Impacts of climate variability and change on drought characteristics in the Niger River Basin,West Africa

West Africa has been afflicted by droughts since the declining rains of the 1970s. Therefore, this study examines the characteristics of drought over the Niger River Basin (NRB), investigates the influence of the drought on the river flow, and projects the impacts of future climate change on drought. A combination of observation data and regional climate simulations of past (1986–2005) and future climates (2046–2065 and 2081–2100) were analyzed. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were used to characterize drought while the standardized runoff index (SRI) was used to quantify river flow. Results of the study show that the historical pattern of drought is consistent with previous studies over the Basin and most part of West Africa. RCA4 ensemble gives realistic simulations of the climatology of the Basin in the past climate. Generally, an increase in drought intensity and frequency are projected over NRB. The coupling between SRI and drought indices was very strong (P < 0.05). The dominant peaks can be classified into three distinct drought cycles with periods 1–2, 2–4, 4–8 years. These cycles may be associated with Quasi-Biennial Oscillation (QBO) and El-Nino Southern Oscillation (ENSO). River flow was highly sensitive to precipitation in the NRB and a 1–3 month lead time was found between drought indices and SRI. Under RCP4.5, changes in the SPEI drought frequency range from 1.8 (2046–2065) to 2.4 (2081–2100) month year^?1 while under RCP8.5, the change ranges from 2.2 (2046–2065) to 3.0 month year^?1 (2081–2100). Niger Middle sub-basin is likely to be mostly impacted in the future while the Upper Niger was projected to be least impacted. Results of this study may guide policymakers to evolve strategies to facilitate vulnerability assessment and adaptive capacity of the basin in order to minimize the negative impacts of climate change.     

Response of runoff to climate change in the Wei River basin,China

Abstract

Climate change will likely have severe effects on water shortages, flood disasters and the deterioration of aquatic systems. In this study, the hydrological response to climate change was assessed in the Wei River basin (WRB), China. The statistical downscaling method (SDSM) was used to downscale regional climate change scenarios on the basis of the outputs of three general circulation models (GCMs) and two emissions scenarios. Driven by these scenarios, the Soil and Water Assessment Tool (SWAT) was set up, calibrated and validated to assess the impact of climate change on hydrological processes of the WRB. The results showed that the average annual runoff in the periods 2046–2065 and 2081–2100 would increase by 12.4% and 45%, respectively, relative to the baseline period 1961–2008. Low flows would be much lower, while high flows would be much higher, which means there would be more extreme events of droughts and floods. The results exhibited consistency in the spatial distribution of runoff change under most scenarios, with decreased runoff in the upstream regions, and increases in the mid- and lower reaches of the WRB.

Editor Z.W. Kundzewicz; Associate editor D. Yang     

Projection of future rainfall for the North China Plain using two statistical downscaling models and its hydrological implications

This study projected the future rainfall (2046–2065 and 2081–2100) for the North China Plain (NCP) using two stochastic statistical downscaling models, the non-homogeneous hidden Markov model and the generalized linear model for daily climate time series, conditioned by the large-scale atmospheric predictors from six general circulation models for three emission scenarios (A1B, A2 and B1). The results indicated that the annual total rainfall, the extreme daily rainfall and the maximum length of consecutive wet/dry days would decline, while the number of annual rainfall days would slightly increase (correspondingly rainfall intensity would decrease) in the NCP, in comparison with the base period (1961–2010). Moreover, the summer monsoon rainfall, which accounted for 50–75 % of the total annual rainfalls in NCP, was projected to decrease in the latter half of twenty-first century. The spatial patterns of change showed generally north–south gradients with relatively larger magnitude decrease in the northern NCP and less decrease (or even slightly increase) in the southern NCP. This could result in decline of the annual runoff by ?5.5 % (A1B), ?3.3 % (A2) and ?4.1 % (B1) for 2046–2065 and ?5.3 % (A1B), ?4.6 % (A2) and ?1.9 % (B1) decrease for 2081–2100. These rainfall changes, combined with the warming temperature, could lead to drier catchment soil profiles and further reduce runoff potential, would hence provide valuable references for the water availability and related climate change adaption in the NCP.     

Distributed modelling of future changes in hydrological processes of Spencer Creek watershed

The hydrologic impact of climate change has been largely assessed using mostly conceptual hydrologic models. This study investigates the use of distributed hydrologic model for the assessment of the climate change impact for the Spencer Creek watershed in Southern Ontario (Canada). A coupled MIKE SHE/MIKE 11 hydrologic model is developed to represent the complex hydrologic conditions in the Spencer Creek watershed, and later to simulate climate change impact using Canadian global climate model (CGCM 3·1) simulations. Owing to the coarse resolution of GCM data (daily GCM outputs), statistical downscaling techniques are used to generate higher resolution data (daily precipitation and temperature series). The modelling results show that the coupled model captured the snow storage well and also provided good simulation of evapotranspiration (ET) and groundwater recharge. The simulated streamflows are consistent with the observed flows at different sites within the catchment. Using a conservative climate change scenario, the downscaled GCM scenarios predicted an approximately 14–17% increase in the annual mean precipitation and 2–3 °C increase in annual mean maximum and minimum temperatures for the 2050s (i.e., 2046–2065). When the downscaled GCM scenarios were used in the coupled model, the model predicted a 1–5% annual decrease in snow storage for 2050s, approximately 1–10% increase in annual ET, and a 0·5–6% decrease in the annual groundwater recharge. These results are consistent with the downscaled temperature results. For future streamflows, the coupled model indicated an approximately 10–25% increase in annual streamflows for all sites, which is consistent with the predicted changes in precipitation. Overall, it is shown that distributed hydrologic modelling can provide useful information not only about future changes in streamflow but also changes in other key hydrologic processes such as snow storage, ET, and groundwater recharge, which can be particularly important depending on the climatic region of concern. The study results indicate that the coupled MIKE SHE/MIKE 11 hydrologic model could be a particularly useful tool for understanding the integrated effect of climate change in complex catchment scale hydrology. Copyright © 2010 John Wiley & Sons, Ltd.     

Climate change and uncertainty assessment over a hydroclimatic transect of Michigan

Predictions of a warmer climate over the Great Lakes region due to global change generally agree on the magnitude of temperature changes, but precipitation projections exhibit dependence on which General Circulation Models and emission scenarios are chosen. To minimize model- and scenario-specific biases, we combined information provided by the 3rd phase of the Coupled Model Intercomparison Project database. Specifically, the results of 12 GCMs for three emission scenarios B1, A1B, and A2 were analyzed for mid- (2046–2065) and end-century (2081–2100) intervals, for six locations of a hydroclimatic transect of Michigan. As a result of Bayesian Weighted Averaging, total annual precipitation averaged over all locations and the three emission scenarios increases by 7 % (mid-)–10 % (end-century), as compared to the control period (1961–1990). The projected changes across seasons are non-uniform and precipitation decreases by 3 % (mid-)–5 % (end-) for the months of August and September are likely. Further, average temperature is very likely to increase by 2.02–2.85 °C by the mid-century and 2.58–4.73 °C by the end-century. Three types of non-additive uncertainty sources due to climate models, anthropogenic forcings, and climate internal variability are addressed. When compared to the emission uncertainty, the relative magnitudes of the uncertainty types for climate model ensemble and internal variability are 149 and 225 % for mean monthly precipitation, and they are respectively 127 and 123 % for mean monthly temperature. A decreasing trend of the frost days and an increasing trend of the growing season length are identified. Also, a significant increase in the magnitude and frequency of heavy rainfall events is projected, with relatively more pronounced changes for heavy hourly rainfall as compared to daily events. Quantifying the inherent natural uncertainty and projecting hourly-based extremes, the study results deliver useful information for water resource stakeholders interested in impacts of climate change on hydro-morphological processes.     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Impact and uncertainties of climate change on the hydrology of the Mara River basin,Kenya/Tanzania

The impact and uncertainty of climate change on the hydrology of the Mara River basin (MRB) was assessed. Sixteen global circulation models (GCMs) were evaluated, and five were selected for the assessment of future climate scenarios in the basin. Observed rainfall and temperature data for the control period (1961–1990) were combined with expected GCMs output using the delta and direct statistical downscaling methods and three greenhouse gas emission scenarios (A1B, A2 and B1). Uncertainties of climate change were addressed through compare and contrast of results across diverse GCMs, future climate scenarios and the two downscaling methods. Both methods produced a relatively similar annual rainfall amount, but their monthly and daily pattern showed considerable differences. The relative advantages and disadvantages of implementing one method over the other were also explored. The hydrologic impact of climate change in the basin was assessed using Soil and Water Assessment Tool. The model was calibrated and validated with observed data in the control period with (Nash–Sutcliff efficiency, coefficient of determination) results of (calibration: 0.68, 0.69) and (validation: 0.43, 0.44) at Mara Mines. Results have shown a statistically significant increase in flow volume of the Mara River flow at Mara Mines for the year 2046–2065 and 2081–2100. With due attention to the limitations, findings of this study have a wider application for water resources sustainability analysis in the MRB in the face of uncertainties due to climate change. Copyright © 2012 John Wiley & Sons, Ltd.     

The impact of climate change on runoff in the Yarlung Tsangpo River basin in the Tibetan Plateau

The Tibetan Plateau (TP) is the “water tower of Asia” and it plays a key role on both hydrology and climate for southern and eastern Asia. It is critical to explore the impact of climate change on runoff for better water resources management in the TP. However, few studies pay attention to the runoff response to climate change in large river systems on the TP, especially in data-sparse upstream area. To complement the current body of work, this study uses two rainfall-runoff models (SIMHYD and GR4J) to simulate the monthly and annual runoff in the upstream catchments of the Yarlung Tsangpo River basin (YTR) under historical (1962–2002) and future (2046–2065 A1B scenario) climate conditions. The future climate series are downscaled from a global climate model (MIROC3.2_hires) by a high resolution regional climate model (RegCM3). The two rainfall-runoff models successfully simulate the historical runoff for the eight catchments in the YTR basin, with median monthly runoff Nash–Sutcliffe Efficiency of 0.86 for SIMHYD and 0.83 for GR4J. The mean annual future temperature in eight catchments show significant increase with the median of +3.8 °C. However, the mean annual future precipitation shows decrease with the median of ?5.8 % except in Lhatse (+2.0 %). The two models show similar modeling results that the mean annual future runoff in most of catchments (seven in eight) shows decrease with the median of ?13.9 % from SIMHYD and ?15.2 % from GR4J. The results achieved in this study are not only helpful for local water resources management, but also for future water utilization planning in the lower reaches region of the Brahmaputra.     

Impact of climate change on 24‐h design rainfall depth estimation in Qiantang River Basin,East China

The frequency and magnitude of extreme meteorological or hydrological events such as floods and droughts in China have been influenced by global climate change. The water problem due to increasing frequency and magnitude of extreme events in the humid areas has gained great attention in recent years. However, the main challenge in the evaluation of climate change impact on extreme events is that large uncertainty could exist. Therefore, this paper first aims to model possible impacts of climate change on regional extreme precipitation (indicated by 24‐h design rainfall depth) at seven rainfall gauge stations in the Qiantang River Basin, East China. The Long Ashton Research Station‐Weather Generator is adopted to downscale the global projections obtained from general circulation models (GCMs) to regional climate data at site scale. The weather generator is also checked for its performance through three approaches, namely Kolmogorov–Smirnov test, comparison of L‐moment statistics and 24‐h design rainfall depths. Future 24‐h design rainfall depths at seven stations are estimated using Pearson Type III distribution and L‐moment approach. Second, uncertainty caused by three GCMs under various greenhouse gas emission scenarios for the future periods 2020s (2011–2030), 2055s (2046–2065) and 2090s (2080–2099) is investigated. The final results show that 24‐h design rainfall depth increases in most stations under the three GCMs and emission scenarios. However, there are large uncertainties involved in the estimations of 24‐h design rainfall depths at seven stations because of GCM, emission scenario and other uncertainty sources. At Hangzhou Station, a relative change of ?16% to 113% can be observed in 100y design rainfall depths. Copyright © 2012 John Wiley & Sons, Ltd.     

Climate change and resilience of tributary thermal refugia for salmonids in eastern Canadian rivers

Abstract

River water temperature regimes are expected to change along with climate over the next decades. This work focuses on three important salmon rivers of eastern Canada, two of which warm up most summers to temperatures higher than the Atlantic salmon lethal limit (>28°C). Water temperature was monitored at 53 sites on the three basins during 2–18 summers, with about half of these sites either known or potential thermal refugia for salmon. Site-specific statistical models predicting water temperature, based on 10 different climate scenarios, were developed in order to assess how many of these sites will remain cool enough to serve as refugia in the future (2046–2065). The results indicate that, while 19 of the 23 identified refugia will persist, important increases in the occurrence and duration of temperature events in excess of 24°C and 28°C, respectively, in the mainstems of the rivers, will lead to higher demands for thermal refugia in the salmonid populations.

Editor Z.W. Kundzewicz; Associate editor T. Okruszko     

Daily rainfall projections from general circulation models with a downscaling nonhomogeneous hidden Markov model (NHMM) for south‐eastern Australia

An ensemble of stochastic daily rainfall projections has been generated for 30 stations across south‐eastern Australia using the downscaling nonhomogeneous hidden Markov model, which was driven by atmospheric predictors from four climate models for three IPCC emissions scenarios (A1B, A2, and B1) and for two periods (2046–2065 and 2081–2100). The results indicate that the annual rainfall is projected to decrease for both periods for all scenarios and climate models, with the exception of a few scenarios of no statistically significant changes. However, there is a seasonal difference: two downscaled GCMs consistently project a decline of summer rainfall, and two an increase. In contrast, all four downscaled GCMs show a decrease of winter rainfall. Because winter rainfall accounts for two‐thirds of the annual rainfall and produces the majority of streamflow for this region, this decrease in winter rainfall would cause additional water availability concerns in the southern Murray–Darling basin, given that water shortage is already a critical problem in the region. In addition, the annual maximum daily rainfall is projected to intensify in the future, particularly by the end of the 21st century; the maximum length of consecutive dry days is projected to increase, and correspondingly, the maximum length of consecutive wet days is projected to decrease. These changes in daily sequencing, combined with fewer events of reduced amount, could lead to drier catchment soil profiles and further reduce runoff potential and, hence, also have streamflow and water availability implications. Copyright © 2012 John Wiley & Sons, Ltd.     

Impacts of climate variability on water quality with best management practices in sub‐tropical climate of USA

Efficiency of non‐point source pollution control methods may be altered in future climate. This study investigated climate change impacts on sediment and nutrient transport, and efficiency of best management practices (BMPs), in the Upper Pearl River Watershed (UPRW) in Mississippi. The Soil and Water Assessment Tool was applied to the UPRW using observed flow, sediment and nutrient data. Water quality samples were collected at three US geological survey gauging stations. The model was successfully calibrated and validated for daily time steps (Nash Sutcliffe efficiency and coefficient of determination – R² up to 0.7) using manual and automatic (sequential uncertainty fitting version 2) methods from February 2010 to May 2011. Future weather scenarios were simulated using the LARS‐WG model, a stochastic weather generator, with Community Climate System Model, global climate model, which was developed by the National Center for Atmospheric Research in the USA. On the basis of the Special Report on Emissions Scenarios A1B, A2 and B1 of the Intergovernmental Panel on Climate Change, climate change scenarios were simulated for the mid (2046–2065) and late (2080–2099) century. Effectiveness of four BMPs (Riparian buffer, stream fencing, sub‐surface manure applications and vegetative filter strips) on reducing sediment and nutrient were evaluated in current and future climate conditions. Results show that sediment, nitrogen and phosphorus loadings will be increased up to a maximum of 26.3%, 7.3% and 14.3%, respectively, in future climate conditions. Furthermore, the effectiveness of BMPs on sediment removal will be reduced in future climate conditions, and the efficiency of nitrogen removal will be increased, whereas phosphorus removal efficiency will remain unchanged. Copyright © 2013 John Wiley & Sons, Ltd.     

Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling

ABSTRACT

Numerous statistical downscaling models have been applied to impact studies, but none clearly recommended the most appropriate one for a particular application. This study uses the geographically weighted regression (GWR) method, based on local implications from physical geographical variables, to downscale climate change impacts to a small-scale catchment. The ensembles of daily precipitation time series from 15 different regional climate models (RCMs) driven by five different general circulation models (GCMs), obtained through the European Union (EU)-ENSEMBLES project for reference (1960–1990) and future (2071–2100) scenarios are generated for the Omerli catchment, in the east of Istanbul city, Turkey, under scenario A1B climate change projections. Special focus is given to changes in extreme precipitation, since such information is needed to assess the changes in the frequency and intensity of flooding for future climate. The mean daily precipitation from all RCMs is under-represented in the summer, autumn and early winter, but it is overestimated in late winter and spring. The results point to an increase in extreme precipitation in winter, spring and summer, and a decrease in autumn in the future, compared to the current period. The GWR method provides significant modifications (up to 35%) to these changes and agrees on the direction of change from RCMs. The GWR method improves the representation of mean and extreme precipitation compared to RCM outputs and this is more significant, particularly for extreme cases of each season. The return period of extreme events decreases in the future, resulting in higher precipitation depths for a given return period from most of the RCMs. This feature is more significant with downscaling. According to the analysis presented, a new adaption for regulating excessive water under climate change in the Omerli basin may be recommended.     

Impacts of climate variability and human activity on streamflow decrease in a sediment concentrated region in the Middle Yellow River

The Kuye River is the primary tributary located in the sediment concentrated regions in the Middle Yellow River in China. Significant decrease in streamflow has been observed in the Kuye River. The non-parametric Mann–Kendall test was applied to detect the change in annual streamflow for the period of 1960 to 2006. Mean annual streamflow in the Kuye River was 84.9 mm from 1960 to 1979 (period I), while it decreased to 58.2 mm from 1980 to 1998 (period II) and 20.5 mm from 1999 to 2006 (period III), respectively. The climate elasticity method and the hydrological modeling method were individually employed to assess the impact of climate variability and human activities on the decrease in streamflow. The results showed that climate variability was responsible for 29.6 and 27.1 % of the streamflow decrease from the climate elasticity method and the hydrological modeling method, respectively; while human activities accounted for 70.4 and 72.9 % of the streamflow decrease in period II. In period III, climate variability contributed 40.9 and 39.3 % of the streamflow decrease from the climate elasticity method and the hydrological modeling method, respectively; while human activities accounted for 59.1 and 60.7 % of the streamflow decrease. Therefore, human activities were the main reason of the streamflow decrease. Soil conservation measures (planting trees, improving pastures, building terraces and sediment-trapping dams) and coal mining led to the streamflow reduction in the Kuye River.     

Using reanalysis‐driven regional climate model outputs for hydrology modelling

Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale.     

PMP and PMF estimations in sparsely-gauged Andean basins and climate change projections

Abstract

Mixed-regime Andean basins present a complex scenario for flood analysis. In this study, we propose a methodology for incorporating orographic effects influenced by mountainous barriers in the Probable Maximum Precipitation (PMP) estimation method in sparsely-gauged basins. The proposed methodology is applied to the Puclaro Reservoir basin in Chile, which is affected by the Andes. The PMP estimations were calculated by applying statistical and hydrometeorological approaches to the baseline (1960–1999) and climate change scenarios (2045–2065) determined from projections of the ECHAM5 general circulation model. Temperature projections for the 2040–2065 period show that there would be a rise in the catchment contributing area that would lead to an increase in the average liquid precipitation over the basin. Temperature projections would also affect the maximization factors in the calculation of the PMP, as precipitable water content, raising it to 126.6% and 62.5% under scenarios A2 and B1, respectively; the probable maximum flood (PMF) would increase to +175.5% under the A2 scenario. These projections would affect the safety of dam design and would be generalizable to zones with similar mixed hydrology and climate change projections. We propose that the methodology presented could be also applied to basins with similar characteristics.

Editor Z.W. Kundzewicz; Associate editor A. Porporato     

Multi-site bias correction of climate model outputs for hydro-meteorological impact studies: An application over a watershed in China

Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre-processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists between different sites, which could negatively affect simulations from a distributed hydrological model. In this study, three multi-variate bias correction (MBC) methods—initially proposed to correct the inter-variable correlation or multi-variate dependence of climate model outputs—are used to correct biases in distributional properties and spatial dependence at multiple weather stations. To reveal the benefits of correcting spatial dependence, two distribution-based single-site bias correction methods are used for comparison. The effects of multi-site correction on hydro-meteorological extremes are assessed by driving a distributed hydrological model and then evaluating the model performance in terms of several meteorological and hydrological extreme indices. The results show that the multi-site bias correction methods perform well in reducing biases in spatial correlation measures of raw global climate model outputs. In addition, the multi-site methods consistently reproduce watershed-averaged meteorological variables better than single-site methods, especially for extreme values. In terms of representing hydrological extremes, the multi-site methods generally perform better than the single-site methods, although the benefits vary according to the hydrological index. However, when applying the multi-site methods, the original temporal sequence of precipitation occurrence may be altered to some extent. Overall, all multi-site bias correction methods are able to reproduce the spatial correlation of observed meteorological variables over multiple stations, which leads to better hydrological simulations, especially for extremes. This study emphasizes the necessity of considering spatial dependence when applying bias correction to ccc outputs and hydrological impact studies.     

High resolution climate change scenarios: implications for British runoff

Nationwide changes in spatially well‐resolved patterns of British runoff were investigated under two climate change scenarios derived from general circulation model (GCM) output. A physical process‐based hydrological model (HYSIM) was used to simulate effective runoff across a 10 km×10 km British grid under baseline and future climate conditions. A gridded baseline climatology for precipitation and the Penman variables was used to validate HYSIM across Britain using grid cell‐specific parameters derived from land use and soil type. The climate change scenarios were constructed from the Hadley Centre's high resolution equilibrium GCM (UKHI) for 2050 and transient GCM (UKTR) for 2065. Future effective runoff was simulated under both scenarios by applying changes in precipitation and the Penman variables to the baseline climatology. Annual effective runoff is shown to increase throughout most of Britain under the UKHI scenario for 2050, whilst it decreases over much of England and Wales under the UKTR scenario for 2065. Both scenarios show an increasing gradient in runoff between a wetter northern Britain and a drier south‐eastern Britain. This gradient is more pronounced under the UKTR scenario. Changes in effective runoff for winter and summer show an increase in seasonality under both scenarios. Winter runoff is shown to increase most in northern Britain under both scenarios, whilst summer runoff is shown to experience major reductions over much of England and Wales under the UKTR scenario. If these simulations are realized, Britain may expect an accentuated north to south‐east imbalance in available water resources. If this is combined with a temporal imbalance suggested by the increased seasonality, there could be problems for the future management of British water resources. Copyright © 1999 John Wiley & Sons, Ltd.     

Regional regression models for hydro‐climate change impact assessment

Hydro‐climatic impacts in water resources systems are typically assessed by forcing a hydrologic model with outputs from general circulation models (GCMs) or regional climate models. The challenges of this approach include maintaining a consistent energy budget between climate and hydrologic models and also properly calibrating and verifying the hydrologic models. Subjective choices of loss, flow routing, snowmelt and evapotranspiration computation methods also increase watershed modelling uncertainty and thus complicate impact assessment. An alternative approach, particularly appealing for ungauged basins or locations where record lengths are short, is to predict selected streamflow quantiles directly from meteorological variable output from climate models using regional regression models that also include physical basin characteristics. In this study, regional regression models are developed for the western Great Lakes states using ordinary least squares and weighted least squares techniques applied to selected Great Lakes watersheds. Model inputs include readily available downscaled GCM outputs from the Coupled Model Intercomparison Project Phase 3. The model results provide insights to potential model weaknesses, including comparatively low runoff predictions from continuous simulation models that estimate potential evapotranspiration using temperature proxy information and comparatively high runoff projections from regression models that do not include temperature as an explanatory variable. Copyright © 2014 John Wiley & Sons, Ltd.     

Teleconnection between ENSO and climate in South China

This study investigates the features of the teleconnection between El Niño–Southern Oscillation (ENSO) and the climate over the region with the latitude from 21°N to 25°N and longitude from 111°E to 116°E in South China for the period from 1960 to 2005. The climate variables analyzed are the monthly means of daily maximum temperature (Tmax), minimum temperature (Tmin), precipitation and relative humidity (RH), which are recorded at 20 weather stations over the region. The cross correlation coefficients between the ENSO index and those climate variable anomalies are calculated to evaluate the strength of the teleconnection. The analysis results reveal that ENSO has positive influence on most of the climate variables in the study region. Specifically, ENSO has significant effects on Tmin (but not Tmax) with the corresponding time delay of about 4 months. In addition, ENSO has considerable influence on precipitation and RH over the study region with teleconnection lag time of around 2 and 1 month, respectively.