Seasonal Distribution Of Precipitation Research Articles

Seasonal precipitation distribution determines ecosystem CO2 and H2O exchange by regulating spring soil water–salt dynamics in a brackish wetland

Abstract The intensification of the global hydrological cycle is anticipated to increase the variability of precipitation patterns. Brackish wetlands respond to changes in precipitation patterns by regulating the absorption and release of CO2 and H2O to maintain the stability of ecosystem functions. However, there is limited understanding of how the inter‐seasonal precipitation distribution (SPD) affects ecosystem CO2 and H2O exchange compared with annual precipitation totals. Here, we conducted four consecutive years of field experiments in a brackish wetland, manipulating the proportion of precipitation across different seasons while maintaining a constant annual precipitation total. We utilized five inter‐SPD proportions (+73%, +56%, control (CK), −56%, −73%) to examine the effects of SPD on ecosystem CO2 and H2O exchange. Our findings revealed that the annual ecosystem CO2 and H2O fluxes showed a trend of decreasing with the decrease in spring precipitation distribution. Among them, the annual net ecosystem CO2 exchange, evapotranspiration, carbon use efficiency and water use efficiency were shown to be more sensitive to decrease in spring precipitation distribution and increase in summer and autumn precipitation distribution. This negative asymmetric response pattern suggests that annual ecosystem CO2 and H2O exchange is primarily governed by seasonal precipitation variability, with spring soil water–salt dynamics identified as the key driver. Therefore, this association can be explained by the fact that drought of the early growth stage exacerbates soil salinization and inhibits vegetation colonization and growth, thereby greatly impairing the annual CO2–H2O exchange capacity of brackish wetlands. Our results emphasized that the spring extreme precipitation‐induced soil water–salt conditions will greatly influence CO2 and H2O exchange in brackish wetlands in the future. These findings are crucial for improving predictions of the carbon sequestration and water‐holding capacity of brackish wetlands. Read the free Plain Language Summary for this article on the Journal blog.

Assessment of relationship between sea surface temperature (SST) changes and precipitation types in Nigeria from 2000 to 2022

This study investigated the circulation patterns associated with rainfall variations across Nigeria’s different climatic zones (2000–2022). Data was acquired from MERRA2, gridded ERSST5 provided by the NOAA, and observation data records obtained from the Climatic Research Unit (CRU). Principal Component Analysis (PCA) was employed to compare different precipitation types, while the Multinomial Logistic Regression Model was employed to examine the influence of SSTa on precipitation presenting regression coefficients with a significance level set at p < 0.05. Five distint standard precipitation index (SPI) classes: very dry, dry, normal, wet and very wet were categorised using prcipitation data. Analysis was done in R-studio and involved data preparation, model training, and evaluation, with emphasis on interpreting the coefficients to discern the impact of SST anomalies on precipitation for each specified level. The results show that TOP, AVP, LSP, and CNP varied: spatially, the northern region received low moisture budget from the Atlantic Ocean while the temporal distribution of precipitation across different climatic zones indicate high variability in precipitation across these zones. The mean SSTa in the WAf region were predominantly positive (0.5 and 1). The lowest (highest) global SST values were prevalent during the DJF (JJA) season(s) whereas, the monthly distribution of SSTa for the WAf region reveal neutral (2000–2016) and El Niño (2016–2022) episodes. The analysis of NIF values indicates a varied but generally stronger relationship between WAf SST anomalies and precipitation types compared to nino3.4 SST versus precipitation types. As a signal for prediction of seasonal and spatial distribution of precipitation across Nigeria’s different climatic zones, this outcome can support planning for food security, water and biodiversity conservation, and climate change adaptation and mitigation.

Accuracy of historical precipitation from CMIP6 global climate models under diversified climatic features over India

The importance of global climate models (GCMs) is increasingly recognized due to their excellent ability to accurately predict climatic factors. These capabilities prove invaluable to water resources engineers as they facilitate effective planning and strategic decision-making. Finally, evaluating the performance of GCMs is very important because it allows us to simulate and predict different climate scenarios, empowering us to make informed choices. Therefore, the purpose of this study is to determine the degree of discordance between historical simulated data produced by the CMIP6 models and historical observational data over different climate zones of India. The ability of 24 different GCMs to reproduce the geographical and seasonal distribution of Indian precipitation has been tested by analyzing the daily historical precipitation forecasts from these models. These models have been used to estimate the degree of uncertainty associated with the spatiotemporal variability of precipitation forecasts. More than 20% percent bias (PBIAS) is observed to occur predominantly in four climate classifications: polar tundra, temperate, cold, and tropical monsoon. In some regions of India, the CMIP6 models produce overestimated or underestimated results. The locations identified indicate that there have been changes of more than 20% PBIAS near Sivalik Range, Naga Hills, and Western Ghats. The precipitations of those regions that have been underestimated also imply that those locations have different climatic conditions. This study also highlights that CMIP6 GCMs are yet to produce better results near several Indian mountainous regions depending upon climates. The outcomes of this study will be very useful for reconstructing modeled data for that specific regions.

Research on the Causes and Prevention Measures of Urban Waterlogging

Flood disaster is one of the major natural disasters in China, due to the influence of monsoon climate, seasonal distribution of precipitation and unbalanced spatial distribution, urban flood disasters in China are prone to occur frequently, endangering the safety of life and property of urban residents, and also affecting the safe operation and sustainable development of cities. Therefore, the prevention and control of urban waterlogging is not only a major livelihood project, but also a major development project. In view of the repeated failures of urban waterlogging prevention and control in China, this paper analyzes the causes of urban waterlogging from four aspects: climate change, urban expansion, drainage system, and emergency rescue, analyzes the mechanism of urban waterlogging from the perspective of disaster system theory, and finally summarizes the coping strategies of urban waterlogging based on four aspects: stormwater management, institutional mechanism, engineering construction, and emergency management, in order to provide reference and reference for urban waterlogging control.

Distinct response patterns of soil micro-eukaryotic communities to early-season and late-season precipitation in a semiarid grassland

Semiarid ecosystems are susceptible to changes in precipitation regimes. However, how soil micro-eukaryote communities in semi-arid ecosystems respond to variations in the seasonal distribution of precipitation remains elusive. A 5-year field experiment was performed to investigate the effect of changed precipitation distributions throughout the growing season on soil micro-eukaryotic communities in the temperate steppe. Early-season precipitation did not significantly affect the biomass of arbuscular mycorrhizal fungi (AMF) nor the composition and structure of AMF and protist communities. Late-season precipitation significantly increased the biomass of AMF while significantly altering the relative abundance of dominant genera in AMF and protist communities. There was a clear difference in the community structure of AMF and protists between the late-season precipitation and natural precipitation. In addition, late-season precipitation decreased the amount of consumer protists but enhanced the abundance of parasitic protists. These results indicate that the impact of precipitation limitations in the middle growing season can be weakened by increased precipitation in the early growing seasons, while an increase in precipitation later in the growing season does not improve the impact of a decrease in precipitation in the middle growing season. Our findings highlight the potential impact of intra-annual changes in precipitation in soil micro-eukaryotic communities in arid and semiarid regions.

Метеорологические условия и их сезонное распределение в ущелье Гушары бассейна реки Варзоб (Таджикистан)

The results of monitoring meteorological conditions in the middle part of the Varzob river basin in the Gushary gorge are presented. It is shown that for the period 1946–2021, atmospheric precipitation remains almost constant and the average annual temperature is characterized by an increasing trend. The seasonal distribution of temperature and atmospheric precipitation in the Gushary gorge of the Varzob river basin is characterized by the fact that the maximum precipitation corresponds to the spring season with maximum temperature in the summer season. It has been established that the average long-term temperature in the summer season in the Gushary gorge does not exceed 25°C that is due to the protection of the gorge from the penetration of air masses by high mountain ranges. A study and comparison of evapotranspiration in the gorge for 1950 and 2021 showed that for the period of more than seventy years there have been no significant changes in evapotranspiration.

Hydrological regimes and evaporative flux partitioning at the climatic ends of High Mountain Asia

Abstract High elevation headwater catchments are complex hydrological systems that seasonally buffer water and release it in the form of snow and ice melt, modulating downstream runoff regimes and water availability. In High Mountain Asia (HMA), where a wide range of climates from semi-arid to monsoonal exist, the importance of the cryospheric contributions to the water budget varies with the amount and seasonal distribution of precipitation. Losses due to evapotranspiration and sublimation are to date largely unquantified components of the water budget in such catchments, although they can be comparable in magnitude to glacier melt contributions to streamflow. &amp;#xD;Here, we simulate the hydrology of three high elevation headwater catchments in distinct climates in HMA over 10 years using an ecohydrological model geared towards high-mountain areas including snow and glaciers, forced with reanalysis data. &amp;#xD;Our results show that evapotranspiration and sublimation together are most important at the semi-arid site, Kyzylsu, on the northernmost slopes of the Pamir mountain range. Here, the evaporative loss amounts to 28% of the water throughput, which we define as the total water added to, or removed from the water balance within a year. In comparison, evaporative losses are 19% at the Central Himalayan site Langtang and 13% at the wettest site, 24K, on the Southeastern Tibetan Plateau. At the three sites, respectively, sublimation removes 15%, 13% and 6% of snowfall, while evapotranspiration removes the equivalent of 76%, 28% and 19% of rainfall. In absolute terms, and across a comparable elevation range, the highest ET flux is 413 mm yr-1 at 24K, while the highest sublimation flux is 91 mm yr-1 at Kyzylsu. During warm and dry years, glacier melt was found to only partially compensate for the annual supply deficit.

ML-based regionalization of climate variables to forecast seasonal precipitation for water resources management

Numerous dams and reservoirs have been constructed in South Korea, considering the distribution of seasonal precipitation which highly deviates from the actual one with high precipitation amount in summer and very low amount in other seasons. These water-related structures should be properly managed in order to meet seasonal demands of water resources wherein the forecasting of seasonal precipitation plays a critical role. However, owing to the impact of diverse complex weather systems, seasonal precipitation forecasting has been a challenging task. The current study proposes a novel procedure for forecasting seasonal precipitation by: (1) regionalizing the influential climate variables to the seasonal precipitation with k-means clustering; (2) extracting the features from the regionalized climate variables with machine learning-based algorithms such as principal component analysis (PCA), independent component analysis (ICA), and Autoencoder; and (3) finally regressing the extracted features with one linear model of generalized linear model (GLM) and another nonlinear model of support vector machine (SVM). Two globally gridded climate variables-mean sea level pressure (MSLP) and sea surface temperature (SST)-were teleconnected with the seasonal precipitation of South Korea, denoted as accumulated seasonal precipitation (ASP). Results indicated that k-means clustering successfully regionalized the highly correlated climate variables with the ASP, and all three extraction algorithms-PCA, ICA, and Autoencoder-combined with the GLM and SVM models presented their superiority in different seasons. In particular, the PCA combined with the linear GLM model performed better, and the Autoencoder combined with the nonlinear SVM model did better. It can be concluded that the proposed forecasting procedure of the seasonal precipitation, combined with several ML-based algorithms, can be a good alternative.

Effect of seasonal distribution in precipitation on soil nitrogen mineralization in a subtropical forest

Soil N mineralization is a key process of nutrient cycling in ecosystems. The mechanism of the seasonal distribution of precipitation on soil N mineralization remains unclear. We conducted a precipitation manipulation experiment in a subtropical forest in the middle and lower reaches of the Yangtze River in China from 2020 to 2022, with three treatments, including control (CK), decreased precipitation in the dry season with extremely increased precipitation in the wet season (T1), and decreased precipitation in the dry season with proportionally increased precipitation in the wet season (T2). With in situ resin core method, we explored the effect of seasonal distribution of precipitation on soil N mineralization. The results showed that T1 and T2 significantly decreased dry season net nitrification rate by 57.9% and 72.5% and the net N mineralization rate by 82.5% and 89.6%, respectively, and significantly increased wet season net nitrification rate by 64.3% and 79.5% and net N mineralization rate by 64.2% and 81.1%, respectively. Proportionally increased precipitation in the wet season was more conducive to soil N mine-ralization process than extremely increased precipitation in the wet season. Results of the structural equation model showed that change in seasonal distribution of precipitation could significantly affect soil N mineralization processes in the subtropical forest by changing soil water content, ammonium nitrogen, microbial biomass nitrogen, and soil C:N. Our results had important reference for understanding soil nitrogen cycling and other ecological processes, and were conducive to more accurate assessment on the impacts of future changes in seasonal precipitation pattern on subtropical forest ecosystems.

IWRAM: A hybrid model for irrigation water demand forecasting to quantify the impacts of climate change

The phenomenon of climate change exerts a substantial influence on the water consumption patterns of agricultural crops, thereby affecting the overall demand for irrigation water and regional water security. The quantitative assessment of future changes in irrigation water requirements has great importance for long-term water resource planning and the water-energy-food-ecosystem nexus. This work employed a comprehensive approach (IWRAM) by integrating many models and techniques, including a global climate model (CanESM2), a statistical downscaling model (SDSM), a bias correction technique (QTM), a temperature-based crop phenology method (GDD), and deep learning technology (LSTM), in order to assess the potential impact of climate change on irrigation water demand. Therein, the QTM is utilized for the purpose of bias correction for the CanESM2-SDSMed daily precipitation data. Additionally, the LSTM is implemented to forecast ETo using the CanESM2-SDSMed daily temperature data. The findings from the case study conducted in the Jinghuiqu irrigation area (JIA) indicate that the application of bias correction techniques resulted in notable enhancements in the frequency distribution of predicted precipitation data. Consequently, this led to a more coherent relationship between historical observed data and anticipated future data, aligning them more closely with natural patterns. The deep learning technique exhibited a strong degree of concordance, suggesting its exceptional capacity for modeling daily reference evapotranspiration. By using the IWRAM model, relatively accurate predictions were made for future precipitation, temperature, crop evapotranspiration, and effective precipitation. Consequently, an estimation was made regarding the anticipated need for irrigation water in JIA. These results highlighted that JIA total irrigation water demand (or net irrigation water demand) will increase by 121 mm (net irrigation water: 189 mm) and 119 mm (net irrigation water: 187 mm) in the year 2050 under RCP 2.6 and 8.5 scenarios, respectively. Climate change could potentially affect the seasonal distribution of precipitation. Therefore, it is crucial for the authorities in JIA to not only take into account the overall irrigation water requirement but also give priority to the water requirements of each stage of crop growth.

Holocene and late Pleistocene bioclimatic change inferred from δ13C of organic carbon in buried alluvial soils in the tallgrass prairie, northeastern Kansas

The tallgrass prairie of the Central Plains is one of the largest North American biomes, yet little is known about the history of late-Quaternary bioclimatic change in the region because settings that are favorable for the preservation of traditional paleoenvironmental proxies, such as pollen and plant macrofossils, are rare. However, the valleys of large streams contain thick soil-stratigraphic sequences that span the Holocene and terminal Pleistocene and are archives of other paleoenvironmental proxies. To better understand late-Quaternary bioclimatic change in the tallgrass prairie, we analyzed the stable carbon isotope (δ13C) composition of soil organic matter (SOM) preserved in buried soils in floodplain, terrace, and alluvial fan deposits in the Big Blue River valley of northeast Kansas. A combination of eight cores and three outcrops at six localities were investigated, and 27 radiocarbon ages provide a robust numerical chronology. Based on our analysis, the tallgrass prairie has experienced significant bioclimatic changes over the past 27,000 years. From the LGM to the beginning of the late Holocene (ca. 25,000–4500 cal yr BP), there is an overall increase in C4 plant contributions to SOM, suggesting general warming for that period. However, warming and C4 grass expansion occurred in a non-linear fashion, with minor variations in the abundance of C4 vs C3 plants reflecting either small fluctuations in temperatures during general warming trends or other environmental factors, such as landscape disturbance, periods of drought, or the seasonal distribution of precipitation. The δ13C record also suggests that a major warming event occurred in the region between ca. 3000–1300 cal yr BP, when the contribution of C4 biomass to SOM exceeded 95 %. This climatic event does not appear to be an anomaly, as multiple paleoenvironmental proxies indicate a brief but exceedingly warm and dry interval occurred from 3300 to 2800 cal yr BP.

Productivity, water and nitrogen utilization of intensified dryland farming with annual forages on the Chinese Loess plateau

Understanding the relationships of productivity performance and water utilization and soil nitrogen dynamics after annual forage planting during the fallow period (F) in winter wheat (Triticum aestivum L.; W) mono-cropping is critically important for maintaining sustainable livestock and grain production in semiarid regions. We used 2 years (2017–2019) of data to investigate soil nitrogen dynamics, production, water utilization, and fallow efficiency when forage rape (Brassica campestris L.; R) and common vetch (Vicia sativa L.; V) were planted in a 3-month summer fallow of the W-F-W-F cropping system. Three cropping systems were comprised of winter wheat-summer fallow-winter wheat-summer fallow (W-F-W-F), winter wheat-forage rape-winter wheat-forage rape (W-R-W-R), and winter wheat-forage rape-winter wheat-common vetch (W-R-W-V). The results showed that the annual forage planting decreased the average NO3−-N content by 54.8% compared with the W-F-W-F cropping system. Compared with the W-F-W-F cropping system, planting annual forage in summer fallow increased the average system forage production by 4.93 t ha−1. Local total annual precipitation can meet crop-water requirements, and the limiting factor for agricultural production was the drought due to the uneven seasonal distribution of precipitation. In comparison to the W-F-W-F cropping system, annual forage planting decreased the average available soil moisture storage by 50.3 mm above the 80 cm soil layer. Compared with that in the W-R-W-R (23.21 t ha−1) and W-F-W-F (30.25 t ha−1) cropping systems, the crop productivity in the W-R-W-V cropping system (33.23 t ha−1) was relatively stable and high because the reduction in subsequent winter wheat yield (2.96 t ha−1) was adequately offset by the forage yield (5.15 t ha−1). Adding forage rape to the W-F-W-F cropping system decreased system crop-water productivity (CWP) by 40.9%. However, the CWP, precipitation use efficiency (PUE), and soil nitrate in the W-R-W-V cropping system increased by 30.4, 30.1, 110.9, and 82.0%, respectively, compared with those in the W-R-W-R cropping system. Therefore, the W-R-W-V cropping system is recommended for better water and fertility management as well as grain and forage production in semiarid regions. However, further study is required to involve drought years for better evaluation of the effect of long-term precipitation variability on the crop productivity.

Precipitation δ18O paced the seasonal δ18O variations of terrestrial snail body water and shells in the East Asian monsoon region

Terrestrial snails are sensitive to climate changes and their shell's oxygen isotope composition (δ18Oshell) is widely used in studies of paleoclimate reconstructions. However, the interpretation of δ18Oshell remains complex due to the combined effects from different factors such as precipitation δ18O (δ18Op), relative humidity (RH), and temperature. Furthermore, no systematic studies have addressed the transformation of oxygen isotope signals from rainfall to the aragonite shell via snail body water. Here we present the results of Cathaica fasciola body water δ18O (δ18OBW) and its content, which were sampled every two days throughout the growing season in 2021 in Xi'an. Meanwhile, the soil water δ18O (δ18OSW), δ18Op, rainfall amount, RH and temperature were also simultaneously monitored, and two high-resolution (at ∼0.3 mm interval) δ18Oshell series were obtained from two live snails collected in September and October 2021, respectively. These data reveal that the δ18OBW mimic the variations of δ18Op, and they are positively correlated (r2 = 0.84) for precipitation events and the correlation coefficient only increases slightly (r2 = 0.88) when RH is included in the multivariate analysis. During non-precipitation intervals, there is an obvious negative correlation between δ18OBW and RH. Moreover, the variations of two δ18Oshell series are broadly consistent with the theoretically calculated δ18Oshell from δ18OBW/δ18Op at seasonal scale. Collectively, this study demonstrates that δ18Op is the first-order control on the seasonal variations of δ18OBW and δ18Oshell, while RH play its role during non-precipitation periods. Importantly, this study lays good foundation for reconstructing terrestrial seasonal distribution of precipitation and extreme rainfall events using fossil shells in the geological past.

The extreme wet and large precipitation size increase carbon uptake in Eurasian meadow steppes: Evidence from natural and manipulated precipitation experiments

The distribution of seasonal precipitation would profoundly affect the dynamics of carbon fluxes in terrestrial ecosystems. However, little is known about the impacts of extreme precipitation and size events on ecosystem carbon cycle when compared to the effects of average precipitation amount. The study involved an analysis of carbon fluxes and water exchange using the eddy covariance and chamber based techniques during the growing seasons of 2015–2017 in Bayan, Mongolia and 2019–2021 in Hulunbuir, Inner Mongolia, respectively. The components of carbon fluxes and water exchange at each site were normalized to evaluate of relative response among carbon fluxes and water exchange. The investigation delved into the relationship between carbon fluxes and extreme precipitation over five gradients (control, dry spring, dry summer, wet spring and wet summer) in Hulunbuir meadow steppe and distinct four precipitation sizes (0.1–2, 2–5, 5–10, and 10–25 mm d−1) in Bayan meadow steppe. The wet spring and summer showed the greatest ecosystem respiration (ER) relative response values, 76.2% and 73.5%, respectively, while the dry spring (−16.7%) and dry summer (14.2%) showed the lowest values. Gross primary production (GPP) relative response improved with wet precipitation gradients, and declined with dry precipitation gradients in Hulunbuir meadow steppe. The least value in net ecosystem CO2 exchange (NEE) was found at 10–25 mm d−1 precipitation size in Bayan meadow steppe. Similarly, the ER and GPP increased with size of precipitation events. The structural equation models (SEM) satisfactorily fitted the data (χ2 = 43.03, d.f. = 11, p = 0.215), with interactive linkages among soil microclimate, water exchange and carbon fluxes components regulating NEE. Overall, this study highlighted the importance of extreme precipitation and event size in influencing ecosystem carbon exchange, which is decisive to further understand the carbon cycle in meadow steppes.

Effects of elevated CO2 concentration and experimental warming on morphological, physiological, and biochemical responses of winter wheat under soil water deficiency.

Global climate change and freshwater scarcity have become two major environmental issues that constrain the sustainable development of the world economy. Climate warming caused by increasing atmospheric CO2 concentration can change global/regional rainfall patterns, leading to uneven global seasonal precipitation distribution and frequent regional extreme drought events, resulting in a drastic reduction of available water resources during the critical crop reproduction period, thus causing many important food-producing regions to face severe water deficiency problems. Understanding the potential processes and mechanisms of crops in response to elevated CO2 concentration and temperature under soil water deficiency may further shed lights on the potential risks of climate change on the primary productivity and grain yield of agriculture. We examined the effects of elevated CO2 concentration (e[CO2]) and temperature (experimental warming) on plant biomass and leaf area, stomatal morphology and distribution, leaf gas exchange and mesophyll anatomy, rubisco activity and gene expression level of winter wheat grown at soil water deficiency with environmental growth chambers. We found that e[CO2] × water × warming sharply reduced plant biomass by 57% and leaf photosynthesis (P n) 50%, although elevated [CO2] could alleviated the stress from water × warming at the amount of gene expression in RbcL3 (128%) and RbcS2 (215%). At ambient [CO2], the combined stress of warming and water deficiency resulted in a significant decrease in biomass (52%), leaf area (50%), P n (71%), and G s (90%) of winter wheat. Furthermore, the total nonstructural carbohydrates were accumulated 10% and 27% and increased R d by 127% and 99% when subjected to water × warming and e[CO2] × water × warming. These results suggest that water × warming may cause irreversible damage in winter wheat and thus the effect of "CO2 fertilization effect" may be overestimated by the current process-based ecological model.

Regional difference in precipitation seasonality over China from CMIP6 projections

AbstractBased on historical simulations and projections under two scenarios of the Shared Socioeconomic Pathways (SSP2‐4.5 and SSP5‐8.5) provided by 32 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate the future precipitation seasonality changes over China by two indices, the dimensionless seasonality index (DSI) and the relative seasonality index (SI), and the associated physical mechanisms by moisture budget diagnosis. The results show that relative to the baseline period 1995–2014, the DSI is enhanced across almost all of China at the end of the 21st century, with stronger magnitudes in southeastern China and the southern Tibetan Plateau and weaker magnitudes in northwestern China, and national‐mean enhancements are 15% (7%–21%) and 23% (15%–29%) for the multimodel median (25th–75th percentile range across models) under the SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Projected SI changes show large regional differences, with decreases in northern China and increase in southern China, leading to little change for the national mean. The DSI and SI changes are associated with uneven distributions of changes in absolute amounts and relative proportions of precipitation among seasons, respectively. Further analysis reveals that the contributions of different moisture budget terms to precipitation changes vary across seasons, resulting in changes in the seasonal distribution of precipitation and thus the DSI and SI changes. Specifically, the thermodynamic component of vertical moisture advection primarily determines the greater magnitude of increased precipitation in summer than in winter in most of China, resulting in the DSI change. The reduction in residual term dominates the diminished winter precipitation in southern China, leading to more precipitation concentrated in summer for given annual precipitation and thus the intensification in SI, while the opposite holds true in northern China.

Adaptation to seasonal drought in irrigation districts of south China: A copula-based fuzzy-flexible stochastic multi-objective approach for precise irrigation planning

The occurrence of seasonal drought has led to periodic water shortages in irrigation districts of south China, posing significant agricultural challenges on water and food security. Consequently, there is a pressing need for high-efficient water resources management from a sustainability perspective. To address this issue, a copula-based fuzzy-flexible stochastic multi-objective programming (CFSMP) model was proposed to facilitate irrigation planning under the combined influence of seasonal dry and wet conditions. The model was aimed to simultaneously achieve maximum economic benefit, water productivity and green water use efficiency, considering the economic, social, and environmental spheres of the irrigation district. The CFSMP model represented an innovative solution that was capable of (1) precisely describing the response of crop yield to water supply at ten-day time scale, which can be matched with practical irrigation demand; (2) characterizing joint distributions of seasonal dry and wet conditions of precipitation during different crop growth periods based on the copula function; (3) reconciling conflicts on objectives among economic, social, and environmental spheres of the irrigation district in complex and uncertain environments. The proposed model was applied to the Dongfeng Reservoir Irrigation District in south China. The downscaled ten-day Jensen water production functions of main grain and cash crops were fitted to recognize precise distributions of crop water demand and water sensitivity index. The Frank copula, with the smallest value of squared Euclidean distribution, was selected to describe the joint distribution of seasonal precipitation and gain insights into actual seasonal water availability and water scarcity. The results showed that the proposed model produced more water-saving and yield-promoting irrigation planning policies than the current quota. The average water allocation of rice, wheat, maize, rape, and citrus decreased by 352.6 mm, 10.6 mm, 67.7 mm, 7.44 mm, and 54.6 mm, respectively. Except for a yield reduction of 8.8% for rape, crop yields of rice, wheat, maize, and citrus were improved by 33.3%, 45.4%, 29.9%, and 12.3%, respectively. The coordination degree of the model reached 0.827, demonstrating excellent performance on generating coordinated and robust solutions. Therefore, the CSFMP model has the potential to alleviate seasonal drought in south China and can be applied in similar regions with comparable resources crisis.

Multiyear versus single‐year El Niño events: Contrasting their impacts on South American seasonal precipitation

AbstractDifferences in the seasonal distribution of precipitation over South America (SA) associated with single‐year (SY) and multiyear (MY) El Niño (EN) events were analysed based on reanalysis data for the 1901–2012 period. The results suggest that Pacific Sea Surface Temperature (SST) anomalies associated with MY EN events interact with the tropical Atlantic and Indian Oceans SST from austral fall to spring, after the event's first year, affecting SA precipitation distribution in subsequent seasons. Compared to SY EN events, which reproduce well the north–south dipolar precipitation anomaly pattern over SA, the MY EN events show differences in the intensity and positioning of precipitation anomalies. Precipitation decreases over northern SA during all seasons are observed for SY and MY EN events except for differences in magnitudes. Variations in the position and longitudinal extension of the downward motions of Walker circulation in response to these events explain these differences. Over southern and southeastern SA, differences in anomaly positioning are more evident. The positive precipitation anomalies over these regions in the austral spring and summer are weakened and southward shifted during the MY EN in relation to those during SY EN events. These variations are associated with the Rossby‐wave train pattern path that depends on the EN and season. Consequently, the associated local atmospheric circulation patterns also depend on the season. The intense (weak) South American low‐level jet (SALLJ) for the first year of MY EN contrasts with the weak or inexistence (intense) SALLJ for the SY EN during summer (springer). Complementary to previous studies, the results indicate that the differences in the intensity and duration of the SY and MY EN events contribute to changes in the EN‐related atmospheric teleconnection pattern that impacts SA precipitation. The results can be useful for climate prediction and monitoring purposes.

Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS

Abstract. This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12 km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth-generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions. First, how well are the 3 and 24 h precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) 4 km Stage IV and Oregon State University Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data on 3 and 24 h timescales, respectively. Our analysis suggests that the 12 km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3 and 24 h precipitation maximum over the CONUS, in contrast to ERA5, which generally underestimates these quantities mainly over the eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3 and 24 h annual and seasonal maximum precipitation. WRF exhibits seasonally dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics but is susceptible to common regional climate model biases. Consequently, if used as input data for domain-specific models, we suggest moderate bias correction be applied to the dynamically downscaled product.

Climate Variability and Trends in Imotski, Croatia: An Analysis of Temperature and Precipitation

This paper examines the long-term changes in temperature and precipitation in the karst region of Imotski, Croatia, which is of particular interest due to its abundance of karst phenomena. This study analyses temperatures and precipitation on monthly and annual scales at two climatological stations in the region, Imotski and Ričice. Linear regression, the Theil–Sen estimator (β), and the Mann–Kendall test were used to determine the trends and statistical significance. The homogeneity of the data was checked using the Standard Normal Homogeneity Test (SNHT), and the F-test and t-test were used to test the significance of the mean shift between the two subseries. Additionally, the coefficient of variability, standardized rainfall anomaly, and precipitation concentration index were employed to analyze the precipitation variability. The study found a statistically significant (p &lt; 0.05) upward trend in the mean (β = 0.0437) and maximum (β = 0.0590) annual air temperature at the Imotski station and the mean (β = 0.0387) annual temperature at the Ričice station. The SNHT test showed a statistically significant (p &lt; 0.05) shift in the mean annual temperatures after 2007 and maximum annual temperatures after 1998 at the Imotski station. Similarly, a statistically significant (p &lt; 0.05) shift in the mean annual temperatures after 2011 and the maximum annual temperatures after 1998 was found at the Ričice station. A seasonal distribution of precipitation is observed at both the Ričice and Imotski stations, with a downward trend (β = −2.7693) at Ričice and an upward trend (β = 6.0575) at Imotski; however, neither trend is statistically significant (p &gt; 0.05). An increase in the intensity of dry periods and the occurrence of extreme events was also noted. The climatological analysis, conducted for the first time in this area, is a crucial step toward understanding local climate patterns and making informed decisions toward sustainable development and adaptation strategies.