Space-time precipitation reflecting climate change

Abstract

A methodology has been developed and applied to an eastern Nebraska, USA, case study to estimate the space-time distribution of daily precipitation under climate change. The approach is based on the analysis both of the type and of the Markov properties of atmospheric circulation patterns (CPs), and a stochastic linkage between daily (here 500 hPa) CP types and daily precipitation events. Historical data and General Circulation Model (GCM) output of daily CPs corresponding to 1 × CO₂ and 2 × CO₂ are considered. Time series of both local and regional precipitation corresponding to each of those cases were simulated and their statistical properties were compared. Under the dry continental climate of eastern Nebraska, a highly variable spatial response to climate change was obtained. Most of the local and the regional average precipitation values reflect, under 2 × CO₂, a somewhat wetter and a more variable precipitation regime in eastern Nebraska. The sensitivity of the results to the GCM utilized should be considered.     

Regional precipitation and temperature scenarios for climate change

Abstract

To investigate the consequences of climate change on the water budget in small catchments, it is necessary to know the change of local precipitation and temperature. General Circulation Models (GCM) cannot provide regional climate parameters yet, because of their coarse resolution and imprecise modelling of precipitation. Therefore downscaling of precipitation and temperature has to be carried out from the GCM grids to a small scale of a few square kilometres. Daily rainfall and temperature are modelled as processes conditioned on atmospheric circulation. Rainfall is linked to the circulation patterns (CPs) using conditional probabilities and conditional rainfall amount distribution. Both temperature and precipitation are downscaled to several locations simultaneously taking into account the CP dependent spatial correlation. Temperature is modelled using a simple autoregressive approach, conditioned on atmospheric circulation and local areal precipitation. The model uses the classification scheme of the German Weather Service and a fuzzy rule-based classification. It was applied in the Aller catchment for validation using observed rainfall and temperature, and observed classified geopotential pressure heights. GCM scenarios of the ECHAM model were used to make climate change predictions (using classified GCM geopotential heights); simulated values agree fairly well with historical data. Results for different GCM scenarios are shown.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

区域气候模式RegCM3对华北地区未来气候变化的数值模拟

采用高水平分辨率区域气候模式进行区域未来气候变化预估,对理解全球增暖对区域气候的潜在影响和科学评估区域气候变化有很好的参考价值.这里对国家气候中心使用25 km高水平分辨率区域气候模式RegCM3单向嵌套全球模式MIROC3.2_hires在观测温室气体(1951—2000)和IPCC A1B温室气体排放情景下(2001—2100)进行的共计150年长时间模拟结果,进行华北地区未来气温、降水和极端气候事件变化的分析.模式检验结果表明:模式对当代(1981—2000)气温以及和气温有关的极端气候事件(霜冻日数、生长季长度)的空间分布和数值模拟较好;对降水及和降水有关的极端气候事件(强降水日期、降水强度、五日最大降水量)能够模拟出它们各自的主要空间分布特征,但在模拟数值上存在偏大、偏强的误差.和全球模式驱动场相比,区域模式模拟的气温、降水和极端气候事件有明显的改进.2010—2100年华北地区随时间区域平均气温升高幅度逐渐增大,随之霜冻日数逐渐减少,生长季长度逐渐增多;同时随温室效应的不断加剧,未来降水呈增加的趋势,强降水日期和五日最大降水量逐渐增多、降水强度逐渐增大.从空间分布看,21世纪末期(2081—2100)气温、降水以及有关的极端气候事件变化比21世纪中期(2041—2060)更加明显.     

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.     

Utility of high-resolution climate model simulations for water resources prediction over the Korean Peninsula: a sensitivity study / Utilité de simulations de modèle climatique à haute résolution pour la prévision des ressources en eau dans la Péninsule Coréenne: étude de sensibilité

Abstract

Abstract The utility of simulations of Global Climate Models (GCMs) for regional water resources prediction and management on the Korean Peninsula was assessed by a probabilistic measure. Global Climate Model simulations of an indicator variable (e.g. surface precipitation or temperature) were used for discriminating high vs low regional observations of a target variable (e.g. watershed precipitation or reservoir inflow). The formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two distributions. High resolution Atmospheric Model Intercomparison Project-II (AMIP-II) type GCM simulations performed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and AMIP-I type GCM simulations performed by the Korean Meteorological Research Institute (METRI) were used to obtain information for the indicator variables. Observed mean areal precipitation and temperature, and watershed-outlet discharge values for seven major river basins in Korea were used as the target variables. The results suggest that the use of the climate model nodal output from both climate models in the vicinity of the target basin with monthly resolution will be beneficial for water resources planning and management analysis that depends on watershed mean areal precipitation and temperature, and outlet discharge.     

Assessing climate change impacts on the ecohydrology of the Jinghe River basin in the Loess Plateau,China

Abstract

Quantifying the impacts of climate change on the hydrology and ecosystem is important in the study of the Loess Plateau, China, which is well known for its high erosion rates and ecosystem sensitivity to global change. A distributed ecohydrological model was developed and applied in the Jinghe River basin of the Loess Plateau. This model couples the vegetation model, BIOME BioGeochemicalCycles (BIOME-BGC) and the distributed hydrological model, Water and Energy transfer Process in Large river basins (WEP-L). The WEP-L model provided hydro-meteorological data to BIOME-BGC, and the vegetation parameters of WEP-L were updated at a daily time step by BIOME-BGC. The model validation results show good agreement with field observation data and literature values of leaf area index (LAI), net primary productivity (NPP) and river discharge. Average climate projections of 23 global climate models (GCMs), based on three emissions scenarios, were used in simulations to assess future ecohydrological responses in the Jinghe River basin. The results show that global warming impacts would decrease annual discharge and flood season discharge, increase annual NPP and decrease annual net ecosystem productivity (NEP). Increasing evapotranspiration (ET) due to air temperature increase, as well as increases in precipitation and LAI, are the main reasons for the decreasing discharge. The increase in annual NPP is caused by a greater increase in gross primary productivity (GPP) than in plant respiration, whilst the decrease in NEP is caused by a larger increase in heterotrophic respiration than in NPP. Both the air temperature increase and the precipitation increase may affect the changes in NPP and NEP. These results present a serious challenge for water and land management in the basin, where mitigation/adaption measures for climate change are desired.

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

Citation Peng, H., Jia, Y.W., Qiu, Y.Q., and Niu, C.W., 2013. Assessing climate change impacts on the ecohydrology of the Jinghe River basin in the Loess Plateau, China. Hydrological Sciences Journal, 58 (3), 651–670.     

Assessment of future precipitation indices in the Shikoku region using a statistical downscaling model

In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0–15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios.     

The effects of climate change and urbanization on the runoff of the Rock Creek basin in the Portland metropolitan area,Oregon, USA

Climate changes brought on by increasing greenhouse gases in the atmosphere are expected to have a significant effect on the Pacific Northwest hydrology during the 21st century. Many climate model simulations project higher mean annual temperatures and temporal redistribution of precipitation. This is of particular concern for highly urbanized basins where runoff changes are more vulnerable to changes in climate. The Rock Creek basin, located in the Portland metropolitan area, has been experiencing rapid urban growth throughout the last 30 years, making it an ideal study area for assessing the effect of climate and land cover changes on runoff. A combination of climate change and land cover change scenarios for 2040 with the semi‐distributed AVSWAT (ArcView Soil and Water Assessment Tool) hydrological model was used to determine changes in mean runoff depths in the 2040s (2030–2059) from the baseline period (1973–2002) at the monthly, seasonal, and annual scales. Statistically downscaled climate change simulation results from the ECHAM5 general circulation model (GCM) found that the region would experience an increase of 1·2 °C in the average annual temperature and a 2% increase in average annual precipitation from the baseline period. AVSWAT simulation shows a 2·7% increase in mean annual runoff but a 1·6% decrease in summer runoff. Projected climate change plus low‐density, sprawled urban development for 2040 produced the greatest change to mean annual runoff depth (+5·5%), while climate change plus higher‐density urban development for 2040 resulted in the smallest change (+5·2%), when compared with the climate and land cover of the baseline period. This has significant implications for water resource managers attempting to implement adaptive water resource policies to future changes resulting from climate and urbanization. Copyright © 2008 John Wiley & Sons, Ltd.     

GCM-related uncertainty for river flows and inundation under climate change: the Inner Niger Delta

ABSTRACT

A semi-distributed hydrological model of the Niger River above and including the Inner Delta is developed. GCM-related uncertainty in climate change impacts are investigated using seven GCMs for a 2°C increase in global mean temperature, the hypothesised threshold of “dangerous” climate change. Declines in precipitation predominate, although some GCMs project increases for some sub-catchments, whilst PET increases for all scenarios. Inter-GCM uncertainty in projected precipitation is three to five times that of PET. With the exception of one GCM (HadGEM1), which projects a very small increase (3.9%), river inflows to the Delta decline. There is considerable uncertainty in the magnitude of these reductions, ranging from 0.8% (HadCM3) to 52.7% (IPSL). Whilst flood extent for HadGEM1 increases (mean annual peak +1405 km²/+10.2%), for other GCMs it declines. These declines range from almost negligible changes to a 7903 km² (57.3%) reduction in the mean annual peak.

Editor Z.W. Kundzewicz; Associate editor not assigned     

Impact of the spatial variability of daily precipitation on hydrological projections: A comparison of GCM‐ and RCM‐driven cases in the Han River basin,Korea

In this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes.     

全球变暖背景下东亚气候变化的最新情景预测

在最新的SRES A2和B2温室气体排放情景下,利用国际上7个气候模式针对未来全球变暖的数值模拟结果,本文着重分析了东亚区域气候21世纪的变化趋势. 研究揭示：中国大陆年均表面气温升高过程与全球同步,但增幅在东北、西部和华中地区较大,且表现出明显的年际变化;全球年均表面气温增幅纬向上大体呈带状分布,两极地区最为明显,并在北极地区达到最大;此外,21世纪后半段北半球高纬度地区的年平均强升温幅度主要来自于冬季增温. 在21世纪前50年,温室气体含量的增加除在一定程度上会增加青藏高原大部分夏季降水量外,不会对中国大陆其余地区的年、季节平均降水量产生较大影响;但持续的温室气体含量增加将最终导致大陆降水量几乎是全域性的增加.     

Possible climate change/variability and human impacts,vulnerability of drought-prone regions,water resources and capacity building for Africa

ABSTRACT

This review article discusses the climate, water resources and historical droughts of Africa, drought indices, vulnerability, impact of global warming and land use for drought-prone regions in West, southern and the Greater Horn of Africa, which have suffered recurrent severe droughts in the past. Recent studies detected warming and drying trends in Africa since the mid 20th century. Based on the Fourth Assessment Report of the Intergovernmental Panel on Climate Change and the Coupled Model Intercomparison Project Phase 5 (CMIP5), both northern and southern Africa are projected to experience drying, such as decreasing precipitation, runoff and soil moisture in the 21st century and could become more vulnerable to the impact of droughts. The daily maximum temperature is projected to increase by up to 8°C (RCP8.5 of CMIP5), precipitation indices such as total wet day precipitation (PRCPTOT) and heavy precipitation days (R10 mm) could decrease, while warm spell duration (WSDI) and consecutive dry days (CDD) could increase. Uncertainties of the above long-term projections, teleconnections to climate anomalies such as ENSO and the Madden-Julian Oscillation, which could also affect the water resources of Africa, and capacity building in terms of physical infrastructure and non-structural solutions are also discussed. Given that traditional climate and hydrological data observed in Africa are generally limited, satellite data should also be exploited to fill the data gap for Africa in the future.

Editor D. Koutsoyiannis; Associate editor N. Ilich     

中国平均降水和极端降水对气候变暖的响应:CMIP5模式模拟评估和预估

基于24个CMIP5全球耦合模式模拟结果,分析了中国区域年平均降水和ETCCDI强降水量(R95p)、极端强降水量(R99p)对增暖的响应.定量分析结果显示,CMIP5集合模拟的当代中国区域平均降水对增温的响应较观测偏弱,而极端降水的响应则偏强.对各子区域气温与平均降水、极端降水的关系均有一定的模拟能力,并且极端降水的模拟好于平均降水.RCP4.5和RCP8.5情景下,随着气温的升高,中国区域平均降水和极端降水均呈现一致增加的趋势,中国区域平均气温每升高1℃,平均降水增加的百分率分别为3.5%和2.4%,R95p增加百分率为11.9%和11.0%,R99p更加敏感,分别增加21.6%和22.4%.就各分区来看,当代的区域性差异较大,未来则普遍增强,并且区域性差异减小,在持续增暖背景下,中国及各分区极端降水对增暖的响应比平均降水更强,并且越强的极端降水敏感性越大.未来北方地区平均降水对增暖的响应比南方地区的要大,青藏高原和西南地区的R95p和R99p增加最显著,表明未来这些区域发生暴雨和洪涝的风险将增大.     

Hydrologic impact of regional climate change for the snowfed and glacierfed river basins in the Republic of Tajikistan: hydrological response of flow to climate change

The warming of the Earth's atmosphere system is likely to change temperature and precipitation, which may affect the climate, hydrology and water resources at the river basins over the world. The importance of temperature change becomes even greater in snow or glacier dominated basins where it controls the snowmelt processes during the late‐winter, spring and summer months. In this study hydrologic responses of streamflow in the Pyanj and Vaksh River basins to climate change are analysed with a watershed hydrology model, based on the downscaled atmospheric data as input, in order to assess the regional climate change impact for the snowfed and glacierfed river basins in the Republic of Tajikistan. As a result of this analysis, it was found that the annual mean river discharge is increasing in the future at snow and glacier dominated areas due to the air temperature increase and the consequent increase in snow/ice melt rates until about 2060. Then the annual mean flow discharge starts to decrease from about 2080 onward because the small glaciers start to disappear in the glacier areas. It was also found that there is a gradual change in the hydrologic flow regime throughout a year, with the high flows occuring earlier in the hydrologic year, due to the warmer climate in the future. Furthermore, significant increases in annual maximum daily flows, including the 100‐year return period flows, at the Pyanj and Vaksh River basins toward the end of the 21st century can be inferred from flood frequency analysis results. Copyright © 2012 John Wiley & Sons, Ltd.     

Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation

We applied a simple statistical downscaling procedure for transforming daily global climate model (GCM) rainfall to the scale of an agricultural experimental station in Katumani, Kenya. The transformation made was two-fold. First, we corrected the rainfall frequency bias of the climate model by truncating its daily rainfall cumulative distribution into the station’s distribution based on a prescribed observed wet-day threshold. Then, we corrected the climate model rainfall intensity bias by mapping its truncated rainfall distribution into the station’s truncated distribution. Further improvements were made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme to correct the time structure problem inherent with daily GCM rainfall. Results of the simple and hybridized GCM downscaled precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model for a more objective evaluation of their performance using a non-linear measure based on mutual information based on entropy. This study is useful for the identification of both suitable downscaling technique as well as the effective precipitation variables for forecasting crop yields using GCM’s outputs which can be useful for addressing food security problems beforehand in critical basins around the world.     

Stormwater quantity and quality response to climate change using artificial neural networks

This research investigates the potential impacts of climate change on stormwater quantity and quality generated by urban residential areas on an event basis in the rainy season. An urban residential stormwater drainage area in southeast Calgary, Alberta, Canada is the focus of future climate projections from general circulation models (GCMs). A regression‐based statistical downscaling tool was employed to conduct spatial downscaling of daily precipitation and daily mean temperature using projection outputs from the coupled GCM. Projected changes in precipitation and temperature were applied to current climate scenarios to generate future climate scenarios. Artificial neural networks (ANNs) developed for modelling stormwater runoff quantity and quality used projected climate scenarios as network inputs. The hydrological response to climate change was investigated through stormwater runoff volume and peak flow, while the water quality responses were investigated through the event mean value (EMV) of five parameters: turbidity, conductivity, water temperature, dissolved oxygen (DO) and pH. First flush (FF) effects were also noted. Under future climate scenarios, the EMVs of turbidity increased in all storms except for three events of short duration. The EMVs of conductivity were found to decline in small and frequent storms (return period < 5 years); but conductivity EMVs were observed to increase in intensive events (return period ≥ 5 years). In general, an increasing EMV was observed for water temperature, whereas a decreasing trend was found for DO EMV. No clear trend was found in the EMV of pH. In addition, projected future climate scenarios do not produce a stronger FF effect on dissolved solids and suspended solids compared to that produced by the current climate scenario. Copyright © 2010 John Wiley & Sons, Ltd.     

A study on the impact of climate change on streamflow at the watershed scale in the humid tropics

Abstract

Climate change is recognized to be one of the most serious challenges facing mankind today. Driven by anthropogenic activities, it is known to be a direct threat to our food and water supplies and an indirect threat to world security. Increase in the concentration of carbon dioxide and other greenhouse gases in the atmosphere will certainly affect hydrological regimes. The consequent global warming is expected to have major implications on water resources management. The objective of this research is to present a general approach for evaluating the impacts of potential climate change on streamflow in a river basin in the humid tropical zone of India. Large-scale global climate models (GCMs) are the best available tools to provide estimates of the effect of rising greenhouse gases on rainfall and temperature. However the spatial resolution of these models (250 km?×?250 km) is not compatible with that of watershed hydrological models. Hence the outputs from GCMs have to be downscaled using regional climate models (RCMs), so as to project the output of a GCM to a finer resolution (50 km?×?50 km). In the present work, the projections of a GCM for two scenarios, A2 and B2 are downscaled by a RCM to project future climate in a watershed. Projections for two important climate variables, viz. rainfall and temperature are made. These are then used as inputs for a physically-based hydrological model, SWAT, in order to evaluate the effect of climate change on streamflow and vegetative growth in a humid tropical watershed.

Citation Raneesh, K. Y. & Santosh, G. T. (2011) A study on the impact of climate change on streamflow at the watershed scale in the humid tropics. Hydrol. Sci. J. 56(6), 946–965.     

Hydrological impacts of climate change predicted for an inland lake catchment in Ontario by using monthly water balance analyses

Potential hydrological impacts of climate change on long‐term water balances were analysed for Harp Lake and its catchment. Harp Lake is located in the boreal ecozone of Ontario, Canada. Two climate change scenarios were used. One was based on extrapolation of long‐term trends of monthly temperature and precipitation from a 129‐year data record, and another was based on a Canadian general circulation model (GCM) predictions. A monthly water balance model was calibrated using 26 years of hydrological and meteorological data, and the model was used to calculate hydrological impact under two climate change scenarios. The first scenario with a warmer and wetter climate predicted a smaller magnitude of change than the second scenario. The first scenario showed an increase in evaporation each month, an increase in catchment runoff in summer, fall and winter, but a decrease in spring, resulting in a slight increase in lake level. Annual runoff and lake level would increase because the precipitation change overrides evaporation change. The second scenario with a warmer, drier climate predicted a greater change, and indicated that evaporation would increase each month, runoff would increase in many months, but would decrease in spring, causing the lake level to decrease slightly. Annual runoff and lake level would decrease because evaporation change overrides precipitation change. In both scenarios, the water balance changes in winter and spring are pronounced. Copyright © 2009 John Wiley & Sons, Ltd.     

Nonlinear dynamics and recurrence analysis of extreme precipitation for observed and general circulation model generated climates

A statistical framework based on nonlinear dynamics theory and recurrence quantification analysis of dynamical systems is proposed to quantitatively identify the temporal characteristics of extreme (maximum) daily precipitation series. The methodology focuses on both observed and general circulation model (GCM) generated climates for present (1961–2000) and future (2061–2100) periods which correspond to 1xCO₂ and 2xCO₂ simulations. The daily precipitation has been modelled as a stochastic process coupled with atmospheric circulation. An automated and objective classification of daily circulation patterns (CPs) based on optimized fuzzy rules was used to classify both observed CPs and ECHAM4 GCM‐generated CPs for 1xCO₂ and 2xCO₂ climate simulations (scenarios). The coupled model ‘CP‐precipitation’ was suitable for precipitation downscaling. The overall methodology was applied to the medium‐sized mountainous Mesochora catchment in Central‐Western Greece. Results reveal substantial differences between the observed maximum daily precipitation statistical patterns and those produced by the two climate scenarios. A variable nonlinear deterministic behaviour characterizes all climate scenarios examined. Transitions’ patterns differ in terms of duration and intensity. The 2xCO₂ scenario contains the strongest transitions highlighting an unusual shift between floods and droughts. The implications of the results to the predictability of the phenomenon are also discussed. Copyright © 2013 John Wiley & Sons, Ltd.