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This paper deals with the study of stationarity and mean reversion in the temperature anomalies series in the southwestern American cone. In particular, monthly temperatures in 12 Chilean meteorological stations were studied (from the 1960's to nowadays), examining if temperature shocks are expected to remain in the long term or if they are reversible. The results clearly show a significant relationship between the latitude, climate, and the order of integration of the temperatures. The orders of integration tend to be smaller in colder southern parts, therefore impacts of climate change are expected to be more reversible. However, in northern desert areas the orders of integration are larger than 0.5, thus impacts are expected to be maintained for a longer time. [ABSTRACT FROM AUTHOR]

The impact of global climate change on water resources is a pressing concern, particularly in arid and semi-arid regions, where water shortages are becoming increasingly severe. Rainwater harvesting (RWH) offers a promising solution to address these challenges. However, the process of selecting suitable RWH sites is complex. This paper introduces a comprehensive methodology that leverages various technologies and data sources to identify suitable RWH locations in the northern region of Iraq, considering both historical and future scenarios. The study employs remote sensing and geographic information systems to collect and process geospatial data, which are essential for the site selection process. AHP is utilized as a decision-making tool to assess and rank potential RWH locations based on multiple criteria, helping to prioritize the most suitable sites. The WLC approach is used to combine and weigh various factors, enabling a systematic evaluation of site suitability. To account for the uncertainty associated with future climate conditions, a stochastic weather generator is employed to simulate historical and future precipitation data for period (1980–2022) and (2031–2100). This ensures that the assessment considers changing climate patterns. Historical precipitation values ranged from 270 to 490 mm, while future projections indicate a decrease, with values varying from 255 to 390 mm. This suggests a potential reduction in available water resources due to climate change. The runoff for historical rainfall values ranged from 190 mm (poor) to 490 mm (very good). In the future projections, runoff values vary from 180 mm (very poor) to 390 mm (good). This analysis highlights the potential impact of reduced precipitation on water availability. There is a strong correlation between rainfall and runoff, with values of 95% for historical data and 98.83% for future projections. This indicates that changes in precipitation directly affect water runoff. The study incorporates several criteria in the model, including soil texture, historical and future rainfall data, land use/cover, slope, and drainage density. These criteria were selected based on the nature of the study region and dataset availability. The suitability zones are classified into four categories for both historical potential and future projections of RWH zones: very high suitability, covering approximately 8.2%. High suitability, encompassing around 22.6%. Moderate suitability, constituting about 37.4%. Low suitability, accounting for 31.8% of the study region. For the potential zones of RWH in the future projection, the distribution is as follows: very high suitability, approximately 6.1%. High suitability, around 18.3%. Moderate suitability, roughly 31.2%. Low suitability, making up about 44.4% of the study region. The research's findings have significant implications for sustainable water resource management in the northern region of Iraq. As climate change exacerbates water scarcity, identifying suitable RWH locations becomes crucial for ensuring water availability. This methodology, incorporating advanced technology and data sources, provides a valuable tool for addressing these challenges and enhancing the future of water management to face of climate change. However, more investigations and studies need to be conducted in near future in the study region. [ABSTRACT FROM AUTHOR]

Uncertainty sources in climate change impact studies can be addressed in two distinct phases, climate modelling and impact assessment. Generally, uncertainty from the climate modelling phase is the larger contributor and the impact phase uncertainty is sometimes marginalized. This paper aims to develop integrated uncertainty analysis approaches that deal with uncertainties at the impact level with all sources included. The existing uncertainty analysis method of Reliability Ensemble Averaging (REA) is modified for impact studies by bracketing the hydrological model parameter uncertainty. A novel probabilistic approach referred to as Bootstrapped Ensemble Uncertainty Modelling (BEUM) is also proposed for uncertainty analysis at the impact phase. Modified REA and BEUM are applied to streamflow projections of the Hulu Langat basin with uncertainties stemming from emission scenarios, general circulation models (GCM), downscaling method, and a hydrological model parametrization for a comprehensive integrated uncertainty assessment. The modified REA increased the reliability measure of each streamflow scenario and improved the uncertainty coverage percentage when compared with the original REA. The REA eliminated the projections of the less reliable GCMs, and therefore reduced uncertainty bands compared to the BEUM. The BEUM can create a large ensemble of streamflow projections through nonparametric bootstrapping of the limited impact scenarios and define the streamflow distribution for every quantile. Therefore, BEUM is able to encompass larger uncertainty intervals and explain the behavior of ensemble members through probabilistic distributions that can assist in decision making for climate change mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of climate change on the hydrological and meteorological parameters of the Navrood Watershed (a Caspian Hyrcanian forest watershed) in the north of Iran. Outputs of seven CMIP6 GCMs (Tmin, Tmax, and precipitation) under two scenarios, SSP2-4.5 and SSP5-8.5, were utilized. This study considered the historical period (1994–2014), the near future (2025–2049), the middle future (2050–2074), and the far future (2075–2099). The EDCDFm and MBA methods were used for bias correction of the outputs of GCMs and combining GCMs, respectively. The LARS-WG model was used for statistical downscaling. the runoff was calculated by the IHACRES model. Based on the results, the annual Tmin under SSP2-4.5 and SSP5-8.5 will increase as much as 1.04 °C and 1.25 °C in the near future, 1.55 °C and 2.48 °C in the middle future, and 2.09 °C and 4.11 °C in the far future, respectively. The annual Tmax under SSP2-4.5 and SSP5-8.5 will increase as much as 1.59 °C and 1.38 °C in the near future, 1.98 °C and 3.02 °C in the middle future, and 2.58 °C and 4.94 °C in the far future, respectively. The annual precipitation (PCP) under SSP2-4.5 and SSP5-8.5 will increase as much as 6.81% and 7.11% in the near future, 6.15% and 4.43% in the middle future, and 8.63% and 6.58% in the far future, respectively. Finally, the annual runoff under SSP2-4.5 and SSP5-8.5 will increase as much as 16.3% and 15.4% in the near future, 14.8% and 10.6% in the middle future, and 19.2% and 15.1% in the far future, respectively. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007–2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov–Smirnov test, the corrected Akaike criterion AICc, the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28–32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. [ABSTRACT FROM AUTHOR]

Drought is a major natural hazard that can have devastating impacts on regional agriculture, water supply, ecological environment and social economy. Extreme events, including droughts, are expected to be severer and more frequent because of climate warming. This paper presents an analysis of observed changes and future projections of meteorological drought in the Daqing River Basin (DRB), North China, based on the modified Standardized Precipitation Index (SPImod). Observed changes of meteorological drought are analyzed based on the precipitation series of 26 meteorological stations from 1971 to 2011, and future projections are made under two representative concentration pathways (RCPs 4.5 and 8.5) from 2021 to 2050. The result shows a change from wet to dry detected in most areas of the DRB in summer, winter and annual series. The drought risk is higher under the condition of "OR" and "AND" return period during the observation period, and the high-risk regions cover a large area. The drought risk from 2021 to 2050 will increase across the basin under RCP 4.5 and RCP 8.5, compared with which in the base period from 1971 to 2000. Most regions will be at a higher drought risk under the condition of "OR" return period, and the area with high-risk under RCP 4.5 is slightly larger than that under RCP 8.5. Under the condition of "AND" return period, the high-risk regions will cover the area from the northwest to the northeast, while the area of high-risk regions under RCP 8.5 will be larger than that under RCP 4.5. The result of this paper will be helpful for performing an efficient water resource management in the DRB under the impacts of global warming and climate change. [ABSTRACT FROM AUTHOR]

Climate change is a major concern the world over more so for a predominantly agrarian country like India. In this paper we analyze the time horizon dynamics of crop and climate variables at the regional level in India. We also analyze the co-movements of crop yields with temperature and rainfall to observe the coherence across heterogenous time horizons. We employ Bai-Perron structural break and Continuous wavelet transform methods on yearly data of seven crop yields and climate variables. Observed variables are analyzed from 1956 to 2010 for the un-divided state of Andhra Pradesh, India. Breakpoint analysis shows an increase of around 1.0° temperature with two observed break points. Rainfall depicts no systematic change with fluctuations being largely random. The framework of wavelets-based time–frequency analysis employed in this study captures climate and crop dynamics at heterogeneous time horizons, allowing one to study the impact of climate and crop yields at both short and longer time-horizons. Wavelet based coherence analysis exhibited significant co-movement between climatic and crop variables. Given shifts in climate patterns and subsequent shifts in co-movements across time horizons at the regional level, policy makers and crop scientists should design time specific and locally viable adaption and mitigation policies to tackle the impact of climate change on crops and livelihoods. [ABSTRACT FROM AUTHOR]

Climate change and urbanization are significantly magnifying flood hazard, leading to a greater vulnerability of urban concentrations. This paper investigates the impact of climate change on urban flooding using future projected rainfall data and a calibrated hydraulic model. Two urban watersheds in Delhi, India (the Qudesia Nallah catchment and the Jahangirpuri drain catchment) are considered to evaluate the climate change impact on urban flooding. Regional climate models (RCMs) are used to project future precipitation, which is then utilized by the hydraulic model to evaluate the impact on flooding. Climate data from three RCMs extracted from the Coordinated Regional Climate Downscaling Experiment (CORDEX) are used to study the impact of climate change for historical (1990–2016) and future scenario (Representative Concentration Pathway (RCP) 4.5, 2021–2100). The rainfall projections are fed as 2-, 5-, 10-, and 20-year return periods to a calibrated hydrodynamic Storm Water Management Model (SWMM). The results show that the flooded nodes vary between 2–6 and 12–43, respectively, in the Qudesia Nallah catchment and the Jahangirpuri drain catchment under present conditions but increase from 11 to 51 and 42 to 91, respectively, for future climate conditions. The results suggest that the risk of occurrence of flooding, duration, and frequency in the two study areas will increase in the future when compared to those under the present conditions. The results also indicate that the damage induced by the 20-year return period rainfall at the present time will likely be caused just by the 2-year return period in the future. This is due to the greater likelihood of rainfall extremes in the region. The potential flooding sites identified in this study will provide the urban municipalities with substantive information to perform ameliorative strategies. [ABSTRACT FROM AUTHOR]

Five downscaling techniques, namely the statistical downscaling model, the automated statistical downscaling method, the change factor (CF) method, the advanced CF method, the Weather generator (LarsWG5) method, are applied to the upstream basin of the Huaihe River. Changes in regional climate scenarios and hydrology variables are compared in future periods to investigate the uncertainty associated with the downscaling techniques. Paired-sample T test is applied to evaluation the significant of the difference of the means between the observed data and the downscaled data in the future. The Xinanjiang rainfall-runoff model is employed to simulate the rainfall-runoff relation. The results demonstrate that the downscaling techniques utilized herein predict an increased tendency in the future. The increases range of maximum temperature (T) is between 3.7 and 4.7 °C until the time period of 2070-2099 (2080s). While, the increases range of minimum temperature (T) is between 2.8 and 4.9 °C until 2080s. The research presented herein determined that there is an increase predicted for the peaks over threshold (discussed in the paper) and a decrease predicted for the peaks below the threshold (discussed in the paper) in the future, which illustrates that the temperature would rise gradually in the future. Precipitation changes are not as obvious as temperatures changes and tend to be influence by the season. Most downscaling techniques predict increases, and others indict decreases. The annual mean precipitation range changes between 3.2 and 53.3 %, and moreover, these changes vary from season to season. [ABSTRACT FROM AUTHOR]

It is an undisputed fact that weather risk increases over time due to climate change. However, qualification of this statement with regard to the type of weather risk and geographical location is needed. In this paper we compare alternative tests for trend detection and discuss their sensitivity. We use local t tests, change point tests and Mann-Kendall tests to analyze the trends of weather risk indices that are relevant from an agricultural viewpoint. Local test procedures offer more information about the timing and the kind of change in weather risk than global tests do. We also use quantile regression to analyze changes in the tails of weather index distributions. These methods are applied to temperature and rainfall based weather indices in three different climatic zones. Our results show that weather risk follows different patterns depending on the type of risk and the location. We also find differences in the sensitivity of the statistical test procedures. [ABSTRACT FROM AUTHOR]

The water-energy-food (WEF) Nexus encompasses complex and interdependent relations and its examination requires content-specific concepts and approaches. Managing, conserving and maximizing the potential of each component is a major global concern considering the many challenges to be faced in the 21st century. The aim of this study was to identify, in the literature, recommendations for public policy, research and development, and practices for the WEF Nexus, aimed at promoting sustainable development considering stochastic and risk elements. In this regard, this paper presents a literature review of the contribution scientific studies have made toward better understanding the importance of the WEF Nexus in the context of sustainable development. Research indicates that the WEF Nexus cannot be discussed as independent sectors, highlighting the need for integrated policies and inter-sectoral and international cooperation to promote sustainable development. Therefore, the effective management of the WEF Nexus requires science-based data using risk and stochastic elements to assist policy and decision-making. Thus, in a situation of rapid global changes, decision-making processes for this Nexus must be assisted by multidisciplinary, multi-stakeholder and cross-sectoral approaches, aimed at avoiding the unintended effects of a single sector approach (e.g., energy policies stimulating hydroelectricity production need to consider factors affecting conservation and food production). With regard to the effects of climate change on the WEF Nexus, risk and stochastic elements must be considered when developing a science-based model for the sustainable management of WEF resources. [ABSTRACT FROM AUTHOR]

Stochastic point processes for rainfall are known to be able to preserve the temporal variability of rainfall on several levels of aggregation (e.g. hourly, daily), especially when the cluster approach is used. One major assumption in most of the applications todate is the stationarity of the rainfall properties in time, which must be reconsidered under a climate change hypothesis. Here, we propose new theoretical developments of a Poisson-based model with cluster, namely the Neyman-Scott Rectangular Pulses Model, which treats storm frequency as a nonstationary function. In this paper, storm frequency is modelled as a linear function of time in order to reproduce an increase (or decrease) of the mean annual precipitation. The basic theory is reconsidered and the moments are derived up to the third order. Then, a calibration method based on the generalized method of moments is proposed and discussed. An application to a rainfall time series from Uccle illustrates how this model can reproduce a trend for the average rainfall. This work opens new avenues for future developments on transient stochastic rainfall models and highlights the major challenges linked to this approach. [ABSTRACT FROM AUTHOR]

Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions. [ABSTRACT FROM AUTHOR]

In order to explore the evolvement mechanism of hydrological elements under climate change and the interaction impacts, the hydro-meteorological elements, like temperature, precipitation, runoff, and evaporation, are studied by statistical analysis method and cause analysis approach in this paper. Initially, in terms of climate change across Anhui province, the average temperature data from 1960 to 2009 at 15 meteorological stations are selected from China Meteorological Data Sharing Network to analyze the spatio-temporal variation of temperature characteristics. Secondly, from the perspective of Xin'anjiang Basin, evolution rules of hydro-meteorological elements are studied through deviation variation of every 10 years and the long-term development trend from 1981 to 2010 at 9 hydrological stations. And the influence of climate change on runoff is analyzed according to the principle of runoff formation, and the response of runoff to precipitation and temperature is predicted with the simulating future climate change by climate models. The results show that, through statistics, the average temperature in Anhui province is in a fluctuating downward state from 1960 to 1990. After 1990, the uptrend is significant. In Xin'anjiang Basin, the annual precipitation declines overally, especially at some stations it is obvious. The variation of runoff is in a downtrend consistent with precipitation, but it is not noticeable, taken as a whole. The average evaporation changes unremarkably, except in summer and winter. Under three scenarios of A1B, A2 and B1, it is assumed that temperature, precipitation and runoff are all increasing in the future. Through cause analysis, these mainly seem to be affected by the circulation of weather background and sea surface temperature. Based on the analysis of hydro-meteorological elements under climate change and the forecasting of their future changes, the development of regional water resources can be grasped which is of great significance for human interests to water supply scheme, water conservancy project planning, and the economic-social sustainablility. [ABSTRACT FROM AUTHOR]

Current drought studies have already used the concept of equal ensemble streamflow prediction, but lack other kinds of ensemble prediction models for comparison of drought probabilistic prediction. Therefore, in this paper, an equal ensemble drought prediction (EEDP) model, a weighted ensemble drought prediction (WEDP) model, and a conditional ensemble drought prediction (CEDP) model were established and elaborately compared for drought prediction. The EEDP model directly uses the concept of ensemble streamflow prediction and assigns an equal weight to each ensemble member. The WEDP model assigns weights to ensemble members given the similarity of climate indexes between the historical and forecast year. The CEDP model considers the climate information as predictors and generates conditional ensembles of drought given the climate indexes. The verification of the proposed models was carried out using 26 meteorological stations in Jiangxi province (China), using the standard precipitation index to depict meteorological drought conditions in October, November, and December. The results show that compared to the EEDP model, the WEDP and CEDP models remarkably improved the accuracy and reduced the uncertainty of the drought prediction, indicating that a model considering climate information is much better than a purely statistical model. Meanwhile, the CEDP model outperformed the WEDP model in parameter estimation and accuracy. The prediction in southern Jiangxi province gets the best results compared with other regions. Our results provide a basis for drought planning and management in Jiangxi province. [ABSTRACT FROM AUTHOR]

Due to the widespread uncertainties in agricultural water resources systems and climate change projections, the traditional optimization methods for agricultural water management may have difficulties in generating rational and effective optimal decisions. In order to get optimal future agricultural water allocation schemes for arid areas with consideration of climate change conditions, the model framework established in this paper integrates a statistical downscaling model, back propagation neural networks, and an evapotranspiration model (the Hargreaves model) with inexact irrigation water allocation optimization model under future climate change scenarios. The model framework, which integrates simulation models and optimization models, considers the interactions and uncertainties of parameters, thereby reflecting the realities more accurately. It is applied to the Yingke Irrigation Area in the midstream area of the Heihe River Basin in Zhangye city, Gansu Province, northwest China. Then, water allocation schemes in planning year (2047) under multiple future Representative Concentration Pathways (RCP) scenarios and the status quo (2016) are compared, in order to evaluate the practicability of generated water allocation schemes. The results show that the water shortages of economic crops are improved compared with the status quo under all RCP scenarios while those of the grain crops present opposite results. Meanwhile, the economic benefits decrease from the status quo to planning year under all future scenarios. This phenomenon is directly related to the amount of irrigation water allocation and is indirectly related to the changes of meteorological conditions. The model framework can reveal the regular pattern of hydro-meteorological elements with the impact of climate change. Meanwhile, it can generate irrigation water allocation schemes under various RCPs scenarios which could provide valuable decision support for water resources managers. [ABSTRACT FROM AUTHOR]

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7-8):2261-2278. 10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15-32. 10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability. [ABSTRACT FROM AUTHOR]

The estimation of long-term sea level variability is of primary importance for a climate change assessment. Despite the value of the subject, no scientific consensus has yet been reached on the existing acceleration in observed values. The existence of this acceleration is crucial for coastal protection planning purposes. The absence of the acceleration would enhance the debate on the general validity of current future projections. Methodologically, the evaluation of the acceleration is a controversial and still open discussion, reported in a number of review articles, which illustrate the state-of-art in the field of sea level research. In the present paper, the well-proven direct scaling analysis approach is proposed in order to describe the long-term sea level variability at 12 worldwide-selected tide gauge stations. For each of the stations, it has been shown that the long-term sea level variability exhibits a trimodal scaling behaviour, which can be modelled by a power law with three different pairs of shape and scale parameters. Compared to alternative methods in literature, which take into account multiple correlated factors, this simple method allows to reduce the uncertainties on the sea level rise parameters estimation. [ABSTRACT FROM AUTHOR]

The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO2eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO2eq./kWh and 167.70 Gg C year−1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions. [ABSTRACT FROM AUTHOR]

In order to understand the behavior of the glaciers, their mass balance should be studied. The loss of water produced by melting, known as glacier discharge, is one of the components of this mass balance. In this paper, a vine copula structure is proposed to model the multivariate and nonlinear dependence among the glacier discharge and other related meteorological variables such as temperature, humidity, solar radiation and precipitation. The multivariate distribution of these variables is expressed as a mixture of four components according to the presence or not of positive discharge and/or positive precipitation. Then, each of the four subgroups is modelled with a vine copula. The conditional probability of zero discharge for given meteorological conditions is obtained from the proposed joint distribution. Moreover, the structure of the vine copula allows us to derive the conditional distribution of the glacier discharge for the given meteorological conditions. Three different prediction methods for the values of the discharge are used and compared. The proposed methodology is applied to a large database collected since 2002 by the GLACKMA association from a measurement station located in the King George Island in the Antarctica. Seasonal effects are included by using different parameters for each season. We have found that the proposed vine copula model outperforms a previous work where we only used the temperature to predict the glacier discharge using a time-varying bivariate copula. [ABSTRACT FROM AUTHOR]

China is exposed to a wide range of natural hazards, and disaster losses have escalated over the past decade. Owing to the pressure from natural disasters, along with changes in climate, social conditions, and regional environment, assessment of social vulnerability (SV) to natural hazards has become increasingly urgent for risk management and sustainable development in China. This paper presents a new method for quantifying SV based on the projection pursuit cluster (PPC) model. A reference social vulnerability index (SVI) at the county level was created for the Yangtze River Delta area in China for 1995, 2000, 2005, and 2009. The result of social vulnerability assessment was validated using data of actual losses from natural disasters. The primary findings are as follows: (i) In the study area, the major factors that impact SVI are regional per capita GDP and per capita income. (ii) The study area was more vulnerable in 1995 than in later years. SV of the whole region had decreased over the study period. (iii) Most part of Shanghai and the southeast part of Jiangsu Province had been the least vulnerable within the region. From this least vulnerable zone to the periphery of the region, the situation deteriorated. The highest SVI values in all evaluated years were found in the northern, western, or southern tips of the Yangtze River Delta. [ABSTRACT FROM AUTHOR]

Understanding low flow variability is critical for assessing water quality and health of riverine ecosystems in a river basin. Low flows are dependent on human water abstraction as well as the climate variability. This paper investigates the changing nature of low flows and their association with large-scale climate variability for different watersheds in the State of Texas, USA. For this purpose, we employed trend, wavelet analysis and linear as well as nonlinear correlations to identify important changes in low flow characteristics for three stream-gauging stations selected from different (i.e. Brazos, Colorado and Trinity) river basins located in Texas for the time period of 1916-1959 and 1960-2003. We also investigated the teleconnections between low flow variables and the large-scale climate indices (NINO 3.4, SOI and PDO) using cross wavelet analysis as well as their linear and non-linear correlation relationship. Our results indicated that the low flow magnitudes have shown considerable different characteristics for selected river basins during two separate time periods (1916-1959 and 1960-2003). Based on cross wavelet analysis, we identified that the low flows in selected stations of Colorado and Trinity River basins are likely to be influenced by all three large-scale climate indices. In addition to that, we identified that low flows are more nonlinearly associated with climate indices. Among the selected River basins, the stronger association between low flows and large scale climate indices are observed for Trinity River basin. The results from this study can help in better understanding of low flow hydrology and their potential relationship with large scale indices. [ABSTRACT FROM AUTHOR]

Floods have changed in a complex manner, triggered by the changing environment (i.e., intensified human activities and global warming). Hence, for better flood control and mitigation in the future, bivariate frequency analysis of flood and extreme precipitation events is of great necessity to be performed within the context of changing environment. Given this, in this paper, the Pettitt test and wavelet coherence transform analysis are used in combination to identify the period with transformed flood-generating mechanism. Subsequently, the primary and secondary return periods of annual maximum flood (AMF) discharge and extreme precipitation (Pr) during the identified period are derived based on the copula. Meanwhile, the conditional probability of occurring different flood discharge magnitudes under various extreme precipitation scenarios are estimated using the joint dependence structure between AMF and Pr. Moreover, Monte Carlo-based algorithm is performed to evaluate the uncertainties of the above copula-based analyses robustly. Two catchments located on the Loess plateau are selected as study regions, which are Weihe River Basin (WRB) and Jinghe River Basin (JRB). Results indicate that: (1) the 1994-2014 and 1981-2014 are identified as periods with transformed flood-generating mechanism in the WRB and JRB, respectively; (2) the primary and secondary return periods for AMF and Pr are examined. Furthermore, chance of occurring different AMF under varying Pr scenarios also be elucidated according to the joint distribution of AMF and Pr. Despite these, one thing to notice is that the associate uncertainties are considerable, thus greatly challenges measures of future flood mitigation. Results of this study offer technical reference for copula-based frequency analysis under changing environment at regional and global scales. [ABSTRACT FROM AUTHOR]

The analysis of phenological changes in vegetation is essential for the assessment of the response and adaptation of ecosystems/agro-ecosystems to climate change. This study analyses spatial and temporal changes in phenological events (phenophases) and in the climatic growing season in southern and south-eastern Romania, based on mean monthly temperature values recorded between 1961 and 2010 at 24 weather stations, spread out uniformly in the study area. By using the histophenogram method for extracting the mean phenophases length (eight in total, i.e. growing season onset, budding-leafing, flowering, fruiting, maturing, dissemination, start of leaf loss and end of leaf loss) and that of the overall growing season, this paper aims to analyse current phenological changes (in three periods, 1991-2000, 2001-2010 and 1991-2010) in relation to 1961-1990, which is the reference interval in various global climatic studies. Following the analysis of the theoretical phenology length, based on the temperature thresholds (between 5 and 25 °C) that differentiate biological cycles of vegetation, results showed an overall increase in phenological activity (especially in the past decade, 2001-2010), except for two phenophases (fruiting and dissemination), for which largely dominant length decreases were identified. Quantitatively, increases (and null changes, in a few cases) in phenological length generally range between 0-5 days/0-10% (e.g. in the budding-leafing phenophase) and 11-15 days/10.1-20% (maturing), considering the absolute changes or percentages in the three periods, compared to the reference interval 1961-1990. For the most part, the current decrease in the fruiting and dissemination phenophases falls in the interval −1 to −5 days/−10.1 to −20%, compared to the reference period. At the same time, it was noticed that the entire growing season has been expanding, especially in the past decade, when numerous increase instances of up to 15 days were recorded, corresponding to a dominant interval of 0-10% from the period 1961-1990. The results can be particularly useful for adapting the different types of crops to future climate changes, considering that the study area has a high agro-ecological importance. [ABSTRACT FROM AUTHOR]

Water reuse is a viable option to increase urban water supply, especially under new realities of climate change and increasing anthropogenic activities. A sustainable water reuse application should be cost-effective and have acceptable health risk to consumers. Water reuse application evaluation is complex because data acquisitions are usually associated with the problems of uncertainty, hesitancy, and parameterization. In this paper, a generalized intuitionistic fuzzy soft set (GIFSS)-based decision support framework is proposed to provide an effective approach to describe uncertainty and hesitancy in an intuitionistic fuzzy number. In addition, the modified measures of comparison and similarity are proposed to compare water reuse applications. Then, the proposed framework is applied to the City of Penticton (British Columbia, Canada) to evaluate seven water reuse applications. The evaluation results show that the applications of garden flower watering and public parks watering are the most preferred alternatives, which are consistent with the existing practice in the city. Furthermore, the results are highly affected by the generalized parameter and the weights of evaluation criteria. Both the comparison measure-based and similarity measure-based evaluations within the same GIFSS-based framework produce consistent results, indicating an applicable and efficient methodology. [ABSTRACT FROM AUTHOR]

Sea-level rise, as a result of global warming, may lead to more natural disasters in coastal regions where there are substantial aggregations of population and property. Thus, this paper focuses on the impact of sea-level rise on the recurrence periods of extreme water levels fitted using the Pearson type III (P-III) model. Current extreme water levels are calculated using observational data, including astronomical high tides and storm surges, while future extreme water levels are determined by superposing scenario data of sea-level rise onto current extreme water levels. On the basis of a case study using data from Shandong Province, China, results indicated that sea-level rise would significantly shorten the recurrence periods of extreme water levels, especially under higher representative concentration pathway (RCP) scenarios. Results showed that by the middle of the century, 100-year current extreme water levels for all stations would translate into once in 15-30 years under RCP 2.6, and once in ten to 25 years under RCP 8.5. Most seriously, the currently low probability event of a 1000-year recurrence would become common, occurring nearly every 10 years by 2100, based on projections under RCP 8.5. Therefore, according to this study, corresponding risk to coastlines could well be increase in future, as the recurrence periods of extreme water levels would be shortened with climate change. [ABSTRACT FROM AUTHOR]

Ecological security is a fundamental component of regional security that has drawn increased attention worldwide over the past two decades. This paper presents a novel approach to assess the status of land ecological security (LES) in Shanghai, China from 1992 to 2011 using spatial variables and a logistic regression model. The LES status of 1745 points within the study area in 1992, 2001 and 2011 was sampled systematically using a 2 × 2 km grid sample frame and evaluated based on an expert method with ten experts from five fields. A five-point Likert scale was used to score the LES status as very insecure, insecure, neutral, secure or very secure. We identified several explanatory factors to the LES status, including distance-based variables describing the proximities to urban center, developed areas and sources of pollution, as well as variables regarding the density of built-up areas and the mean value of normalized difference vegetation index. A logistic regression model was used to quantify the relationship between LES scores and the spatial variables at each of the three time points, resulting in a series of maps illustrating the LES patterns of Shanghai in 1992, 2001 and 2011. The results show that LES is either very insecure or insecure at the center of Shanghai and at its district centers, and the LES of the entire Shanghai municipality has deteriorated significantly from 1992 to 2011. This research contributes to an enhanced understanding of LES changes resulting from rapid urbanization and industrialization of the Shanghai municipality and provides a methodological framework to study LES elsewhere. [ABSTRACT FROM AUTHOR]

Coastal flood risk will likely increase in the future due to urban development, sea-level rise, and potential change of storm surge climatology, but the latter has seldom been considered in flood risk analysis. We propose an integrated dynamic risk analysis for flooding task (iDraft) framework to assess coastal flood risk at regional scales, considering integrated dynamic effects of storm climatology change, sea-level rise, and coastal development. The framework is composed of two components: a modeling scheme to collect and combine necessary physical information and a formal, Poisson-based theoretical scheme to derive various risk measures of interest. Time-varying risk metrics such as the return period of various damage levels and the mean and variance of annual damage are derived analytically. The mean of the present value of future losses (PVL) is also obtained analytically in three ways. Monte Carlo (MC) methods are then developed to estimate these risk metrics and also the probability distribution of PVL. The analytical and MC methods are theoretically and numerically consistent. A case study is performed for New York City (NYC). It is found that the impact of population growth and coastal development on future flood risk is relatively small for NYC, sea-level rise will significantly increase the damage risk, and storm climatology change can also increase the risk and uncertainty. The joint effect of all three dynamic factors is possibly a dramatic increase of the risk over the twenty-first century and a significant shift of the probability distribution of the PVL towards high values. In a companion paper (Part II), we extend the iDraft to perform probabilistic benefit-cost analysis for various flood mitigation strategies proposed for NYC to avert the potential impact of climate change. [ABSTRACT FROM AUTHOR]

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

The present study attempts to investigate potential impacts of climate change on floods frequency in Bazoft Basin which is located in central part of Iran. A combination of four general circulation models is used through a weighting approach to assess uncertainty in the climate projections. LARS-WG model is applied to downscale large scale atmospheric data to local stations. The resulting data is in turn used as input of the hydrological model Water and Energy Transfer between Soil, plants and atmosphere (WetSpa) to simulate runoff for present (1971-2000), near future (2020-2049) and far future (2071-2100) conditions. Results demonstrate good performance of both WetSpa and LARS-WG models. In addition in this paper instantaneous peak flow (IPF) is estimated using some empirical equations including Fuller, Sangal and Fill-Steiner methods. Comparison of estimated and observed IPF shows that Fill-Steiner is better than other methods. Then different probability distribution functions are fit to IPF series. Results of flood frequency analysis indicate that Pearson III is the best distribution fitted to IPF data. It is also indicated that flood magnitude will decrease in future for all return periods. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel functional clustering method, the Bagging Voronoi K-Medoid Aligment (BVKMA) algorithm, which simultaneously clusters and aligns spatially dependent curves. It is a nonparametric statistical method that does not rely on distributional or dependency structure assumptions. The method is motivated by and applied to varved (annually laminated) sediment data from lake Kassjön in northern Sweden, aiming to infer on past environmental and climate changes. The resulting clusters and their time dynamics show great potential for seasonal climate interpretation, in particular for winter climate changes. [ABSTRACT FROM AUTHOR]

One of the primary challenges of current times is climate change, which has negative effects on any nation’s ability to pursue sustainable development. Key environmental and human living circumstances are impacted by climate change, which also affects the foundation for social and economic development. In this research, the key factors for climate change are obtained from the literature survey. Also, the integrated technique of the linear diophantine DEMATEL approach is initiated. The decision-making trial and evaluation laboratory (DEMATEL) has been widely used to solve MCDM problems. In addition, the Linear Diophantine Fuzzy Set (LDFS\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\mathscr{L}\mathscr{D}\mathscr{F}\mathscr{S}$$\end{document}) has several advantages over other prevailing fuzzy sets, which permits the consideration of input variables that contain elements of uncertainty and ambiguity. The primary intent of this paper is to utilize our proposed approach to prioritize the criteria for climate change. As a result, the identification of the main criteria for climate change will be able to reduce future disasters related to climate change and lead to a sustainable future. [ABSTRACT FROM AUTHOR]

Salinization threatens the viability of water resources and is common in many important inland freshwater lakes worldwide, especially in arid and semi-arid areas. Bosten Lake is a typical inland freshwater lake that has evolved into a subsaline lake and is located in the arid region of Northwest China. The water resources of Bosten Lake are important for supplying regional drinking water and agricultural irrigation and for economic development. In this study, changes in salinity with time and space were analyzed in Bosten Lake. Overall, the salinity increased from 0.39 g/L in 1958 to 1.87 g/L in 1987, reaching its highest value in 1987. After 1987, the salinity decreased to 1.17 g/L in 2003 and increased to 1.45 g/L in 2010. Increased salinity adversely affects aquatic lake systems, regional eco-environments and water resource use, and has become a serious environmental problem in Bosten Lake. Thus, the causes of increasing salinity are discussed in this paper. Overall, the influences of climate variations and human activities resulted in the salinization of the lake. Understanding the salinization processes in Bosten Lake can be useful for implementing actions that improve water quality and water resource use in the lake. [ABSTRACT FROM AUTHOR]

Climate change in North China would result in significant changes in temperature, precipitation and their spatial/temporal distributions. Consequently, these induced changes will have profound effects on the hydrological cycle and water resources in both agricultural and natural ecosystems. Panjiakou reservoir in the middle Luanhe River basin-a tributary of the Haihe River basin-is one of the important sources of water for industrial and agricultural development in Beijing, Tianjin and Hebei province, China. Any significant change in the magnitude and/or timing of runoff from the reservoir induced by changes in climatic variables would have significant implication for the economic prosperity in North China. This paper investigates the impacts of climate change on hydrological processes in the Luanhe River basin as follows. Firstly, spatial and temporal patterns of precipitation, temperature and runoff at both annual and seasonal scales from 1957 to 2000 in the Luanhe River basin are analyzed using Mann-Kendall trend analysis, linear regression methods and inverse distance weighted interpolation. For the impact study, four Global Climate Models (GCMs) (named CSIRO, HadCM3, CNRM and GFDL) were used to produce precipitation and temperature data under A2 scenario by mean of a widely used quantile-quantile transformation. Projected meteorological variables were used to force a two-parameter hydrologic model to simulate the hydrological response to climate change in the future (2021-2050). Moreover, a sensitivity analysis is conducted to assess how precipitation and temperature affect the runoff. Results suggested that most part of the Luanhe River basin was dominated by significant increasing trends of temperature and no significant trends of precipitation in annual and seasonal scale during the past decades. Annual, spring and autumn runoffs present significant decreasing trends in the Panjiakou reservoir basin. Meanwhile, runoff is more strongly related to precipitation than to temperature. All GCMs projected precipitation and temperature series after bias correction indicated increasing temperature and increasing precipitation trends for the period 2021-2050 except that CNRM showed a slight decreasing trend in precipitation. Great enhancements can be found in projected runoff except CNRM by driving the two-parameter water balance model. The study provides valuable information on the assessment of the impact of the climate change on water resources in the Luanhe River basin as well for allocating and designing water resources projects. [ABSTRACT FROM AUTHOR]

China's urban environments are particularly vulnerable to flooding due to climate change and rapid urbanization. Study of the urban flood risk analysis has significantly increased over the past decade, and this paper therefore reviews the main results (i.e. theoretical basis, methods, techniques, case studies) obtained in the literature from China. We focus on the following topics: (1) urban flood hazard analysis, (2) exposure and vulnerability analysis, and (3) urban flood risk assessment. Recent advances made in the research area are presented with suggestions for further research to improve the availability and reliability of urban flood risk analysis. [ABSTRACT FROM AUTHOR]

Existing models of post-fire erosion have focused primarily on using empirical or deterministic approaches to predict the magnitude of response from catchments given some initial rainfall and burn conditions. These models are concerned with reducing uncertainties associated with hydro-geomorphic transfer processes and typically operate at event timescales. There have been relatively few attempts at modelling the stochastic interplay between fire disturbance and rainfall as factors which determine the frequency and severity with which catchments are conditioned (or primed) for a hazardous event. This process is sensitive to non-stationarity in fire and rainfall regime parameters and therefore suitable for evaluating the effects of climate change and strategic fire management on hydro-geomorphic hazards from burnt areas. In this paper we ask the question, 'What is the first-order effect of climate change on the interaction between fire disturbance and storms?' The aim is to isolate the effects of fire and rainfall regimes on the frequency of extreme erosion events. Fire disturbance and storms are represented as independent stochastic processes with properties of spatial extent, temporal duration, and frequency of occurrence, and used in a germ-grain model to quantify the annual area affected by extreme erosion events due to the intersection of fire disturbance and storms. The model indicates that the frequency of extreme erosion events will increase as a result of climate change, although regions with frequent storms were most sensitive. [ABSTRACT FROM AUTHOR]

Water resources are influenced by various factors such as weather, topography, geology, and environment. Therefore, there are many difficulties in evaluating and analyzing water resources for the future under climate change. In this paper, we consider climate, land cover and water demand as the most critical factors affecting change in future water resources. We subsequently introduce the procedures and methods employed to quantitatively evaluate the influence of each factor on the change in future water resources. In order to consider the change in land cover, we apply the Multi-Regression approach from the cellular automata-Markov Chain technique using two independent variables, temperature and rainfall. In order to estimate the variation of the future runoff due to climate change, the data of the SRES A2 climate change scenario were entered in the SLURP model to simulate a total of 70 years, 2021-2090, of future runoff in the Han River basin in Korea. However, since a significant amount of uncertainties are involved in predicting the future runoff due to climate change, 50 sets of daily precipitation data from the climate change scenario were generated and used for the SLURP model to forecast 50 sets of future daily runoff. This process was used to minimize the uncertainty that may occur when the prediction process is performed. For future water balance analysis, the future water demand was divided into low demand, medium demand and high demand categories. The three water demand scenarios and the 50 daily runoff scenarios were combined to form 150 sets of input data. The monthly water balance within the Han River basin was then calculated using this data and the Korean version of Water Evaluation and Planning System model. As a result, the future volume of water scarcity of the Han River basin was predicted to increase in the long term. It is mostly due to the monthly shift in the runoff characteristic, rather than the change in runoff volume resulting from climate change. [ABSTRACT FROM AUTHOR]

The risk of climate change and international market fluctuations complicate crop production. Wheat is considered one of the most strategic products in food security. Dryland farming of wheat is prevalent in many parts of the world, and it occupies a large part of the cultivated land. However, its performance is highly dependent on weather conditions and changes. Yield prediction models could be used for planning purposes when dealing with changes in yield. This study analyzed the effectiveness of the least absolute shrinkage and selection operator (LASSO) model in selecting variables for predicting dryland wheat yield in southwestern Iran. The model was used with 45 weather-based variables across annual, seasonal, and monthly time frames. The results showed that temperature, evaporation, and extreme temperatures followed by radiation and precipitation variables categories, are effective meteorological variables in estimating dryland farming wheat yield in the study area. Monthly timescale could estimate yield with minimum error compared to other timescales. However, considering all selected variables regardless of their timescale (total) results, the best estimation in most districts with the model's R2 and normalized root mean square error (NRMSE) varied between 57.98–99.50 and 1.46–21.94, respectively. Therefore, the LASSO regression could be used reliably for each district considering the most effective meteorological parameters in that region for accurate decision-makers policies. [ABSTRACT FROM AUTHOR]

Mankind is currently facing a crucial challenge in terms of climate change which may result in severe drought in many areas across the world. The present study analysed the joint return periods of meteorological drought events for historical and future projections across the Vidarbha region of India. To describe the drought occurrences in the area, the study used the standardized precipitation evapotranspiration index. The properties of each drought event, viz., duration, severity, and peak, were identified to model the multivariate drought risks. Several copula families were evaluated using statistical tests for joint dependency modelling of drought properties. The obtained joint distribution and selected univariate marginals are further used for estimating trivariate return periods for the historical and future climate change scenarios. The drought properties exhibited good interdependency justifying the use of a trivariate framework. The observed historical data of rainfall, maximum and minimum temperature were collected from the India Meteorological Department. The latest future projections of climate variables from coupled model intercomparison project phase 6 (CMIP6) datasets were acquired from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) for six general circulation models. The copula-based trivariate frequency analysis was performed to compare the return periods associated with 'moderate' and 'above moderate drought' (i.e., combination of severe and extreme drought) events for the reference period (1981–2020) and future periods (2021–2100) under two SSP scenarios, SSP2-4.5 and SSP5-8.5. The 'AND' condition return period showed high drought risk during historical period covering a larger area. The findings of the study suggested that for 'AND' condition the trivariate frequency of above moderate droughts would increase in the future across Vidarbha region. The analysis showed that during near future more area will be under the risk of above moderate drought conditions compared to the far future, and the high-risk zones will be more under SSP5-8.5 than SSP2-4.5 scenario. The frequency of moderate drought episodes will be higher than the above moderate drought events, but the percentage area in the high-risk zone will be lower. The 'OR' condition return period projected alleviation of drought risk in the future, while the far future projected more area under drought risk compared to near future for the SSP scenarios. [ABSTRACT FROM AUTHOR]

Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from 'Semi-arid' towards 'Arid' class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions ('Humid' to 'Sub-humid'). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions. [ABSTRACT FROM AUTHOR]

Interactions and feedback between the atmosphere, oceans, and land lead to natural climatic variations responsible for severe weather events such as Extreme Precipitation (EP). Understanding the lagged/delayed effect of teleconnections would improve the predictive capabilities, especially in a complex, non-linear, and non-stationary hydro-climatic system. The objective of the study is two-fold: (i) selection of lagged climate indices to improve the efficacy of the NS model using Granger Causality and (ii) non-stationary modeling of extreme precipitation. This study proposes a new Generalized Regression Neural Network based Non-linear Granger (NL-GRNN) causality approach. The climate indices with their optimal time-lags having significant Granger causality are selected for modeling EP in a Non-Stationary Generalized Extreme Value (NSGEV) framework. Seventeen climate indices are considered and applied for two regions of different climatic conditions, namely Chennai, India, and San Diego, North America. Results show that NL-GRNN based NSGEV models are found to be computationally efficient and show better performance compared to the linear- and artificial neural network based Granger causality approaches. It is observed that the ENSO-related indices and the northern hemisphere climate variability modes such as AO, PNA, and NAO dominantly influence EP over Chennai and San Diego, respectively. The outcomes of the study will be useful in planning and design of water infrastructure, disaster management and disease outbreak. [ABSTRACT FROM AUTHOR]

The southwestern foothills of the Himalayan Mountain range have been experiencing a surge of catastrophic landslides in the last two decades, as a tragic result of the adverse effects of climate change. This research is about the landslide risk assessment (LRA) which has not been explored yet in the landslide-prone district Muzaffarabad, Pakistan. Landslide susceptibility (spatial probability) was analyzed using random forest model while landslide hazard (temporal probability) was analyzed using Poisson probability model. A random forest-based landslide susceptibility map depicts an accuracy of 0.90. A landslide hazard map was generated by multiplying the temporal probability with the spatial probability and classified as well. Semi-quantitative danger pixels and a fuzzy set theory approach for LRA have been adopted to estimate future landslide risks in the region. The pixel-based LRA approach indicates that 14, 18 and 20 km2 area of settlement while, the fuzzy set theory-based approach depicts that 15, 19 and 21 km2 area of the settlement are under very high landslide risk for 1-, 3-, and 5- year return period respectively. Both approaches produced risk maps that designated various risk zones with almost the same area coverage and results. The LRA maps were classified into five classes including very high (1.99%, 2.33%, 2.80%), high (2.16%, 2.53%, 3.04%), moderate (8.02%, 9.79%, 11.22%), low (17.76%, 22.94%, 23.20%), and very low (70.08%, 62.40%, 59.74%) risk zones for 1, 3 and 5 years return period respectively. This research will assist planners and scientists in developing high-precision management strategies for landslide-affected natural resources, especially in the context of the increasing impact of geomorphic hazards on climate change. [ABSTRACT FROM AUTHOR]

Based on the daily data of relative humidity from 23 meteorological stations in the Tarim River Basin of northwest China during the period from 1961 to 2010, this paper analyzed the multifractal and long memory property of humidity process. Main findings are as follows: (1) The processes present scaling and multifractal property. (2) The left-skewed multifractal spectrum f( α) indicates that the time series of relative humidity is predominated by small fluctuations. (3) There exists long memory with the δ ∈ (0, 0.5) in the processes, except for Kalpin and Aksu's exhibiting non-stationary long memory with the parameter δ being 0.67 and 0.69 respectively. (4) We found that on the whole, the degree of multifractality exhibits a strengthening trend with the longitude and latitude increasing, but decreasing trend with elevation rising; For length of long memory, we investigated that on the whole, the δ values increased with the longitude and latitude increasing, which indicates that the bigger the longitude and latitude is, the longer the memory of humidity process is, but the higher the elevation is, the shorter the memory of humidity process is. [ABSTRACT FROM AUTHOR]

Most studies on separating the effects of climate change and human activities on runoff are mainly conducted at an annual scale with few analyses over different time scales, which is especially essential for regional water resources management. This paper investigates the impacts of climate change and human activities on runoff changes at annual, seasonal and monthly time scales in the Zhang River basin in North China. Firstly, the changing trends and inflection point are analyzed for hydro-climatic series over different time scales. Then the hydrological modeling based method and sensitivity based method are used to separate the effects. The results show that the effect of climate change is stronger than that of human activities on annual runoff changes. However, the driving factors on runoff are different at seasonal scale. In the wet season, the effect of human activities on runoff, accounting for 57 %, is stronger than that of climate change, while in the dry season climate change is the dominant factor for runoff reduction and the contribution rate is 72 %. Furthermore, the effects of climate change and human activities on monthly runoff changes are various in different months. The separated effects over different time scales in this study may provide more scientific basis for the water resources adaptive management over different time scales in this basin. [ABSTRACT FROM AUTHOR]

In the wake of global and regional climate change and heightened human activities, runoff from some rivers in the world, especially in the arid and semi-arid regions, has significantly decreased. To reveal the varying characteristics leading to the change in runoff, detecting the influencing factors has been important in recent scientific discussions for water resources management in drainage basins. In this paper, an investigation into attributing the runoff response to climate change and human activities were conducted in two catchments (Wushan and Shetang), situated in the upper reaches of Weihe River in China. Prior to the identification of the factors that influenced runoff changes, the Mann-Kendall test was adopted to identify the trends in hydro-climate series. Also, change-points in the annual runoff were detected through Pettitt's test and the precipitation-runoff double cumulative curve method. It is found that both catchments presented significant negative trend in annual runoff and the detected change-point in runoff occurs in 1993. Hence, the pre-change period and post-change period are defined before and after 1993, respectively. Then, runoff response to climate change and human activities was quantitatively evaluated on the basis of hydrologic sensitivity analysis and hydrologic model simulation. They provided similar estimates of the percentage change in mean annual runoff for the post-change period over the considered catchments. It is found that the decline in annual runoff over both catchments can be mainly attributed to the human activities, the reduction percentages due to human activities range from 59 to 77 %. The results of this study can provide a reference for the development, utilization and management of the regional water resources and ecological environment protection. [ABSTRACT FROM AUTHOR]

Reliable projections of extremes at finer spatial scales are important in assessing the potential impacts of climate change on societal and natural systems, particularly for elevated and cold regions in the Tibetan Plateau. This paper presents future projections of extremes of daily precipitation and temperature, under different future scenarios in the headwater catchment of Yellow River basin over the 21st century, using the statistical downscaling model (SDSM). The results indicate that: (1) although the mean temperature was simulated perfectly, followed by monthly pan evaporation, the skill scores in simulating extreme indices of precipitation are inadequate; (2) The inter-annual variabilities for most extreme indices were underestimated, although the model could reproduce the extreme temperatures well. In fact, the simulation of extreme indices for precipitation and evaporation were not satisfactory in many cases. (3) In future period from 2011 to 2100, increases in the temperature and evaporation indices are projected under a range of climate scenarios, although decreasing mean and maximum precipitation are found in summer during 2020s. The findings of this work will contribute toward a better understanding of future climate changes for this unique region. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of spatial functional extrapolation in the framework of spatial autoregressive Hilbertian processes of order one (SARH(1) processes) introduced in Ruiz-Medina (J Muitivar Anal 102:292-305, ). Moment-based estimators of the operators involved in the state equation of these processes are computed by projection into a suitable orthogonal basis. Specifically, the eigenfunction basis diagonalizing the autocovariance operator is considered. An estimation algorithm is designed for the implementation of the resulting SARH(1)-plug-in projection extrapolator from temporal curves irregularly distributed in space. Its performance is illustrated with a real-data example, where the problem of spatial functional extrapolation of ocean surface temperature profiles is addressed. This problem is crucial in the assessment of climate change anomalies. The data are collected from the public oceanographic bio-optical database: The World-wide Ocean Optics Database. Cross Validation (C.V.) procedures are applied for the evaluation of the estimation results derived. [ABSTRACT FROM AUTHOR]

This paper presents the analyses of regional climate change features and the local urbanization effects on different weather variables over Southeast China. The weather variables considered are: daily mean (T), minimum (T), and maximum (T) near surface air temperature, diurnal temperature range (DTR), relative humidity (RH), and precipitation (P). With analysis of two datasets (a station dataset for the period from 1960 to 2005 that is mainly used and a grid dataset for the period 1960-2000), this study reveals that the trends in the variations of these weather variables can be separated into two periods, before and after 1984. Before 1984, there were no significant urbanization effects, and T, RH, and P steadily increased but T decreased, resulting in a considerable decrease in DTR and a slight decrease in T. After 1984, T and T increased considerably, and the urbanization influence on T, but not T, is observable. The urbanization effect causes an extra increasing trend in T with a rate of about 0.6°C/decade and, accordingly, extra decreasing trends in DTR and RH. The analysis of the seasonal trends reveals that the urbanization influence results in a near-uniform increase of T for all four seasons and a strong decrease of RH in summer and autumn. Moreover, there is no significant change in P at the annual scale and an increasing rate of 11.8%/decade in summer. With the urbanization influence, a considerable increase in P is noticeable at the annual scale; specifically, the increasing rates of 18.6%/decade in summer and 13.5%/decade in autumn are observed. [ABSTRACT FROM AUTHOR]

Interannual variability is an important modulator of synoptic and intraseasonal variability in South America. This paper seeks to characterize the main modes of interannual variability of seasonal precipitation and some associated mechanisms. The impact of this variability on the frequency of extreme rainfall events and the possible effect of anthropogenic climate change on this variability are reviewed. The interannual oscillations of the annual total precipitation are mainly due to the variability in austral autumn and summer. While autumn is the dominant rainy season in the northern part of the continent, where the variability is highest (especially in the northeastern part), summer is the rainy season over most of the continent, thanks to a summer monsoon regime. In the monsoon season, the strongest variability occurs near the South Atlantic Convergence Zone (SACZ), which is one of the most important features of the South American monsoon system. In all seasons but summer, the most important source of variability is ENSO (El Niño Southern Oscillation), although ENSO shows a great contribution also in summer. The ENSO impact on the frequency of extreme precipitation events is also important in all seasons, being generally even more significant than the influence on seasonal rainfall totals. Climate change associated with increasing emission of greenhouse gases shows potential to impact seasonal amounts of precipitation in South America, but there is still great uncertainty associated with the projected changes, since there is not much agreement among the models' outputs for most regions in the continent, with the exception of southeastern South America and southern Andes. Climate change can also impact the natural variability modes of seasonal precipitation associated with ENSO. [ABSTRACT FROM AUTHOR]