily water demands and renewable daily water supply to estimate the potential stress. Water stress indices developed include the Normalized Deficit Cumulated to represent multiyear droughts by computing the maximum cumulative deficit between demand and supply over the study period (1949–2009) and the Normalized Deficit Index representing drought associated with maximum cumulative deficit each year. These water stress indices map directly to storage requirements needed to buffer multiyear and within-year climate variability and can reveal the dependence on exogenous water transferred by rivers/canals to the area. Future climate change and variability can be also incorporated into this framework to inform climate-driven drought for additional storage development and potential applications of water trading across counties.

Degradation of freshwater ecosystems and the services they provide is a primary cause of increasing water insecurity, raising the need for integrated solutions to freshwater management. While methods for characterizing the multi-faceted challenges of managing freshwater ecosystems abound, they tend to emphasize either social or ecological dimensions and fall short of being truly integrative. This paper suggests that management for sustainability of freshwater systems needs to consider the linkages between human water uses, freshwater ecosystems and governance. We present a conceptualization of freshwater resources as part of an integrated social-ecological system and propose a set of corresponding indicators to monitor freshwater ecosystem health and to highlight priorities for management. We demonstrate an application of this new framework -the Freshwater Health Index (FHI) - in the Dongjiang River Basin in southern China, where stakeholders are addressing multiple and conflicting freshwater demands. By combining empirical and modeled datasets with surveys to gauge stakeholders' preferences and elicit expert information about governance mechanisms, the FHI helps stakeholders understand the status of freshwater ecosystems in their basin, how ecosystems are being manipulated to enhance or decrease water-related services, and how well the existing water resource management regime is equipped to govern these dynamics over time. This framework helps to operationalize a truly integrated approach to water resource management by recognizing the interplay between governance, stakeholders, freshwater ecosystems and the services they provide.

Frequency and duration of flood events are analyzed using Dartmouth Flood Observatory's (DFO) global flood database to detect significant trends and regime shifts during 1985–2015 at global and latitudinal scales. Three classes of flood duration (i.e. short: 1–7, moderate: 8–20, and long: 21 days and above) are also considered for this analysis. The non-parametric Mann-Kendall trend test and Pettitt change-point analysis are used to evaluate three hypotheses (H1, H2, and H3) addressing potential monotonic trends and regime shifts in flood frequency, moments of the duration, and the frequency of a specific flood duration type. The results show that long duration flood frequency has increased across most spatial scales with significant change-point observed in the 2000s. In the tropics, floods have increased four-fold since the 2000s. This increase is 2.5 fold in the north mid-latitudes. There is no monotonic trend in the frequency of short duration floods across all global and latitudinal scales. There is also a significant increasing trend in the annual median and tails of flood durations globally and in each latitudinal belt. The possible causes of these trends are analyzed using a Generalized Linear Model framework and also discussed qualitatively. This analysis provides the framework for understanding simultaneously changing climate and socioeconomic conditions and how they relate to the frequency and persistence in the organization of global and local dynamical systems that cause hydrologic extremes.

The Masinga Reservoir located in the upper Tana River basin, Kenya, is extremely important in supplying the country's hydropower and protecting downstream ecology. The dam serves as the primary storage reservoir, controlling streamflow through a series of downstream hydroelectric reservoirs. The Masinga dam's operation is crucial in meeting power demands and thus contributing significantly to the country's economy. La Niña–related prolonged droughts of 1999–2001 resulted in severe power shortages in Kenya. Therefore, seasonal streamflow forecasts contingent on climate information are essential to estimate preseason water allocation. Here, the authors utilize reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with constructed analog SSTs and multimodel precipitation forecasts developed from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project to improve water allocation during the April–June and October–December seasons for the Masinga Reservoir. Three-month-ahead inflow forecasts developed from ECHAM4.5, multiple GCMs, and climatological ensembles are used in a reservoir model to allocate water for power generation by ensuring climatological probability of meeting the end-of-season target storage required to meet seasonal water demands. Retrospective reservoir analysis shows that inflow forecasts developed from single GCM and multiple GCMs perform better than use of climatological values by reducing the spill and increasing the allocation for hydropower during above-normal inflow years. Similarly, during below-normal inflow years, both of these forecasts could be effectively utilized to meet the end-of-season target storage by restricting releases for power generation. The multimodel forecasts preserve the end-of-season target storage better than the single-model inflow forecasts by reducing uncertainty and the overconfidence of individual model forecasts.