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The Beas River, located in the Western Himalayan mountainous regions in India, is one of the major tributaries of the Indus River. However, recent changes of precipitation and related climatic teleconnections in this river basin have rarely been investigated yet. In this study, the trend and concentration characteristics of precipitation during1982–2010 are investigated by using Mann–Kendall trend test and two kinds of concentration indices. The climatic teleconnections are explored with the help of cross correlation, wavelet transform and composite analysis, revealing the relationship of precipitation with climatic indices of Indian summer monsoon (ISM), El Nino/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO). The results indicate that: (1) Precipitation of most of the stations increased in the monsoon season while precipitation of all the stations decreased in the non-monsoon seasons. As a result, the annual precipitation of the majority of the stations was on the decrease. (2) A general increase in the precipitation Gini coefficient and precipitation concentration degree (PCD) was detected. Moreover, the precipitation concentration period (PCP) is mainly within the period from May to August, and more PCP occurred in the monsoon months recently. (3) The relationship between monsoon precipitation and ISM is not significant in the Beas River basin. The relationship between precipitation and ENSO in winter is less significant than in the monsoon season, and the relationship of monsoon/winter precipitation with IOD is not as evident as that with ENSO. Besides, ENSO and NAO play important roles in the changes of monsoon and winter precipitation in the Beas River basin. [ABSTRACT FROM AUTHOR]

Abstract Rivers originating from the Tianshan Mountains, known collectively as the "water tower of Central Asia", are a key source of fresh water to the densely populated lowlands. Despite of the significance of water resources, our knowledge on the discharge regime in the alpine regions is limited, due to the paucity of in situ measurements and the complexity of contributing sources including rainfall, snowmelt and glacier-melt. In this study, the streamflow regime for the headwater catchment of Manas River basin (MRB) in the Tianshan Mountains is investigated through application of a hydrologic modeling framework, which is based upon an appropriate modification of the hydrological model HBV-D (Hydrologiska Byrans Vattenbalansavdelning-D). The daily precipitation and average temperature are reconstructed based on meteorological station data and remote-sensing observations for the period 1967–2007. Thereafter, the modified hydrological model is evaluated and the temporal distribution of runoff components is quantified via the model simulation. Our primary results include the following: (1) our modified version of the HBV-D model, with stronger physics basis in snow/glacier module and higher spatial resolution, is superior to the original HBV-D model in simulating daily streamflow processes and is capable of reproducing the variations of glacier area and glacier volume during the historical period. (2) Snowmelt is shown to dominate runoff processes in pre-monsoon season, accounting for approximately 61%, 76% and 74% of streamflow in April, May and June, respectively. The monthly contributions from glacier-melt, occurring in the region above 3000 m a.m.s.l (above mean sea level), range from 27% to 44% during the July–September period. (3) The average annual discharge in the MRB is sourced to 48% by snowmelt, 27% by glacier-melt, and the rest by rainfall. Particularly in 1981 when rainfall and snowmelt runoff were substantially reduced, the contribution of the glacier-melt to the river discharge reached 40%, suggesting that the glacier melt contributions to the streamflow in the MRB are especially significant during the dry years in ensuring a sustained water supply. The modified HBV-D model is expected to provide a useful modeling tool in simulating runoff regime in high-altitude mountainous regions and the results constitute a highly significant initial contribution to the formulation of effective adaptation strategies for water resource management under climate change. Highlights • Compared with the spatial discretization and glacier dynamic method, snow/glacier melt method plays a more important role in improving the performance of HBV-D model in simulating daily streamflow processes. • The modified HBV-D model not only simulates the daily streamflow well, but also is capable of reproducing change rates of glacier area and glacier volume. • The contribution of meltwater from snow and glacier to the average annual streamflow exceeds 70% at the Manas river basin. [ABSTRACT FROM AUTHOR]

Identifying the links between variations in large-scale climate patterns and precipitation is of tremendous assistance in characterizing surplus or deficit of precipitation, which is especially important for evaluation of local water resources and ecosystems in semi-humid and semi-arid regions. Restricted by current limited knowledge on underlying mechanisms, statistical correlation methods are often used rather than physical based model to characterize the connections. Nevertheless, available correlation methods are generally unable to reveal the interactions among a wide range of climate oscillations and associated effects on precipitation, especially on extreme precipitation. In this work, a probabilistic analysis approach by means of a state-of-the-art Copula-based joint probability distribution is developed to characterize the aggregated behaviors for large-scale climate patterns and their connections to precipitation. This method is employed to identify the complex connections between climate patterns (Atlantic Multidecadal Oscillation (AMO), El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)) and seasonal precipitation over a typical semi-humid and semi-arid region, the Haihe River Basin in China. Results show that the interactions among multiple climate oscillations are non-uniform in most seasons and phases. Certain joint extreme phases can significantly trigger extreme precipitation (flood and drought) owing to the amplification effect among climate oscillations. [ABSTRACT FROM AUTHOR]

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide. [ABSTRACT FROM AUTHOR]

The Indicators of Hydrologic Alteration (IHA) based on 33 parameters in five dimensions (flow magnitude, timing, duration, frequency and change rate) have been widely used in evaluation of hydrologic alteration in river systems. Yet, inter-correlation seriously exists amongst those parameters, therefore constantly underestimates or overestimates actual hydrological changes. Toward the end, a new method (Representative-IHA, RIHA) is developed by removing repetitions based on Criteria Importance Through Intercriteria Correlation (CRITIC) algorithm. RIHA is testified in evaluating effects of future climate change on hydro-ecology in the Niger River of Africa. Future flows are projected using three watershed hydrological models forced by five general circulation models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios. Results show that: (1) RIHA is able to eliminate self-correlations amongst IHA indicators and identify the dominant characteristics of hydrological alteration in the Upper Niger River, (2) March streamflow, September streamflow, December streamflow, 30-day annual maximum, low pluses duration and fall rates tends to increase over the period 2010–2099, while July streamflow and 90-day annual minimum streamflow shows decrease, (3) the Niger River will undergo moderate flow alteration under RCP8.5 in 2050s and 2080s and low alteration other scenarios, (4) future flow alteration may induce increase water temperatures, reduction dissolved oxygen and food resources. Consequently, aquatic biodiversity and fish community of Upper Niger River would become more vulnerable in the future. The new method enables more scientific evaluation for multi-dimensional hydrologic alteration under the context of climate change. [ABSTRACT FROM AUTHOR]

Abstract: Projections of changes in climate extreme are important in assessing the potential impacts of climate change on social and natural systems. This article presents future projections of climate extremes in the Tibetan Plateau constructed from ensembles of coupled general circulation models (CGCMs) contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4), under a range of emission scenarios. The extremes of temperature and precipitation are described by seven indices, namely, the frost day (FD), percentage of nights when Tmin >90th percentile (TN90), consecutive dry days (CDD), annual count of days when precipitation >=10mm (R10), maximum 5-day precipitation total (R5D), simple daily intensity index (SDII), and annual total precipitation when precipitation >95th percentile (R95T). Results indicate that frost days decrease over the Tibetan Plateau in the 21st century. More frequent warm nights are also projected in the plateau. The increases of these temperature extremes under A2 and A1B scenarios are more pronounced than under B1. Heavy precipitation events for single days and pentads are projected to increase in their intensity over most parts of Tibetan Plateau. CDD, R10, R5D, R95T and SDII collectively suggest more extreme precipitation in the region (2011–2020). In addition, impacts of climate extremes changes on local water resources and fragile ecosystem are discussed as an extension of this article. The findings will be beneficial to project regional responses in this unique region to global climate change, and then to formulate regional strategies against the potential menaces of climate change. [Copyright &y& Elsevier]