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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.

Groundwater in north India remains a vital food and water security resource for more than one billion people. Both summer monsoon drying, and winter warming pose considerable challenges for rapidly declining groundwater. However, their impacts on irrigation water demands and groundwater storage under the observed and projected future climate remain unexplored. Using in situ observations, satellite data, and a hydrological model that considers the role of irrigation and groundwater pumping, we show that summer monsoon drying and winter warming accelerate groundwater depletion in north India during the observed climate, which will continue in the projected future climate. Summer monsoon precipitation has significantly (P‐value = 0.04) declined (∼8%) while winters have become warmer in north India during 1951–2021. Both satellite (GRACE/GRACE‐FO) and hydrological model‐based estimates show a rapid groundwater depletion (∼1.5 cm/year) in north India with a net loss of 450 km3 of groundwater during 2002–2021. The summer monsoon drying followed by winter warming cause a substantial reduction in groundwater storage due to reduced groundwater recharge and enhanced pumping to meet irrigation demands. Summer monsoon drying and winter warming will continue to affect groundwater storage in north India in the future. For instance, summer monsoon drying (10%–15% deficit for near‐far periods) followed by substantial winter warming (1–4°C) in the future will further accelerate groundwater depletion by increasing (6%–20%) irrigation water demands and reducing groundwater recharge (6%–12%). Groundwater sustainability measures including reducing groundwater abstraction and enhancing the groundwater recharge during the summer monsoon seasons are needed to ensure future agricultural production. Plain Language Summary: Groundwater in north India is facing rapid depletion due to summer monsoon drying and winter warming. The decline in summer monsoon precipitation and warmer winters have led to major droughts, resulting in a massive loss of groundwater between 2002 and 2021. Summer monsoon drying coupled with winter warming climate will increase irrigation water demands and reduce groundwater recharge. This necessitates the implementation of groundwater sustainability measures to ensure future agricultural production during monsoon droughts. Key Points: Examined the role summer monsoon drying and winter warming on groundwater storage in IndiaRapid groundwater depletion (∼1.5 cm/year) resulted in a net loss of 450 km3 of groundwater during 2002–2021Summer monsoon drying followed by substantial winter warming under the warming climate will further accelerate groundwater depletion [ABSTRACT FROM AUTHOR]

The potential for extreme climate events to cluster in space and time has driven increased interest in understanding and predicting compound climate risks. Through a case study on floods in the Ohio River Basin, we demonstrated that low-frequency climate variability could drive spatial and temporal clustering of the risk of regional climate extremes. Long records of annual maximum streamflow from 24 USGS gauges were used to explore the regional spatiotemporal patterns of flooding and their associated large-scale climate modes. We found that the dominant time scales of flood risk in this basin were in the interannual (6–7 years), decadal (11–13 years), and secular bands and that different sub-regions within the Ohio River Basin responded differently to large-scale forcing. We showed that the leading modes of streamflow variability were associated with ENSO and secular trends. The low-frequency climate modes translated into epochs of increased and decreased flood risk with multiple extreme floods or the absence of extreme floods, thus informing the nature of compound climate-induced flood risk. A notable finding is that the secular trend was associated with an east-to-west shift in the flood incidence and the associated storm track. This is consistent with some expectations of climate change projections. [ABSTRACT FROM AUTHOR]

Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for nonstationary conditions informed by the global climate model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly pressure change events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multivariable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100‐year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast United States. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the Northeast United States. This SPG provides physically plausible weather ensembles for water resource studies involving climate change. Key Points: This article introduces a nonparametric stochastic process in generating synthetic precipitations for nonstationary climateThe basis of this algorithm is highly related to the physical mechanism of precipitation formation defined by pressure change events [ABSTRACT FROM AUTHOR]

Mass livestock mortality events during severe winters, a phenomenon that Mongolians call dzud, cause the country significant socioeconomic problems. Dzud is an example of a compound event, meaning that multiple climatic and social drivers contribute to the risk of occurrence. Existing studies argue that the frequency and intensity of dzud events are rising due to the combined effects of climate change and variability, most notably summer drought and severe winter conditions, on top of socioeconomic dynamics such as overgrazing. Summer droughts are a precondition for dzud because scarce grasses cause malnutrition, making livestock more vulnerable to harsh winter conditions. However, studies investigating the association between climate and dzud typically look at a short time frame (i.e., after 1940), and few have investigated the risk or the recurrence of dzud over a century-scale climate record. This study aims to fill the gaps in technical knowledge about the recurrence probability of dzud by estimating the return periods of relevant climatic variables: summer drought conditions and winter minimum temperature. We divide the country into three regions (northwest, southwest, and east Mongolia) based on the mortality index at the soum (county) level. For droughts, our study uses as a proxy the tree-ring-reconstructed Palmer drought severity index (PDSI) for three regions between 1700–2013. For winter severity, our study uses observational data of winter minimum temperature after 1901 while inferring winter minimum temperature in Mongolia from instrumental data in Siberia that extend to the early 19th century. Using a generalized extreme value distribution with time-varying parameters, we find that the return periods of drought conditions vary over time, with variability increasing for all the regions. Winter temperature severity, however, does not change with time. The median temperature of the 100-year return period for winter minimum temperature in Mongolia over the past 300 years is estimated as -26.08 ∘ C for the southwest, -27.99 ∘ C for the northwest, and -25.31 ∘ C for the east. The co-occurrence of summer drought and winter severity increases in all the regions in the early 21st century. The analysis suggests that a continued trend in summer drought would lead to increased vulnerability and malnutrition. Prospects for climate index insurance for livestock are also discussed. [ABSTRACT FROM AUTHOR]

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers. [ABSTRACT FROM AUTHOR]

Mass livestock mortality events during severe winters, a phenomenon that Mongolians call dzud, cause the country significant socioeconomic problems. Dzud is an example of a compound event, meaning that multiple climatic and social drivers contribute to the risk of occurrence. Existing studies argue that the frequency and intensity of dzud are rising due to the combined effects of climate change and variability, most notably summer drought and severe winter conditions, on top of socioeconomic dynamics such as overgrazing. Summer droughts are a precondition for dzud because scarce grasses cause malnutrition, which in turn makes livestock more vulnerable to harsh winter conditions. However, these studies typically look at a short time frame (i.e., after 1940); few have investigated either the risk or the recurrence of dzud over a century-scale climate record. This study aims to fill the gaps in technical knowledge about the recurrence probability of dzud by estimating the return levels of relevant climatic variables: summer drought conditions and winter minimum temperature. We divide the country into three regions (Northwest, Southwest, and East Mongolia) based on the mortality index at the soum (county) level. For droughts, our study uses as a proxy the tree-ring reconstructed Palmer Drought Severity Index (PDSI) for three regions between 1700-2013. For winter severity, our study uses observational data of winter minimum temperature after 1901 while inferring winter minimum temperature in Mongolia from instrumental data in Siberia that extends to the early 19th century. The Generalized Extreme Value (i.e., the statistical method to infer the probability of very rare or extreme events) shows that the return levels of drought conditions are changing over time, with variability increasing for all the regions. Winter severity, however, is constant. The median 100-year return levels of the winter minimum temperature in Mongolia have been, over the past 300 years, -26.08 °C for the Southwest, -27.99 °C for the Northwest, and -25.31 °C for the East. This study thus suggests that continued summer drought would lead to increased vulnerability and malnutrition. Here, we link meteorological characteristics to socioeconomic impacts related to livestock populations and draws attention to the need for livestock index insurance. [ABSTRACT FROM AUTHOR]

Producing enough food to feed a growing population is a great future challenge, especially for vulnerable areas in Africa. There is limited understanding of food security under future climate conditions, particularly under the warming target stipulated in the Paris Agreement. Maize is the most widely cultivated crop in Africa. Taking maize as an example, we present an integrated assessment of maize supply and demand under 1.5°C and 2.0°C warming scenarios, considering the combined impacts of climate change, technology development and population increase. We find that global warming of 1.5°C or 2.0°C would shorten maize growth duration, aggravate droughts, and consequently reduce yield with a spatially explicit pattern. Maize yield would decrease more under global warming of 2.0°C versus 1.5°C. Benefit of rising CO2 concentration could not fully offset the yield loss due to climate change under global warming of 1.5°C. Technology development can significantly improve the ratio of maize supply to demand, which is however subject to future projections on population and technology development. Under a reasonable logarithmic technology development scenario, maize security would become worse in most of the countries in Africa. Our findings highlight the importance of technology development and adaptation strategies to meet the challenges of food security in the vulnerable regions. Key Points: Maize yield would decrease more under 2.0 than 1.5°C warming scenarioUnder a reasonable logarithmic technology development scenario, maize security would become worse in most of the countries in AfricaTechnology development and adaptation strategies are essential to meet the challenges of food security in the vulnerable regions [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]

The assessment and implementation of structural or financial instruments for climate risk mitigation requires projections of future climate risk over the operational life of each proposed instrument. A point often neglected in the climate adaptation literature is that the physical sources of predictability differ between projects with long and short planning periods: While historical and paleo climate records emphasize low‐frequency modes of variability, anthropogenic climate change is expected to alter their occurrence at longer time scales. In this paper we present a set of stylized experiments to assess the uncertainties and biases involved in estimating future climate risk over a finite future period, given a limited observational record. These experiments consider both quasi‐periodic and secular change for the underlying risk, as well as statistical models for estimating this risk from an N‐year historical record. The uncertainty of IPCC‐like future scenarios is considered through an equivalent sample size N. The relative importance of estimating short‐ or long‐term risk depends on the investment life M. Shorter design lives are preferred for situations where interannual to decadal variability can be successfully identified and predicted, highlighting the importance of sequential investment strategies for adaptation. Key Points: Quasi‐periodic and secular climate signals, with different identifiability and predictability, control future uncertainty and riskAdaptation strategies must consider how uncertainties in risk projections influence success of decision pathwaysStylized experiments reveal how bias and variance of climate risk projections influence risk mitigation over a finite planning period [ABSTRACT FROM AUTHOR]

In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean-atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non-Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro-Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56-years record (1951-2004) of daily rainfall measurements from 7 stations in Agro-Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter-annual and seasonal variability, as well as average and extreme rainfall patterns. Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed. [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]

This study explores whether it is possible to reconstruct the frequency of extreme precipitation occurrence across the contiguous United States (CONUS) using the Living Blended Drought Atlas (LBDA), a 500 year paleoclimate reconstruction of the summer (June-August) Palmer Drought Severity Index (PDSI). We first identify regions of the country where the LBDA may reflect the occurrence of extremes based on their seasonality and contribution to total annual moisture delivery. Correlation measures are used to assess the relationship between the frequencies of extreme precipitation occurrence and both the instrumental monthly PDSI and the annual LBDA-estimated PDSI. Extreme precipitation is found to account for a large portion of total precipitation west of the Mississippi River and clusters in particular seasons (winter and summer), supporting a strong relationship with the LBDA without much information loss from the instrumental PDSI data. Dimension reduction techniques are used to explore the joint spatiotemporal structure of extreme precipitation occurrence and LBDA across the country. The primary modes of variability of the LBDA and extreme precipitation occurrence relate remarkably well for a region centered over the southwest that exhibits an ENSO-like time-frequency structure. Generalized linear models (GLMs) are used to demonstrate the feasibility of reconstructing the annual extreme precipitation frequency over the 500 year prehistoric record at two sites in the southwest and Southern Plains. GLM-based reconstructions show a high degree of structured variability in the likelihood of extreme precipitation occurrences over the prehistoric record. [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]

We live in a world where biophysical and social processes are tightly coupled. Hydrologic systems change in response to a variety of natural and human forces such as climate variability and change, water use and water infrastructure, and land cover change. In turn, changes in hydrologic systems impact socioeconomic, ecological, and climate systems at a number of scales, leading to a coevolution of these interlinked systems. The Harvard Water Program, Hydrosociology, Integrated Water Resources Management, Ecohydrology, Hydromorphology, and Sociohydrology were all introduced to provide distinct, interdisciplinary perspectives on water problems to address the contemporary dynamics of human interaction with the hydrosphere and the evolution of the Earth's hydrologic systems. Each of them addresses scientific, social, and engineering challenges related to how humans influence water systems and vice versa. There are now numerous examples in the literature of how holistic approaches can provide a structure and vision of the future of hydrology. We review selected examples, which taken together, describe the type of theoretical and applied integrated hydrologic analyses and associated curricular content required to address the societal issue of water resources sustainability. We describe a modern interdisciplinary science of hydrology needed to develop an in-depth understanding of the dynamics of the connectedness between human and natural systems and to determine effective solutions to resolve the complex water problems that the world faces today. Nearly, every theoretical hydrologic model introduced previously is in need of revision to accommodate how climate, land, vegetation, and socioeconomic factors interact, change, and evolve over time. [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]

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]

Many studies report that hydrologic regimes are modulated by large-scale modes of climate variability such as the El Niño Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO). Climate-informed frequency analysis models have therefore been proposed to condition the distribution of hydrologic variables on climate indices. However, standard climate indices may be poor predictors in some regions. This paper therefore describes a regional frequency analysis framework that conditions the distribution of hydrologic variables directly on atmospheric or oceanic fields, as opposed to predefined climate indices. This framework is based on a two-level probabilistic model describing both climate and hydrologic data. The climate data set (predictor) is typically a time series of atmospheric of oceanic fields defined on a grid over some area, while the hydrologic data set (predictand) is typically a regional data set of station data (e.g., annual average flow at several gauging stations). A Bayesian estimation framework is used, so that a natural quantification of uncertainties affecting hydrologic predictions is available. A case study aimed at predicting the number of autumn flood events in 16 catchments located in Mediterranean France using geopotential heights at 500 hPa over the North-Atlantic region is presented. The temporal variability of hydrologic data is shown to be associated with a particular spatial pattern in the geopotential heights. A cross-validation experiment indicates that the resulting probabilistic climate-informed predictions are skillful: their reliability is acceptable and they are much sharper than predictions based on standard climate indices and baseline predictions that ignore climate information. [ABSTRACT FROM AUTHOR]

The presence of rich ENSO variability in the long unforced simulation of GFDL's CM2.1 motivates the use of tools from dynamical systems theory to study variability in ENSO predictability, and its connections to ENSO magnitude, frequency, and physical evolution. Local Lyapunov exponents (LLEs) estimated from the monthly NINO3 SSTa model output are used to characterize periods of increased or decreased predictability. The LLEs describe the growth of infinitesimal perturbations due to internal variability, and are a measure of the immediate predictive uncertainty at any given point in the system phase-space. The LLE-derived predictability estimates are compared with those obtained from the error growth in a set of re-forecast experiments with CM2.1. It is shown that the LLEs underestimate the error growth for short forecast lead times (less than 8 months), while they overestimate it for longer lead times. The departure of LLE-derived error growth rates from the re-forecast rates is a linear function of forecast lead time, and is also sensitive to the length of the time series used for the LLE calculation. The LLE-derived error growth rate is closer to that estimated from the re-forecasts for a lead time of 4 months. In the 2,000-year long simulation, the LLE-derived predictability at the 4-month lead time varies (multi)decadally only by 9-18 %. Active ENSO periods are more predictable than inactive ones, while epochs with regular periodicity and moderate magnitude are classified as the most predictable by the LLEs. Events with a deeper thermocline in the west Pacific up to five years prior to their peak, along with an earlier deepening of the thermocline in the east Pacific in the months preceding the peak, are classified as more predictable. Also, the GCM is found to be less predictable than nature under this measure of predictability. [ABSTRACT FROM AUTHOR]

While secular changes in regional sea levels and their implications for coastal zone management have been studied extensively, less attention is being paid to natural fluctuations in sea levels, whose interaction with a higher mean level could have significant impacts on low-lying areas, such as wetlands. Here, the long record of sea level at Key West, FL is studied in terms of both the secular trend and the multi-scale sea level variations. This analysis is then used to explore implications for the Everglades National Park (ENP), which is recognized internationally for its ecological significance, and is the site of the largest wetland restoration project in the world. Very shallow topographic gradients (3-6 cm per km) make the region susceptible to small changes in sea level. Observations of surface water levels from a monitoring network within ENP exhibit both the long-term trends and the interannual-to-(multi)decadal variability that are observed in the Key West record. Water levels recorded at four long-term monitoring stations within ENP exhibit increasing trends approximately equal to or larger than the long-term trend at Key West. Time- and frequency-domain analyses highlight the potential influence of climate mechanisms, such as the El Niño/Southern Oscillation and the North Atlantic Oscillation (NAO), on Key West sea levels and marsh water levels, and the potential modulation of their influence by the background state of the North Atlantic Sea Surface Temperatures. In particular, the Key West sea levels are found to be positively correlated with the NAO index, while the two series exhibit high spectral power during the transition to a cold Atlantic Multidecadal Oscillation (AMO). The correlation between the Key West sea levels and the NINO3 Index reverses its sign in coincidence with a reversal of the AMO phase. Water levels in ENP are also influenced by precipitation and freshwater releases from the northern boundary of the Park. The analysis of both climate variability and climate change in such wetlands is needed to inform management practices in coastal wetland zones around the world. [ABSTRACT FROM AUTHOR]

India is a poster child for groundwater depletion and chronic water stress. Often, water sustainability is measured through an estimate of the difference between the average supply and demand in a region. However, water supply and demand are highly variable in time and space. Hence, measures of scarcity need to reflect temporal imbalances even for a fixed location. We introduce spatially distributed indices of water stress that integrate over time variations in water supply and demand. The indices reflect the maximum cumulative deficit in a regional water balance within year and across years. This can be interpreted as the amount that needs to be drawn from external storage (either aquifers or surface reservoirs or interarea transfers) to meet the current demand pattern given a variable climate and renewable water supply. A simulation over a long period of record (historical or projected) provides the ability to quantify risk. We present an application at a district level in India considering more than a 100 year data set of rainfall as the renewable supply, and the recent water use pattern for each district. Consumption data are available through surveys at the district level, and consequently, we use this rather than river basins as the unit of analysis. The rainfall endogenous to each district is used as a potentially renewable water supply to reflect the supply-demand imbalances directly at the district level, independent of potential transfers due to upstream-induced runoff or canals. The index is useful for indicating whether small or large surface storage will suffice, or whether the extent of groundwater storage or external transfers, or changes in demand are needed to achieve a sustainable solution. Implications of the analysis for India and for other applications are discussed. [ABSTRACT FROM AUTHOR]

Six water emergencies have occurred since 1981 for the New York City (NYC) region despite the following: 1) its perhumid climate, 2) substantial conservation of water since 1979, and 3) meteorological data showing little severe or extreme drought since 1970. This study reconstructs 472 years of moisture availability for the NYC watershed to place these emergencies in long-term hydroclimatic context. Using nested reconstruction techniques, 32 tree-ring chronologies comprised of 12 species account for up to 66.2% of the average May-August Palmer drought severity index. Verification statistics indicate good statistical skill from 1531 to 2003. The use of multiple tree species, including rarely used species that can sometimes occur on mesic sites like Liriodendron tulipifera, Betula lenta, and Carya spp., seems to aid reconstruction skill. Importantly, the reconstruction captures pluvial events in the instrumental record nearly as well as drought events and is significantly correlated to precipitation over much of the northeastern United States. While the mid-1960s drought is a severe drought in the context of the new reconstruction, the region experienced repeated droughts of similar intensity, but greater duration during the sixteenth and seventeenth centuries. The full record reveals a trend toward more pluvial conditions since ca. 1800 that is accentuated by an unprecedented 43-yr pluvial event that continues through 2011. In the context of the current pluvial, decreasing water usage, but increasing extra-urban pressures, it appears that the water supply system for the greater NYC region could be severely stressed if the current water boom shifts toward hydroclimatic regimes like the sixteenth and seventeenth centuries. [ABSTRACT FROM AUTHOR]

Summary: Several studies have shown that statistics of streamflow time series, in particular empirical moments, scale with physical properties of the drainage basin, such as the catchment area. Those scaling laws have been extensively used to estimate statistics of streamflow series at ungauged sites. The role of climate variability and change has not been considered in such models. Further, most studies are based on classical statistics, where parameter uncertainties are usually neglected or not formally considered. In this paper we develop and apply hierarchical Bayesian models, to both assess regional and at-site trends in time in a spatial scaling framework, and simultaneously provide a rigorous framework for assessing and reducing parameter and model uncertainties. The models are tested with reconstructed natural inflow series from over 40 hydropower sites in Brazil with catchments areas varying from 2588 to 823,555km2. Both annual maximum flood series and monthly streamflow are considered. Cross-validated results show that the Hierarchical Bayesian models are able to skillfully estimate monthly and flood flow probability distribution parameters for sites that were not used in model fitting. The models developed can be used to provide record augmentation at sites that have short records, or to estimate flow at ungauged sites, even in the absence of an assumption of time stationarity. Since model uncertainties are accounted for, the precision of the estimates can be quantified and hypotheses tests for regional and at-site trends can be formally made. A formal inclusion of climate predictors to facilitate seasonal forecasting or climate change scenario development is also feasible. This is indicated, but not developed here. [Copyright &y& Elsevier]

The article presents an analysis and interpretation of global sea surface temperature (SST) data using independent component analysis (ICA) and principal component analysis (PCA). It states that PCA represents multivariates and expands the variance explicated by components. It compares the capacity of PCA, the Varimax rotation and ICA in demonstrating climate unpredictability in SST anomaly (SSTA) data distributed worldwide.

Summary: Efficient management of water and energy is an important goal of sustainable development for any nation. Streamflow forecasts, have been used in complex optimization models to maximize water use efficiency and electrical energy production. In this paper we develop a statistical model for the long term forecasts of hydroenergy inflow into the Brazilian hydropower system, which consists of more than 70 hydropower reservoirs. At present, the planning of reservoir operation and energy production in Brazil is made with no reliable long term (one season or longer lead times) streamflow forecasts. Here we use the NINO3 index and the main modes of the tropical Pacific thermocline structure as climate predictors in order to achieve skillfull forecasts at long leads. Cross-validated results show that about 50% of the total hydroenergy inflow can be predicted with moderate accuracy up to 20 month lead time. [Copyright &y& Elsevier]

The agriculture sector in India accounts for more than 85% of the total water use for irrigation. Within irrigation, the share of groundwater in the net irrigated area is around 50% to 55%, which is responsible for two-thirds of the total agricultural production. Agriculture also accounts for non-point-source pollution that arises from excessive use of fertilizers and pesticides. The Green Revolution was partly based on increased fertilizer use and intensive irrigation. During the past three decades, food production was tripled and poverty witnessed a decline. However, agriculture intensification led to overexploitation and pollution of groundwater resources, causing degradation of the environment and depletion of natural resources. Over and above, absence of significant public investment in surface water-based irrigation infrastructure, free or highly subsidized electricity, unregulated groundwater pumping, and irrational pricing policies have promoted cropping choices independent of resource endowments of water and energy. Over the long term, the current trends toward resource depletion and degradation, combined with a lack of proper reforms and policies that ensure agricultural sustainability and threats from climate change, pose a huge risk to farmers' income and can force poorer farmers out of the agricultural sector. In turn, these problems would lead to increased migration and poverty and decreased crop production, which are serious threats to food security. In this paper, we have discussed various challenges to water sustainability and food security of India. [ABSTRACT FROM AUTHOR]

Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study. [ABSTRACT FROM AUTHOR]

Results from diagnostic analyses to understand the seasonal evolution of the large-scale climatic state responsible for the development and melt of the winter snowpack, and spring–early summer precipitation in the Churchill Falls region on the Québec-Labrador Peninsula, Canada, are presented in the context of the development of an empirical model for seasonal to annual streamflow forecasting, with a special emphasis on the May–July spring freshet. Teleconnection indices and gridded global measures of atmospheric circulation inferred from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis are used as climatic indicators. Composite and correlation analyses are applied to the climatic indicators conditioned on the spring streamflow for identification of potential predictors. Meridional and zonal atmospheric fluxes over the Atlantic and the Pacific Oceans emanating from regionally persistent sea surface temperature/sea level pressure modes are identified as potential carriers of information. We speculate on the ocean-atmosphere and regional hydrologic mechanisms that may be involved in lending multiseasonal predictability to streamflows in the region. [ABSTRACT FROM AUTHOR]

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. This study investigates flood hazards of the Brahmaputra River, Bangladesh. Based on a tree ring reconstruction of seasonal river discharge, climate modelling, and historic documentation of flood events, the authors suggest flood hazard risk is underestimated by ~24–38% in the present day compared to the past 700 years. [ABSTRACT FROM AUTHOR]

Comments on Kevin Trenberth and James Hurrell's criticisms on C. Wunsch's `The Interpretation of Short Climate Records.' Authors' belief that statistical inferences that may be drawn from short time series are strongly conditioned by scientific taste; Trenberth and Hurrell's misuse of the autoregressive moving average model.

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]

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]

Six water emergencies have occurred since 1981 for the New York City (NYC) region despite the following: 1) its perhumid climate, 2) substantial conservation of water since 1979, and 3) meteorological data showing little severe or extreme drought since 1970. This study reconstructs 472 years of moisture availability for the NYC watershed to place these emergencies in long-term hydroclimatic context. Using nested reconstruction techniques, 32 tree-ring chronologies comprised of 12 species account for up to 66.2% of the average May-August Palmer drought severity index. Verification statistics indicate good statistical skill from 1531 to 2003. The use of multiple tree species, including rarely used species that can sometimes occur on mesic sites like Liriodendron tulipifera, Betula lenta, and Carya spp., seems to aid reconstruction skill. Importantly, the reconstruction captures pluvial events in the instrumental record nearly as well as drought events and is significantly correlated to precipitation over much of the northeastern United States. While the mid-1960s drought is a severe drought in the context of the new reconstruction, the region experienced repeated droughts of similar intensity, but greater duration during the sixteenth and seventeenth centuries. The full record reveals a trend toward more pluvial conditions since ca. 1800 that is accentuated by an unprecedented 43-yr pluvial event that continues through 2011. In the context of the current pluvial, decreasing water usage, but increasing extra-urban pressures, it appears that the water supply system for the greater NYC region could be severely stressed if the current water boom shifts toward hydroclimatic regimes like the sixteenth and seventeenth centuries. [ABSTRACT FROM AUTHOR]

Coastal areas are highly vulnerable to flooding, due to hydrological extreme events such heavy rainfalls and/or storm surges. Such events are supposed to be increasing in the next future due to the emission in atmosphere of anthropogenic greenhouse gases, although such predictions are affected by notable uncertainties, especially at the local scale. Due the particular value of coastal regions, which generally are densely populated areas, as well as, sites of agriculture and industrial activities, is crucial the assessment of the future hydraulic risks associated to hydrological extreme events, and then the identification of policies to mitigate such risks taking into account such climate uncertainties, e.g. a global sea level rise. In this study, in order to assess the future hydraulic risk in coastal regions, as well as, to identify optimal adaptive defence policies, a risk analysis model is developed to calculate the present day and future flood risk, accounting for climate change uncertainties and mitigation measures. Such model juxtaposes a number of coupled/nested models as: a) downscaling model to link heavy rainfall events at the local scale to large scale atmospheric circulation patterns; b) bivariate extreme value model to estimate the statistical dependence between rainfall extreme occurrence and sea level rise due storm surge; c) hydraulic simulation-optimization model to identify the optimal flood defence infrastructure size for given probability distribution of occurrence of hydrological extreme events; d) mathematical decision model which is aimed to identify the best policies of mitigation of hydraulic risk and the times taking in account the uncertainties in hydrological extreme event predictions. Sea level pressure and meridional and zonal winds fields were used to calculate storm surges. Bivariate Point Process Method (BPPM) is used to calculate Joint probability density function between extreme daily rainfall amount and daily extreme storm tide depth. The simulation-optimization model calculates, for assigned extreme rainfall events and sea levels joint density probability functions, sets of Pareto optimal solutions which are obtained by defining two optimality criteria consisting in: minimizing both the cost of the flood defence infrastructure system and the flooding hydraulic risk. This latter is calculated as Expected Annual Damage (EAD) expressed as the joint probability density function between extreme rainfall and storm surge and damages, which are related to a given defence system state, water depth – damage curve and land use (CORINE Land Cover). Then, we developed an adaptive dynamic decision process to select, among the different optimal Pareto solution sets, the most appropriate set of interventions to make in a flood defence system and the right time to make these interventions. The adaptive dynamic risk analysis model is applied to the study case of Pontina Plain, a large reclamation region (about 100000 ha) in the south of Lazio region (Italy), particularly vulnerable to extreme events - as extreme rainfall amount and sea level rise due to storm surge at the sea outfall of the river- which in the past caused the crisis of hydraulic network system with flooding of large areas and collapse of levees. [ABSTRACT FROM AUTHOR]