Future Precipitation Research Articles

Projecting Climate Change Impact on Precipitation Patterns during Different Growth Stages of Rainfed Wheat Crop in the Pothwar Plateau, Pakistan

In rainfed areas, precipitation variations directly impact wheat growth stages such as emergence, tillering, jointing and booting, and maturity. Evaluating the impact of climate change on precipitation patterns during these critical growth stages is crucial for adapting climate change and ensuring global food security. In this study, projections of five General Circulation models (GCMs) under two shared socioeconomic pathways (SSPs) were used to predict the changing characteristics of precipitation during four main growth stages of wheat in the rainfed region of the Pothwar Plateau, Pakistan. Historical datasets of daily precipitation at six weather stations were analyzed to check the past changes in the precipitation patterns. During the baseline period (1985–2014), the annual average precipitation decreased at a rate of −9.75 mm/decade, while the amount of precipitation during the rabi season (wheat-growing season) decreased at a rate of −20.47 mm/decade. An increase in the precipitation was found during the fourth (flowering) stage of crop growth, while the first three stages experienced a decrease in the precipitation amount. The multimodal ensembled data, under the SSP2-4.5 scenario, revealed a significant decline (at the rate of −16.63 mm/decade) in the future annual precipitation. However, it is projected that, under SSP2-4.5, there may be a slight increase (4.03 mm/decade) in the total precipitation amount during the future rabi season. Under the SSP5-8.5 scenario, average annual precipitation exhibited a slightly increasing trend, increasing by 1.0 mm/decade. However, during the rabi season, there was a possibility of a decrease in precipitation amount, with a rate of 11.64 mm/decade. It is also expected that the precipitation amount may vary significantly during the crown root initiation, jointing and booting, and flowering stages in the near future. These results provide a framework for the planning of wheat production in the Pothwar region of Pakistan, taking into account the potential impact of shifting weather patterns, particularly in terms of uneven precipitation.

Investigating the skills of HighResMIP in capturing historical and future mean precipitation shifts over Pakistan

AbstractClimatic change over the globe due to global warming affects the characteristics of climate variables that have critical implications on large fraction of population that depends on agriculture for livelihood like Pakistan. Consequently, this study examined how high horizontal grid resolution CMIP6 models simulate the observed precipitation variability during 1981–2014 and further explored the future changes during 2017–2050 under high emission scenario SSP5‐8.5 over Pakistan region. The performances of 12 (CMIP6) High Resolution Model Inter‐comparison Project version 1.0 (hereafter; HighResMIP) GCMs and their ensemble means in reproducing the observed climate were calculated at each station in the study domain and formed the basis for deriving HighResMIP ranking. Further, the study employed Shannon's Entropy and a modified version of Criteria Importance through Inter‐Criteria Correlation (D‐CRITIC) method to build an ensemble mean from the best performing models. Evaluation of HighResMIP GCMs performance revealed that most models showed mixed signals in the region, with fewer models such as HadGEM3‐GC31‐HH, HadGEM3‐GC31‐HM and HadGEM3‐GC31‐MM showing good agreement with the observed precipitation. Overall, HighResMIP multi‐model ensemble outperforms precipitation distribution over individual models. D‐CRITIC based ensemble mean implies higher increase in precipitation than entropy approach. Future changes depict an increase in mean annual in the northern region relative to the historical period. A pronounced increase of about 16%–18% in precipitation was noted in HadGEM3‐GC31‐HH and HiRAM‐SIT‐HR. Conversely, FGOAL‐f3‐H project noteworthy reduction (21%) in precipitation in the near future (2017–2050). The projected seasonal precipitation shows upsurge pattern of 5%–28% in pre‐monsoon season, whereas the reduction in monsoon precipitation is projected to be 29%–40%. The findings of this study can help in building future climate resilience and developing strategic policies in Pakistan.

The combined impact of climate change scenarios and land use changes on water resources in a semi-arid watershed

This study proposes a combined approach involving climate change scenarios and varied land use and land cover (LULC) projections in a semi-arid watershed (El Grou) located in Morocco. The Soil and Water Assessment Tool (SWAT) model was coupled with land use scenarios generated using the Cellular Automata-Markov model for three dates (2030, 2040, and 2050) and future climate projections from the CMIP5 model under RCP 4.5 and 8.5 scenarios. Future precipitation and temperature projections indicate a decrease in annual precipitation by 5 % to 34 % and an increase in average temperatures, particularly during the summer months. LULC changes reveal a significant decline in agricultural lands and forests, with an expansion of bare soil by 2050. These changes, analyzed through three SWAT models representing different time periods, indicate a decrease in surface runoff by 41 % to 73 %, a reduction in total water yield by 21 % to 53 %, and a decline in groundwater recharge, posing significant challenges for water resource management and ecosystem health. The study underscores the need for integrated land and water management strategies, incorporating climate change adaptation measures, to ensure sustainable water resource utilization and build resilience in the El Grou watershed and similar semi-arid regions.

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble

Abstract. ​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.

Enhancing Monsoon Predictions for the Upper Chambal Catchment Through Temporal and Spatial Downscaling of Predicted Future Precipitation

Enhancing Monsoon Predictions for the Upper Chambal Catchment Through Temporal and Spatial Downscaling of Predicted Future Precipitation

Projection of Changes in Rainfall and Drought Based on CMIP6 Scenarios on the Ca River Basin, Vietnam

In this study, future precipitation and drought in the Ca river basin, Vietnam, were projected based on an ensemble of 27 CMIP6 models for four climate change scenarios. The impact of climate change on precipitation and drought was investigated. Monthly precipitation observation data were adjusted using the bias correction method. To detect drought events, the Standard Precipitation Index (SPI) was employed. Changes in drought were assessed using SPI3, SPI6, and SPI12. Although the amount of annual total precipitation slightly increased, the drought events may become more severe. There is a high likelihood of increased drought intensity and severity in Vietnam due to climate change. The frequency of droughts is likely to change depending on the location and climate change scenario. We found that the frequency and severity of droughts may be altered depending on the window size of SPI. The short-term drought events will be more frequent and severe, and long-term drought events will become more severe in the Ca river basin.

Projected urban flood risk assessment under climate change and urbanization based on an optimized multi-scale geographically weighted regression

Previous research has primarily focused on evaluating flood risks influenced by single factors, often neglecting the combined impacts of climate change and urbanization on historical and future flood risks. This study utilized 12 assessment indicators across three criteria layers. It employed the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and an optimized Multi-scale Geographically Weighted Regression (MGWR) to develop a comprehensive assessment framework for historical and projected urban flood risks under combined scenarios of RCPs and SSPs. The risk of future extreme precipitation events increased under 4 RCPs. Compared with 2010, Beijing's urban land area is expected to increase by an average of 53.64 % in 2060 under 5 SSPs, accompanied by rapid socioeconomic development. MGWR based on K-means optimization improves operational speed by reducing the number of patches. The areas classified as high and highest flood risk levels increased by 38.87 % and 60.17 % from the 2010s to the 2060s, indicating an upward trend in future flood risk. Higher flood risk is observed under the high emissions combination scenario. Structural equation modeling (SEM) revealed that road network density (RND), runoff depth (RD), and distance to road (DRO) were the main influencing factors contributing to flood risks in Beijing.

Qualifying uncertainty of precipitation projections over China: mitigating uncertainty with emergent constraints

Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.

Nonstationary multi-site design flood estimation and application to design flood regional composition analysis

Impacts of climate change and human activities may lead to changes in the spatiotemporal composition of the design flood as well as its size. Previous studies mainly focused on changes in design flood size, while there has been relatively little research on changes in its regional composition. In this study, a nonstationary multi-site design flood estimation method is developed, which is useful for the design flood regional composition analysis under nonstationary conditions. Dynamic copula models are first constructed to analyze the change in the joint distribution of the nonstationary multi-site flood variables with the consideration of the nonstationarity of the marginal distribution and copula structure parameters. Then the design flood combinations in multi-site for a specified design standard are calculated by comprehensively applying the equivalent reliability method, the expectation conditional and the most-likely conditional combination strategies, which considers the future precipitation change and design lifespan length impacts on the design flood. Finally, the uncertainty of the multi-site design flood estimation caused by the model parameters uncertainty is evaluated. A case study, based on the annual maximum 7-day (AM7) flood volume in the Yichang (YC) and Cuntan (CT) sites, is conducted to illustrate this method. Results show that flood quantiles in the YC and CT sites exhibit an increasing trend as the precipitation projections will increase in the future, but the flood quantiles in the YC site are less compared to the historical period because of the huge regulation and storage effect of the Three Gorges Reservoir. In addition, the design flood combination in the CT and YC sites are calculated and the CT design floods from the expectation combination strategy are bigger than those provided by the most-likely combination strategy.

Tectonically Controlled Establishment of Modern‐Like Precipitation Patterns in East and Central Asia During the Early Late Miocene

AbstractDeciphering how modern precipitation patterns became established in monsoon‐dominated East Asia and the arid interior Asia is crucial for predicting future precipitation trends under accelerated global warming and increased climate extremes. However, this effort is hindered by a scarcity of quantitative paleo‐precipitation data in this region. Here we reconstruct the pattern of Middle to Late Miocene paleo‐precipitation across an east‐to‐west transect from the summer monsoon‐dominated East Asian region through the transition zone and into interior Asia. Our work is based on a newly established precipitation calculation equation and quantitative pollen‐based precipitation conversion. Analysis indicates a common trend of precipitation across the studied region prior to ca, 11 Ma, followed by a clear divergence of precipitation variations between East and interior Asia since at least 11–9 Ma. This divergence is characterized by increasing precipitation in East Asia, but a coeval decrease in rainfall in the transition zone and interior Asia. The timing of this precipitation divergence was contemporaneous with intense tectonic activity in the northern Tibetan Plateau, which differentially affected the efficacy of water vapor transport into East and interior Asia. Modeling work using different topographic settings corroborates this tectonic influence. Our study demonstrates the early establishment of modern‐like precipitation patterns in East‐interior Asia at least in the early Late Miocene.

Analyzing the Contribution of Moisture Sources to Precipitation Changes Under 1.5/2.0°C Stabilized Warming Scenarios: A Study Over Northwest China

AbstractChanges in precipitation and its causes under future warming scenarios in Northwest China are of great concern. Based on the dynamical downscaling results of the Weather Research and Forecasting model (WRF), this study quantitatively identified the contribution of local evaporation and external moisture transport to future precipitation changes in western and eastern Northwest China (WNWC and ENWC) by calculating the precipitation recycling ratio (PRR). The results showed that the WRF could reasonably reproduce the observed spatial and temporal distribution of precipitation and evaporation during the historical reference period of 1985–2014. Under the 1.5 and 2.0°C stabilized warming scenarios from 2071 to 2100, there is a stronger precipitation growth in WNWC than in ENWC, and the PRR generally increases in WNWC and decreases in ENWC. Further analysis indicates that external moisture transport is the dominant contributor to the increased precipitation in all cases with its contribution exceeding 70%, while the difference of precipitation growth in two sub‐regions arises from the summer moisture transport at the southern boundary as the inflow in WNWC and outflow in ENWC. Compared to the negligible contribution of evaporation in ENWC (less than 7%), evaporation contributes more than 23% to the precipitation increase in WNWC under future warming scenarios, which may be related to the strong land‐atmosphere coupling there. Furthermore, an additional warming of 0.5°C leads to an increase in precipitation for both sub‐regions, which is mainly due to the contribution of external moisture transport.

Key drivers of vulnerability to rainfall flooding in New Orleans

IntroductionFuture urban stormwater flood risk is determined by the confluence of both climate-driven changes in precipitation patterns and the effectiveness of flood mitigation systems, such as urban drainage and pump systems. This is especially true in coastal cities protected by levee systems like New Orleans, where even present-day rainfall would be enough to cause serious flooding in the absence of extensive stormwater drainage and pumping. However, while the uncertainties associated with climate change have been well studied, uncertainties in infrastructure performance and operation have received less attention.MethodsWe investigated how these interrelated sets of uncertainties drive flood risk in New Orleans using a Robust Decision Making (RDM) approach. RDM is a framework for Decision Making Under Deep Uncertainty (DMDU) that leverages simulation models to facilitate exploration across many possible futures and the identification of decision-relevant scenarios. For our work, we leveraged a detailed Storm Water Management Model (SWMM) representation of the New Orleans urban stormwater management system to examine flood depths across the city when faced with different levels of future precipitation, sea-level rise, drainage pipe obstruction, and pumping system failure. We also estimated direct flood damage for each neighborhood in the city for this scenario ensemble. These damage estimates were then subjected to vulnerability analysis using scenario discovery—a technique designed to determine which combinations of uncertainties are most stressful to the system in terms of an outcome of interest (excess flood damage).ResultsOur results suggest that key drivers of vulnerability depend on geographic scale. Specifically, we find that possible climate-driven precipitation increases are the most important determinant of vulnerability at the citywide level. However, for some individual neighborhoods, infrastructure operation challenges under present day conditions are a more significant driver of vulnerability than possible climate-driven precipitation increases.

Tree species mixing effects on the radial growth of Pinus massoniana and Castanopsis hystrix: A comparison between even-aged and uneven-aged stands

The establishment of mixed-species forests with complex structures and high biodiversity has emerged as a key approach for enhancing productivity and maintaining the stability of plantation forests. However, how the mixing effect changes with stand development and climate variation is still unclear. Additionally, little is known about how the mixing effects vary between even- and uneven-aged mixed planting strategies based on the same species combinations and site conditions. In this study, we used dendrochronological methods to reconstruct 37 years of annual growth series for even-aged mixed plantations (EMs) and uneven-aged mixed plantations (UEMs) of Pinus massoniana and Castanopsis hystrix, as well as their respective monocultures at the individual tree (286 trees in total) level. We found that in EMs, the growth of P. massoniana and C. hystrix was 20.8 % lower and 26.2 % higher than those of their monocultures, respectively. However, the growth of P. massoniana remained unchanged during the first 17 years of mixing, after which it was significantly suppressed by C. hystrix. The growth of P. massoniana and C. hystrix in the UEMs was 20.8 % and 26.2 % lower than that in their monocultures, respectively. Remarkably, the mixing effect on the growth of P. massoniana shifted from suppression to promotion after 30 years of mixing. Furthermore, the relative growth of P. massoniana in the UEMs increased with dry season precipitation. These results indicate that transforming pure plantations into uneven-aged mixed plantations may be a crucial forest management strategy for enhancing tree growth and to improving adaptation to future precipitation pattern changes. However, in EMs, thinning should be conducted before fast-growing, shade-intolerant species experience intense light competition.

Machine learning and CORDEX-Africa regional model for assessing the impact of climate change on the Gilgel Gibe Watershed, Ethiopia

Climate change is one of the most pressing challenges of our time, profoundly impacting global water resources and sustainability. This study aimed to predict the long-term effects of climate change on the Gilgel Gibe watershed by integrating machine learning (ML) methods and climate model scenarios. Utilizing an ensemble mean of four regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa project, we forecast future climatic conditions. Although global and regional climate simulations offer valuable insights, their limitations necessitate alternative approaches, such as ML, for improved accuracy. Employing an ensemble ML model with Random Forest (RF), Extra Tree (ET), and CatBoost (CB) algorithms, we assessed various bias-correction methods using historical data from 1993 to 2009. Our results highlight the effectiveness of distribution mapping (DM) in capturing temperature variability and precipitation patterns, using the power transpiration (PT) method to represent precipitation variability. Projections indicate a decline in future precipitation under the RCP 8.5 (−32.2%) and SSP 4.5 (−88.8%) for 2024–2049, with further decreases expected for 2050–2099. Conversely, temperatures will rise under RCP 4.5 (TMAX 0.67 °C) and RCP 8.5 (TMAX 0.25 °C and TMIN 1.11 °C) in the near term, exacerbated by higher emissions under SSP 4.5 and 8.5. By leveraging an ensemble mean of four observed RCMs in an ML framework, our study successfully reproduced future Coupled Model Intercomparison Project (CMIP5) and (CMIP6) climatic datasets, with the CB model demonstrating superior performance in predicting future precipitation and temperature trends. These findings offer valuable insights for shaping future climate scenarios and informing policy decisions for the Gilgel Gibe Watershed, thereby enhancing water resource management in the basin and its environs.

The climate niche of Homo Sapiens

The increasing atmospheric concentrations of greenhouse gases will place humans in climates that are unprecedented in the evolution of the species. I use the ecological definition of the human niche in climate space, and combine this with a new constellation of methods from extreme value statistics to study human occupation near the boundaries of that niche. I find that the temperature distribution has a thin tail whereas the tail of precipitation is thick. This thick tail reflects that humans are used to a wide range of rainfall regimes, so future precipitation changes, although leading to unprecedented rainfall, are less likely to pose a major challenge. An increase in temperature, on the other hand, will put hundreds of millions of people in heat that is not just unprecedented and but also hard to imagine from extrapolating current temperatures. These findings are qualitatively similar but an order of magnitude smaller than previous studies.

Projection of extreme climate change in the Asian arid region and the Tibetan Plateau in the early and middle 21st century based on NEX-GDDP-CMIP6

In the context of global warming, it is anticipated that both the intensity and the frequency of future global extreme high precipitation (EHP) and extreme high temperature (EHT) events will increase. To evaluate the future extreme climate changes in the Asian arid region and Tibetan Plateau, this study applied the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) to assess the changes in EHP (Rx5d and R95pTOT) and EHT (TX90p and TXx) under different emission scenarios in the 21st century. Findings suggest that both the frequency and the intensity of the extreme indices will increase, exhibiting accelerated growth under higher emission scenarios, particularly under the SSP5-8.5 emission scenario. It is suggested that the growth of EHT and EHP in the early subperiod of the 21st century (2026–2045) will be relatively moderate, with small differences between different emission scenarios. However, by the middle subperiod of the 21st century (2041–2060), the differences between different emission scenarios will become larger than the 2035s and the growth will become more intense. In western central Asia, TX90p, TXx, Rx5d, and R95pTOT increase by 9.7%–14.2% (13.3%–24.7%), 1.3°C–1.7°C (1.6°C–2.7°C), 6.5%–8.9% (8.2%–8.8%), and 18.1%–27.0% (25.6%–30.0%) by the early (middle) subperiod; in eastern central Asia, TX90p, TXx, Rx5d, and R95pTOT increase 8.1%–12.0% (11.3%–21.1%), 1.4°C–1.8°C (1.9°C–2.9°C), 7.4%–9.7% (10.4%–13.8%), and 20.2%–29.3% (32.0%–40.8%) by the early (middle) subperiod; and over the Tibetan Plateau, TX90p, TXx, Rx5d, and R95pTOT increase 12.5%–17.4% (17.0%–31.0%), 1.2°C–1.5°C (1.6°C–2.5°C), 7.2%–10.0% (9.9%–15.0%), and 26.6%–33.1% (36.1%–55.3%) by the early (middle) subperiod.摘要近年来, 随着全球变暖, 大部分地区极端气候事件的风险都有所增加. 作为中国一带一路建设核心区域的亚洲干旱区和青藏高原区域面临着严峻的极端气候风险. 本文利用NEX-GDDP-CMIP6数据预估了未来21世纪早期 (2026–2045) 和中期 (2041–2060) 不同排放情景下亚洲干旱区和青藏高原相较气候参考期 (1995–2014) 极端高温指数和极端降水指数的变化. 预估结果显示, 在不同排放情景下极端高温指数和极端降水指数都有所上升, 且在SSP5-8.5情景下增长最多, 其中NEX-GDDP-CMIP6不同模式对极端高温指数的模拟一致性高于极端降水指数. 21世纪中期相比早期极端高温指数和极端降水指数的增长更加严重.

Future changes in global rainfall erosivity: Insights from the precipitation changes

Quantifying future changes in rainfall erosivity is essential to address potential water erosion risks. This study reveals a significant power function relationship between rainfall erosivity and precipitation both globally and across various climatic zones. Building on this foundation, new empirical models were developed to project relative changes in rainfall erosivity. These models, predicted on the understanding that General Circulation Models (GCMs) provide more accurate predictions of climate change trends than exact meteorological values, utilizes the ratio of modeled historical precipitation to future precipitation under various scenarios as the variable, formulated as a power function. Data from three GCMs runs from both the Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP6–including historical, mid-term, and long-term periods–were employed. The analysis suggests that by the end of the 21st century, global annual rainfall erosivity could increase by 2.6% under the Shared Socio-Economic Pathway (SSP)1–2.6 scenario, by 3.5% under SSP2-4.5, and by a significant 6.4% under SSP5-8.5, relative to the 2000–2010 baseline. Furthermore, over 76% of the global land area is projected to experience an increase in rainfall erosivity over the century. Regions with projected changes in rainfall erosivity, whether increase or decrease, are likely to face more pronounced changes from mid-century onwards. The CMIP6 exhibits improved model consistency over its predecessor, CMIP5, indicating a greater water erosion risk as global warming progresses. These projections offer insights for strategies to combat soil degradation due to climate changes.

Statistical downscaling of future temperature and precipitation projections in Iraq under climate change scenarios

The research was aimed to; (i) study the observed climate in Iraq from 1971 to 2021 for annually, monthly and seasonally averages of temperature and precipitation for nine meteorological stations; ii) produce RCP4.5 and RCP8.5 scenarios for Iraq region under historical (1900–2005) and future periods (2006–2100) for temperature and precipitation using NCAR-CCSM4 database; and iii) examine the anomaly for near future (2025–2034) and medium future (2050–2059), related to 1986–2005. To evaluate the performance of the monthly averages of temperature and precipitation, three bias correction techniques i.e., Delta, Empirical Quantile Mapping, and Quantile Mapping were applied to nine metrological stations. The results revealed that the Delta is the most efficient method to bias correction for the monthly averages related to temperature and precipitation. The annual average temperature increased from 22.9 C° in 2006, based on both RCP4.5 and RCP8.5, to 24.0 C° and 27.2 C°, respectively, and the anomaly increased from −0.9 C° and −1.9 C° in 2006, to 0.6 C° and 2.4 C°. The precipitation annual and winter month's averages under RCP4.5 and RCP8.5 from 2006 to 2100, 2020–2039 and 2040–2059 showed that southwest region in Iraq (Basra, Missan and Thi-Qar provinces) had more drought and low precipitation than the other regions. From 2029 to 2034 and 2050–2054, rainy years have been predicted, using RCP4.5. Based on RCP.8.5, from 2025 to 2034 and 2050–2055, should witness rainy years, followed by a drought period. The results expected to support decision-makers in incorporating a national initiative to break down social barriers, encourage growth prospects, particularly in southern Iraq.

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

Downscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.

Elevation dependent precipitation changes in historical and future times in the Himalayan Upper Ganga Basin, India

This study examines long-term (1991–2092) precipitation trends in the Upper Ganga Basin (UGB) across multiple elevations, using historical data (1991–2022) and future Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model datasets (2022–2092). By identifying the top five best-fit models from 29 climate model datasets, this study assesses historical patterns and project future changes in the Near-Future-Term (NFT) (2023–2056) and Far-Future-Term (FFT) (2061–2092) under moderate and extreme scenarios. Based on the evaluation of 29 CMIP6 climate models precipitation products, five models namely: BCC-CSM2-MR, CMCC-CM2-SR5, INM-CM5–0, KIOST-ESM, and NESM3 were selected as the best models describing precipitation over UGB and future precipitation of these models were bias corrected using linear scaling bias correction approach. Also it was observed that in the UGB region usually the climate models are much more reliable in capturing the precipitation pattern in lower and mid elevation regions as compared to higher-elevation regions. Further analysis of best five climate models' future precipitation indicates a substantial increase in wet spell occurrences in the south-western part of UGB, while the north-western and central regions are expected to see a rise in dry spell counts, although dry spell increase is insignificant. The mid-elevation range from 2200 to 4050 m which historically receives the most precipitation and is expected to continue experiencing the highest volume of precipitation in the future. The lower elevation range (287–1500 m) has anticipated the most substantial increase in future precipitation. Analysis suggests a future rise in low precipitation events (2.5 mm/day to 10 mm/day) at high elevations (4050 m to 7399 m) and an increase in events exceeding 10 mm/day in mid to low elevations (287 m to 4050 m). Although an increase in low precipitation events is expected across the entire UGB, it is particularly noteworthy that the region between 287 m to 4050 m is likely to experience a greater prevalence of these events. Historically, high elevations witnessed most of the years with over 20 days of annual snowfall. However, future scenarios indicate a prominent shift, predominantly anticipating <20 days per year of snowfall, signifying a significant change in snowfall patterns within the UGB. An analysis of snowfall patterns within the UGB indicates that historically, in the elevation range of 1500 m to 2200 m, some years have experienced 3–7 days of snowfall per year, however, under the SSP245 (NFT) scenario, this is projected to decrease to 2–5 days per year. Conversely, in scenarios SSP245 (FFT), SSP585 (NFT), and SSP585 (FFT), it is anticipated that there will be no snowfall in this elevation range in the future.