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There have been debates and differences of opinion over the validity of using drop size distribution (DSD) models to characterize precipitation microphysics and to retrieve DSD parameters from multiparameter radar measurements. In this paper, simulated and observed rain DSDs are used to evaluate moment estimators. Seven estimators for gamma DSD parameters are evaluated in terms of the biases and fractional errors of five integral parameters: radar reflectivity (ZH), differential reflectivity (ZDR), rainfall rate (R), mean volume diameter (Dm), and total number concentration (NT). It is shown that middle-moment estimators such as M234 (using the second-third-fourth moments) produce smaller errors than lower- and higher-moment estimators if the DSD follows the gamma distribution. However, if there are model errors, the performance of M234 degrades. Even though the DSD parameters can be biased in moment estimators, integral parameters are usually not. Maximum likelihood (ML) and L-moment (LM) estimators perform similarly to low-moment estimators such as M012. They are sensitive to both model error and the measurement errors of the low ends of DSDs. The overall differences among M234, M246, and M346 are not substantial for the five evaluated parameters. This study also shows that the discrepancy between the radar and disdrometer observations cannot be reduced by using these estimators. In addition, the previously found constrained-gamma model is shown not to be exclusively determined by error effects. Rather, it is equivalent to the mean function of normalized DSDs derived through Testud's approach, and linked to precipitation microphysics. [ABSTRACT FROM AUTHOR]

The relative skill of two vertical-profile-of-reflectivity (VPR) correction techniques for daily accumulations on a selected dataset and a real-time dataset has been verified. The first technique (C1) adjusts the 1-h rainfall amounts already derived on a Cartesian CAPPI map at an altitude of 1.5 km in a “one step” procedure using the range-dependent space–time-averaged VPR over the 1-h interval. The C2 technique corrects the nonconvective polar reflectivity measurements of each 5-min radar cycle that are also centered at a height of 1.5 km according to a VPR that is similarly derived but over a shorter time interval. The results emphasize the importance of applying a VPR correction scheme—in particular, in a climatic regime in which most of the liquid precipitation falls from stratiform echoes. The crucial importance of the choice of datasets is also underlined, causing differences in the final assessment that may be greater than those between the various algorithms. Both techniques perform well with selected events of low bright band and thus with the greatest potential for improvement—in particular, when the bias is removed in a post facto analysis. However, when the VPR algorithm is tested in a real-time environment consisting of less strong or higher brightband situations and faces a variety of day-to-day precipitation, the improvement is substantially lower. RMS errors are reduced only from 61% to 48% in contrast with the reduction from 117% to 43% seen with the smaller sample of selected events. This is because other sources of error—in particular, the variability in the radar reflectivity–rainfall rate (Z–R) relationship—are often of the same magnitude as the VPR errors. An example is provided that shows how the bias from an improper Z–R relationship can reduce the true skill of a real-time VPR correction scheme. [ABSTRACT FROM AUTHOR]

• IMERG captures well the temporal-spatial variations of mean TC rainfall. • IMERG underestimates the mean TC rainfall, especially for heavy TC precipitation. • The performance of IMERG on TC precipitation shows dependence on land cover types. • MHS (IR + morph) performs the best (worst) in characterizing TC precipitation. Long-term precipitation data with high temporal-spatial resolution and high precision is crucial for the monitoring of tropical cyclone (TC) precipitation and the understanding of its response to global climate change. The performance of the Final run of Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06B product to characterize the TC precipitation is evaluated during the TC season (May to September) from 2014 to 2019 against a dense network of gauges over eastern China, considering the impact of land cover types (LCTs), different techniques (morphing) and instruments, i.e., passive microwave (PMW) and infrared (IR) sensors. Overall, IMERG captures well the spatial pattern and temporal variations of mean TC rainfall rate (RR) over eastern China but shows underestimations of mean TC RR compared to gauge observations, with the mean error (ME) being −0.78 mm/h, which are mainly contributed by the underestimations of heavy TC RR (ME: −12.1 mm/h). Moreover, the IMERG performance shows dependence on the LCTs and IMERG components (i.e., morph, IR + morph, and PMW sensors). The overestimation/underestimation of light/heavy TC precipitation events is most obvious over water/evergreen broadleaf forests, with the bias being 1.2/0.5. Differently, IMERG even slightly underestimates light TC precipitation events over grasslands (bias = 0.88). Among four IMERG components, Microwave Humidity Sounder (MHS) performs the best in characterizing all TC precipitation events, followed by morph, Special Sensor Microwave Imager/Sounder (SSMIS), and finally IR + morph, indicating that IR algorithms might need to be further improved. The mean TC RR is slightly overestimated by MHS over most regions of eastern China with positive ME exceeding 2.8 mm/h, while is largely underestimated by IR + morph among four components with most MEs lower than −1.2 mm/h. This study may provide an observational reference for the continued development and future improvement of the IMERG product. [ABSTRACT FROM AUTHOR]

In recent years, various early warning signals of critical transition have been presented, such as autocorrelation at lag 1 [AR(1)], variance, the propagator based on detrended fluctuation analysis (DFA-propagator), and so on. Many studies have shown that the climate system has the characteristics of long-term memory (LTM). Will the LTM characteristics of the climate system change as it approaches possible critical transition points? In view of this, the present paper first studies whether the LTM of several folding (folded bifurcation) models changes consistently as they approach their critical points slowly by the rescaled range (R/S) analysis. The results of numerical experiments show that when the control parameters of the folding model are close to its critical threshold, the Hurst exponent H exhibits an almost monotonic increase (significance level α = 0.05). We compare the performance of R/S with the existing indicators, including AR(1), variance, and DFA-propagator, and find that R/S is a perfectly valid alternative. When there is no extra false noise, AR(1) and variance have good early warning effects. After the addition of extra Gaussian white noise of different intensities, the values of AR(1) and variance change significantly. As a result, the DFA-propagator based on AR(1) calibration also changed significantly. Compared with the other three indicators, the early warning effect of H has stronger ability to resist the interference of external false signals. To further verify the validity of increasing H, paleoclimate reconstruction of Cariaco Basin sediment core grayscale record with long trends filtered out is studied by R/S analysis. The other three early warning signals are calculated in the same way. The data contain a well-known abrupt climate change: the transition between the Younger Dryas (YD) and the Holocene. We find that approximately 300 years before this abrupt climate change occurred, before 11.7 kyr BP, the LTM exponents for Cariaco Basin deglacial grayscale data present an obvious increasing trend at a significant level of α = 0.05. Meanwhile, the variation trend of H and DFA-propagator is basically similar. This shows that increasing H by R/S analysis is an effective early warning signal, which indicates that a dynamic system is approaching its possible critical transition points; H is a completely valid alternative signal for AR(1) and DFA-propagator. The main conclusion of this paper is based on numerical experiments. The precise relationship between H and the stability of the underlying state approaching the transition needs to be further studied. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are widely used to assess future impacts associated with climate change at regional and local scales. RCMs must represent relevant climate variables in the present‐day climate to be considered fit‐for‐purpose for impact assessment. This condition is particularly difficult to meet over complex regions such as the Andes‐Amazon transition region, where the Andean topography and abundance of tropical rainfall regimes remain a challenge for numerical climate models. In this study, we evaluate the ability of 30 regional climate simulations (6 RCMs driven by 10 global climate models) to reproduce historical (1981–2005) rainfall climatology and temporal variability over the Andes‐Amazon transition region. We assess spatio‐temporal features such as spatial distribution of rainfall, focusing on the orographic effects over the Andes‐Amazon "rainfall hotspots" region, and seasonal and interannual precipitation variability. The Eta RCM exhibits the highest spatial correlation (up to 0.6) and accurately reproduces mean annual precipitation and orographic precipitation patterns across the region, while some other RCMs have good performances at specific locations. Most RCMs simulate a wet bias over the highlands, particularly at the eastern Andean summits, as evidenced by the 100%–2,500% overestimations of precipitation in these regions. Annual cycles are well represented by most RCMs, but peak seasons are exaggerated, especially at equatorial locations. No RCM is particularly skillful in reproducing the interannual variability patterns. Results highlight skills and weaknesses of the different regional climate simulations, and can assist in the selection of regional climate simulations for impact studies in the Andes‐Amazon transition zone. Plain Language Summary: Regional climate models (RCMs) are useful numerical tools to investigate future climate change impacts (e.g., future water availability, frequency of floods and droughts, regional warming). Regarding regional scale, RCMs are expected to perform better than global climate models due to finer spatial resolution. However, in the Andes‐Amazon transition region, assessing the performance of RCMs is challenging due to complex terrain and scarcity of observations. This region is of critical importance for the water cycle of local and regional ecological systems, but has been often overlooked in RCM assessments. Here, we evaluate how 30 regional climate simulations perform in representing precipitation regional contrasts, wet‐dry seasons, and year‐to‐year changes over the Andes‐Amazon transition region. We find that models perform differently over specific regions, with prominent overestimations at high altitudes by most RCMs. However, Eta RCM has the best performance regarding regional patterns of precipitation and its wet‐dry fluctuations. Besides overestimations during austral summer and spring, wet‐dry seasonal fluctuations are well simulated by most RCMs, but none excels in representing wet‐dry yearly fluctuations. Strengths and weaknesses of different regional climate simulations are shown, and can help choose the most appropriate simulations for distinct impact studies in this region. Key Points: Precipitation output from 30 regional climate simulations is assessed over the Andes‐Amazon in terms of climatology and temporal variabilityThe spatio‐temporal behavior of seasonality is well reproduced by most simulations, with overestimations during austral summer and springWhile orographic precipitation is a major challenge for most regional climate models, Eta satisfactorily reproduces climate patterns in the Andes‐Amazon region [ABSTRACT FROM AUTHOR]

Flood is a reoccurring disaster throughout the world. With the increasing effects of climate change, the occurrence of floods is predicted to increase, impeding development and growth. Moving forward towards 2030, the United Nations calls for a strategic recovery, rehabilitation and reconstruction that embodies the "build back better" approach. Existing disaster risk reduction efforts for floods have focused on restorative and preventive measures through an integrated architectural response that encompasses the experiential and inherited knowledge of communities living in flood-prone areas together with technological and disaster risk reduction operations for flood resilience. The study utilised current literature reviews to identify environmental, social and governance (ESG) domains for risk reduction and three key strategies from the ESG domains, extending towards urban design for disaster management opportunities. Results were collated and analysed through a thematic literature review from local and international knowledge. The findings suggested that it is vital to respond to and attend to the ESG-integrated approach to promote and sustain resilient urban design and architecture. The study presented a holistic urban design framework towards flood resilience. [ABSTRACT FROM AUTHOR]

The hydrological cycle, or water cycle, is one of the most important geochemical cycles on our planet. Normal functioning of its mechanisms (evaporation/evapotranspiration, condensation, and precipitation) is very important for the well-being of human beings. However, the acceleration of the hydrological cycle, mainly due to global warming, is increasing the frequency and intensity of extreme events (floods, droughts, and alterations in water resources) in many regions around the globe. This acceleration or intensification occurs because of rising temperature, which intensifies and speeds up evaporation (probable increase of 5.2%) and precipitation (probable increase of 6.5%); hence this scenario is escalating climate change. According to the datasets retrieved from the Global Land Data Assimilation System (GLDAS) of NASA, rain precipitation rate has shown changes in various regions of the world. Consequently, extreme and frequent events of heavy precipitation, floods, and droughts are also deteriorating the quality of water and preventing recharge of water reservoirs. Although some regions of the world will experience positive outcomes of this scenario in terms of water availability (due to frequent intense precipitation), most of the world’s regions are expected to face the daunting issue of water unavailability, as predicted by many researchers. [ABSTRACT FROM AUTHOR]

Objectives : Climate change is expected to have major impacts on plant species distribution worldwide. These changes can affect plant species in three ways: the timing of seasonal activities (phenology), physiology and distribution. This study aims to predict the effect of shifting climatic conditions on the major vegetation units along an aridity gradient through Namibia. Study area : Namibia's vegetation is characterised by open woodland in the northeast to low open shrubland in the southern part of the country. These differences are a result of increasing aridity from north to south with a rainfall gradient from 100 mm to 600 mm. Namibia is projected to have an increase in annual mean temperature of 2°C by the end of the 21st century. Methods : A vegetation classification was done for 1,986 relevés using cluster analysis, a Multi-Response Permutation Procedure and indicator species analysis. The current distribution of the vegetation classes was modelled with Random Forest. Future projections for the most important climate variables were used to model the potential distribution of the vegetation units in 2080. This modelling approach used two scenarios of Representative Concentration Pathways (4.5 and 8.5) from two Global Climate Models – the IPSL–CM5A–LR and HAdGEM2–ES. Results : The predicted distribution shows a high expansion potential of Eragrostis rigidior---Peltophorum africanum mesic thornbush savannas, Combretum africanum-Terminalia sericea broad-leafed savannas and Senegalia mellifera-Dichrostachys cinerea degraded thornbush savannas towards the south under both scenarios. Conclusions : The model indicated the ability to classify and predict vegetation units to future climatic conditions. Half of the vegetation units are expected to undergo significant contraction. Overall, RCP8.5 conditions favour the proliferation of certain vegetation types, particularly Combretum collinum-Terminalia sericea broad-leafed savannas and Senegalia mellifera-Dichrostachys cinerea degraded thornbush savannas, potentially displacing other vegetation types. Taxonomic reference : Klaassen and Kwembeya (2013) for vascular plants, except Kyalangalilwa et al. (2013) for the genera Senegalia and Vachellia s.l. (Fabaceae). Abbreviations : CDM = Community Distribution Model; CMIP5 = Coupled Model Inter-comparison Project Phase 5; EVI = Enhanced Vegetation Index; GCM = General Circulation Model; IV = Indicator Value; ISA = Indicator Species Analysis; MAP = mean annual precipitation; MAT = mean annual temperature; MRPP = Multi-Response Permutation Procedure; NMS = Non-Metric Multidimensional Scaling; RF = Random Forest; RCPs = Representative Concentration Pathways; SDM = species distribution model. [ABSTRACT FROM AUTHOR]

The Patagonian Andean foreland system includes several intermountain basins filled with a Miocene non-marine record deposited under syn-tectonic conditions related to the Andean uplift and a regional climate change triggered by a rain shadow effect. Many of those basins, such as the Collón Cura basin in Neuquén Province, Argentina, present a well-preserved fluvial record (i.e. the Limay Chico Member of the Caleufú Formation). Sedimentological and palaeomagnetic studies have allowed the interpretation of coeval transverse distributary fan and axial mixed-load fluvial systems deposited between 10.6 ± 0.2 and 12.8 Ma. The basin infill arrangement shows that, while the axial mixed-load fluvial system exhibits an aggradational stacking pattern, the transverse distributary fluvial fan system denotes three different orders of stratigraphic patterns: (i) large-scale progradation of the transverse fluvial fan system over a time scale of 106 year; (ii) intermediate-scale progradational-retrogradational transverse intra-basinal fluvial fan episodes over a time scale of 105 year; and (iii) small-scale transverse lobe progradation over a time scale of 105-104 year. These patterns were interpreted as transverse sediment flux variations triggered by variable external forcings. To decouple those forcings, we estimated the Collón Cura basin equilibrium time at 3-5 × 105 year and compared it with the time scale over which different external forcings varied in the Patagonian Andean and foreland regions during Miocene times. Large-scale progradation is linked to an increase in sediment flux triggered by a long-term tectonically driven exhumation forcing associated with the Miocene Patagonian Andean contractional phase. Intermediate-scale progradational-retrogradational episodes are linked to variations in sediment flux due to a mid-term tectonic forcing associated with the western fault system activity. The small-scale fan lobe progradation is related to increases in sediment flux triggered by indistinguishable short-term autogenic processes and/or high-frequency tectonic and climatic forcings. This contribution shows the applicability and limitations of the basin equilibrium time concept to decouple external forcings from the geological record, considering their magnitude, nature and time scale, as well as the basin characteristics. [ABSTRACT FROM AUTHOR]

This article investigates the Urban Heat Island (UHI) effect and its interplay with precipitation patterns in the rapidly urbanizing western coastal cities of India. Focusing on the socio-economic dimensions of urbanization, it examines the consequences of increased surface temperatures due to altered urban landscapes and heightened anthropogenic activities. The study evaluates the correlations between UHI intensity, population density, and precipitation during summer and winter across multiple decades-- 1991, 2001, 2011, and 2021--using the Spearman rank correlation coefficient. Findings indicate a robust positive correlation between surface temperature and population density in summer, with an inverse relationship to precipitation. In contrast, winter data shows an opposite trend, with unclear connections to population density, suggesting that other factors may significantly affect precipitation. The seasonal and temporal analysis of these correlations sheds light on the urban climate's complexity and informs urban planning strategies to mitigate adverse socio-economic impacts related to climate change. This study underscores the need for sustainable urban development practices that consider the intricate dynamics between urban growth and environmental transformations, highlighting the necessity for climate-responsive planning in the socio-economic development of Indian western coastal cities. [ABSTRACT FROM AUTHOR]

This article explores the application of thermodynamic perturbations to a historical midlatitude, wintertime, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14-km variable-resolution simulations completed with the U.S. Department of Energy's global Energy Exascale Earth System Model (E3SM). We show that the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under preindustrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks from around +1 to +2 K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential nonlinearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB. [ABSTRACT FROM AUTHOR]

Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh. [ABSTRACT FROM AUTHOR]

Climate change poses significant threats to the water reservoir, with potential repercussions for Pakistan's economy. This study investigates the impact of climatic variations on seepage in Tarbela dam, Pakistan, by analyzing hydro-climatological factors. The research employs statistical multivariant time series (SARIMA and ARDL) and machine learning (ANN and Catboost) modeling techniques to assess the influence of climatic variability on seepage dynamics. The analysis focuses on annual and seasonal climatic variables from a data set spanning from 2003 to 2015, aiming to explore the relationship between climatic variability and dam seepage. The SARIMA model is utilized to estimate water inflow, temperature, rainfall, and reservoir level, validating the integration of multiple data sets. Machine learning models demonstrate superior performance, achieving high R2 scores during the training, validation, and testing. The findings derived from the ARDL analysis reveal a significant increase in seepage with rising water inflow, rainfall, and reservoir level. Among the machine learning models, CatBoost performs the best. The study recommends implementing appropriate adaptive approaches to mitigate any negative impact on Tarbela dam seepage. Moreover, this research serves as a guide for enhancing the dam's power generation capacity and improving irrigation water management in the face of existing crises in Pakistan. [ABSTRACT FROM AUTHOR]

Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Predicting, mapping, and evaluating the flood inundation risk (FIR) is important for coastal tourism. This study developed a spatial tourism-aimed framework by integrating a Weighted k Nearest Neighbors (WkNN) algorithm, geographic information systems, and environmental indexes, such as precipitation and soil. These model inputs were standardized and weighted using inverse distance calculation and integrated into WkNN to infer the regional probability and distribution of the FIR. Zhejiang province, China, was selected as a case study. The evaluation results were mapped to denote the likelihood of an FIR, which was then validated by the historical Maximum Inundation Extent (MIE) extracted from the World Environment Situation Room. The results indicated that 80.59% of the WkNN results reasonably confirmed the MIE. Among the matched areas, 80.14%, 90.13%, 65.50%, and 84.14% of the predicted categories using WkNN perfectly coincided with MIE at high, medium, low, and very low risks, respectively. For the entire study area, approximately 2.85%, 64.83%, 10.8%, and 21.51% are covered by a high, medium, low, and very low risk of flood inundation. Precipitation and elevation negatively contribute to a high-medium risk. Drainage systems positively alleviate the regional stress of the FIR. The results of the evaluation illustrate that in most inland areas, some tourism facilities are located in high-medium areas of the FIR. However, most tourism facilities in coastal cities are at low or very low risk, especially from Hangzhou-centered northern coastal areas to southern Wenzhou areas. The results can help policymakers make appropriate strategies to protect coastal tourism from flood inundation. Moreover, the evaluation accuracy of WkNN is higher than that of kNN in FIR. The WkNN-based framework provides a reasonable method to yield reliable results for assessing FIR. The framework can also be extended to other risk-related research under climate change. [ABSTRACT FROM AUTHOR]

A new reanalysis-based approach is proposed to examine how reconstructions of extreme weather events differ in warmer or cooler counter-factual worlds. This approach offers a novel way to develop plausible storylines for some types of extreme events. As a proof of concept, a reanalysis of a severe windstorm that occurred in February 1903 is translated into a warmer world where it produces higher wind speeds and increased rainfall, suggesting that this storm would be more damaging if it occurred today rather than 120 years ago. The study discusses different methodologies for understanding how climate change has affected extreme events and proposes a reanalysis-based approach to translate extreme events into different climates. The study also highlights the potential of this approach for policymakers in planning for worst-case events and attributing changes in observed weather events to human influence. [Extracted from the article]

Settlement crises in ancient cultures of Western Asia are commonly thought to be caused by climatic events such as severe droughts. However, the insufficient climate proxy situation in this region challenges the inference of clear relationships between climate and settlement dynamics. We investigate the Holocene climatic changes on the Varamin Plain in the context of the climatic history of Western Central Asia by using a transient comprehensive Earth System Model simulation (8 ka BP to pre-industrial), a high-resolution regional snapshot simulation and a synthesis of pollen-based climate reconstructions. In line with the reconstructions, the models reveal only slightly varying mean climatic conditions on the Varamin Plain but indicate substantial changes in seasonality during the Holocene. Increased precipitation during spring, combined with lower temperature and potentially stronger snow accumulation on the upstream Alborz mountains may have led to an increased water supply on the alluvial fan during the vegetation period and thus to more favourable conditions for agricultural production during the Mid-Holocene compared to modern times. According to the model, dry periods on the Central Iranian Plateau are related to particularly weak Westerly winds, fostering the subsidence in the mid-troposphere and hampering precipitation over the region. The model reveals that dry periods have spatially heterogenous manifestations, thus explaining why they do not appear in all proxy records in the wider study region. In fact, the climatic signal may depend on local environmental conditions. The interaction of the topography with the atmospheric circulation leads to additional spatial heterogeneity. Although our results provide several indications for a connection between climate and settlement dynamics, the small overall changes in moisture call into question whether climate is the main driver for settlement discontinuities on the Central Iranian Plateau. To shed further light on this issue, more high-resolution long-term proxy records are needed. [ABSTRACT FROM AUTHOR]

Climate change has a strong impact on the environment in mountain areas, where ecosystems have adapted to climatic conditions that change with elevation. In this study, the response of temperature and precipitation climatic indices in the complex orography setting of the Great Alpine Region is discussed. The high‐resolution gridded dataset that is presented has been produced with the Weather Research Forecasting (WRF) convection permitting regional model. Two 30‐year periods have been considered (1979–2008, 2039–2068), obtained by downscaling global climate simulations with historical setting and RCP8.5 emission scenario. Both daily temperature and precipitation statistics have been found to be significantly different in the two periods, consisting in an overall projected warming and drying of the region. The dependence of the projected changes on elevation is highlighted, indicating a larger warming at medium and high elevations as well as a limited or nonexisting drying at high elevations. Physical mechanisms at the base of those differences are presented and discussed. [ABSTRACT FROM AUTHOR]

In the last 20 years, a significant mortality of Fagus sylvatica L. (European beech) has been documented in central and northern European forests. Surprisingly, no beech die off occurred at the southern limit. This fact leads us to hypothesize that European beech populations growing at the southern limit of the distribution might have a significant phenotypic plasticity to better cope with low water availability and rising temperatures. To check this hypothesis, we evaluated the ecophysiological behavior of F. sylvatica growing along an altitudinal transect in Calabria (Italy). We selected three study sites (750 m a.s.l., 976 m a.sl. m a.s.l., 1450 m a.s.l.) showing narrow ranges of temperature, rainfall and air humidity. Trees growing at 976 m a.s.l. showed the highest stomatal conductance values during the entire experimental period. The lowest gas exchange and highest leaf mass area were recorded in plants growing at 750 m a.s.l. In the European beech growing at 1450 m a.s.l., higher vessel density, lower mean vessel diameter and higher vessel grouping index values were recorded. Overall, our results highlighted that the measured populations show a considerable phenotypic plasticity leading them to adjust anatomical and physiological traits in response to narrow ranges of environmental parameters. Despite that, the distribution of F. sylvatica seems to be limited to areas with a growing season rainfall of at least 400 mm and vapor pressure deficit (VPD) values < 3 kPa, which may represent the main environmental thresholds which strongly limit the beech growth and, therefore, influence the ability of this species to cope with future environmental conditions. [ABSTRACT FROM AUTHOR]

Understanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies. Plain Language Summary: Tropical Storms (TS) are one of the world's most damaging natural hazards which result in colossal socio‐economic losses to life, infrastructure, and property, especially in low‐lying delta rivers basins like the Ganges and Mekong. Knowledge of changes to TS activity under climate change can therefore be helpful in better disaster risk mitigation and climate adaptation. Previous modeling studies have used coarse‐resolution global climate models unable to capture key TS characteristics. In this study, we utilized finer resolution (up to ∼25 km at six hourly time‐steps) CMIP6 HighResMIP models and two different tracking algorithms (trackers) to resolve a part of this uncertainty. Our results project a decline to the frequency of future TS but an increase in the strength of TS (in terms of intensity and Available Cyclone Energy, qualitatively similar for both trackers). These findings can be used to assess the future resilience of existing infrastructure systems to Tropical Storms across these densely populated basins. Key Points: We used multiple CMIP6 HighResMIP models and two trackers to estimate the change in the Tropical Storms (TS) characteristics over the Ganges and Mekong basinsOur results show a decline in the future frequency of TS but increase in the future intensity and Available Cyclone Energy of TS over both basinsBoth tracking algorithms produce qualitatively similar but quantitatively different results [ABSTRACT FROM AUTHOR]

Wireless transmissions at more than 10GHz can experience signal fading caused by the presence of hydrometeor particles in the atmosphere. Among these hydrometeors, rain is the largest contributor to the fading mechanism. Rain fade can be predicted or calculated using rain rate measurements. With climate change affecting the world due to the rise of carbon dioxide in the air, it is expected to affect the distribution of rainfall, which ultimately affects rain fade. This paper investigates the effects of climate change on rain rate distribution in Malaysia. Ten years of Hydro-Estimator data containing rainfall rates in Peninsular Malaysia and the Sabah and Sarawak region from 2011 to 2020 were collected and analyzed. Using the linear regression method, a small increment of rain rate distribution at 0.01% annual probability was detected for all regions, indicating a climate change effect on the rain rate distribution. For Peninsular Malaysia, the rate was 0.2356mm/hr per year while for the Sabah and Sarawak region, it was 0.4046mm/hr per year. An increase in the rain rate would increase the rain fade, causing signal losses and distortions in high-frequency wireless communication signals. The evaluation of climate change effects on the rain rate and rain fade distributions can help in developing a long-term prediction of the signal performance in 5G systems and high-frequency radio link frequencies due to hydrometeors. [ABSTRACT FROM AUTHOR]

Climate change is imminent and threatens the largest watershed in the world, the Amazon basin. As general circulation models may fail to represent cloud-scale phenomena, precipitation in a changing climate under global warming is still a factor of great uncertainty, especially in Tropical regions. In this study, we used long-term high-resolution simulations from a global cloud-resolving model under the scope of the Coupled Model Intercomparison Project (CMIP6) to verify the climate change impacts on the mesoscale convective systems (MCSs) over the Amazon basin. We generated a complete spatial, temporal, and statistical characterization of the MCSs for the past (1950–1960), present (2000–2010), and near-future (2040–2050). We found that MCSs are a critical mechanism for precipitation, especially in austral winter. The simulations are consistent with the observed precipitation and MCSs patterns over the Amazon basin, indicating that MCSs are less frequent compared to the past and are expected to continuously decline in the near-future. Most decreases are projected from September to December, while an increase between June to August, mainly in the southern portion of the Amazon basin. In addition, the investigation presented here shows the great potential of using a global cloud-resolving model under the CMIP6 scope to investigate mesoscale systems in a warming climate. [ABSTRACT FROM AUTHOR]

Mole drain performance is known to vary temporally and spatially due to variations in soil properties, installation conditions, mole channel integrity, and weather patterns. In fine-textured, low-permeability soil profiles, moles can be installed to supplement an underlying tile drain system. However, moles are often not included in such designs. The objective of this modeling study was to investigate the performance impacts of variations in mole integrity and design in such a soil profile during a range of rainfall event scenarios. A finite element software package (SEEP/W) was used to model a field site having (system 1) subsurface tile drains (0.9 m depth, 15 m spacing) with gravel aggregate(10 to 50 mm) and intersecting mole drains (0.6 m depth, 1.4 m spacing). The field site was subjected to a pedological survey to characterize the soil profile, while an on-site weather station and end-of-pipe flowmeters provided rainfall and discharge data from which the model could be calibrated. The calibrated model showed close agreement between modeled and observed subsurface discharge in the validation period (coefficient of mass residual = 0.12, index of agreement = 0.94, model efficiency = 0.74). The model was then used to evaluate the impact of three alternative designs: tile drains only, a common practice in similar soils (system 2); a design similar to system 1 but with the saturated hydraulic conductivity (Ks) of the mole-drained layer decreased to mimic a reduction in mole drain integrity and effectiveness (system 3); and a design similar to system 1 but with Ks of the mole-drained layer increased to mimic improved soil disturbance and fissuring during installation (system 4). These systems were analyzed using the calibration (event A) and validation (event B) rainfall events as well as two notional rainfall scenarios: a "fixed rainfall" scenario (event C) with a rainfall rate of 2 mm h-1 applied to all systems for 50 h and a "historical rainfall" scenario (event D) with annual (30 year) average daily values for the area (taken as the average monthly totals divided by the number of days per month) applied over a year. Results showed that the modeled designs exhibited similar relative behavior in all simulated rainfall scenarios. Systems 1 and 4 consistently outperformed systems 2 and 3 in terms of average and peak discharge and water table control capacity. Across rainfall events, system 2 (without mole drains) was the least effective and was seen to decrease drain discharge by an average of 63% and reduce mean water table depth by an average of 72% relative to systems 1 and 4. Results showed the importance of mole channels in supplementing tile drainage on fine soils, as well as the importance of mole integrity for optimal performance. Such a tool could provide decision support in the drainage system design process and assess the implications of design variations on cost, expected performance, and likely returns to the landowner by estimating seasonal variations in drainage discharge and water table position. Identifying and characterizing the major soil types on a farm through soil profile pedological descriptions and collation of real soil physical and meteorological data is essential to prescribe appropriate drainage designs and prioritize areas for drainage installation in light of technical feasibility and cost estimates. With highresolution data, the software can be calibrated for other drainage system and climate change scenarios. [ABSTRACT FROM AUTHOR]

We classified and characterized precipitation features (PFs) at annual and diurnal timescales during 1998-2011 over the tropical Americas and adjoining oceans using data from two instruments on board the Tropical Rainfall Measuring Mission (TRMM). Our scope included geographic distribution, frequency, area, rainfall rate, and polarization-corrected temperatures (PCTs) of PFs in an effort to contrast different regions over Colombia, the Amazon River Basin, and the adjoining oceanic regions (Pacific Ocean and Caribbean Sea). Our results showed that mesoscale convective systems (MCSs) over the Caribbean Sea exhibited colder minimum PCTs than Pacific Ocean MCSs. Pacific Ocean MCSs had higher rainfall rates, however, than Caribbean Sea MCSs. Despite the fact that MCSs represented <1% of the total precipitation systems found, the MCSs contributed more rainfall over the study regions. Over the Pacific Ocean, for example, MCSs contributed approximately 57% of the total rainfall contributed by all precipitation systems identified over that region. Additionally, MCSs over the Pacific coast have their maximum frequency during the morning hours (0000-0600 Local Time) offshore and centred over the Pacific Ocean at 77.5°W. The annual cycle of PFs showed a marked bimodal annual cycle over Colombia, whereas over the Amazon Basin, Pacific Ocean, and Caribbean Sea, PFs were characterized by a unimodal annual cycle. [ABSTRACT FROM AUTHOR]

Climate change over the past several decades has resulted in shifting rainfall pattern and modifying rainfall intensity, which has exacerbated hydrological processes and added the uncertainty and instability to these processes. This study ascertained impacts of potential future rainfall change on hydrological processes at the Jianfengling (JFL) tropical mountain rainforest watershed in Hainan Island, China using the BASINS (Better Assessment Science Integrating Point and Nonpoint Sources)-HSPF (Hydrological Simulation Program-FORTRAN)-CAT (Climate Assessment Tool) modeling system. The HSPF model was calibrated and validated with available measured data prior to its applications. Three simulation scenarios were then performed to gain a better understanding of the impacts of different rainfall rates and storm intensities on stream discharge, surface water runoff from forest land, and water outflow from the JFL watershed outlet. Results showed that a 10% increase in rainfall rate could result in 1.3 times increase in stream discharge, surface runoff, and water outflow. A potential future wet climate could have profound impacts on hydrological processes at the JFL watershed, whereas a potential future dry climate could result less impacts on stream discharge, surface runoff, and water outflow at the same watershed. Our simulation further revealed that climate change driven by extreme rain storms had greater impacts on annual surface runoff than on annual stream discharge. The coupled CAT-HSPF model is a useful tool to modify historical rainfall data for projecting future rainfall variation impacts on forest hydrological processes due to climate change. This approach would be able to extend to other regions around the world. [ABSTRACT FROM AUTHOR]

Climate change and unsustainable land management practices have resulted in extensive soil degradation, including alteration of soil structure (i.e., aggregate and pore size distributions), loss of soil organic carbon, and reduction of water and nutrient holding capacities. Although soil structure, hydrologic processes, and biogeochemical fluxes are tightly linked, their interaction is often unaccounted for in current ecohydrological, hydrological and terrestrial biosphere models. For more holistic predictions of soil hydrological and biogeochemical cycles, models need to incorporate soil structure and macroporosity dynamics, whether in a natural or agricultural ecosystem. Here, we present a theoretical framework that couples soil hydrologic processes and soil microbial activity to soil organic carbon dynamics through the dynamics of soil structure. In particular, we link the Millennial model for soil carbon dynamics, which explicitly models the formation and breakdown of soil aggregates, to a recent parameterization of the soil water retention and hydraulic conductivity curves and to solute and O2 diffusivities to soil microsites based on soil macroporosity. To illustrate the significance of incorporating the dynamics of soil structure, we apply the framework to a case study in which soil and vegetation recover over time from agricultural practices. The new framework enables more holistic predictions of the effects of climate change and land management practices on coupled soil hydrological and biogeochemical cycles. Plain Language Summary: Soil degradation due to climate change and unsustainable land management practices is a global phenomenon that threatens food security and Earth livability at large. While soil degradation involves modifications of both physical and biological properties of soils, mathematical models to predict these changes have focused independently on these two aspects, limiting our ability to holistically assess climate and human drivers of soil degradation. Here, we connected recent advances in modeling physical and biological soil processes to develop a unified framework that can account more holistically for potential changes in soil properties over time. The potential of this framework to predict soil changes is illustrated through an analysis of a case study of soil and vegetation recovery from agricultural practices. This work may represent an important step toward predicting the effects of land use and climate changes on soil degradation, hence enabling the design of more sustainable land management strategies. Key Points: A framework linking soil structure, carbon, and hydrology is needed for holistic predictions under environmental and land use changesThe novel Millennial model for soil carbon cycling is linked to a recent soil hydraulic parameterizationAggregated carbon is used as a proxy for soil macroporosity to simultaneously model the changes in soil properties and microbial activity [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]

The relevance of the article is due to the importance of the problem of water drainage at modern construction sites. When designing buildings and structures, it is necessary to take into account the possibility of efficient collection and drainage of water from the surface of the roof and the territory. The goal is to represent relevant information about the methods of hydraulic calculation of rainwater elements used in modern engineering practice. Among the methods used in the research, the analytical method, synthesis method, functional method, deduction method, and other approaches were used. The scientific and practical task related to the development of new and improvement of known methods of modelling rain run-off in built-up areas, taking into account the regulatory influence of individual elements of water drainage structures, is being successfully carried out. The results of this study are very important for ensuring the reliability of water drainage systems in the settlements of Ukraine, especially under the conditions of the growth of urban development and changes in climatic conditions. To develop practical surface run-off management plans, it is necessary to scientifically model the run-off level, taking into account the climatic characteristics and parameters of the run-off basin, as well as the predicted changes over time. The task of modelling the behaviour of surface run-off in urbanized areas is complex and it contains many factors. The study shows that reliable modelling results of complex rainwater drainage systems with regulating facilities can be obtained only with the help of multifactorial and multistep models implemented with the help of computer programs. The practical value of the research is that it helps to improve the design and operation of water drainage and water supply systems. [ABSTRACT FROM AUTHOR]

In this study, the hydrometeorological impacts of climate change in the Upper Godavari River basin in India are quantified for historical and future periods using a well-calibrated hydrological model H08. The study provides a quantitative assessment of various hydrological fluxes projected for the future that are useful in water resource planning and management. The results revealed that under RCP4.5 (RCP8.5) climate scenarios, the rainfall in the entire basin was projected to increase by ~24.4% (33%) and temperature by ~2.3°C (4.65°C), the runoff increased by ~44.8% (63%) and the evapotranspiration increased by ~9.8% (12%), by the end of 21st century in the basin. Further, changes were larger in the wet season for all the variables except temperature, which had a larger change in the dry season. This shows that rainfall will be more frequent in the wet season and will result in high runoff generation that may result in a flood-like situation. These results are useful for adaptive measures required to deal with possible climate change impacts in the basin to ensure food and water security. [ABSTRACT FROM AUTHOR]

The largest reservoir of drinkable water on Earth is surface water. It is crucial for maintaining ecosystems and enabling people to adapt to diverse climate changes. Despite surface freshwater is essential for life, the current research shows a striking lack of understanding in its spatial and temporal dynamics of variations in outflow and storage across a sizable country: India. Numerous restrictions apply to current research, including the use of insufficient machine learning techniques and limited data series. This work uses cutting-edge and SOTA-method to use the available data and machine learning to accurately understand spatial and temporal dynamics of variations in surface freshwater outflow and storage using extended data series. The authors did the examination of thematic maps produced using ArcMap 10.8 from June'2005 to June'2020 using JRC dataset to track changes in the intensity of surface water. Google Earth Engine in Python API has been devised to detect changes in surface water levels and quantifying shifting map trends. Raster image viewing, editing, and calculation are done with ArcMap. For determining the relationship between declines in Surface water levels, changes in rainfall intensity and land surface temperature, variables were averaged over 13 rivers for 15 years. The change in surface water is reliant on independent variables of change in land surface temperature and rainfall intensity. The authors use the correlation between these parameters to achieve an average R-squared adjusted value of 0.402. The study's findings contribute to a better understanding of the matter and can be used across the world. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations in reference evapotranspiration (ETo) are sensitive to the meteorological data used in its estimation. The sensitivity of the ASCE standardized ETo equation to meteorological variables from GOES-PRWEB dataset was evaluated for the island of Puerto Rico. Island wide, ETo is most sensitive to daily mean relative humidity (RHmean), followed by solar radiation, daily maximum (Tmax) and minimum (Tmin) air temperatures, and wind speed with average absolute relative sensitivity coefficients (SCs) of 0.98, 0.57, 0.50, 0.27, and 0.12, respectively. The derived SCs guided the prioritization of bias correction of meteorological data for ETo estimation from two downscaled climate models (CNRM and CESM). The SCs were applied to evaluate how meteorological variables contribute to model errors and projected future changes in ETo from 1985–2005 to 2040–2060 at irrigated farms in the south. Both models project a 5.6% average increase in annual ETo due to projected increases in Tmax and Tmin and a decrease in RHmean. Despite ETo being most sensitive to relative changes in RHmean, the contributions from RHmean, Tmax, and Tmin to future changes in ETo are similar. CESM projects increases in ETo in March, November, and December, increasing the potential for crop water stress. Study limitations are discussed. [ABSTRACT FROM AUTHOR]

Healthy forests are essential to human life because they provide food, energy, and other benefits including carbon sequestration. The objective of this study was to assess the forests health status in Mediterranean ecosystems, specifically, arid to semi-arid. Four forest reserves directed by Royal Society for the Conservation of Nature, Jordan were evaluated. Plant health indicators [(gas exchange (photosynthesis, stomatal conductance, transpiration), chlorophyll, middy stem water potential (Ψsmd), relative water content], regeneration, lichens, plant disease, as well as soil variables (respiration CO2-C, electrical conductivity (EC), pH, microorganisms' abundance) were measured. The Ψsmd values in those semi-arid/arid ecosystems were within the normal ranges (−0.3 to −1.3 MPa) in spring but was under extreme water stress (−1.6 to −5.3 MPa) in summer in three reserves. Similarly, gas exchange variables reduced by 25%–90% in summer (compared to spring) across the studied forests. Although the regeneration (seedling per 1000 m2) was higher than 100 in two forest (Ajloun and Dibbeen), the number of seedlings in hiking sites was extremely low in both forests. Soil health indicators reveled that soil respiration CO2-C were higher than 25 mg kg−1 in two forests [Ajloun, Dibbeen, (except hiking zone)]. The mean soil saprophytes (number g−1) ranged from 86 to 377 across the forests reserves. In addition, the mean arbuscular mycorrhizal fungi (spores 100g−1 soil) was between 350 and 877. Soil EC was consistently optimal (less than 0.5 dS m−1) and pH was slightly basic (7.5–8.3) across the reserves. The results revealed that the fluctuation of rainfall and anthropogenic pressures (grazing, hiking) led to partial forest degradation. When forests (Dana Biosphere Reserve) received 81 mm annual precipitation, Ψsmd values in Juniperus phoenicea at summer ranged from −4.4 to −5.3 MPa, regeneration and lichens were less than 20 per 1000 m2, and several trees were dead after infected with soil and air borne pathogens including wilt diseases and die back. Intensive hiking activities (Dibbeen forests, tourism area) and heavy grazing (Yarmouk frosts) reduced regeneration, lichens and soil respiration. Interestingly, the native species had better water relations (RWC, Ψsmd) and gas exchange performance than the introduced species. Overall, it is better to grow native species, and exclude anthropogenic pressure on the territory of introduced species. The conservation programs must persist to sustain several native historical forest trees including Juniperus phoenicea (>600 year old), Quercus ithaburensis (>500 year old), and Pinus halepensis (>100 year old) at Mediterranean semi-arid forests. [ABSTRACT FROM AUTHOR]

Projections of changes in tropical cyclone (TC) characteristics under climate change are of great interest to those affected by TCs. In a recent paper, Knutson et al. combined a large number of previous results to produce projections consisting of distributions of possible future TC frequencies, intensities, and rainfall rates. These distributions provide a great resource for users of TC information. However, to apply the distributions to impacts models may require the user to solve a number of technical challenges including modeling correlations, fitting distributions, interpolation, and converting the projections to properties at landfall. I consider the frequency and intensity changes, and implement solutions for each of these challenges using a combination of recently published research and a new methodology. This allows me to produce a dataset of TC projections that give frequency changes as a function of landfall region and intensity. Mean intensity changes can then be derived from frequency changes. The projections are presented in a format that allows them to be used in impacts models without further processing. The interpolation and landfall adjustments tend to increase the frequency changes. As a result, my projections give increasing mean frequencies of hurricane-strength landfalling TCs in four out of six global basins, with mean frequencies increasing by up to 16% for a 2°C increase in global mean surface temperature. My projections are highly uncertain, but include uncertainty estimates. They are designed to be a useful resource for anyone interested in possible future TC frequencies and intensities. [ABSTRACT FROM AUTHOR]

The main worrying feature of climate change is its rapid evolution, in extent and variation, becoming less and less predictable. In this paper, we have reviewed the available literature and elaborated original data to outline how climate change will affect the grapevine cultivation and wine quality. We start by discussing which features of climate change will impact grapevine production most. The effects of heatwaves, air and soil temperature, extreme rainfall events, atmospheric evaporative demand, wildfires, and smoke are addressed. An increased frequency and intensity of heat waves since 2010 is shown in four grapevine production areas of Northern Italy. The focus then shifts to the impacts of the predicted increase in temperature and drought on frost risks, grapevine phenology, yield, berry quality and water needs as well as vine and vineyard carbon budgets. Climate change will challenge the achievement of current yields and wine quality as well as the ability of vineyards to sequester atmospheric carbon, but such effects will likely depend on the characteristics of the growing environments and on the varieties present. Climate change-related threats to grapevine call for a rapid implementation of adaptation strategies. [ABSTRACT FROM AUTHOR]

An updated glacier inventory is important for understanding the current glacier dynamics in the conditions of actual accelerating glacier retreat observed around the world. Here, we present a detailed analysis of the glaciation areas of the Zhetysu Alatau Range (Tien Shan) for 1956–2016 using well-established semiautomatic methods based on the band ratios. The total glacier area decreased by 49 ± 2.8% or by 399 ± 11.2 km2 from 813.6 ± 22.8 km2 to 414.6 ± 11.6 km2 during 1956–2016, while the number of glaciers increased from 985 to 813. Similar rates of area change characterized the periods 1956–2001, 2001–2012, 2012–2016, and 2001–2016: −296.2 ± 8.3 (−0.8% a−1), −63.7± 1.8 (−1.1% a−1), −39.1 ±1.1 (−2.2% a−1) and −102.8 ± 2.9 (−1.3% a−1) km2, respectively. The mean glacier size decreased from 0.57 km2 in 2001 to 0.51 km2 in 2016. Most glaciation areas of the Zhetysu Alatau faced north (north, northwest, and northeast), covered 390.35 ± 11 km2, and were located in altitudes between 3000 and 4000 m.a.s.l. With shrinkage rates of about −0.8% and −1.3% a−1 for the periods of 1956–2001 and 2001–2016, our results show that study area has the highest shrinkage rate compared to other glacierized areas of Central Asian mountains, including Altai, Pamir, and even the inner ranges of Tien Shan. It was found that a significant increase in temperature (0.12 °C/10 years) plays a main role in the state of glaciers. [ABSTRACT FROM AUTHOR]

The most critical issue that was the main research interest is its groundwater quality which is vital for public health concerns. Groundwater is a significant worldwide water supply for diverse communities, especially in dryland regions. Groundwater quality assessment in desert systems is largely hindered by the lack of hydrological data and the remote location of desert Oases. This study provides a preliminary understanding of the influences of climate, land usage, and population growth on the groundwater quality in El-Farafra Oasis in the Western Desert in Egypt from 2000 to now. Therefore, the study's main objective was to determine the extent of change in temporal water quality and the factors causing it. The present study integrates chemical analyses and geospatial modeling better to assess groundwater quality in the study area. A chemical analysis of thirty-one groundwater samples from wells representing each study area was carried out during three time periods (2000, 2010, and 2022). Several chemical properties of groundwater samples gathered from wells in the research area were analyzed. Furthermore, the groundwater quality trend from 2000 to the present was identified using three approaches: Wilcox and Schoeller Diagram in Aq.QA software, interpolation in the ArcGIS software, and Ground Water Quality Index (GWQI). Moreover, the influence of changing land usage on groundwater quality was studied, and it was found that the increase in agriculture and urbanization areas is linked to groundwater quality degradation. The findings revealed that the barren area in 2000, 2010, and 2022 was 371.7, 362.0, and 343.2 km2, respectively, which indicates a substantial decrease of 6.2% within this research timeframe. In contrast, agriculture and human-made structures have expanded by 1.8%. Also, population growth has led to an increase in water consumption as the population has grown at a rate of 7.52% annually from 2000 to 2020. As the climatic condition increases from 2000 to 2022, these changes could extend to the water quality in shallow aquifers with increasing evaporation. Based on the water quality spatial model, it is found that, despite a declining tendency in the rate of precipitation and an expansion in agricultural areas and population growth, the water quality was still appropriate for human and farming consumption in large areas of the study area. The presented approach is applicable to the assessment of groundwater in desert regions in the Middle East area. [ABSTRACT FROM AUTHOR]

The present study is aimed at examining the rainfall changes and their relationship to vegetation dynamics in the central-coastal region of Bangladesh. Different statistical tests were used in the study to investigate the rainfall changes, such as the Mann-Kendall test and Sen's slope estimation. The normalized difference vegetation index (NDVI) time series were collected from Google Earth Engine and processed in R programming to examine the level and extent of vegetation coverage. A correlation analysis was used to determine the association between the total rainfall (June–September) and mean NDVI (October–November). Results depict that annual rainfall decreased in all the stations, with the highest values in Senbag (Z = -5.16 and Q = 55.24). The post-monsoon variability exhibited an increasing trend in the majority of the stations, whereas other seasons showed a decreasing trend with the highest values in Feni (pre-monsoon Z = -2.37 and monsoon Z = -2.49). The relationship between rainfall and vegetation dynamics (NDVI) was observed, which was a moderate to strong positive lag correlation throughout the region. It was also noticed that the locations with higher rainfall fluctuations have a weaker association with the NDVI. Reduced rainfall can reduce vegetation productivity by disrupting growth seasons, lowering soil water content, and increasing salinity in the area. It is expected that the findings will contribute to a better understanding of the impacts of climate change induced rainfall variability and the response of the vegetative environment to it. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth's hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection. Plain Language Summary: Atmospheric rivers (ARs) are long and narrow weather features often referred to as "rivers in the sky." They often transport water from lower latitudes to higher latitudes typically across climate zones and produce precipitation necessary for local climates. Understanding ARs in a warming climate is challenging because of the variety of ways an AR can be defined on gridded data sets. Unlike weather features such as tropical cyclones where identification methodologies are similar, algorithms that determine the characteristics of ARs vary depending on the science question posed. Because there is no real consensus on AR identification methodology, we aim to quantify the algorithmic uncertainty in AR metrics and precipitation. We compare 16 different ways of defining an AR on gridded data sets and present the range of possibilities in which an AR could change under global warming. Generally, ARs are projected to increase but the amount of that increase is a function of the algorithm. Across all algorithms and focus regions, AR precipitation is projected to become more extreme. Key Points: High‐resolution historical and future simulations are used to evaluate atmospheric river detection tools (ARDT) uncertaintyARDTs mostly show increases in frequency and intensity of future atmospheric rivers (ARs) but the scale of response is dependent on algorithmic restrictivenessMost regions experience an increase in precipitation volume coming from extreme ARs [ABSTRACT FROM AUTHOR]

Many aspects of tropical cyclone (TC) properties at the surface have been changing but any systematic vertical changes are unknown. Here, we document a recent trend of high thick clouds of TCs. The global inner‐core high thick cloud fraction measured by satellite has decreased from 2002 to 2021 by about 10% per decade. The TC inner‐core surface rain rate is also found to have decreased during the same period by a similar percentage. This suppression of high thick clouds and rain has been largest during the intensification phase of the strongest TCs. Hence, these two independent and consistent observations suggest that the TC inner‐core convection has weakened and that TCs have become shallower recently at least. For this period, the lifetime maximum intensity of major TCs has not changed and this suggests an increased efficiency of the spin‐up of TCs. Plain Language Summary: As the atmosphere is becoming warmer under the climate change, many aspects of tropical cyclone (TC) have been changing or are expected to change, for example, TC intensity and TC height. The height of stronger TCs is expected to become taller. We present the first satellite observations of TC clouds near the TC top and demonstrate that the thick clouds of the inner part of TC, which is the part with the most intense winds and rain, has actually decreased during the period between 2002 and 2021. The decrease of high thick clouds within the inner part of TC is also consistent with the observed decreased rain there. However, the lifetime maximum intensity has not changed for this period. This all suggests that the strongest convection of a TC has weakened, and become shallower. TC may also have been spinning up more efficiently converting less energy to similar maximum intensities. Key Points: Cloud fraction (CF) of tropical cyclone (TC) high deep clouds has recently declined in the inner core while the outer‐region change is much smallerThe decline was most significant during the intensification periodThe percentage change of TC inner‐core CF of high thick cloud was similar to that of TC inner‐core rain rate [ABSTRACT FROM AUTHOR]

Water resources and the water cycle in high mountain areas are significantly impacted by climate change. In this study, the midstream section of the Yarlung Tsangpo River basin, situated in the southern part of the Tibetan Plateau, was chosen as the target area, and the Soil Water Assessment Tool (SWAT) was used to assess how climate change may affect hydrological processes. The SWAT model proved effective for runoff and snow cover area simulation. Surface runoff, interflow, and groundwater accounted for 47.2%, 24.4%, and 28.4% of the total runoff, respectively. The spatial distribution of runoff was mainly influenced by precipitation and glacier distribution, whereas the spatial distributions of individual runoff components were mainly influenced by soil properties. Overall, the total runoff as well as its components (surface runoff, interflow, and groundwater) increased at a rate of 0.03–0.83%/10 yr (p > 0.05) in the study area during 1983–2017, which could be attributed to the increase in precipitation. Surface runoff peaked earlier (August) than interflow and groundwater (September), owing to the longer convergence time of interflow and groundwater. Future predictions showed a warming and wetting trend (p < 0.05) in the study area from 2020 to 2100 under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The total runoff was projected to increase at a rate of 0.92–3.56%/10 yr, and the change of total runoff mainly came from the increase of surface runoff. [ABSTRACT FROM AUTHOR]

Europe witnessed a strong increase in climate variability and enhanced climate-induced extreme events, such as hot drought periods, mega heat waves, and persistent flooding and flash floods. Intensified land degradation, land use, and landcover changes further amplified the pressure on the environmental system functionalities and fuelled climate change feedbacks. On the other hand, global satellite observations detected a positive spectral greening trend—most likely as a response to rising atmospheric CO2 concentrations and global warming. But which are the engines behind such shifts in surface reflectance patterns, vegetation response to global climate changes, or anomalies in the environmental control mechanisms? This article compares long-term environmental variables (1948–2021) to recent vegetation index data (Normalized Difference Vegetation Index (NDVI), 2001–2021) and presents regional trends in climate variability and vegetation response across Europe. Results show that positive trends in vegetation response, temperature, rainfall, and soil moisture are accompanied by a strong increase in climate anomalies over large parts of Europe. Vegetation dynamics are strongly coupled to increased temperature and enhanced soil moisture during winter and the early growing season in the northern latitudes. Simultaneously, temperature, precipitation, and soil moisture anomalies are strongly increasing. Such a strong amplification in climate variability across Europe further enhances the vulnerability of vegetation cover during extreme events. [ABSTRACT FROM AUTHOR]

This study aims to reveal the relationship of the expansion of the salt-covered area of Lake Tuz in Turkey with the drought in the region and the change of the groundwater storage. The changes in the salt-covered area in Lake Tuz between 1985 and 2021 (37 years), which have been obtained by August satellite images, are compared with those in groundwater and the 12-month Standardized Precipitation–Evapotranspiration Index (SPEI) variations in the lake. The results show that the salt-covered area on the lake surface was most strongly correlated with the SPEI spring (−0.78, t test; p < 0.01) during the period of August 1985–2021, implying that the drought conditions in spring determines 62% of the salt-covered area on the lake in August. The groundwater storage (GWS) in June and July and the salt-covered area in August in Lake Tuz also depict a moderate correlation of −0.60 at 1% significance level from 1985 to 2021. The results illustrate that the SPEI and GWS values decreased, while the salt-covered area on the lake surface expanded in the Lake Tuz Basin over the past 37 years. The trends of the salt-covered area, SPEI, and GWS became especially pronounced after 1999, during which the SPEI change and similar changing trends appeared in the GWS and salt-covered area. These patterns of increase in drought severity and decreasing groundwater storage are expected to increasingly compromise the future of Lake Tuz and cause ecological and environmental problems in the near future. [ABSTRACT FROM AUTHOR]

Central Oklahoma is undergoing investment in new intermodal transportation and rehabilitation of its infrastructure. Despite a highly variable historical climate, future changes resulting from anthropogenic climate change may be outside of the range for which infrastructure was designed. We examined 21st century trends, focusing on weather and climate extremes of demonstrated importance to transportation professionals as identified through expert input. We assessed trends from a suite of 15 global climate models (GCMs) using two emissions scenarios and two high-resolution statistically downscaled datasets. This ensemble provided a quantitative range for potential future climate conditions whilst revealing uncertainties associated with different models and downscaling methods. Our results support the general consensus of a reduction in the frequency of cold temperatures, freeze–thaw cycles, and winter weather; however, for the latter, there is not necessarily a reduction in intensity. Extreme heat days (e.g., days ≥100 °F) increased by factors of 3–6, with this upper range associated with high greenhouse gas emissions, while the seasonal duration of extreme heat extended by 4–10 weeks. Projected return intervals for heavy rainfall increased in frequency and magnitude in the mid and late 21st century. Although the contribution of the emissions pathway to these changes is evident, different extreme value distributions and the varying simulations of precipitation from the GCMs have a large effect on magnitudes, leading to a range of possible futures to consider in infrastructure design. Precipitation metrics, particularly at the extremes, were more sensitive to the selection of downscaled data, as compared with temperature metrics. Our approach represents a resource for transportation professionals seeking to identify changing risk probabilities at regional to local scales, as a precursor to planning and adaptation. [ABSTRACT FROM AUTHOR]

Climate change has a greater impact on high-altitude and mountain regions than on lowlands. The first priority should be to detect these changes in high mountainous regions, as they will directly affect water resources in the main stems. The present study examines the effect of climate change on snow-rain phase separation in the future in the Behesht Abad and Koohrang zones of Iran's central Zagros mountains, one of the country's highest mountain ranges. The study region's 1985–2019 precipitation, temperature, and precipitation type data were used for this purpose. Future climate change was predicted using the National Center for Environmental Protection's (NCEP) atmospheric reanalysis data and the CanESM2 model for the three scenarios RCP 2.6, RCP 4.5, and RCP 8.5. Furthermore, the SDSM 5.3 model was used to perform the downscaling. The original and modified Mann-Kendall tests were used to detect data trends. Different methods were investigated, and observational reports were compared to separate the precipitation phase during the basic period, and in conclusion, the methods Kienzle and USCE yielded good results. According to the findings of this study, climate change will cause significant changes in the temperature distribution of precipitation in the studied mountain region in the future. The probability of extreme precipitation will increase along with a decrease in precipitation rate variability relative to the historical period during the fall-winter season. Additionally, the changes will increase precipitation during warmer temperatures and decrease it during periods of cooler temperatures and change in snow and rain rates in the future, relative to the historical period. [ABSTRACT FROM AUTHOR]

This article reviews the state of the art in the use of space-borne observations for analyzing extreme rainfall and flood events in Africa. Floods occur across many space and timescales, from very localized flash flood events to slow propagation of discharge peaks in large rivers. We discuss here how satellite data can help us understand the genesis and impacts of these flood events, monitor their evolution, and better constrain prediction models, thereby improving early warning and population protection. To illustrate these topics, we reanalyze major flood events that occurred in Niger, Mozambique, Central African Republic and Ivory Coast, using satellite information. [ABSTRACT FROM AUTHOR]

Intraseasonal variations of precipitation and its microstructure are investigated using measurements of the Equatorial Atmospheric Radar (EAR) facilities at Kototabang, west Sumatra, Indonesia (0.20°S, 100.32°E, 864 m above sea level). Raindrop size distribution (DSD) observations are obtained from a 2D-Video Disdrometer (2DVD) with a near continuous record of operation over eight consecutive years (2003–2010). Precipitation types are classified using 1.3-GHz wind profiler observation, and are partitioned according to active and inactive convective phases of Madden–Julian Oscillation (MJO). It is found that precipitation systems during the inactive phase are more continental in nature than those during the active phase. Cloud propagation from brightness temperature data indicates that Sumatra receives the rainfall mainly from maritime clouds during the active phase, while it is mainly from the continental clouds (land-based convection) during the inactive phase. Other remarkable differences between active and inactive phase precipitation systems are also observed from the vertical structure of precipitation. The precipitation during the inactive phase has deeper storms, a higher reflectivity aloft, more lightning activity and less stratiform characteristics, as compared to the active phase. Assessment of cloud effective radius of the Moderate Resolution Imaging Spectroradiometer (MODIS) data also shows a slight difference in the cloud droplet between the active and the inactive MJO phases. Different convective storms in different MJO phases lead to different DSD characteristics and Z – R relationships. The DSD during the inactive phase tends to have a higher concentration of medium and large-size drops than the active counterpart, consistent with the previous study during the first campaign of Coupling Processes in the Equatorial Atmosphere project. Although the DSD parameters and coefficient of Z – R relationships fall within the range of tropical maritime precipitation, mass-weighted mean diameter ( D m ) for the deep convective rains during the inactive phase are somewhat larger than that for maritime and closer to the continental cluster. Therefore, continental-like DSDs are somewhat dominant during the inactive phase, consistent with the intraseasonal variation of precipitation structure. The causative processes of the observed difference in the DSD for the two phases have also been discussed with the help of satellite and radar data. Evaporation and updraft associated with the intense convection during the inactive phase seem to eliminate the small-sized drops from the spectra. Finally, radar reflectivity during the inactive phase is larger than that during the active MJO phase, at the same rainfall rate. This condition can limit the accuracy of radar-derived rainfall estimates for the tropics when applying a single Z – R relation to the two MJO phases, particularly for deep convective rains. [ABSTRACT FROM AUTHOR]

Rainfall intensity extremes are relevant to many aspects of climatology, climate change, and landsurface processes. Intensity is described and analysed using a diversity of approaches, reflecting its importance in these diverse areas. The characteristics of short-interval intensity extremes, such as the maximum 5-min intensity, are explored here. It is shown that such indices may have marked diurnal cycles, as well as seasonal variability. Some indices of intensity, such as the SDII (simple daily intensity index), provide too little information for application to landsurface processes. Upper percentiles of the intensity distribution, such as the 95th and 99th percentiles (Q95 and Q99) are used as indices of extreme intensity, but problematically are affected by changes in intensity below the nominated threshold, as well as above it, making the detection of secular change, and application to sites with contrasting rainfall character, challenging. For application to landsurface processes, a new index is introduced. This index (RQ95), is that intensity or rainfall rate above which 5% of the total rainfall is delivered. This index better reflects intense rainfall than does Q95 of even 5-min accumulation duration (AD) rainfall depths. Such an index is helpful for detecting secular change at an observing station, but, like Q95, remains susceptible to the effects of change elsewhere in the distribution of intensities. For understanding impacts of climate and climate change on landsurface processes, it is argued that more inclusive indices of intensity are required, including fixed intensity criteria. [ABSTRACT FROM AUTHOR]

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002–2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of − 0.80, − 0.58, − 0.43, and − 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of − 0.68, − 0.23, and − 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA–Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods. [ABSTRACT FROM AUTHOR]

The mass budget of southern Patagonian glaciers is characterized by an extreme amount of surface ablation. To understand the processes controlling surface mass balance, we analyzed in situ data including meteorological variables and ablation stakes for the 25 years between 1996 and 2020 near the terminus of Glaciar Perito Moreno in southern Patagonia in South America. The mean annual temperature has increased over the study period at a rate of 0.2°C decade−1. An energy-balance model was applied to calculate a point surface mass balance, based on meteorological records. The average point surface mass balance is estimated to be −16.3 m water equivalent (w.e.) yr−1 between 1996 and 2020, decreasing at a rate in the range from −0.4 to −0.9 m w.e. yr−1 decade−1. The greatest contribution to the surface energy balance was due to the sensible heat flux, and its variation drove the surface mass balance variation. The meteorological and surface mass balance records were compared with the Southern Annular Mode and El Niño–Southern Oscillation, which change the atmospheric circulation over southern Patagonia and influence surface mass balance near the terminus of the glacier. Our long-term dataset investigates the detailed meteorological conditions and surface mass balance and their connection with the large-scale climate variability over the last 25 years, reported for the first time in Patagonia. [ABSTRACT FROM AUTHOR]