Regionalisation of precipitation for the Iberian Peninsula and climate change

Temporal variability of precipitation over the Iberian Peninsula (IP) has high spatial gradients. Therefore, statistics of the temporal behaviour of precipitation and derived quantities over the IP must be estimated taking into account these spatial gradients. Some statistics can be displayed over a map. However there are statistics, such as Probability Density Functions at each location of the IP, that are impossible to display in a map. Because of this, it is mandatory to reduce the number of degrees of freedom which, in this case, consists of a reduction of the time series representative of the IP domain. In this work, we present a spatial partition of the IP region into areas of similar precipitation. For that, an observed dataset of daily-total precipitation for the years between 1951 and 2003 was used. The land-only high resolution data was obtained on a regular grid with 0.2° resolution in the IP domain. This data was subjected to a k-means Cluster Analysis in order to divide the IP into K regions. The clustering was performed using the squared Euclidean distance. Four clusters of IP grid points, defining 4 IP regions, were identified. The grid points in each region share the same time-varying behaviour which is different from region to region. The annual precipitation discriminates the following regions: (1) north Iberia, (2) a large region extending from the centre to the Mediterranean shores of the IP, (3) a large region ranging from the centre to the western and southwestern shores of the Iberia, and (4) northwest Iberia. The regions obtained for the four seasons of the year are similar. These results are consistent with the thermodynamic characteristics described in the available literature. These Iberian regions were used to assess climate change of seasonal precipitation from the multi-model ensemble of the fifteen simulations provided by the European project ENSEMBLES. Probability Density Functions of annual- and seasonal-total precipitation, consecutive dry days, and total precipitation above the 95th percentile, averaged in each region were estimated for a reference climate (1961–1960), a near-future climate (2021–2050), and a distant-future climate (2069–2098). Climate change projections are based on comparisons of these functions between each future climate and the reference climate.Finally, we emphasize that: (i) the methodology used here, based on Cluster Analysis, can be used to regionalise other areas of the world, and (ii) the identified regions of the IP can be used to represent the Iberian precipitation by four time series that can be subjected to further analysis, whose results can be presented in a concise manner.     

An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

In the present paper, an ensemble approach is proposed to estimate possible modifications caused by climate changes in the extreme precipitation regime, with the rain gauge Napoli Servizio Idrografico (Naples, Italy) chosen as test case. The proposed research, focused on the analysis of extremes on the basis of climate model simulations and rainfall observations, is structured in several consecutive steps. In the first step, all the dynamically downscaled EURO‐CORDEX simulations at about 12 km horizontal resolution are collected for the current period 1971–2000 and the future period 2071–2100, for the RCP4.5 and the RCP8.5 concentration scenarios. In the second step, the significance of climate change effects on extreme precipitation is statistically tested by comparing current and future simulated data and bias‐correction is performed by means of a novel approach based on a combination of simple delta change and quantile delta mapping, in compliance with the storm index method. In the third step, two different ensemble models are proposed, accounting for the variabilities given by the use of different climate models and for their hindcast performances. Finally, the ensemble models are used to build novel intensity–duration–frequency curves, and their effects on the early warning system thresholds for the area of interest are evaluated.     

Robust inferences on climate change patterns of precipitation extremes in the Iberian Peninsula

This work presents a methodology to make statistical significant and robust inferences on climate change from an ensemble of model simulations. This methodology is used to assess climate change projections of the Iberian daily-total precipitation for a near-future (2021–2050) and a distant-future (2069–2098) climates, relatively to a reference past climate (1961–1990).Climate changes of precipitation spatial patterns are estimated for annual and seasonal values of: (i) total amount of precipitation (PRCTOT), (ii) maximum number of consecutive dry days (CDD), (iii) maximum of total amount of 5-consecutive wet days (Rx5day), and (iv) percentage of total precipitation occurred in days with precipitation above the 95th percentile of the reference climate (R95T). Daily-total data were obtained from the multi-model ensemble of fifteen Regional Climate Model simulations provided by the European project ENSEMBLES. These regional models were driven by boundary conditions imposed by Global Climate Models that ran under the 20C3M conditions from 1961 to 2000, and under the A1B scenario, from 2001 to 2100, defined by the Special Report on Emission Scenarios of the Intergovernmental Panel on Climate Change.Non-parametric statistical methods are used for significant climate change detection: linear trends for the entire period (1961–2098) estimated by the Theil-Sen method with a statistical significance given by the Mann-Kendall test, and climate-median differences between the two future climates and the past climate with a statistical significance given by the Mann-Whitney test. Significant inferences of climate change spatial patterns are made after these non-parametric statistics of the multi-model ensemble median, while the associated uncertainties are quantified by the spread of these statistics across the multi-model ensemble. Significant and robust climate change inferences of the spatial patterns are then obtained by building the climate change patterns using only the grid points where a significant climate change is found with a predefined low uncertainty.Results highlight the importance of taking into account the spread across an ensemble of climate simulations when making inferences on climate change from the ensemble-mean or ensemble-median. This is specially true for climate projections of extreme indices such CDD and R95T. For PRCTOT, a decrease in annual precipitation over the entire peninsula is projected, specially in the north and northwest where it can decrease down to 400 mm by the middle of the 21st century. This decrease is expected to occur throughout the year except in winter. Annual CDD is projected to increase till the middle of the 21st century overall the peninsula, reaching more than three weeks in the southwest. This increase is projected to occur in summer and spring. For Rx5day, a decrease is projected to occur during spring and autumn in the major part of the peninsula, and during summer in northern Iberia. Finally, R95T is projected to decrease around 20% in northern Iberia in summer, and around 15% in the south-southwest in autumn.     

Evaluating the transferability of monthly water balance models under changing climate conditions

ABSTRACT

Monthly water balance models (MWBMs) are often used for making flow projections under climate change. As such, these models should provide accurate flow simulations; however, they are seldom evaluated in this regard. This paper presents a comprehensive framework intended for the evaluation of the applicability of MWBMs under changing climatic conditions. The framework consists of analyses of consistency in model performance, parameter estimates and simulated water balance components, and a subjective assessment of model transferability. Four MWBMs – abcd, Budyko, GR2M and WASMOD – are used to simulate runoff in the Wimmera catchment affected by the Millennium drought. Although abcd and Budyko slightly outperformed GR2M and WASMOD, none of the models performed well in transfer to the driest period. The greatest variability is detected in simulated groundwater storage and baseflow; thus, these model components should be improved and/or enhanced calibration strategies should be employed to advance the transferability of MWBMs under changing climate.     

A two-stage time series model for monthly hydrological projections under climate change in the Lim River basin (southeast Europe)

ABSTRACT

Climate change projections of precipitation and temperature suggest that Serbia could be one of the most affected regions in southeastern Europe. To prepare adaptation measures, the impact of climate changes on water resources needs to be assessed. Pilot research is carried out for the Lim River basin, in southeastern Europe, to predict monthly flows under different climate scenarios. For estimation of future water availability, an alternative approach of developing a deterministic-stochastic time series model is chosen. The proposed two-stage time series model consists of several components: trend, long-term periodicity, seasonality and the stochastic component. The latter is based on a transfer function model with two input variables, precipitation and temperature, as climatic drivers. The Nash-Sutcliffe model efficiency for the observed period 1950–2012 is 0.829. The model is applied for the long-term hydrological prediction under the representative concentration pathway (RCP) emissions scenarios for the future time frame 2013–2070.     

Distributional change of monthly precipitation due to climate change: comprehensive examination of dataset in southeastern United States

A number of watersheds are selected from the Hydro‐Climate Data Network over southeastern United States to examine possible changes in hydrological time series, e.g. precipitation, introduced by changing climate. Possible changes in monthly precipitation are examined by three different methods to detect second order stationarity, abrupt changes in the variance and smooth changes in quantiles of the time series. An analysis of second order stationarity shows that precipitation in eight of the 56 watersheds display nonstationary behaviour. Change‐point analyses reveal that changes in the long‐term variance of monthly precipitation are only detected for a few sites. As a complementary analysis tool, quantile regression aims to detect potential changes of different percentiles of the monthly precipitation over time. Several sites show diverging trends in the quantiles, which implies that the range and thus variance of the data, is increasing. As distinct change‐points are not identified, this suggests that the effect is small and cumulative. Results are analysed in detail, and possible explanations are provided. This type of thorough analysis provides a basis for understanding the possible redistribution of water cycle. It also provides implications for water resources management and hydrological engineering facility design and planning. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluating the performance of climate models in reproducing the hydrological characteristics of rainfall events

ABSTRACT

Rainfall events largely control hydrological processes occurring on and in the ground, but the performance of climate models in reproducing rainfall events has not been investigated enough to guide selection among the models when making hydrological projections. We proposed to compare the durations, intensities, and pause periods, as well as depths of rainfall events when assessing the accuracy of general circulation models (GCMs) in reproducing the hydrological characteristics of observed rainfall. We also compared the sizes of design storm events and the frequency and severity of drought to demonstrate the consequences of GCM selection. The results show that rainfall and extreme hydrological event projections could significantly vary depending on climate model selection and weather stations, suggesting the need for a careful and comprehensive evaluation of GCM in the hydrological analysis of climate change. The proposed methods are expected to help to improve the accuracy of future hydrological projections for water resources planning.     

Several characteristics of contemporary climate change in the Tibetan Plateau

Characteristics of contemporary climate change in the Tibetan Plateau have been investigated based on the observational data of monthly mean air temperature, monthly mean maximum and minimum air temperatures, and precipitation amount at 217 stations in the Plateau and its adjacent areas in 1951–1998, in which the temperature data at Lhasa, Lanzhou, Kunming and Chengdu were extended to a period of 1935–1950. The following conclusions can be drawn. (1) The air temperature in the Tibetan Plateau decreased from the 1950s to the 1960s, afterwards it began warming up to the 1990s. The data at the Lhasa Station beginning from 1935 have indicated that the air temperature at the station was the highest in the 1940s, then it became cooling until the 1960s. After the 1960s, it began warming until the 1990s. However, the air temperature at Lhasa in the 1990s still did not reach as high as in the 1940s. (2) Since the 1960s, there has existed a cooling belt below 3000 m altitude above sea level, which is located in eastern and southeastern Tibetan Plateau, and there has existed a strong warming belt from south to north in 85–95° E. Because there are very nonhomogeneous and positive-negative alternating changes between cooling and warming belts, the air temperature is not linearly increased with increasing height. (3) Since the 1960s, there has existed a precipitation decreasing belt distributed over southwestern to northeastern Plateau as well as over a below 3000 m a.s.l. area in southeastern Plateau. The warming with decreasing precipitation occurs in the central area of the Plateau and the above 3000 m western Plateau; the warming with increasing precipitation occurs in the northern and southern Plateau; and the cooling with decreasing precipitation occurs in the below 3000 m southeastern Plateau.     

朝鲜半岛的核试验时期

过去20年中, 北朝鲜在核不扩散问题的外交政策方面一直与西方国家争论不休. 迫于前苏联的压力, 北朝鲜于1985年勉强加入了1968年签订的《核武器不扩散条约》. 但是, 在加入该条约之后, 北朝鲜就停止了必须履行的谈判, 而且也没有令其与国际原子能机构（IAEA）签署的全面安全协议（CSA）生效. 该协议本应于1986年生效, 但直到1992年4月10日它才有了合法约束力. 同年, 国际原子能机构基于其在伊拉克的经验, 在核安全方面实施了新方法. 在原有系统下, 国际原子能机构采用了一种“材料审计”的方法, 该方法只是检查其所申报核材料的存货清单是否正确. 然而, 自1992年开始, 国际原子能机构对接受其检查的国家所申报内容的完备性也开展了评估工作.     

The Local Magnitude Scale in the Korean Peninsula

A formula to determine the local magnitude (M_L) following Richters original definition was empirically derived for the Korean Peninsula. A total of 1,644 digital seismograms from 142 Korean earthquakes that occurred from 1997 to 2000 were corrected for instrument response and convolved with the nominal Wood-Anderson torsion seismograph response to be appropriate for the original definition of M_L. Then, the zero-to-peak amplitude was measured in millimeters on the synthetic Wood-Anderson seismogram. Multiple regression analysis was conducted to determine distance and station correction terms for the measured peak amplitudes. The best-fit solution for M_L yielded the following formula for the Korean Peninsula:where A() and S denote the peak amplitude on the synthetic Wood-Anderson seismogram at distance and the station correction term, respectively. Note that the second term, distance correction, was adjusted with Richters M_L, taking into consideration attenuation differences between the Korean Peninsula and southern California, where Richter originally introduced M_L. On average, the magnitudes determined in this study are nearly the same as those determined by the Korea Institute of Geoscience and Mineral Resources (KIGAM), but are larger than those of the Korea Meteorological Administration (KMA) by as much as 0.36.     

Variations in terrestrial oxygen sources under climate change

Science China Earth Sciences - The terrestrial ecosystem is an important source of atmospheric oxygen, and its changes are closely related to variations in atmospheric oxygen level. However, few...     

Robust strategies for climate change adaptation in the agricultural sector under deep climate uncertainty

Stochastic Environmental Research and Risk Assessment - Adaptive planning in climate change condition is a significant challenge for effective management of water resources and agricultural...     

Progress in plant phenology modeling under global climate change

Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic forces. Plant phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon, water and energy fluxes between the vegetation and near-surface atmosphere. Therefore, an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon, water and energy balance of terrestrial ecosystems. Phenological studies have developed rapidly under global change conditions, while the research of phenology modeling is largely lagged. Inaccurate phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water cycles.Understanding the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier issues. In this review, we first summarized the drivers of plant phenology and overviewed the development of plant phenology models. Finally, we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions.     

Potentially modified hydropower production under climate change in the Italian Alps

We present an assessment of the potential impacts of climate change on hydropower production within a paradigmatic, very highly exploited cryospheric area of upper Valtellina valley in the Italian Alps. Based on dependable and unique hydrological measures from our high‐altitude hydrometric network Idrostelvio during 2006–2015, we set up the Poly‐Hydro model to mimic the cryospheric processes driving hydrological flow formation in this high‐altitude area. We then set up an optimization tool, which we call Poly‐Power, to maximize the revenue of the plant manager under given hydrological regimes, namely, by proper operation of the hydroelectric production scheme (reservoirs, pipelines, and power plants) of the area. We then pursue hydrological projections until 2100, feeding Poly‐Hydro with the downscaled outputs of three general circulation models from the Intergovernmental Panel on Climate Change Fifth Assessment Report, under the scenarios Representative Concentration Pathway (RCP) 2.6, RCP 4.5, and RCP 8.5. We assess hydrological flows in two reference decades, that is, at half century (2040–2049), and end of century (2090–2099). We then feed the so obtained hydrological scenarios as inputs to Poly‐Power, and we project future production of hydroelectric power, with and without reoperation of the system. The average annual stream flows for hydropower production decreases along the century under our scenarios (?21 to +7%, on average ? 5% at half century; ?17 to ?2%, average ? 8%, end of century), with ice cover melting unable to offset such decrease. Reduction in snowfall and increase in liquid rainfall are the main factors affecting the modified hydrological regime. Energy production (and revenues) at half century may increase under our scenarios (?9 to +15%, +3% on average). At the end of century in spite of a projected increase on average (?7 to +6%, +1% on average), under the warmest scenario RCP 8.5 decrease of energy production is consistently projected (?4% on average). Our results provide an array of potential scenarios of modified hydropower production under future climate change and may be used for brain storming of adaptation strategies.     

Reservoir performance under uncertainty in hydrologic impacts of climate change

Relatively few studies have addressed water management and adaptation measures in the face of changing water balances due to climate change. The current work studies climate change impact on a multipurpose reservoir performance and derives adaptive policies for possible future scenarios. The method developed in this work is illustrated with a case study of Hirakud reservoir on the Mahanadi river in Orissa, India, which is a multipurpose reservoir serving flood control, irrigation and power generation. Climate change effects on annual hydropower generation and four performance indices (reliability with respect to three reservoir functions, viz. hydropower, irrigation and flood control, resiliency, vulnerability and deficit ratio with respect to hydropower) are studied. Outputs from three general circulation models (GCMs) for three scenarios each are downscaled to monsoon streamflow in the Mahanadi river for two future time slices, 2045–65 and 2075–95. Increased irrigation demands, rule curves dictated by increased need for flood storage and downscaled projections of streamflow from the ensemble of GCMs and scenarios are used for projecting future hydrologic scenarios. It is seen that hydropower generation and reliability with respect to hydropower and irrigation are likely to show a decrease in future in most scenarios, whereas the deficit ratio and vulnerability are likely to increase as a result of climate change if the standard operating policy (SOP) using current rule curves for flood protection is employed. An optimal monthly operating policy is then derived using stochastic dynamic programming (SDP) as an adaptive policy for mitigating impacts of climate change on reservoir operation. The objective of this policy is to maximize reliabilities with respect to multiple reservoir functions of hydropower, irrigation and flood control. In variations to this adaptive policy, increasingly more weightage is given to the purpose of maximizing reliability with respect to hydropower for two extreme scenarios. It is seen that by marginally sacrificing reliability with respect to irrigation and flood control, hydropower reliability and generation can be increased for future scenarios. This suggests that reservoir rules for flood control may have to be revised in basins where climate change projects an increasing probability of droughts. However, it is also seen that power generation is unable to be restored to current levels, due in part to the large projected increases in irrigation demand. This suggests that future water balance deficits may limit the success of adaptive policy options.     

The effects of climate change on historical and future extreme rainfall in Antalya,Turkey

Abstract

There is increasing concern that flood risk will be exacerbated in Antalya, Turkey as a result of global-warming-induced, more frequent and intensive, heavy rainfalls. In this paper, first, trends in extreme rainfall indices in the Antalya region were analysed using daily rainfall data. All stations in the study area showed statistically significant increasing trends for at least one extreme rainfall index. Extreme rainfall datasets for current (1970–1989) and future periods (2080–2099) were then constructed for frequency analysis using the peaks-over-threshold method. Frequency analysis of extreme rainfall data was performed using generalized Pareto distribution for current and future periods in order to estimate rainfall intensities for various return periods. Rainfall intensities for the future period were found to increase by up to 23% more than the current period. This study contributed to better understanding of climate change effects on extreme rainfalls in Antalya, Turkey.     

Possible change in Korean streamflow seasonality based on multi‐model climate projections

Seasonality in hydrology is closely related to regional water management and planning. There is a strong consensus that global warming will likely increase streamflow seasonality in snow‐dominated regions due to decreasing snowfall and earlier snowmelt, resulting in wetter winters and drier summers. However, impacts to seasonality remain unclear in rain‐dominated regions with extreme seasonality in streamflow, including South Korea. This study investigated potential changes in seasonal streamflow due to climate change and associated uncertainties based on multi‐model projections. Seasonal flow changes were projected using the combination of 13 atmosphere–ocean general circulation model simulations and three semi‐distributed hydrologic models under three different future greenhouse gas emission scenarios for two future periods (2020s and 2080s). Our results show that streamflow seasonality is likely to be aggravated due to increases in wet season flow (July through September) and decreases in dry season flow (October through March). In South Korea, dry season flow supports water supply and ecosystem services, and wet season flow is related to flood risk. Therefore, these potential changes in streamflow seasonality could bring water management challenges to the Korean water resources system, especially decreases in water availability and increases in flood risk. Copyright © 2012 John Wiley & Sons, Ltd.     

Mapping of climatic parameters under climate change impacts in Iran

ABSTRACT

This research aims to provide a comprehensive evaluation of climate change effects on temperature, precipitation and potential evapotranspiration over the country of Iran for the time periods 2010–2039, 2040–2069 and 2070–2099, and under scenarios A2 and B2. After preparation of measured temperature and precipitation data and calculation of potential evapotranspiration for the base time period of 1960–1990 for 46 meteorological stations (with a nationwide distribution), initial zoning of these three parameters over the country was attempted. Maximum and minimum temperatures and values of precipitation were obtained from the HadCM3 model under scenarios A2 and B2 for the three time periods, and these data were downscaled. Corresponding maps were prepared for the three parameters in the three time periods, and spatial and temporal variations of these climatic parameters under scenarios A2 and B2 were extracted and interpreted. Results showed that the highest increase in temperature would occur in western parts of the country, but the highest increase of potential evapotranspiration would occur in the central region of Iran. However, precipitation would vary temporally and spatially in different parts of the country depending on the scenario used and the time period selected.

Editor Z. W. Kundzewicz; Associate editor not assigned     

A study of the infiltration characteristics of undisturbed soil under simulated rainfall

Infiltration is the single most important parameter in deriving the net quick response rainfall which contributes to stream flood discharges. Rainfall simulation is used to study the infiltration characteristics in a typical catchment, the Six Mile Water in N. Ireland. The design of the simulator was such that it could be easily moved from one test area to another within the catchment to examine the effect of soil and slope variation. The simulator was first calibrated in controlled laboratory conditions and later the calibration was checked in the field. The simulator was mounted over an undisturbed plot of 37 m² and the surface runoff from the area measured by means of a collecting channel located along a lower edge of the plot. Soil moisture variations were monitored using a soil moisture neutron probe. Soil classification tests and gravimetric moisture contents were carried out on each plot. The field tests were carried out with variations in rainfall intensity, initial conditions, changing seasons, and for different plots within the catchment area. The results obtained are unique in that they present data obtained under field conditions for undisturbed soil within a natural catchment. The infiltration behaviour was found to depend upon rainfall intensity, initial conditions of the plot under consideration, seasonal temperature, and a slope of the plot. The data showed that while a classical Horton type equation for infiltration was suitable for the later stages of each test result when significant surface runoff was taking place, the model failed to represent early response adequately due to storage effects being omitted in the equation. A modified form of Horton equation is proposed, which models more accurately the infiltration characteristics of the full period of each test run.     

Model and parameter uncertainty in IDF relationships under climate change

Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty.