Assessing the effect of climate natural variability in water resources evaluation impacted by climate change

Water resource assessment on climate change is crucial in water resource planning and management. This issue is becoming more urgent with climate change intensifying. In the current research of climate change impact, climate natural variability (fluctuation) has seldom been studied separately. Many studies keep attributing all changes (e.g. runoff) to climate change, which may lead to wrong understanding of climate change impact assessment. Because of lack of long enough historical series, impacts of climate variability have been always avoided deliberately. Based on Latin hypercube sampling technique, a block sampling approach was proposed for climate variability simulation in this study. The widely used time horizon (1961–1991) was defined as baseline period, and the runoff variation probability affected by climate natural variability was analysed. Allowing for seven future climate projections in total of three GCMs (CSIRO, NCAR, and MPI) and three emission scenarios (A1B, A2, and B1), the impact of future climate change on water resources was estimated in terms of separating the contribution from climate natural variability. Based on the analysis of baseline period, for the future period from 2021 to 2051, the impact of climate natural variability may play a major part, whereas for the period from 2061 to 2091, climate change attributed to greenhouse gases may dominate the changing process. The results show that changes from climate variability possess a comparable magnitude, which highlights the importance to separate impacts of climate variability in assessing climate change, instead of attributing all changes to climate change solely. Copyright © 2012 John Wiley & Sons, Ltd.     

Simulation of future climate scenarios with a weather generator

Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.     

The effect of climate and land use change on flow duration in the Maryland Piedmont region

This paper describes the use of a continuous streamflow model to examine the effects of climate and land use change on flow duration in six urbanizing watersheds in the Maryland Piedmont region. The hydrologic model is coupled with an optimization routine to achieve an agreement between observed and simulated streamflow. Future predictions are made for three scenarios: future climate change, land use change, and jointly varying climate and land use. Future climate is modelled using precipitation and temperature predictions for the Canadian Climate Centre (CCC) and Hadley climate models. Results show that a significant increase in temperature under the CCC climate predictions produces a decreasing trend in low flows. A significant increasing trend in precipitation under the Hadley climate predictions produces an increasing trend in peak flows. Land use change by itself, as simulated by an additional 10% increase in imperviousness (from 20·5 to 30·5%), produces no significant changes in the simulated flow durations. However, coupling the effects of land use change with climate change leads to more significant decreasing trends in low flows under the CCC climate predictions and more significant increasing trends in peak flows under Hadley climate predictions than when climate change alone is employed. These findings indicate that combined land use and climate change can result in more significant hydrologic change than either driver acting alone. Copyright © 2008 John Wiley & Sons, Ltd.     

Necessity for post-processing dynamically downscaled climate projections for impact and adaptation studies

This work aims to answer if post-processing climate model outputs will improve the accuracy of climate change impact assessment and adaptation evaluation. To achieve this aim, the daily outputs of CSIRO Conformal Cubic Atmospheric Model for periods 1960–1979, 1980–1999 and 2046–2065, and observed daily climate data (1960–1979, 1980–1999) were used by a stochastic weather generator, the Long Ashton Research Station-Weather Generator to construct long time series of local climate scenarios (CSs). The direct outputs of climate models (DOCM) and CSs were then fed into the Agricultural Production System sIMulator—Wheat model to calculate seasonal climate variables and production components at three locations spanning northern, central and southern wheat production areas in New South Wales (NSW), Australia. This study firstly compared the differences in climate variables and production components derived from DOCM and CSs against those from observed climate for period 1960–1979. The impact difference arising from the use of DOCM and CSs for the future period 2046–2065 was then quantified. Simulation results show that (1) both the median/mean and distribution/variation of climate variables and production components associated with CSs were closer to those derived from observed climate when compared to those from DOCM in most of the cases (median/mean, distribution/variation, climate variables, production components and locations); (2) the difference in the mean and distribution of climate variables and production components derived from DOCM and observed climate was significant in most of the cases; (3) longer dry spells in both winter and spring were found from CSs across the three locations considered in comparison with those from DOCM; (4) the median growing season (GS) rainfall total, GS average maximum temperature, GS average solar radiation, GS length and final wheat yield were lower from DOCM than those from CSs and vice versa for GS rainfall frequency and GS average minimum temperature in 2055; (5) the mean and distribution of these climate variables and production components arising from the use of DOCM and CSs are significantly different in most of the cases. This implied that using the direct outputs of spatially downscaled general circulation model without implementing post-processing procedures may lead to significant errors in projected climate impact and the identified effort in tackling climate change risk. It is therefore highly recommended that post-processing procedures be used in developing robust CSs for climate change impact assessment and adaptation evaluation.     

The implications of bias correction methods and climate model ensembles on soil erosion projections under climate change

Climate change will most likely cause an increase in extreme precipitation and consequently an increase in soil erosion in many locations worldwide. In most cases, climate model output is used to assess the impact of climate change on soil erosion; however, there is little knowledge of the implications of bias correction methods and climate model ensembles on projected soil erosion rates. Using a soil erosion model, we evaluated the implications of three bias correction methods (delta change, quantile mapping and scaled distribution mapping) and climate model selection on regional soil erosion projections in two contrasting Mediterranean catchments. Depending on the bias correction method, soil erosion is projected to decrease or increase. Scaled distribution mapping best projects the changes in extreme precipitation. While an increase in extreme precipitation does not always result in increased soil loss, it is an important soil erosion indicator. We suggest first establishing the deviation of the bias-corrected climate signal with respect to the raw climate signal, in particular for extreme precipitation. Furthermore, individual climate models may project opposite changes with respect to the ensemble average; hence climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that the impact of climate change on soil erosion can only accurately be assessed with a bias correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models. © 2018 John Wiley & Sons, Ltd.     

Paleoclimate data assimilation: Its motivation,progress and prospects

Reconstructing past climate is beneficial for researchers to understand the mechanism of past climate change, recognize the context of modern climate change and predict scenarios of future climate change. Paleoclimate data assimilation (PDA), which was first introduced in 2000, is a promising approach and a significant issue in the context of past climate research. PDA has the same theoretical basis as the traditional data assimilation (DA) employed in the fields of atmosphere science, ocean science and land surface science. The main aim of PDA is to optimally estimate past climate states that are both consistent with the climate signal recorded in proxy and the dynamic understanding of the climate system through combining the physical laws and dynamic mechanisms of climate systems represented by climate models with climate signals recorded in proxies (e.g., tree rings, ice cores). After investigating the research status and latest advances of PDA abroad, in this paper, the background, concept and methodology of PAD are briefly introduced. Several special aspects and the development history of PAD are systematically summarized. The theoretical basis and typical cases associated with three frequently-used PAD methods (e.g., nudging, particle filter and ensemble square root filter) are analyzed and demonstrated. Finally, some underlying problems in current studies and key prospects in future research related to PDA are proposed to provide valuable thoughts on and a scientific basis for PDA research.     

Hydrological response to future land-use change and climate change in a tropical catchment

Hydrological response to expected future changes in land use and climate in the Samin catchment (278 km²) in Java, Indonesia, was simulated using the Soil and Water Assessment Tool model. We analysed changes between the baseline period 1983–2005 and the future period 2030–2050 under both land-use change and climate change. We used the outputs of a bias-corrected regional climate model and six global climate models to include climate model uncertainty. The results show that land-use change and climate change individually will cause changes in the water balance components, but that more pronounced changes are expected if the drivers are combined, in particular for changes in annual streamflow and surface runoff. The findings of this study will be useful for water resource managers to mitigate future risks associated with land-use and climate changes in the study catchment.     

Homogenization of climate series:The basis for assessing climate changes

Long-term meteorological observation series are fundamental for reflecting climate changes.However,almost all meteorological stations inevitably undergo relocation or changes in observation instruments,rules,and methods,which can result in systematic biases in the observation series for corresponding periods.Homogenization is a technique for adjusting these biases in order to assess the true trends in the time series.In recent years,homogenization has shifted its focus from the adjustments to climate mean status to the adjustments to information about climate extremes or extreme weather.Using case analyses of ideal and actual climate series,here we demonstrate the basic idea of homogenization,introduce new understanding obtained from recent studies of homogenization of climate series in China,and raise issues for further studies in this field,especially with regards to climate extremes,uncertainty of the statistical adjustments,and biased physical relationships among different climate variables due to adjustments in single variable series.     

Projected changes in flood indices in selected catchments in Poland in the 21st century

The aim of this study is to estimate likely changes in flood indices under a future climate and to assess the uncertainty in these estimates for selected catchments in Poland. Precipitation and temperature time series from climate simulations from the EURO-CORDEX initiative for the periods 1971–2000, 2021–2050 and 2071–2100 following the RCP4.5 and RCP8.5 emission scenarios have been used to produce hydrological simulations based on the HBV hydrological model. As the climate model outputs for Poland are highly biased, post processing in the form of bias correction was first performed so that the climate time series could be applied in hydrological simulations at a catchment-scale. The results indicate that bias correction significantly improves flow simulations and estimated flood indices based on comparisons with simulations from observed climate data for the control period. The estimated changes in the mean annual flood and in flood quantiles under a future climate indicate a large spread in the estimates both within and between the catchments. An ANOVA analysis was used to assess the relative contributions of the 2 emission scenarios, the 7 climate models and the 4 bias correction methods to the total spread in the projected changes in extreme river flow indices for each catchment. The analysis indicates that the differences between climate models generally make the largest contribution to the spread in the ensemble of the three factors considered. The results for bias corrected data show small differences between the four bias correction methods considered, and, in contrast with the results for uncorrected simulations, project increases in flood indices for most catchments under a future climate.     

Holocene climate forcings and lacustrine regime shifts in the Indian summer monsoon realm

Extreme climate events have been identified both in meteorological and long-term proxy records from the Indian summer monsoon (ISM) realm. However, the potential of palaeoclimate data for understanding mechanisms triggering climate extremes over long time scales has not been fully exploited. A distinction between proxies indicating climate change, environment, and ecosystem shift is crucial for enabling a comparison with forcing mechanisms (e.g. El-Niño Southern Oscillation). In this study we decouple these factors using data analysis techniques [multiplex recurrence network (MRN) and principal component analyses (PCA)] on multiproxy data from two lakes located in different climate regions – Lonar Lake (ISM dominated) and the high-altitude Tso Moriri Lake (ISM and westerlies influenced). Our results indicate that (i) MRN analysis, an indicator of changing environmental conditions, is associated with droughts in regions with a single climate driver but provides ambiguous results in regions with multiple climate/environmental drivers; (ii) the lacustrine ecosystem was ‘less sensitive’ to forcings during the early Holocene wetter periods; (iii) archives in climate zones with a single climate driver were most sensitive to regime shifts; (iv) data analyses are successful in identifying the timing of onset of climate change, and distinguishing between extrinsic and intrinsic (lacustrine) regime shifts by comparison with forcing mechanisms. Our results enable development of conceptual models to explain links between forcings and regional climate change that can be tested in climate models to provide an improved understanding of the ISM dynamics and their impact on ecosystems. © 2020 John Wiley & Sons, Ltd.     

Climate change regionalization in China(1961–2010)

Since climatic condition is the important foundation for human subsistence and development and the key factor in sustainable development of economy and society, climate change has been a global issue attracting great attentions of politicians, scientists, governments, and the public alike throughout the world. Existing climate regionalization in China aims to characterize the regional differences in climate based on years of the mean value of different climate indexes. However, with the accelerating climate change nowadays, existing climate regionalization cannot represent the regional difference of climate change, nor can it reflect the disasters and environmental risks incurred from climate changes. This paper utilizes the tendency value and fluctuation value of temperature and precipitation from 1961 to 2010 to identify the climate change quantitatively, and completes the climate change regionalization in China(1961–2010) with county administrative regionalization as the unit in combination with China's terrain feature. Level-I regionalization divides China's climate change(1961–2010) into five tendency zones based on the tendency of temperature and precipitation, which are respectively Northeast China-North China warm-dry trend zone, East China-Central China wet-warm trend zone, Southwest China-South China dry-warm trend zone, Southeast Tibet-Southwest China wet-warm trend zone, and Northwest China-Qinghai-Tibet Plateau warm-wet trend zone; level-II regionalization refers to fourteen fluctuation regions based on level-I regionalization according to the fluctuation of temperature and precipitation.     

Climate change impacts on groundwater hydrology – where are the main uncertainties and can they be reduced?

ABSTRACT

This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models.

Editor D. Koutsoyiannis; Associate editor not assigned     

小冰期气候变化主控因子的模拟试验

小冰期是距今最近,特征最明显的寒冷气候事件,对于研究世纪尺度气候变化具有重要意义. 过去的研究结果认为,太阳活动和火山活动的变化是小冰期气候变化的主要原因. 本文应用AGCM SSiB模式分别试验了植被、太阳辐射和火山活动变化对小冰期温度、降水的影响,发现下垫面植被变化对小冰期温度变化影响的量级与太阳辐射和火山活动变化的作用相当,对降水的影响甚至超过太阳活动和火山活动变化的作用,说明对于世纪尺度的气候变化而言,下垫面植被的反馈作用不可忽略. 这对于深入理解小冰期气候变化的机理具有启迪作用,同时也为世纪尺度气候变化研究与气候情景预测提供了新的思路和方法.     

Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis

Understanding and modelling pluvial flood patterns is pivotal for the estimation of flood impacts in urban areas, especially in a climate change perspective. However, urban flood modelling under climate change conditions poses several challenges. On one hand, the identification and collection of climate change data suitable for flood-related evaluations requires consistent computational and scientific effort. On the other hand, large difficulties can arise in the reproduction of the rainfall-runoff transformation process in cases when only little information about the subsurface processes is known. In this perspective, a simplified approach is proposed to address the challenges regarding the quantitative estimation of climate change effects on urban flooding for real case applications. The approach is defined as “bottom-up” because climate change information is not included in flood modelling, but it is only invoked for the interpretation of results. In other words, the challenge faced in this work is the development of a modelling strategy that is expeditious, because it does not require flood simulations for future rainfall scenarios, but only under current climate conditions, thus reducing the overall computational effort; and it is flexible, because results can be easily updated once new climate change data, scenarios or methods become available, without the need of additional flood simulations. To simulate real case applications, the approach is tested for a scenario analysis, where different return periods and hyetograph shapes are used as input for urban inundation modelling in Naples, Italy. The approach can support public and private stakeholders, such as land administrators and water systems managers; moreover, it represents a valuable and effective basis for climate change risk communication strategies.     

The integrated effects of climate and hydrologic uncertainty on future flood risk assessments

This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether hydrologic uncertainties are a significant source of uncertainty relative to other sources such as climate variability and change and (2) whether a statistical characterization of uncertainty from a lumped, conceptual hydrologic model is sufficient to account for hydrologic uncertainties in the modelling process. To investigate these questions, an ensemble of climate simulations are propagated through hydrologic models and then through a reservoir simulation model to delimit the range of flood protection under a wide array of climate conditions. Uncertainty in mean climate changes and internal climate variability are framed using a risk‐based methodology and are explored using a stochastic weather generator. To account for hydrologic uncertainty, two hydrologic models are considered, a conceptual, lumped parameter model and a distributed, physically based model. In the conceptual model, parameter and residual error uncertainties are quantified and propagated through the analysis using a Bayesian modelling framework. The approach is demonstrated in a case study for the Coralville Dam on the Iowa River, where recent, intense flooding has raised questions about potential impacts of climate change on flood protection adequacy. Results indicate that the uncertainty surrounding future flood risk from hydrologic modelling and internal climate variability can be of the same order of magnitude as climate change. Furthermore, statistical uncertainty in the conceptual hydrological model can capture the primary structural differences that emerge in flood damage estimates between the two hydrologic models. Copyright © 2014 John Wiley & Sons, Ltd.     

Relative effects of multi-decadal climatic variability and changes in the mean and variability of climate due to global warming: future streamflows in Britain

Climate change impact assessments conventionally assess just the implications of a change in mean climate due to global warming. This paper compares such effects of such changes with those due to natural multi-decadal variability, and also explores the effects of changing the year-to-year variability in climate as well as the mean. It estimates changes in mean monthly flows and a measure of low flow (the flow exceeded 95% of the time) in six catchments in Britain, using the UKCIP98 climate change scenarios and a calibrated hydrological model. Human-induced climate change has a different seasonal effect on flows than natural multi-decadal variability (an increase in winter and decrease in summer), and by the 2050s the climate change signal is apparent in winter and, in lowland Britain, in summer. Superimposing natural multi-decadal variability onto the human-induced climate change increases substantially the range in possible future streamflows (in some instances counteracting the climate change signal), with important implications for the development of adaptation strategies. Increased year-to-year variability in climate leads to slight increases in mean monthly flows (relative to changes due just to changes in mean climate), and slightly greater decreases in low flows. The greatest effect on low flows occurs in upland catchments.     

On the correlation between precipitation and potential evapotranspiration climate change signals for hydrological impact analyses

State-of-the-art hydrological climate impact assessment involves ensemble approaches to address uncertainties. For precipitation, a wide range of climate model runs is available. However, for particular meteorological variables used for the calculation of potential evapotranspiration (ET_o), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ET_o amounts, in order to preserve the internal physical consistency. This results in constraints on the maximum ensemble size. In this paper, we investigate the correlation between climate change signals of precipitation and ET_o. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ET_o amounts does not result in a significant bias for changes in extreme flow. With these results, future impact studies can be conducted with larger ensemble sizes, resulting in a more complete uncertainty estimation.     

Impact of oceans on climate change in drylands

Drylands account for approximately 41% of the global total land area. Significant warming and rare precipitation in drylands result in a fragile ecology and deterioration of the living environment, making it more sensitive to global climate change. As an important regulator of the Earth's climate system, the oceans play a vital role in the process of climate change in drylands. In modern climate change in particular, the impact of marine activities on climate change in drylands cannot be neglected. This paper reviews the characteristics of climate change in drylands over the past 100 years, and summarizes the researches conducted on the impact of marine activities on these changes. The review focuses on the impact of the Pacific Decadal Oscillation(PDO), Atlantic Multidecadal Oscillation(AMO), El Ni?o and La Ni?a on climate change in drylands, and introduces the mechanisms by which different oceanic oscillation factors synergistically affect climate change in drylands.Studies have shown that global drylands have experienced a significant intensification in warming in the past 100 years, which shows obvious characteristics of interdecadal dry/wet variations. The characteristics of these changes are closely related to the oscillatory factors of the oceanic interdecadal scale. Different phase combinations of oceanic oscillation factors significantly change the land-sea thermal contrast, which in turn affects the westerly jet, planetary wave and blocking frequency, resulting in changes in the temperature and dry/wet characteristics of drylands. With the intensification of climate change in drylands, the impact of marine activities on these regions will reveal new characteristics in the future, which will increase the uncertainty of future climate change in drylands and intensify the impact of these drylands on global climate.     

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.     

Projected risks to groundwater‐dependent terrestrial vegetation caused by changing climate and groundwater abstraction in the Central Perth Basin,Western Australia

The effect of potential climate change on groundwater‐dependent vegetation largely depends on the nature of the climate change (drying or wetting) and the level of current ecosystem dependence on groundwater resources. In south‐western Australia, climate projections suggest a high likelihood of a warmer and drier climate. The paper examines the potential environmental impacts by 2030 at the regional scale on groundwater‐dependent terrestrial vegetation (GDTV) adapted to various watertable depths, on the basis of the combined consideration of groundwater modelling results and the framework for GDTV risk assessment. The methodology was tested for the historical period from 1984 to 2007, allowing validation of the groundwater model results' applicability to such an assessment. Climate change effects on GDTV were evaluated using nine global climate models under three greenhouse gas emission scenarios by applying the climate projections to groundwater models. It was estimated that under dry climate scenarios, GDTV is likely to be under high and severe risk over more than 20% of its current habitat area. The risk is also likely to be higher under an increase in groundwater abstraction above current volumes. The significance of climate change risk varied across the region, depending on both the intensity of the change in water regime and the sensitivity of the GDTV to such change. Greater effects were projected for terrestrial vegetation dependent on deeper groundwater (6–10 m). Copyright © 2013 John Wiley & Sons, Ltd.