Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network

Climate projections for the Huaihe River Basin, China, for the years 2001–2100 are derived from the ECHAM5/MPI-OM model based on observed precipitation and temperature data covering 1964–2007. Streamflow for the Huaihe River under three emission scenarios (SRES-A2, A1B, B1) from 2010 to 2100 is then projected by applying artificial neural networks (ANN). The results show that annual streamflow will change significantly under the three scenarios from 2010 to 2100. The interannual fluctuations cover a significant increasing streamflow trend under the SRES-A2 scenario (2051–2085). The streamflow trend declines gradually under the SRES-A1B scenario (2024–2037), and shows no obvious trend under the SRES-B1 scenario. From 2010 to 2100, the correlation coefficient between the observed and modeled streamflow in SRES-A2 scenario is the best of the three scenarios. Combining SRES-A2 scenario of the ECHAM5 model and ANN might therefore be the best approach for assessing and projecting future water resources in the Huaihe basin and other catchments. Compared to the observed period of streamflows, the projected periodicity of streamflows shows significant changes under different emission scenarios. Under A2 scenario and A1B scenario, the period would delay to about 32–33a and 27–28a, respectively, but under B1 scenario, the period would not change, as it is about 5–6a and the observed period is about 7–8a. All this might affect drought/flood management, water supply and irrigation projects in the Huaihe River basin.     

Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method

In this study, the applicability of the statistical downscaling model (SDSM) in downscaling precipitation in the Yangtze River basin, China was investigated. The investigation includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP/NCAR reanalysis data, the validation of the model using independent period of the NCEP/NCAR reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios. Selected as climate variables for downscaling were measured daily precipitation data (1961–2000) from 136 weather stations in the Yangtze River basin. The results showed that: (1) there existed good relationship between the observed and simulated precipitation during the calibration period of 1961–1990 as well as the validation period of 1991–2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The average R ² values between the simulated and observed monthly and seasonal precipitation for the validation period were 0.78 and 0.91 respectively for the whole basin, which showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under both scenarios A2 and B2, during the prediction period of 2010–2099, the change of annual mean precipitation in the Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a surplus of precipitation in the 2080s as compared to the mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2 and increase by about 5.33% under scenario B2 in the 2080s. The winter and autumn might be the more distinct seasons with more predicted changes of precipitation than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean precipitation in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.     

Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling

Many impact studies require climate change information at a finer resolution than that provided by general circulation models (GCMs). Therefore the outputs from GCMs have to be downscaled to obtain the finer resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach is proposed for predicting the daily precipitation of 138 main meteorological stations in the Yangtze River basin for 2010–2099 by statistical downscaling of the outputs of general circulation model (HadCM3) under A2 and B2 scenarios. After that, the spatial–temporal changes of the amount and the extremes of predicted precipitation in the Yangtze River basin are investigated by Mann–Kendall trend test and spatial interpolation. The results showed that: (1) the amount and the change pattern of precipitation could be reasonably simulated by ASD; (2) the predicted annual precipitation will decrease in all sub-catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and during 2080s, respectively, under A2 scenario. However, they have mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, except for Hanjiang River region during 2080s, as far as B2 scenario is concerned; and (3) the significant increasing trend of the precipitation intensity and maximum precipitation are mainly occurred in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios and the middle of 2040–2060 can be regarded as the starting point for pattern change of precipitation maxima.     

Changes to flow regime on the Niger River at Koulikoro under a changing climate

Abstract

A significant decrease in mean river flow as well as shifts in flood regimes have been reported at several locations along the River Niger. These changes are the combined effect of persistent droughts, damming and increased consumption of water. Moreover, it is believed that climate change will impact on the hydrological regime of the river in the next decades and exacerbate existing problems. While decision makers and stakeholders are aware of these issues, it is hard for them to figure out what actions should be taken without a quantitative estimate of future changes. In this paper, a Soil and Water Assessment Tool (SWAT) model of the Niger River watershed at Koulikoro was successfully calibrated, then forced with the climate time series of variable length generated by nine regional climate models (RCMs) from the AMMA-ENSEMBLES experiment. The RCMs were run under the SRES A1B emissions scenario. A combination of quantile-quantile transformation and nearest-neighbour search was used to correct biases in the distributions of RCM outputs. Streamflow time series were generated for the 2026–2050 period (all nine RCMs), and for the 2051–2075 and 2076–2100 periods (three out of nine RCMs) based on the availability of RCM simulations. It was found that the quantile-quantile transformation improved the simulation of both precipitation extremes and ratio of monthly dry days/wet days. All RCMs predicted an increase in temperature and solar radiation, and a decrease in average annual relative humidity in all three future periods relative to the 1981–1989 period, but there was no consensus among them about the direction of change of annual average wind speed, precipitation and streamflow. When all model projections were averaged, mean annual precipitation was projected to decrease, while the total precipitation in the flood season (August, September, October) increased, driving the mean annual flow up by 6.9% (2026–2050), 0.9% (2051–2075) and 5.6% (2076–2100). A t-test showed that changes in multi-model annual mean flow and annual maximum monthly flow between all four periods were not statistically significant at the 95% confidence level.     

Temporal change of spatial heterogeneity and its effect on regional trend of annual precipitation heterogeneity indices

A statistical test on climate and hydrological series from different spatial resolution could obtain different regional trend due to spatial heterogeneity and its temporal variability. In this study, annual series of the precipitation heterogeneity indices of concentration index (CI) and the number of wet days (NW) along with annual total amount of precipitation were calculated based on at‐site daily precipitation series during 1962–2011 in the headwater basin of the Huaihe River, China. The regional trends of the indices were first detected based on at‐site series by using the aligned and intrablock methods, and field significance tests that consider spatial heterogeneity over sites. The detected trends were then compared with the trends of the regional index series derived from daily areal average precipitation (DAAP), which averages at‐site differences and thus neglects spatial heterogeneity. It was found that the at‐site‐based regional test shows increasing trends of CI and NW in the basin, which follows the test on individual sites that most of sites were characterized by increasing CI and NW. However, the DAAP‐derived regional series of CI and NW were tested to show a decreasing trend. The disparity of the regional trend test on at‐site‐based regional series and the DAAP‐derived regional series arises from a temporal change of the spatial heterogeneity, which was quantified by the generalized additive models for location, scale, and shape. This study highlights that compared with averaging indices, averaging at‐site daily precipitation could lead to an error in the regional trend inference on annual precipitation heterogeneity indices. More attention should be paid to temporal variability in spatial heterogeneity when data at large scales are used for regional trend detection on hydro‐meteorological events associated with intra‐annual heterogeneity.     

Climate changes and their impacts on water resources in semiarid regions: a case study of the Wei River basin,China

Understanding the impacts of climate change and human activity on the hydrological processes in river basins is important for maintaining ecosystem integrity and sustaining local economic development. The objective of this study was to evaluate the impact of climate variability and human activity on mean annual flow in the Wei River, the largest tributary of the Yellow River. The nonparametric Mann–Kendall test and wavelet transform were applied to detect the variations of hydrometeorological variables in the semiarid Wei River basin in the northwestern China. The identifications were based on streamflow records from 1958 to 2008 at four hydrological stations as well as precipitation and potential evapotranspiration (PET) data from 21 climate stations. A simple method based on Budyko curve was used to evaluate potential impacts of climate change and human activities on mean annual flow. The results show that annual streamflow decreased because of the reduced precipitation and increased PET at most stations. Both annual and seasonal precipitation and PET demonstrated mixed trends of decreasing and increasing, although significant trends (P < 0.05) were consistently detected in spring and autumn at most stations. Significant periodicities of 0.5 and 1 year (P < 0.05) were examined in all the time series. The spectrum of streamflow at the Huaxian station shows insignificant annual cycle during 1971–1975, 1986–1993 and 1996–2008, which is probably resulted from human activities. Climate variability greatly affected water resources in the Beiluo River, whereas human activities (including soil and water conservation, irrigation, reservoirs construction, etc.) accounted more for the changes of streamflow in the area near the Huaxian station during different periods. The results from this article can be used as a reference for water resources planning and management in the semiarid Wei River basin. Copyright © 2012 John Wiley & Sons, Ltd.     

Climate change analysis on historical watershed‐scale precipitation by means of long‐term dynamical downscaling

A methodology based on long‐term dynamical downscaling to analyse climate change effects on watershed‐scale precipitation during a historical period is proposed in this study. The reliability and applicability of the methodology were investigated based on the long‐term dynamical downscaling results. For an application of the proposed methodology, two study watersheds in Northern California were selected: the Upper Feather River watershed and the Yuba River watershed. Then, precipitation was reconstructed at 3‐km spatial resolution and hourly intervals over the study watersheds for 141 water years from 1 October 1871 to 30 September 2012 by dynamically downscaling a long‐term atmospheric reanalysis dataset, 20th century global reanalysis version 2 by means of a regional climate model. The reconstructed precipitation was compared against observed precipitation, in order to assess the applicability of the proposed methodology for the reconstruction of watershed‐scale precipitation and to validate this methodology. The validation shows that the reconstructed precipitation is in good agreement with observation data. Moreover, the differences between the reconstructed precipitation and the corresponding observations do not significantly change through the historical period. After the validation, climate change analysis was conducted based on the reconstructed precipitation. Through this analysis, it was found that basin‐average precipitation has increased significantly over both of the study watersheds during the historical period. An upward trend in monthly basin‐average precipitation is not significant in wet months except February while it is significant in dry months of the year. Furthermore, peak values of basin‐average precipitation are also on an upward trend over the study watersheds. The upward trend in peak basin‐average precipitation is more significant during a shorter duration. Copyright © 2016 John Wiley & Sons, Ltd.     

Trends of precipitation in Beijiang River Basin,Guangdong Province,China

An analysis of spatial and temporal trends of precipitation in Beijiang River basin, Guangdong Province, China during 1959–2003 was performed using 17 time series (including monthly, annual, wet season, dry season, early flood period and late flood period totals) both on station based and sub‐basin based data sets. Two nonparametric methods (Mann–Kendall and Sen's T) were used for data analysis. The results showed that (1) downward trends of temporal distribution were mostly detected during the early flood period, especially in May, while upward trends were observed in July and the dry season; (2) downward trends of spatial distribution were mostly detected in the southern Beijiang River basin, while upward trends were observed north of this area. Our results indicated a delayed rainy season and a northward trend of the precipitation belt compared to recent years. Copyright © 2007 John Wiley & Sons, Ltd.     

Impacts of climate change and human activities on surface runoff in the Dongjiang River basin of China

Observed rainfall and flow data from the Dongjiang River basin in humid southern China were used to investigate runoff changes during low‐flow and flooding periods and in annual flows over the past 45 years. We first applied the non‐parametric Mann–Kendall rank statistic method to analyze the change trend in precipitation, surface runoff and pan evaporation in those three periods. Findings showed that only the surface runoff in the low‐flow period increased significantly, which was due to a combination of increased precipitation and decreased pan evaporation. The Pettitt–Mann–Whitney statistical test results showed that 1973 and 1978 were the change points for the low‐flow period runoff in the Boluo sub‐catchment and in the Qilinzui sub‐catchment, respectively. Most importantly, we have developed a framework to separate the effects of climate change and human activities on the changes in surface runoff based on the back‐propagation artificial neural network (BP‐ANN) method from this research. Analyses from this study indicated that climate variabilities such as changes in precipitation and evaporation, and human activities such as reservoir operations, each accounted for about 50% of the runoff change in the low‐flow period in the study basin. Copyright © 2010 John Wiley & Sons, Ltd.     

Streamflow trends and climate linkages in the source region of the Yellow River,China

Much of the discussion on hydrological trends and variability in the source region of the Yellow River centres on the mean values of the mainstream flows. Changes in hydrological extremes in the mainstream as well as in the tributary flows are largely unexplored. Although decreasing water availability has been noted, the nature of those changes is less explored. This article investigates trends and variability in the hydrological regimes (both mean values and extreme events) and their links with the local climate in the source region of the Yellow River over the last 50 years (1959–2008). This large catchment is relatively undisturbed by anthropogenic influences such as abstraction and impoundments, enabling the characterization of widely natural, climate‐driven trends. A total of 27 hydrological variables were used as indicators for the analysis. Streamflow records from six major headwater catchments and climatic data from seven stations were studied. The trend results vary considerably from one river basin to another, and become more accentuated with longer time period. Overall, the source region of the Yellow River is characterized by an overall tendency towards decreasing water availability. Noteworthy are strong decreasing trends in the winter (dry season) monthly flows of January to March and September as well as in annual mean flow, annual 1‐, 3‐, 7‐, 30‐ and 90‐day maxima and minima flows for Maqu and Tangnag catchments over the period 1959–2008. The hydrological variables studied are closely related to precipitation in the wet season (June, July, August and September), indicating that the widespread decrease in wet season precipitation is expected to be associated with significant decrease in streamflow. To conclude, decreasing precipitation, particularly in the wet season, along with increasing temperature can be associated with pronounced decrease in water resources, posing a significant challenge to downstream water uses. Copyright © 2011 John Wiley & Sons, Ltd.     

Wet and dry spell analysis of Global Climate Model-generated precipitation using power laws and wavelet transforms

Climate model simulations for the twenty-first century point toward changing characteristics of precipitation. This paper investigates the impact of climate change on precipitation in the Kansabati River basin in India. A downscaling method, based on Bayesian Neural Network (BNN), is applied to project precipitation generated from six Global Climate Models (GCMs) using two scenarios (A2 and B2). Wet and dry spell properties of monthly precipitation series at five meteorologic stations in the Kansabati basin are examined by plotting successive wet and dry durations (in months) against their number of occurrences on a double-logarithmic paper. Straight-line relationships on such graphs show that power laws govern the pattern of successive persistent wet and dry monthly spells. Comparison of power-law behaviors provides useful interpretation about the temporal precipitation pattern. The impact of low-frequency precipitation variability on the characteristics of wet and dry spells is also evaluated using continuous wavelet transforms. It is found that inter-annual cycles play an important role in the formation of wet and dry spells.     

Simulated runoff responses to land use in the middle and upstream reaches of Taoerhe River basin,Northeast China,in wet,average and dry years

Study on runoff variations and responses can lay a foundation for flood control, water allocation and integrated river basin management. This study applied the Soil and Water Assessment Tool model to simulate the effects of land use on annual and monthly runoff in the Middle and Upstream Reaches of Taoerhe River basin, Northeast China, under the wet, average and dry climate conditions through scenario analysis. The results showed that from the early 1970s to 2000, land use change with an increase in farmland (17.0%) and decreases in forest (10.6%), grassland (4.6%) and water body (3.1%) caused increases in annual and monthly runoff. This effect was more distinct in the wet season or in the wet year, suggesting that land use change from the early 1970s to 2000 may increase the flood potential in the wet season. Increases in precipitation and air temperature from the average to wet year led to annual and monthly (March and from June to December) runoff increases, while a decrease in precipitation and an increase in air temperature from the average to dry year induced decreases in annual and monthly (all months except March) runoff, and moreover, these effects were more remarkable in the wet season than those in the dry season. Due to the integrated effects of changing land use and climate conditions, the annual runoff increased (decreased) by 70.1 mm (25.2 mm) or 197.4% (71.0%) from the average to wet (dry) year. In conclusion, climate conditions, especially precipitation, played an important role in runoff variations while land use change was secondary over the study area, and furthermore, the effects of changes in land use and/or climate conditions on monthly runoff were larger in the wet season. Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of human activities on stream flow in the Biliu River basin,China

The obvious decline in stream flow to the Biliu River reservoir over the period 1990–2005 has raised increasing concerns. Climate change and human activities, which mainly include land use changes, hydraulic constructions and artificial water consumption, are considered to be the most likely reasons for the decline in stream flow. This study centres on a detailed analysis of the runoff response to changes in human activities. Using a distributed hydrological model, (Soil and Water Assessment Tool), we simulated runoffs under different human activity and climate scenarios to understand how each scenario impacts stream flow. The results show that artificial water consumption correlates with the precipitation (wet, normal and dry) of the year in question and is responsible for most of the decrease in runoff during each period and for each different wetness year. A Fuzzy Inference Model is also used to find the relationship between the precipitation and artificial water consumption for different years, as well as to make inferences regarding the future average impact on runoff. Land use changes in the past have increased the runoff by only a small amount, while another middle reservoir (Yunshi) has been responsible for a decrease in runoff since operation began in 2001. We generalized the characteristics of the human activities to predict future runoff using climate change scenarios. The future annual flow will increase by approximately 10% from 2011 to 2030 under normal human activities and future climate change scenarios, as indicated by climate scenarios with a particularly wet year in the next 20 years. This study could serve as a framework to analyse and predict the potential impacts of changes both in the climate and human activities on runoff, which can be used to inform the decision making on the river basin planning and management. Copyright © 2012 John Wiley & Sons, Ltd.     

Multivariate properties of extreme precipitation events in the Pearl River basin,China: Magnitude,frequency, timing,and related causes

Daily precipitation/temperature data collected at 74 weather stations across the Pearl River basin of China (PRBC), for the years 1952–2013, were used to analyse extreme precipitation (EP) processes at annual and seasonal scales in terms of precipitation magnitude, occurrence rates, and timing. Peak‐over‐threshold sampling, modified Mann‐Kendall trend tests, and Poisson regression model were utilized in this study. Causes driving the observed statistical behaviours of EP were investigated, focusing particularly on the impacts of temperature change and the El Niño–Southern Oscillation (ENSO). EP events, which occur mainly during April and September, are most frequent in June. At an annual scale, they are subject to relatively even interannual distributions during the wet season. Significant trends were observed in the magnitude, frequency, and timing of EP events during the dry seasons, although no such trends were seen during the wet seasons. Seasonal shifts in EP can easily trigger sudden flood or drought events and warming temperatures, and ENSO events also have significant impacts on EP processes across the PRBC, as reflected by their increased magnitude and frequency in the western PRBC and decreased precipitation magnitudes in the eastern PRBC during ENSO periods. These results provide important evidence of regional hydrological responses to global climate changes in terms of EP regimes in tropical and subtropical zones.     

Seasonality intensification and long-term winter cooling as a part of the Late Pliocene climate development

A mutual climatic range method is applied to the Mediterranean marine pollen record of Semaforo (Vrica section, Calabria, Italy) covering the period from ∼2.46 Ma to ∼2.11 Ma. The method yields detailed information on summer, annual and winter temperatures and on precipitation during the nine obliquity and precession-controlled ‘glacial’ periods (marine isotope stages 96 to 80) and eight ‘interglacial’ periods (marine isotope stages 95 to 81) characterising this time interval. The reconstruction reveals higher temperatures of at least 2.8 °C in mean annual and 2.2 °C in winter temperatures, and 500 mm in precipitation during the ‘interglacials’ as compared to the present-day climate in the study area. During the ‘glacials’, temperatures are generally lower as compared to the present-day climate in the region, but precipitation is equivalent. Along the consecutive ‘interglacials’, a trend toward a reduction in annual and winter temperatures by more than 2.3 °C, and toward a higher seasonality is observed. Along the consecutive ‘glacials’, a trend toward a strong reduction in all temperature parameters of at least 1.6 °C is reconstructed. Climatic amplitudes of ‘interglacial–glacial’ transitions increase from the older to the younger cycles for summer and annual temperatures. The cross-spectral analyses suggest obliquity related warm/humid–cold/dry ‘interglacial–glacial’ cycles which are superimposed by precession related warm/dry– cold/humid cycles. A time displacement in the development of temperatures and precipitation is indicated for the obliquity band by temperatures generally leading precipitation change at ∼4 kyr, and on the precession band of ∼9.6 kyr in maximum.     

Estimating the effects of climate change on the intensification of monsoonal‐driven stream discharge in a Himalayan watershed

Understanding potential hydrologic influences to continued climate change in Himalayan watersheds is important for management of transnational water resources. This study estimates the climate change impacts on hydrologic processes of the Kali Gandaki watershed from central Himalayan region using the Soil and Water Assessment Tool. Daily predicted stream discharge of the basin for 1981–95 following calibration was accurate with Nash Sutcliffe Efficiency value >0.75. Sensitivity analysis of the hydrologic parameters showed the precipitation and temperature lapse rates as the most sensitive parameters to the stream discharge. To assess the influence of continued climate change on hydrologic processes, we modified the weather inputs for the model using average, minimum and maximum temperature, and precipitation changes for the Special Report on Emission Scenarios B1, A1B and A2 derived from 16 General Circulation Models for 2080s. Mean annual stream discharge was approximately 39% higher than current values for the maximum temperature and precipitation changes of the A2 scenario and 22% less for minimum changes of the same scenario. Stream discharge was projected to be changed by +9% during monsoon season and by ?6% during pre‐monsoon season. Snowfall and snow melt were projected to be 30% and 29%, respectively, less than the current average for the maximum temperature and precipitation changes of the A2 scenario. Future simulations showed potential increase in monsoonal stream discharge associated with projected higher precipitation which when coupled with enhanced summer glacier melt might influence the downstream water availability of the basin. Copyright © 2013 John Wiley & Sons, Ltd.     

Simulating precipitation in the Northeast United States using a climate-informed K-nearest neighbour algorithm

Decadal prediction using climate models faces long-standing challenges. While global climate models may reproduce long-term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate-informed K-nearest neighbour (K-NN)-based stochastic modelling approach to capture the long-term trend and variability while replicating intra-annual statistics. The climate-informed K-NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near-term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate-informed K-NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K-NN weather generator and ISIMIP-2b GFDL general circulation model bias-corrected output in a cross-validation mode. Results indicate that the climate-informed K-NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within-year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra-annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near-term streamflow, which in turn can be used for short-term water resources planning and management.     

Effect of projected climate change on the hydrological regime of the Yangtze River Basin,China

The hydrological response to the potential future climate change in Yangtze River Basin (YRB), China, was assessed by using an ensemble of 54 climate change simulations. The Coupled Model Intercomparison Project 5 simulations under two new Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios were downscaled and used to drive the Variable Infiltration Capacity hydrological model. This study found that the range of temperature changes is homogeneous for almost the entire region, with an average annual increase of more than 2 °C under RCP4.5 and even more than 4 °C under RCP8.5 in the end of the twenty first century. The warmest period (June–July–August) of the year would experience lower changes than the colder ones (December–January–February). Overall, mean precipitation was projected to increase slightly in YRB, with large dispersion among different global climate models, especially during the dry season months. These phenomena lead to changes in future streamflow for three mainstream hydrological stations (Cuntan, Yichang, and Datong), with slightly increasing annual average streamflows, especially at the end of twenty first century. Compared with the percentage change of mean flow, the high flow shows (90th percentile on the probability of no exceedance) a higher increasing trend and the low flow (10th percentile) shows a decreasing trend or lower increasing trend. The maximum daily discharges with 5, 10, 15, and 30-year return periods show an increasing trend in most sub-basins in the future. Therefore, extreme hydrological events (e.g., floods and droughts) will increase significantly, although the annual mean streamflow shows insignificant change. The findings of this study would provide scientific supports to implement the integrated adaptive water resource management for climate change at regional scales in the YRB.     

Impact of climate change on sediment yield for Naran watershed,Pakistan

In this paper, the site-specific impact of climate change on sediment yield has been assessed for the Naran watershed, Pakistan. Observed data has been gathered for period 1961–2010 and HaDCM3 GCM predictors of SRES scenarios A2 and B2 have been downloaded. Future precipitation and temperature time series have been statistically downscaled for time horizon 2011–2040 and 2041–2070. Downscaled data show both increasing and decreasing changes with respect to the observation. Potential sediment yield for future related to climate change has been simulated. The results show that the both snowy and monsoon seasonal stream discharges are expected to increase. This will lead to increase in annual sus-pended sediment yields. Percentage-wise, a less discharge and more sediment yield are expected during the early summer. The study concluded that the climate change and variability are influencing the watershed, and suspended sediment yield is likely to increase in the future.     

Perturbation study of climate change impacts in a snow‐fed river basin

A physically based distributed hydrological model developed at the University of Yamanashi based on block‐wise use of TOPMODEL and the Muskingum–Cunge method (YHyM/BTOPMC), integrated with a simple degree‐day–based snow accumulation/melt sub‐model, was applied to evaluate hydrological responses under changing climatic conditions in the snow‐fed Kali Gandaki River Basin (KGRB) in Western Nepal. Rainy season precipitation (June to September) in the basin takes up about 80% of the annual precipitation, and dry season runoff is largely contributed by snowmelt. Climate change is likely to increase the probability of extreme events and problems related to water availability. Therefore, the study aimed to simulate runoff pattern under changing climatic conditions, which will be helpful in the management of water resources in the basin. Public domain global data were widely used in this study. The model was calibrated and validated with an acceptable degree of accuracy. The results predicted that the annual average discharge will increase by 2.4%, 3.7%, and 5.7% when temperature increases by 1, 2, and 3 °C compared with the reference scenario. Similarly, maximum, minimum, and seasonal discharges in the monsoon and pre‐monsoon seasons will also increase with rising temperature. Snowmelt runoff is found sensitive to temperature changes in the KGRB. Increasing temperature will cause a faster snowmelt, but precipitation will increase the snowpack and also shed a positive effect on the total annual and monsoonal discharge. For the combined scenarios of increasing temperature and precipitation, the annual average discharge will increase. In contrast, discharge during the increasing temperature and decreasing precipitation will tend to decrease. Copyright © 2012 John Wiley & Sons, Ltd.