Future Precipitation Projections Research Articles

A Data Driven Approach for Analyzing the Effect of Climate Change on Mosquito Abundance in Europe

Mosquito-borne diseases have been spreading across Europe over the past two decades, with climate change contributing to this spread. Temperature and precipitation are key factors in a mosquito’s life cycle, and are greatly affected by climate change. Using a machine learning framework, Earth Observation data, and future climate projections of temperature and precipitation, this work studies three different cases (Veneto region in Italy, Upper Rhine Valley in Germany and Pancevo, Serbia) and focuses on (i) evaluating the impact of climate factors on mosquito abundance and (ii) long-term forecasting of mosquito abundance based on EURO-CORDEX future climate projections under different Representative Concentration Pathways (RCPs) scenarios. The study shows that increases in precipitation and temperature are directly linked to increased mosquito abundance, with temperature being the main driving factor. Additionally, as the climatic conditions become more extreme, meaning higher variance, the mosquito abundance increases. Moreover, we show that in the upcoming decades mosquito abundance is expected to increase. In the worst-case scenario (RCP8.5) Serbia will face a 10% increase, Italy around a 40% increase, and Germany will reach almost a 200% increase by 2100, relative to the decade 2010–2020. However, in terms of absolute numbers both in Italy and Germany, the expected increase is similar. An interesting finding is that either strong (RCP2.6) or moderate mitigation actions (RCP4.5) against greenhouse gas concentration lead to similar levels of future mosquito abundance, as opposed to no mitigation action at all (RCP8.5), which is projected to lead to high mosquito abundance for all cases studied.

Increased drought and extreme events over continental United States under high emissions scenario

The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023–2052) compared to the historical period (1989–2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories.

Assessment of rainwater harvesting potential for urban area under climate and land use changes using geo-informatics technology

Globally climate and land use change have put stress on the natural water resources which are depleting rapidly. This study's goal is to explore the rainwater harvesting (RWH) potential for Lahore city. Average annual rainfall records were used along with future projections of precipitation by global climate model GCM (MPI-ESM1–2-HR) under two shared socioeconomic pathways (SSP2 and SSP5) to find the potential of rainwater harvesting. Land use land cover (LULC) maps of past (2010), present (2022) and future time periods (2035 and 2050) and built-up area for Lahore city was calculated in Arc Map using a geometry dialogue box that was estimated to be 1299.86 km2 of land in the year 2050. By the middle of the twenty-first century, rainfall in the Lahore district is predicted to increase by 12% and 16%, from 698.0 mm in the baseline time period (1990–2015) to 781.7 mm and 809.6 mm, respectively. This increased precipitation over the extent of built-up area could be harvested and utilized for different domestic, industrial, recreational and environment protection purposes. The study also concluded that RWH not only save the Lahore from urban flooding due to rain but also have potential to realize about 39.8% of water deficit of total water demand for Lahore. The daily supply capacity from Ravi River and WASA is 632 million gallon which is equal to only 54% of total water demand. The shortage of 487.2 million gallon per day could be succeed by using rainwater which have potential to realize about 19% of water shortage. In a similar manner, it is indicated based on outcomes of this study that method of RWH is very appropriate for major cities like Lahore in order to address present climate and land use change concerns, particularly in water-stressed nations like Pakistan.

Future projections of extreme precipitation in Tropical America and Panama under global warming based on 150‐year continuous simulations using 20‐km and 60‐km atmospheric general circulation models

AbstractThis study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate.

Emergent constraints on future extreme precipitation intensification: from global to continental scales

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.

Evaluation and Projection of Climate Change in the Second Songhua River Basin Using CMIP6 Model Simulations

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

Applicability of CMIP5 and CMIP6 Models in China: Reproducibility of Historical Simulation and Uncertainty of Future Projection

Abstract General circulation model (GCM) outputs archived by the Coupled Model Intercomparison Project (CMIP) are widely used for climate projection. A comprehensive evaluation of the applicability of GCMs is crucial for generating reliable projections. However, previous GCM evaluation studies mainly focused on the reproducibility of the historical simulation to the observation data, ignoring the uncertainty involved in the future climate projection. In this study, an innovative and comprehensive index, named the history–future applicability score (HFAS), is proposed for evaluating the applicability of GCMs. Through incorporating the Taylor skill score (S) and the analysis of variance (ANOVA) within a general framework, the HFAS can reflect both the historical reproducibility and future uncertainty of GCMs. The HFAS framework is applied to quantitatively evaluating the skills of 28 GCMs (14 from CMIP5 and 14 from CMIP6) in modeling temperature and precipitation at 631 reference sites across China. Results indicate that (i) both CMIP5 and CMIP6 models underestimate the historical temperature of China and overestimate the historical precipitation; (ii) the total uncertainties in future temperature and precipitation projections increase along with time; compared to CMIP5, temperature uncertainty of CMIP6 is narrowed, while the precipitation uncertainty is increased; (iii) taking into account historical reproducibility and future uncertainty, CMIP6 models generally have increased applicability compared to CMIP5; and (iv) the best-performing GCMs in modeling temperature and precipitation of China are ACCESS-CM2 and MPI-ESM-LR, respectively, with the HFAS being 68.4 and 63.5. The findings are helpful for researchers to select locally appropriate GCMs and generate reliable regional climate projection.

Future projection of extreme precipitation over the Korean Peninsula under global warming levels of 1.5 °C and 2.0 °C, using large ensemble of RCMs in CORDEX-East Asia Phase 2

Future projection of extreme precipitation over the Korean Peninsula under global warming levels of 1.5 °C and 2.0 °C, using large ensemble of RCMs in CORDEX-East Asia Phase 2

Utility of Open-Access Long-Term Precipitation Data Products for Correcting Climate Model Projection in South China

Insufficient precipitation observations hinder the bias-correction of Global Climate Model (GCM) precipitation outputs in ungauged and remote areas. As a result, the reliability of future precipitation and water resource projections is restricted for these areas. Open-access quantitative precipitation estimation (QPE) products offer a potential solution to this challenge. This study assesses the effectiveness of three widely used, long-term QPEs, including ERA5, PERSIANN-CDR, and CHIRPS, in bias-correcting precipitation outputs from the CMIP6 GCMs. The evaluation involves the reproduction of precipitation distribution, streamflow simulation utility based on a hydrological model, and the accuracy of extreme indices associated with rainstorm/flood/drought events. This study selects the Beijiang basin located in the subtropical monsoon area of South China as the case study area. The results demonstrate that bias-correction using QPEs improves the performance of GCM precipitation outputs in reproducing precipitation/streamflow distribution and extreme indices, with a few exceptions. PCDR generally exhibits the most effective bias-correction utility, consistently delivering reasonable performance across various cases, making it a suitable alternative to gauge data for bias-correction in ungauged areas. However, GCM outputs corrected by ERA5 tend to overestimate overall precipitation and streamflow (by up to about 25% to 30%), while the correction results of CHIRPS significantly overestimate certain extreme indices (by up to about 50% to 100%). Based on the revealed performance of QPEs in correcting GCM outputs, this study provides references for selecting QPEs in GCM-based water resource projections in remote and ungauged areas.

Future Projections of Precipitation and Temperature in Northeast, Thailand using Bias-Corrected Global Climate Models

The increase in anthropogenic greenhouse gases (GHGs) has resulted in global climate change, with the Northeast region of Thailand experiencing the highest rate of change. This poses signifi cant risks of drought and its impact on crop yield production. To better understand the potential consequences of climate change and devise suitable adaptation strategies, this study aims to project precipitation and temperature data for Northeast Thailand from 2015 to 2055, using bias-corrected global climate models under Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results showed that the annual precipitation and temperature increased from 2015 to 2055 under both RCPs. The annual average (minimum-maximum) of precipitation from the CanESM, GFDL, MIROC5, NorESM, and ensemble mean from four models under RCP4.5 were 1,238.83 (904.28-1,629.70), 1,227.82 (865.49- 1,721.31), 1,312.78 (876.60-1,616.38), 1,350.21 (985.55-1,625.00), and 1,282.41 (1,088.43-1,461.49) mm and RCP8.5 were 1,267.96 (864.24-1,712.86), 1,222.20 (863.79-1,835.43), 1,294.07 (843.04-1,752.41), 1,353.14 (1,059.50-1,827.23), and 1,284.34 (1,116.55-1,541.63) mm, respectively. While, those of temperature under RCP4.5 were 29.27 (28.15-30.35), 29.59 (27.98-31.20), 29.12 (27.65-30.70), 28.09 (26.96-29.14), and 29.02 (28.08-29.90) °C and RCP8.5 were 29.50 (28.12-31.40), 29.68 (28.23-31.50), 29.11 (27.51-30.26), 28.37 (27.11-29.88), and 29.17 (28.08-30.66) °C, respectively. These fi ndings suggest that the annual precipitation is lower under RCP4.5 compared to RCP8.5, while the temperature shows an increasing trend under both RCPs. Therefore, it is evident that climate change will manifest differently in Northeast Thailand, depending on local contexts and the measures implemented today. Understanding the impacts and risks of future climate change at a local scale and identifying adaptive solutions pose signifi cant challenges for the future.

Impact of Global Warming on U.S. Summertime Mesoscale Convective Systems: A Simple Lagrangian Parcel Model Perspective

Abstract Mesoscale convective systems (MCSs) are the dominant rainfall producer over the United States during the warm season, causing natural disasters and severe weather every year. Global climate models have large uncertainty in projecting precipitation changes in the future climate. Here, a simple Lagrangian parcel model is used to investigate the impact of global warming on MCS initiation and growth. The single-column parcel model projects a mean precipitation decrease over the central United States and an increase to its east, in agreement with the CMIP5 model projection. It also highlights the crucial role of current climate mean-state model bias in exaggerating the change in future mean precipitation projection by 25%. As for convective population, the model captures the decreased occurrence frequency of weak to moderate convection and increased frequency of strong convection due to the increased CAPE and CIN, in agreement with convection-permitting regional simulations. Novel parameterizations of gust-front propagation speed and subsidence strength are developed as guided by cloud-resolving simulations. The multicolumn parcel model employing those parameterizations captures readily the cold pool–induced upscale growth feature. It simulates smaller mesoscale clusters over the central United States under global warming due to gust-front slowdown and subsidence strength enhancement, which are further attributed to land aridity–induced weakening of initial accumulated precipitation and strengthening of updraft speed, respectively. That said, mesoscale clusters could become bigger under more favorable conditions in future climate, including boundary layer moistening, convection lifetime lengthening, and cold pool mechanical-lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes.

Deep learning for stochastic precipitation generation – deep SPG v1.0

Abstract. We present a deep-neural-network-based single-site stochastic precipitation generator (SPG), capable of producing realistic time series of daily and hourly precipitation. The neural network outputs a wet-day probability and precipitation distributions in the form of a mixture model. The SPG was tested in four different locations in New Zealand, and we found it accurately reproduced the precipitation depth, the autocorrelations seen in the original data, the observed dry-spell lengths, and the seasonality in precipitation. We present two versions of the hourly and daily SPGs: (i) a stationary version of the SPG that assumes that the statistics of the precipitation are time independent and (ii) a non-stationary version that captures the secular drift in precipitation statistics resulting from climate change. The latter was developed to be applicable to climate change impact studies, especially studies reliant on SPG projections of future precipitation. We highlight many of the pitfalls associated with the training of a non-stationary SPG on observations alone and offer an alternative method that replicates the secular drift in precipitation seen in a large-ensemble regional climate model. The SPG runs several orders of magnitude faster than a typical regional climate model and permits the generation of very large ensembles of realistic precipitation time series under many climate change scenarios. These ensembles will also contain many extreme events not seen in the historical record.

Evaluation of future temperature and precipitation projections in Morocco using the ANN-based multi-model ensemble from CMIP6

In present study, values of minimum temperature, maximum temperature and precipitation at 27 observation stations in Morocco are used to implement an artificial neural network based downscaling approach in order to simulate regional climate and to investigate the impact of climate change on the country under different scenarios. For this purpose, the best models representing the country among the 15 GCMs within the scope of the CMIP6 are first identified. Then, using the artificial neural network based statistical downscaling method, a multi-model ensemble is created for each climate parameter. Following the performance evaluation based on different statistical metrics and their aggregated values, a good agreement between the observed and the predicted variables is achieved. This allows us to assess future projections of temperature and precipitation following two climate scenarios, namely the SSP2-4.5 and SSP5-8.5. Spatial as well as temporal changes are evaluated for three different time periods namely, 2025–2049, 2050–2074 and 2075–2100. Both scenarios indicate that an important increase of the minimum and maximum temperatures is expected and it can reach up to 5 °C by the end of the century in some regions of the country. Seasonal variability has also been addressed here under climate change scenarios, and consistent variations with annual changes are also reported during each season, except for the summer where the increase barely goes beyond 1.5 °C. The current analysis also includes the variation of precipitation at both seasonal and annual timescales. The country is likely to experience an important drought during the upcoming years, reaching a decrease of roughly 30% and 50% each year respectively, under the SSP2-4.5 and SSP5-8.5 scenarios by the end of the century. This change is also consistent over the seasons, especially during fall, winter and spring seasons, when Morocco receives its major amount of precipitation.

Historical assessment and future projection of extreme precipitation in CMIP6 models: Global and continental

AbstractIn this study, the new gridded daily precipitation dataset REGEN (Rainfall Estimates on a Gridded Network) was used to verify the performance of 23 global climate models participating in the 6th phase of the Coupled Model Intercomparison Project (CMIP6) in terms of extreme precipitation over global and continents during 1950–2014. Projected changes were also evaluated using CMIP6 multi‐model ensemble (MME) under different Shared Socioeconomic Pathway (SSP) by the end of the 21st century. Although positive biases exist in the climatology of most extreme precipitation indices simulated by the CMIP6 MME compared with observations, most of the CMIP6 models can reproduce the spatial distribution characteristics and temporal evolution trends of global extreme precipitation. The simulation is improved by using the CMIP6 MME, relative to the results from individual models. The projected results showed that in most regions of the world, extreme precipitation will significantly increase in the future, with more significant increase under higher SSPs. By the end of the 21st century, total precipitation of wet days (PRCPTOT), simple daily intensity (SDII), the number of very heavy rain days (R20mm), maximum 1‐day precipitation (Rx1day) and maximum consecutive 5‐day precipitation (Rx5day) over the global will increase by up to 50%, 20%, 70%, 50% and 50%, respectively, under the SSP5‐8.5 scenario. Nevertheless, marked uncertainties exist in the projection of extreme precipitation in South America and in the global for consecutive dry days (CDD) index. Noteworthy, average PRCPTOT, Rx1day and Rx5day over Asia, Europe and North America, as well as of the whole world, have significantly increased where the CDD index is also higher, shown by projected results. In summary, global extreme precipitation will increase in the future, and precipitation will become more uneven in space and time. Moreover, as the SSP scenario becomes higher, the uneven distribution of precipitation will become more pronounced.

How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?

Abstract. Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

Future projections of seasonal temperature and precipitation for India

Ninety climate models, from four consortiums—CMIP5, CMIP6, NEX-GDDP, and CORDEX—are evaluated for the simulation of seasonal temperature and precipitation over India, and subsequently, using the best ones, their future projections are made for the country. NEX-GDDP is found to be the best performer for the simulation of surface air temperature for all the four seasons. For the simulation of precipitation, CMIP6 performs the best in DJF and MAM seasons, while NEX-GDDP performs the best in JJAS and ON seasons. The selected models suggest that temperature will increase over the entire Indian landmass, relatively more over the north-western part of the country. Furthermore, the rate of warming will be more in winter than in summer. The models also suggest that precipitation will increase over central eastern and north-eastern India in the monsoon season, and over peninsular India during post-monsoon months.

Fate of Rainfall Over the North Indian States in the 1.5 and 2°C Warming Scenarios

AbstractRise in mean temperature put a great deal of uncertainty about how weather and climate extremes may play out, particularly in India's varied climatic zones. Consequently, it is important to understand the possible changes in both magnitude and direction of weather and climate extremes like rainfall for different warming levels of 1.5 and 2°C scenarios concerning preindustrial and present levels. Hence in the present study, the precipitation behavior of seven North Indian states that is, Haryana, Himachal Pradesh, J&K, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand carefully studied using CMIP5 models. Future projections of precipitation has been done for the Paris Agreement global warming level of 1.5 and 2°C scenarios. Along with model validation and future projections of precipitation, the return period of extreme rainfall is also discussed to understand the behavior of the occurrence of extreme precipitation. Statistical analysis shows that the ensemble means have the least error as compared to the other six CMIP5 models. Therefore, future analysis has been done with the ensemble mean. Our findings show that the precipitation is likely to decrease in the 1.5°C scenarios, while it is likely to increase in the 2°C scenarios. The occurrence and intensity of extreme rainfall events are likely to be more frequent in all the models. The return period of the extreme rainfall events is likely to increase in all the states in both the warming scenarios. A three‐fold rise is likely to increase extreme rainfall events in the 2°C scenario.

Projections and impact assessment of the local climate change conditions of the Black Volta Basin of Ghana based on the Statistical DownScaling Model

Abstract The uncertainties and biases associated with Global Climate Models (GCMs) ascend from global to regional and local scales which delimits the applicability and suitability of GCMs in site-specific impact assessment research. The study downscaled two GCMs to evaluate effects of climate change (CC) in the Black Volta Basin (BVB) using Statistical DownScaling Model (SDSM) and 40-year ground station data. The study employed Taylor diagrams, dimensionless, dimensioned, and goodness of fit statistics to evaluate model performance. SDSM produced good performance in downscaling daily precipitation, maximum and minimum temperature in the basin. Future projections of precipitation by HadCM3 and CanESM2 indicated decreasing trend as revealed by the delta statistics and ITA plots. Both models projected near- to far-future increases in temperature and decreases in precipitation by 2.05-23.89, 5.41–46.35, and 5.84–35.33% in the near, mid, and far future respectively. Therefore, BVB is expected to become hotter and drier by 2100. As such, climate actions to combat detrimental effects on the BVB must be revamped since the basin hosts one of the largest hydropower dams in Ghana. The study is expected to support the integration of CC mitigation into local, national, and international policies, and support knowledge and capacity building to meet CC challenges.

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

Landslide Susceptibility Mapping under the Climate Change Impact in the Chania Regional Unit, West Crete, Greece

Over the preceding decades, climate change has affected precipitation, the most common factor triggering landslides. The aim of this study is to highlight this impact by examining the precipitation trends in the Chania regional unit, Greece, with the help of the precipitation time series provided by 21 local meteorological stations covering a period from 1955 to 2020. The analysis also focuses on the extreme precipitation events of February 2019, where the monthly cumulated precipitation amount reached 1225 mm, one of the highest ever recorded in Greece. Moreover, an inventory of past and recent landslides was created and the intensity–duration landslide precipitation thresholds were evaluated. Daily simulations of precipitation from three state-of-the-art regional climate models were used to analyze precipitation patterns under two representative concentration pathways (RCPs), 4.5 and 8.5, for the period 2030–2060. The application of the estimated precipitation thresholds on the daily future precipitation projections revealed an increase in the following decades of the precipitation events that can activate a landslide and, therefore, highlighted the climate change impact. Moreover, the mean annual precipitation of the preceding 10 years was evaluated and used along with local hydro-geological data and the recent landslide inventory, providing approximately a 5% more effective landslide susceptibility map compared with the relative maps produced by using the mean annual precipitation evaluated for the control period (1976–2005) and for the preceding 30 years. Thus, landslide susceptibility emerges as a dynamic process and the landslide susceptibility map needs to be regularly updated due to the significant and ongoing changes in precipitation because of climate change.