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This paper presents an investigation of the role of climatic variability on interannual groundwater and streamflow variability in the southeast United States. For this purpose, streamflow and associated groundwater levels are analyzed for 20 basins that are minimally affected by reservoirs and groundwater pumping. Using the spatially averaged monthly precipitation time series obtained from the Precipitation-elevation Regressions on Independent Slopes Model (PRISM), this paper identifies the recharge and discharge periods that influence the groundwater levels during the winter [January, February, March (JFM)] and summer [July, August, September (JAS)] seasons. Recharge-discharge dependency analyses indicate that precipitation during the previous 3 months influences the groundwater level in a given month. Streamflow in any given month depends on the groundwater level during the previous 3 months. Principal component analysis (PCA) on the precipitation, temperature, streamflow, and groundwater data indicates that groundwater levels and streamflow are the two dominant variables influencing the basin hydroclimatology. Furthermore, relating the percentage variance explained from the PCA to baseflow index (BFI) clearly shows that basins with high BFI have higher eigenvalues, indicating that groundwater is a spatial integrator of hydroclimatic processes. Relating the groundwater levels with El Niño/ Southern Oscillation (ENSO) index, NINO3.4, shows that interannual variability in JFM groundwater levels could be partially explained by the ENSO conditions, but the relationship between JAS groundwater levels and JAS NINO3.4 is not statistically significant. Precipitation forecasts from the ECHAM4.5 general circulation model indicate that it is possible to quantify groundwater availability during the winter season on the basis of the forecasted precipitation and ENSO conditions. [ABSTRACT FROM AUTHOR]

Increasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e., Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Niño-southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April-October) and the water year, which is from October of the previous year to September of the current year for a period from 1955-2006. A data-driven model, least-square support vector machine (LSSVM), was developed that incorporated oceanic atmospheric oscillations to increase the streamflow lead time. Based on performance measures, predicted streamflow volumes are in agreement with the measured volumes. Sensitivity analyses, performed to evaluate the effect of individual and coupled oscillations, revealed a stronger presence of coupled PDO, NAO, and ENSO indices within the basin. The AMO index shows a pronounced effect when individually compared with the other oscillation modes. Additionally, model-forecasted streamflow is better than that for climatology. Overall, very good streamflow predictions are obtained using the SVM modeling approach. Furthermore, the LSSVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back-propagation models, artificial neural network, and multiple linear regression. The current paper contributes in improving the streamflow forecast lead time, and identified a coupled climate signal within the basin. The increased lead time can provide useful information to water managers in improving the planning and management of water resources within the Kaidu River Basin. [ABSTRACT FROM AUTHOR]

Increasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e., Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Niño-southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April-October) and the water year, which is from October of the previous year to September of the current year for a period from 1955-2006. A data-driven model, least-square support vector machine (LSSVM), was developed that incorporated oceanic atmospheric oscillations to increase the streamflow lead time. Based on performance measures, predicted streamflow volumes are in agreement with the measured volumes. Sensitivity analyses, performed to evaluate the effect of individual and coupled oscillations, revealed a stronger presence of coupled PDO, NAO, and ENSO indices within the basin. The AMO index shows a pronounced effect when individually compared with the other oscillation modes. Additionally, model-forecasted streamflow is better than that for climatology. Overall, very good streamflow predictions are obtained using the SVM modeling approach. Furthermore, the LSSVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back-propagation models, artificial neural network, and multiple linear regression. The current paper contributes in improving the streamflow forecast lead time, and identified a coupled climate signal within the basin. The increased lead time can provide useful information to water managers in improving the planning and management of water resources within the Kaidu River Basin. [ABSTRACT FROM AUTHOR]

This paper investigated the variability of freshwater discharge (Q) in the Dez River Basin in Iran and its possible links to the North Atlantic Oscillation (NAO) and El Nino Southern Oscillation (ENSO). Nonparametric statistical tests were applied to analyze fluctuations of discharge and teleconnection indexes. The results indicated statistically significant monotonic decreases of the discharge and peak flow (p-value <0.05) across most of the subbasins. Furthermore, a downward step change was detected in discharge time series around the mid-1990s from 14.7% to 43.3% with respect to long-term values. Similarly, almost concurrent shifts detected in the Oceanic Nino Index (ONI) (from þ0.254 to -0.416 in 1998) and the NAO (from +0.196 to -0.155 in 1995) that suggest changing conditions toward more cold phases of ENSO and negative phases of NAO coincided with discharge and monthly peak flow depletion. Comparing the amount of discharges during different phases showed that Q (El Niño) > Q (neutral) > (La Nina) and Q (NAO+) > Q (NAO-) . Furthermore, the Mann-Whitney U test verified statistically significant differences (p-value <0.05) between flows pertaining to different phases of both ENSO and NAO. Correlations between discharge and teleconnection indexes indicated that discharge is apparently affected by teleconnections considering the lag time. [ABSTRACT FROM AUTHOR]

In this paper, a bivariate-copula-based methodology is presented to assess the risk associated with hydroclimatic variability on groundwater levels in an unconfined aquifer at the Manjara basin in India. Rank correlation analysis is used to identify the association between the El Niño-Southern Oscillation (ENSO) index, precipitation, and groundwater levels. It is found that the dependencies among the hydroclimatic variable pairs are statistically significant and the dependence structure can be modeled by using bivariate Archimedean copulas. The groundwater level or depth-to-groundwater table (DGWT) in the study region is found to be responsive toward interannual precipitation variations that are influenced by the ENSO phenomenon. For probabilistic representation of hydroclimate variables, various probability distributions are evaluated and it is found that the precipitation and DGWT are best fitted using lognormal and Weibull distributions, respectively, whereas the ENSO index is best fitted using nonparametric-based normal kernel density function. For modeling joint dependence structure of hydroclimatic variable pairs (precipitation-DGWT, ENSO index-precipitation, and ENSO index-DGWT), appropriate Archimedean copulas (viz, Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank families) are evaluated. On performing standard statistical tests, it is found that the Frank copula is best representing the joint dependence structure for all three variable pairs. Then the Frank copula-based joint distributions are used to derive conditional distributions and to perform risk analysis of groundwater levels. The study suggest that the copula-based methodology can be used effectively for modeling dependence structure of hydroclimatic variables and for risk assessment of groundwater levels under changes in hydroclimatic conditions. [ABSTRACT FROM AUTHOR]

This work reports climate change signals and long-term trend analysis of climate variables, meteorological drought, and extreme climate indexes over the tropical state of Kerala in India. The trend analysis reveals statistically significant decrease of annual and southwest monsoon rainfall (as much as 63 mm and 55 mm per decade, respectively). A decrease in number of annual rainy days (up to 2.8 days=decade) is also reported. Temperature trend analysis indicates an increasing trend with as high as 1.3°C=decade. The spatiotemporal variation of extreme climate indexes across Kerala shows a decreasing trend of extreme precipitation indexes and an increasing trend of extreme temperature indexes. R95 and R95p decreased in northern and southern Kerala whereas R5 index increased in central and southern regions. Warm days have significantly increased whereas cold days exhibit a decreasing trend across the state. The increase in warmer nights is statistically significant whereas colder nights are decreasing in central and southern regions. Meteorological drought using Standardized Precipitation Index (SPI) reveals increasing occurrence of droughts in Kerala with higher frequencies over southern and central Kerala. [ABSTRACT FROM AUTHOR]

Many hydraulic projects as reservoirs, ponds, paddy fields, and soil and water conservation engineering projects have been constructed upstream of theWudaogou station basin in northeast China. This study investigates the impact of climate variability and hydraulic project construction on the runoff decrease through the concept of climate elasticity. The elasticity of streamflow in relation to precipitation and potential evapotranspiration is calculated by four methods: two nonparametric estimators, two parametric estimators. Results show that for the period 1936-2010 the two nonparametric climate elasticity methods show a smaller value of the elasticity of streamflow in relation to precipitation (1.85 and 2.47) but a larger value of the elasticity of streamflow in relation to potential evapotranspiration (-0.02 and -0.48) than the parametric estimators. It is estimated that climate variability is responsible for 47.3% (40.3 mm) of the streamflow reduction between 1936-1956 and 1957-2010, while hydraulic project construction contribute to 52.7% (45.1 mm) of the reduction. From 1957 to 2010, the amount of construction for water conservancy has been growing. To further investigate the effect of hydraulic project construction, the study period (1957-2010) is split into three subperiods using two faint jump points (1975 and 1987), and the effect of hydraulic project construction on the runoff during each subperiod is evaluated. The results indicate that as the cumulative storage of hydraulic projects increases, hydraulic project construction plays an increasingly important role (from 12.8 to 90.8%) in streamflow change. Almost all the results show that streamflow changes due to hydraulic project construction are less than the cumulative storage of the hydraulic projects, which verifies the reliability of the theoretical calculation. [ABSTRACT FROM AUTHOR]

Snow line altitude (SLA) defines the boundary of snow-free and snow-covered area. The efficiency of snow/ice-melt-runoff model and snowy/Himalayan watershed model depends on the estimation accuracy of the mean SLA for varying saturated snow cover (melting area) within seasonal and permanent SLA. Remotely sensed data are still in the phase of development to present quick and dependable input of SLA. This study is aimed to predict seasonal SLA, annual equilibrium line altitude (ELA), maximum annual SLA, melting zone, and their intraseasonal or/and interannual variations based on observed temperature in a watershed of the western Himalayas. The study area extends from 2,100 to 6,000 m altitudes (latitude: 32° 10'N to 32° 25'N; longitude: 77°E to 77° 40'E). Varying catchment participation (VCP) zonal altitude of melting and simulated relation naming applied basic oscillation (ABO) have been developed for SLA movement on weekly and monthly mean temperature data and simulation of monthly observed/estimated SLA, respectively. The VCP results suggested an upward shift of spring SLA by 700 m in March, whereas ABO results showed a 300-m upward shift of ELA during September in two decades from the 1980s. The VCP calibration parameters--temperature lapse rate of 0.554°C=100 m and saturated SLA mean air temperature of 5.75°C--have been designed to predict SLA using temperature and ablating snowpack at Solang (2,485 m altitude). The periodical shift in SLA has been verified with the snow cover disappearance data and combined impact of temperature and solar radiation at two stations. The VCP-computed SLA and ELAvaried by 5% with observed values, whereas the error in ABO-predicted monthly SLA including ELAwas 4%. Simplified VCP pattern variability, ABO trend of SLA, and SLA nomogram have been developed for the snowmelt hydrological models and other applications. [ABSTRACT FROM AUTHOR]

Hydrologic alteration can be attributed to climate and human effects with some confidence. However, when quantifying the hydrologic alteration induced by dams using historical flow records, there is a lack of consideration concerning the impacts of climate variability on hydrologic alteration. Evaluating dam-induced and climate-influence-induced hydrologic alteration has emerged as a key problem, which is the process of quantifying the extent that they have altered the river's hydrologic attributes. Central to this process is restoring the hydrologic attributes without dam influence. This study simulates hydrologic regimes without dam influence and evaluates the dam and climate influences on hydrologic processes in the Lancang-Mekong River. The results showed that the meteorological variables were closely related with runoff. Therefore, a stepwise-regression model was established and validated using meteorological variables with their time lags, and it showed good performance. The simulative evaluation revealed that runoff from the Lancang River was simultaneously and significantly affected by dam construction and climate variability, and the damming effects were mainly concentrated in the primary stage of reservoir operation. Meanwhile, the dam operation increased the minimum and maximum monthly runoff over a year. During the study period, the degree of hydrologic alteration (DHA) indicated that the Manwan Dam compensated for the impacts of climate variability on the Lancang River. [ABSTRACT FROM AUTHOR]

The El Niño Southern Oscillation (ENSO) climate-variability phenomenon greatly affects water availability in the Southeast United States. For example, it is well known that La Niña conditions bring drought to this region. In the past decade, several severe droughts have adversely impacted the water resources of many communities in this region, especially those that rely on surface-water systems. Because small- to mid-size communities are most vulnerable to climate variability, this study was undertaken to develop a climate variability-based community water deficit index (CWDI) for use by water managers in these communities. Although currently available drought indices can be useful tools for monitoring and forecasting purposes, they are not suitable for use in water-supply systems for small- to mid-size communities. The CWDI was conceptualized keeping in mind that it should (1) forecast hydrologic drought, (2) operate at a high spatial resolution, and (3) address both water supply and demand during droughts. The system dynamics-modeling software Structured Thinking Experiential Learning Laboratory with Animation was used to develop the modeling framework to estimate CWDI by evaluating differences in a community water supply and demand, and thus help forecast the severity of an impending drought. Another important feature of the CWDI is its ability to evaluate how drought-management policies can affect the severity of drought. The CWDI was tested in two small- to mid-size communities of this region (Auburn, Alabama, and Griffin, Georgia). The results indicate that the index not only can monitor drought in the studied water-supply systems, but can also forecast ENSO-induced hydrologic droughts in the region and can be used in drought planning. [ABSTRACT FROM AUTHOR]