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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]

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]

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]

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]

Rising surface air temperatures, coupled with delays in the onset of austral summer rains and increased fuel load have amplified forest fire risk over southern Africa. This study investigates interactions between climate change and fire risk in South Africa's northern savanna biome. We employ the CCAM model to simulate the reference climate and project future forest fire risk on the savanna. An ensemble of six CMIP5 GCMs were downscaled to 8 km to project climate change in the far-future (2080 to 2099) under RCP8.5 emission scenario. The models were validated using ERA5-Land reanalyses whilst future projections focused on the 10th, 50th and 90th percentiles. The frequency of high fire risk days was calculated using a McArthur Forest Fire Danger Index (FFDI) which links meteorological variables to fire danger. The ensemble simulated widespread temperature rises of between 4.5 and 6 °C across the savanna, whilst rainfall is projected to decline by up to 20 mm/month, with corresponding decreases in minimum relative humidity. Heat wave days are projected to increase to above 8 days per annum, whilst soil moisture deficiency increases by above 50 mm on the savanna. Consequently, mean annual high fire danger days are projected to reach a peak frequency of 25 days in October, with an autumnal secondary peak. Spatially, greater increases in high FFDI days were projected over the western savanna extending toward neighbouring Botswana. This study contributes to understanding fire risk under unprecedented temperature rises which appear to be modulating fire intensity in the study region. [ABSTRACT FROM AUTHOR]

Climate and extreme hydrometeorological studies are required to reduce risk and vulnerabilities. This study uses different cumulus and microphysics schemes in Weather Research and Forecasting (WRF) model to simulate heavy rainfall events and Cyclone Sidr in Bangladesh, where many extreme events occur. Results show that WRF can capture the cyclone track, intensity, and landfall position. In addition, regionalization and an ensemble method through Bayesian regression model (BRM) are used to improve WRF rainfall simulations. Although regionalization can improve results of the experiments with different schemes, BRM leads to the best performance. To consider uncertainties and evaluate hazards, a probabilistic framework and proper indices based on distributions are used. Spatiotemporal precipitation distributions are used to develop the flash flood index (FFI) to compare the intensity of heavy rainfall events. Results show a large FFI over the central and southeast divisions that indicates the areas prone to the flash flood hazards and high-level risk. We use standardized precipitation index (SPI) in 6- and 12-month time scales based on monthly precipitation of ACCESS-CM2 in 2015–2100 under two scenarios (SSP-126 and 585) to evaluate long-term precipitation changes and drought/flood tendency. In addition, the probability distributions of the precipitation and wind speed are used in reliability analysis of the infrastructure. Target reliability indices determine the proper design rainfall (105 mm d−1) and wind speed (60 m s−1) that leads to a safe design of infrastructure. This study provides an integrated analysis of extreme hydrometeorological events, which is crucial for sustainable adaptation and mitigation plans. [ABSTRACT FROM AUTHOR]

Catastrophe models are widely used for assessing extreme weather risks. One way catastrophe models are applied is to run an analysis, and then run it again with adjusted hazard. This allows, for example, estimation of the impact of seasonal forecasts or climate change projections. One way to implement this approach involves resimulating a year loss table (YLT) from a list of events with adjusted frequencies. The new YLT is usually simulated independently from the original YLT, but this leads to differences between the two YLTs, even if the frequencies have not been adjusted, due to simulation noise. The simulation noise reduces the precision of all estimates of change. We present a new algorithm that attempts to reduce this problem. We create the new YLT incrementally by copying the original YLT and then adding or removing just enough events to capture the change. This algorithm does not involve any approximations, and is no slower. We test the algorithm using a number of simple catastrophe models for U.S. hurricane loss, to which we apply adjustments for climate change. We find that the incremental simulation method is much more precise than independent simulation in all our tests. For example, the estimates of the impact of climate change so far are three times more precise. This equates to the increase in precision that would occur from using nine times as many years of simulation, but for no extra cost. [ABSTRACT FROM AUTHOR]

Global warming has exacerbated the instability of the climate and has led to the frequent occurrence of extreme cold and warm events. The complex geography of the high-latitude region of China's monsoon-continental transition zone makes it extremely sensitive to climate response, understanding temperature changes over long periods of time is crucial to revealing trends in climate change trends. In this study, we constructed a standardized tree-ring width chronology of Korean spruce growing in this area from 1845 to 2016 and used it to analyzed the response of the radial growth of Korean spruce to climatic factors such as temperature and precipitation. The results show that the annual mean temperature was the dominant climatic factor affecting the growth of Korean spruce. Hence, we reconstructed the annual mean temperature series of this region spanning the past 172 years. The analysis results show that the study area experienced five warm periods and five cold periods in the past 172 years. Cold years were dominant before 1960s, while temperature continuously rose and changed drastically in the early twenty-first century. The reconstructed annual mean temperature series has variability cycles of 3a, 7a, 10–12a, 15–22a and 30–40a. The results of this reconstruction enrich the tree-ring database in the representative regions of the monsoon-continental climate transition zone in China and provide a reference for systematically understanding the climate change patterns in the representative regions of the transition zone. [ABSTRACT FROM AUTHOR]

Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ETo), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ETo, was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data. [ABSTRACT FROM AUTHOR]

Formulation of sustainable development plans in the water sector requires reliable estimates of future hydrological conditions. The general circulation models (GCM) are usually used in the prediction of runoff in future periods but the predictions have large uncertainty. This study aimed to propose an approach for reducing the uncertainty of the results when using GCM models. To that end, the IHACRES hydrological model was first calibrated for the baseline period (1981–2005) in the Gharesu basin, Iran. The runoff corresponding to the GCM outputs of temperature and precipitation was then calculated in the historical period using the IHACRES. Twelve top GCMs suitable to the case study for the estimation of runoff were selected by the TOPSIS algorithm. The selected twelve GCMs were combined by the runoff hybrid approach (RHM), as an ensemble model, to reduce the uncertainty of hydrological modeling. The RHM model is formed by weighted integration of the calculated runoff based on the temperature and precipitation achieved from the selected GCMs. Results showed that the mean coefficient of variation (CV) of RHM was 0.76 and the uncertainty of runoff estimation by ensemble modeling was less than that of any single GCM. The RHM model under the RCP4.5 and RCP8.5 scenarios was used to predict runoff in the near future period P1 (2006–2030), mid-future period P2 (2031–2055), and far future period P3 (2056–2080). The annual runoff prediction for the Gharesu basin under scenario RCP4.5 showed an increase of about 1% in the near future period (P1), a decrease of -2.4% in the period P2, and a decrease of -10% in the period P3 relative to the baseline period. Runoff decreased by -7.8, -6.9, and − 1.9%, respectively, in periods P1, P2, and P3 under scenario RCP8.5. The results picture that the studied sub-basin of Karkheh will face more heavy rains and floods in the winter and will be drier than the past. That is an important alarm for water managers to adapte their management strategies. [ABSTRACT FROM AUTHOR]

Pro-environmental behaviors towards climate change can be measured and evaluated in different fields. Typically, surveys are the standard tool for extracting personal information regarding this phenomenon. However, statistical modeling for these surveys is not straightforward, as the response variable is often not explicit. Hence, we propose a set of methodological procedures to deal with pro-environmental behavior data. First, validity evidence through a factorial analysis. Second, indexes are created from factor scores, where one of the latent factors summarizes a target variable. Third, a Beta regression is used to model the index of interest. Fourth, the inferential process is performed from a Bayesian perspective, in which posterior probabilities are used to sort and select the relevant variables. Finally, suitable models are obtained, and conclusions can be drawn from them. As a motivation, we used data from two Chilean surveys to illustrate our methodology as well as interpret and discuss the results. [ABSTRACT FROM AUTHOR]

A compound perspective on hydrological extreme events is of paramount significance as it may lead to damages with larger losses. In this study, an integrated framework, based on downscaled climate variables and hydrological model, i.e. the Soil and Water Assessment Tool, was applied to generate extreme precipitation (Rx1day) and extreme streamflow (Sx1day) series under historical and future climate conditions. Then the potential impacts of climate change for univariate and bivariate joint frequency of extreme precipitation and flood in Xitiaoxi River Basin (XRB), a representative watershed of the Yangtze River Delta, are detected. The compound risk of extreme precipitation and flood under different levels of joint return period for historical and projected periods is estimated by copula‐based two-dimensional approaches. The Rx1day and Sx1day under future scenarios changed by − 0.4% to 11.7% and 0.7% to 20.4%, respectively, compared to historical period based on univariate frequency analysis, indicating the increasing magnitude of the flood in the future. Climate change with different emission scenarios all have a driving effect on the rising coactivity of extreme precipitation and flood under compound flooding frequency analysis. In addition, the enhancement of climate change to extreme events is more apparent for extremes with higher return period and under the periods of 2080s. Moreover, the flood frequency designs are deduced by bivariate joint distribution are safer than that by univariate distribution. This study may provide actionable insights to formulate the planning scheme of flood control and disaster reduction under the changing environment. [ABSTRACT FROM AUTHOR]

A comprehensive investigation of historical hydro-climatic changes at a county level is an essential prerequisite of developing any adaptation or mitigation strategies to deal with the destructive impacts of climate change. In this study, spatial distributions and trends in thirty-seven hydro-climatic mean and extreme indices across Iran were analyzed based on the state-of-the-art reanalysis datasets (ERA5-Land and AgERA5) at the county level from 1986 to 2015 using several nonparametric approaches such as multiple modified Mann–Kendall statistical tests and Sen's Slope estimator. Their interannual oscillations were also examined using continuous wavelet transform to portray a complete picture of the temporal variability of the variables. The micro-scale of the reanalysis datasets enabled us to deal with the sparse distribution of weather stations in Iran, study various hydro-climatic variables that have received less attention, and subsequently provide a comprehensive, all-in-one reference for later policy actions and further studies. Analyzing diverse, intercorrelated variables provides a great opportunity to explore the driving forces of the temporal patterns. The results showed that trends in hydro-climatic mean and extreme indices highly depended on their geographical location and climate zones. The snow and the warm temperate climates encountered larger hydro-climatic changes compared to the arid and semi-arid climates zones. The most noticeable trends were found for temperature which significantly increased over all counties while drying trends were observed in precipitation, surface reservoir content, and runoff. Wind speed and surface albedo in most counties experienced upward and downward trends, respectively, whereas solar radiation, surface air pressure, and evaporation exhibited a high spatial variability. In terms of extreme events, Iran was faced with an increase in hot climate extremes and a decrease in cold and precipitation extremes. At last, the wavelet power spectrum analysis demonstrated the annual and seasonal dominant periods for precipitation and an annual dominant period for temperature, and there were significant periodic variations at 2–3 years for hot and cold spell durations and intensity-based precipitation index and 4–6 years for consecutive dry and wet days. The annual fluctuation of precipitation reduced over the study period, but that of temperature remained almost constant. [ABSTRACT FROM AUTHOR]

Sensitivity analysis of reference crop evapotranspiration (ET0) is a study to measure the sensitivity of ET0 to the changes in meteorological variables. Climate change is one of the biggest challenges to human civilization as it brings significant effects to the water resources and crop production. During the recent years, the assessment of climate change impact on the ET0 estimation received a lot of attention and subsequently sensitivity analysis is carried out to test the sensitivity of ET0 to the meteorological variables. Accordingly, this study aims to estimate the ET0 data, select the best alternative method for ET0 estimation and analyse the sensitivity of ET0 to the changes of climatic factors in Peninsular Malaysia. The ET0 estimation models such as Hargreaves Samani, Blaney-Criddle, Makkink and Turc models were compared with FAO-56 Penman–Monteith. The statistical performances of the ET0 models were evaluated by using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE) and the Coefficient of Determination (R2). The sensitivity analysis of ET0 was carried out by graphical approach, sensitivity coefficient approach, partial correlation coefficient, standardized regression coefficient and zero-order correlation coefficient. In general, the Turc method was suggested as the best alternative method for ET0 estimation in Peninsular Malaysia as the Turc method achieved the least RMSE, NRMSE, MAE and MBE values. Moreover, ET0 was the most sensitive to solar radiation, followed by relative humidity, maximum temperature, minimum temperature and windspeed. [ABSTRACT FROM AUTHOR]

The extreme temperature has become more frequent and intense due to global warming, particularly in dry regions, causing devastating impacts on humans and ecosystems. The transboundary Amu river basin (ARB) is the most vulnerable region in Central Asia (CA) to extreme weather linked to climate change. This study aimed to project warm and cold extremes in ARB for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and two time-horizons, 2020–2059 and 2060–2099, using daily maximum (Tmax) and minimum temperature (Tmin) simulations of global climate models (GCMs) of Coupled Model Inter-comparison Project phase six (CMIP6). Results revealed that the basin's west experiences more hot extremes and the east more cold extremes. Climate change would cause a significant increase in the annual mean of Tmax and Tmin. However, the increase in mean Tmin would be much higher (5.0ºC) than the mean Tmax (4.6ºC). It would cause an increase in the hot extremes and a decrease in the cold extremes in the basin. The higher increase in the hot extremes would be in the west, while the higher decrease in the cold extreme in the basin's east. The number of days above 40℃ would increase from 45 to 60 days in the basin's west and northwest compared to the historical period. The number of days below − 20℃ would decrease up to 45 days in the basin's east. Overall, the decrease in cold extremes would be much faster than the increase in hot extremes. [ABSTRACT FROM AUTHOR]

COVID-19 or Coronavirus (SARS-COV-2) is a pandemic calamity that has locked people in their own houses. The effect of SARS-COV-2 disease has caused a decrease in the economy as businesses, transportation, aviation, and industries have been halted. Many people have died, and many are trying to survive this pandemic. As we all know, the virus of SARS-COV-2 can be transmitted through physical contact, and the government has taken up specific measures like closing up schools and colleges, closing up malls/markets/public places, and imposing lockdown in cities. It is expected that these measures can result in a decreased infection rate. On the one hand, SARS-COV-2 Has halted economic or developmental growth, but on the other hand, our nature i. e. our earth, is being provided with such conditions that it can restore its losses. Air quality has been improved in the lock down time. The emission level of different gases and particulate matters have slowed down in the Covid period. Water bodies have been clean and more transparent and propagate wildlife and fisheries. Due to the SARS-COV-2 lockdown, businesses and industries have halted, impacting the financial needs of many people around the world. The worry about surviving this pandemic and the financial crisis leads them to mental and emotional distress. This review article summarized the emergence of SARS-COV-2 disease and its role on human physical and psychological health. We also described the positive and negative effects of SARS-COV-2 on climate, environmental, and air quality with upcoming challenges for governments and populations around the world. [ABSTRACT FROM AUTHOR]

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision–making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann–Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03–0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest. [ABSTRACT FROM AUTHOR]

Increased greenhouse gas concentration in the atmosphere has led to significant climate warming and changes in precipitation and temperature characteristics. These trends, which are expected to continue, will affect water infrastructure and raise the need to update associated planning and design policies. The potential effects of climate change can be addressed, in part, by incorporating outputs of climate model projections into statistical assessments to develop the Intensity Duration Frequency (IDF) curves used in engineering design and analysis. The results of climate model projections are available at fixed temporal and spatial resolutions. Model results often need to be downscaled from a coarser to a finer grid spacing (spatial downscaling) and/or from a larger to a smaller time-step (temporal downscaling). Machine Learning (ML) models are among the methods used for spatial and temporal downscaling of climate model outputs. These methods are more frequently used for spatial downscaling; fewer studies explore temporal downscaling. In this study, multiple ML models are evaluated to temporally downscale precipitation time-series (available at 3-h time steps) generated by several regional climate models of the North American Regional Climate Change Assessment Program (NARCCAP) under a high-carbon-emission projection. The temporally downscaled time-series for 2-h, 1-h, 30-min, and 15-min durations are intended for subsequent statistical analysis to generate current- and future-climate IDF curves for Maryland. In this study, the behavior of the ML models is explored by assessing performance in predicting large target response quantities, identifying systematic trends in errors, investigating input/output relationships using response functions, and leveraging conventional performance metrics. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibetan Plateau (QTP) is one of the amplifiers of global climate change. The headwater area of the Yellow River Basin (HYRB) on the QTP is the dominant water source region for the whole Yellow River Basin. However, the sensitive responses of hydrological processes to the intensifying climate change are exerting high uncertainties to the water cycle in the HYRB. The aim of this study was to investigate the potential climate change under three Representative Concentration Pathways (RCP 2.6, 4.5, and 8.5) and their hydrological impacts in this region using the ensemble climate data from eight general circulation models (GCMs) and the Soil and Water Assessment Tool (SWAT). Compared to the baseline (1976–2015), the projected climate indicated a rise of 7.3–7.8% in annual precipitation, 1.3–1.9 °C in maximum air temperature, and 1.2–1.8 °C in minimum air temperature during the near future period (2020–2059), and an increment of 9.0–17.9%, 1.5–4.5 °C, and 1.3–4.5 °C in precipitation, maximum and minimum temperature, respectively, during the far future period (2060–2099). The well-simulated SWAT modeling results suggested that due to a wetter and warmer climate, annual average actual evapotranspiration (AET) would increase obviously in the future (31.9–35.3% during the near future and 33.5–54.3% during the far future), which might cause a slight decrease in soil water. Water yield would decrease by 16.5–20.1% during the near future period, implying a worsening water crisis in the future. Till the end of this century, driven by the increased precipitation, water yield would no longer continue to decrease, with a decline by 15–19.5%. Overall, this study can not only provide scientific understanding of the hydrological responses to the future climate in both semi-arid and alpine areas, but also contribute to the decision support for sustainable development of water resources and protection of eco-environment in the HYRB. [ABSTRACT FROM AUTHOR]

The analysis of trends and other non-stationary behaviours at the extremes of a series is an important problem in global warming. This work proposes and compares several statistical tools to analyse that behaviour, using the properties of the occurrence of records in i.i.d. series. The main difficulty of this problem is the scarcity of information in the tails, so it is important to obtain all the possible evidence from the available data. First, different statistics based on upper records are proposed, and the most powerful is selected. Then, using that statistic, several approaches to join the information of four types of records, upper and lower records of forward and backward series, are suggested. It is found that these joint tests are clearly more powerful. The suggested tests are specifically useful in analysing the effect of global warming in the extremes, for example, of daily temperature. They have a high power to detect weak trends and can be widely applied since they are non-parametric. The proposed statistics join the information of M independent series, which is useful given the necessary split of the series to arrange the data. This arrangement solves the usual problems of climate series (seasonality and serial correlation) and provides more series to find evidence. These tools are used to analyse the effect of global warming on the extremes of daily temperature in Madrid. [ABSTRACT FROM AUTHOR]

Global warming is already having a negative impact on vital sectors on which human development depends, such as water resource availability. In this study, the changes and abrupt change timing of climatic extreme indices, aridity and drought over the Region of South Aegean are captured using the Mann–Kendall and Pettitt tests, while the latter variables are correlated with the water volume transported by ships to the region as well as the relevant costs. The region's climate is shifting to warmer conditions with less precipitation, since significantly positive trends were noted with regard to the number of tropical nights, warm nights, warm days, the warm spell duration index and the diurnal temperature range; significant negative trends were observed in relation to the number of cool nights, cool days and the cold spell duration index, with the change-point year for the latter variables being 2006. Inaddition, 7/11 precipitation related indices exhibited a downward trend, while significantly negative trends were observed with regard to the number of consecutive dry days, with the timing of the abrupt change being 2001. The Aridity Index (AI) reveals that the region's climate characterization is changing from dry and sub-humid to semi-arid conditions, whilst the Reconnaissance Drought Index standardized (RDIst) and the Standardized Precipitation Index (SPI) indices suggests an amplification of drought phenomena over the Region. The tourism variables illustrated a significant positive trend, with the timing of the abrupt change being registered during 2006–2009, whilst the correlation analysis between tourism variables and water transfers implies that the surge on water transfer by ships to the Region occurred between 1998 and 2008. This can be mainly attributed to the changes in climate patterns. The correlation analysis documents a strong positive correlation between the water transfer dataset and the diurnal temperature range, and a moderately negative association with the precipitation related indices, annual precipitation, drought phenomena and aridity with 7/11. [ABSTRACT FROM AUTHOR]

This work proposes a new general procedure to stochastically analyze multi-model multivariate wave climate time series projections at different temporal scales. For every projection, it characterizes significant wave height, peak period and mean direction by means of univariate non-stationary distributions capable of capturing cyclic climate behavior over a reference time interval duration. The temporal dependence between the values at a given sea state and previous short-term wave climate is described with a vector autoregressive model (VAR). The multi-model ensemble wave climate characterization is based on a compound distribution of the individual non-stationary distributions and a weighted averaged VAR model. The methodology is applied to bias-adjusted wave climate projections derived using WaveWatch III forced by wind field data from EURO-CORDEX models at a location close to the Mediterranean Spanish coast. Results are compared to hindcast data which shows a clear bi-seasonal behavior. Different temporal references were considered, starting with a 1-year reference period to analyze overall changes in wave climate at scales ranging from days, months and seasons with respect to historic conditions. The results show that the projected wave climate has a very different temporal behavior than hindcast data, delaying and widening/shortening the start and duration of the two main seasons and including shorter term variations. Regarding the energetic content of the sea states, the compound variable highest percentiles of the significant wave height present lower values than the hindcast (≈3−10%) during the traditionally more severe period (November–March) but higher values (≈10−35%) during the calmer months. The projected peak period presents a similar temporal pattern to the hindcast data, while the mean wave direction shows a significant change from the historical bi-modal behavior towards more likely easterly waves throughout the year. Additionally, a 10-year analysis is done to find larger temporal variabilities such as decadal variations associated with the North Atlantic Oscillation. The observed temporal variability in the yearly seasonal pattern throughout the century is addressed by analysing 20-year rolling windows in all the model projections and in the compound variable. The compound distribution shows significant temporal variabilities throughout the century with the most severe periods and more likely severe waves during summer at the end of the century. [ABSTRACT FROM AUTHOR]

The Yalong River basin (YRB), originating from the Tibetan Plateau, is critical for China's western development and electricity transmission. Climate change would affect the streamflow and potentially impact the hydropower in the YRB. In this study, the Automated Statistical Downscaling technique and the hydrological model SWAT were used to reproduce the observed precipitation, temperature and streamflow and predict them in the future. More importantly, the Bayesian Model Averaging (BMA) method was used to appraise the performance of four different Global Climate Models (GCMs) in simulating historical streamflow (1984–1997), and to quantify the uncertainties in future projections (2021–2100) under three Representative Concentration Pathways (RCPs) in the YRB. It was found that (i) the SWAT hydrological model has good applicability in the YRB; (ii) the BMA method can capture the historical streamflow reasonably better than individual GCM projections, providing a scientific reference for the future water resources planning and utilization in the YRB. [ABSTRACT FROM AUTHOR]

Many efficient forecasting models have been found to fail or show low skill due to the changes in the predictor–predictand relationship with the changes in global climate. An attempt has been taken to develop a climate change resilient heatwave prediction model using machine learning (ML) algorithms known as Support Vector Machines (SVM), random forest and artificial neural network. The National Centres for Environmental Prediction/National Centre for Atmospheric Research reanalysis data of ocean-atmospheric variables were used as the predictors of ML models for forecasting the number of heatwave days (HWDs) in the summer of Pakistan. An SVM based recursive feature elimination method was used to select the skilful predictors. The ML models were developed by considering a moving window of 29 years with a time step of 5 years to incorporate the changes in the relation of HWDs with its predictors due to climate change. The result showed changes in the relationship of HWDs with all the ocean-atmospheric variables considered in this study as probable predictors, which indicates the necessity of forward-rolling approach proposed in this study for the development of climate change resilient forecasting model. The relative performance of ML showed the higher capability of SVM to predict HWDs with an %NRMSE of 36, R2 of 0.87, md score of 0.76 and an rSD of 0.88 during the validation period. The result revealed the potential of SVM model to be used for reliable forecasting of heatwaves in the context of climate change. [ABSTRACT FROM AUTHOR]