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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]

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 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]

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]

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]