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This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction. [ABSTRACT FROM AUTHOR]

Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation. Significance Statement: In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment. [ABSTRACT FROM AUTHOR]

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Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

The 16 years of normalized difference vegetation index (NDVI) and precipitation data are used to analyze the time‐lag effects of the growing‐season NDVI response to precipitation at regional scales. This study focuses on the arid and semi‐arid Hulunbuir grassland dominated by perennials in northeast China. The multi‐month time‐lag effects are examined using simple statistical approaches, which can detect the two distinct time‐lags for four subregions with four major land‐cover types. A "positive" time‐lag effect of the growing‐season NDVI response to precipitation is observed at 1‐month (May in the current year) time‐lag and 13‐month (May in the previous year) time‐lag while a "negative" time‐lag effect is observed at 9‐month (September in the previous year) time‐lag. In addition, the prediction results of NDVI based on precipitation indicate that the NDVI prediction model considered the lagged monthly precipitation has good performance. Therefore, revealing the time‐lag effects is very important for accurately predicting the growing‐season NDVI and evaluating the vegetation dynamics. Recommendations for Resource Managers: The multi‐month time‐lag effects of the growing‐season NDVI to precipitation in arid and semi‐arid grasslands in northeastern China is analyzed in this paper. We can detect the two distinct time‐lags for four subregions with four major land‐cover types. The following implications could be realized based on the observations: The "positive" 1‐month (May in the current year) and 13‐month (May in the previous year) time‐lag memory of the growing‐season NDVI response to precipitation can be found.The "negative" 9‐month time‐lag memory of the growing‐season NDVI response to precipitation associated with September (late growing‐season) meteorological condition can be detected. This "negative" effect does not support the green up of the growing‐season in the next year. [ABSTRACT FROM AUTHOR]

Short-termintense precipitation (SIP; i.e., convective precipitation exceeding 20mmh21) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms. [ABSTRACT FROM AUTHOR]

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model's parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models. [ABSTRACT FROM AUTHOR]

In August-September 2021, the western Pacific subtropical high (WPSH) showed significant anomalies, with some areas between the Yangtze and Yellow River basins in China suffering from heavy rainfall and flooding, while extreme heat occurred in Southern China. Using the 6 h reanalysis data of NCEP/NCAR and the daily precipitation observation data, this paper studied the abnormal characteristics and the thermal mechanism of the three-dimensional ridge of the WPSH during the seasonal transition from late summer to early autumn in August-September 2021, and explored its connection with the precipitation in the areas between the Yangtze and Yellow River basins in China during the same period. The results showed that, in August-September 2021, the three-dimensional ridge of the WPSH appeared to be southward at the lower troposphere and tilted northward with height. According to the diagnosis of the atmospheric heat source, it was found that the center of atmospheric cold source in the low latitudes at 850 hPa enhanced the surface high pressure, corresponding to the WPSH ridge shifting southward at the lower troposphere, which led to more water vapor convergence and more precipitation between the Yangtze and Yellow River basins. The latent heat of condensation released by the increased precipitation between the Yangtze and Yellow River basins in China heated the middle and upper troposphere, increasing the intensity of the high pressure in the upper troposphere, corresponding to the WPSH ridge northward at the upper troposphere. This anomalous tilt of the WPSH ridge in August-September 2021 and the intense precipitation processes between the Yangtze and Yellow River basins in China during the same period established a reciprocal feedback mechanism through diabatic heating. Further studies also showed that the anomalous tilt of the WPSH ridge is one of the important modes of its three-dimensional structural anomalies, and that, in years when the ridge tilts from south to north with height anomalously in August-September, the areas between the Yangtze and Yellow River basins are characterized by high precipitation. [ABSTRACT FROM AUTHOR]

The Wenchuan earthquake occurred in Sichuan Province, China, on May 12, 2008, and caused a large number of secondary geological disasters such as collapses, debris flows, and landslides, as well as extensive damage to local vegetation. The earthquake had an impact on the regional climate, seriously affecting the sustainable development of the regional environment and economy. In this study, the normalized difference vegetation index (NDVI) was used to characterize the vegetation status, which was calculated from images acquired by the MODIS sensor. Based on the vegetation cover and precipitation of the area affected by the Wenchuan earthquake over about 70 years, in this paper we analyze the characteristics of vegetation changes and their impact on the climate after the earthquake. The results revealed that the above secondary geological disasters caused by the earthquake destroyed vegetation. After the earthquake, NDVI decreased from 0.394 in 2007 to 0.383 on July 1, 2008, and then increased year by year to 0.413 in 2015, except in 2010. During the two years after the earthquake, several large-scale debris flows occurred in this area, damaging the surface vegetation, so the vegetation coverage was low in 2010. The change of the land cover has an indirect impact on the local precipitation. The annual and seasonal (except winter) precipitation trended downward from 1948 to 2018, with an annual average decrease of 1.6069 mm, but trended upward from 2008 to 2018, with an annual average increase of 14.012 mm, after the Wenchuan earthquake, indicating the recovery of vegetation. The increased vegetation promoted regional precipitation, but the interaction mechanism between the vegetation cover and the precipitation is complex. The effect of the vegetation differs in different regions and cannot be generalized. This study provides theoretical support for vegetation restoration and environmental management in areas with strong earthquakes. [ABSTRACT FROM AUTHOR]

Rainfall and wind speed are two important meteorological variables that have a significant impact on agriculture, human health, and socioeconomic development. While individual rainfall or wind events have been widely studied, little attention has been devoted to studying the lead-lag relationship between rainfall and wind speed, particularly in coastal regions where strong dependence between rainfall and wind speed is expected. Taking China's coastline as the case study, this paper aims to explore the variation trends of wind speed and rainfall and reveal the relationships between rainfall events and wind speeds on days before and after rainfall occurrence, by using meteorological station data from 1960 to 2018. The results show that wind speed tended to decrease while rainfall showed a slight increase for most stations. The daily wind speed increased 2 days before rainfall occurrence and decreased after then, with the highest wind speed observed during rainfall onset regardless of rainfall amount. Moreover, heavier rainfall events are more likely to occur with higher wind speeds. The findings of this study potentially improve the understanding of the dependence of rainfall and wind speed, which could help rainfall or wind-related disaster mitigation. [ABSTRACT FROM AUTHOR]

Restoration of natural vegetation in arid and semi‐arid grasslands is facing severe challenges. The vegetation is easy to lose their vitality, resulting in the loss of the cover in natural grasslands under the high grazing pressure. To address this situation, this paper proposes a novel method for accurately predicting the grazing pressure using the nonlinear autoregressive with exogenous input (NARX) network based on the remote sensing data of normalized difference vegetation index (NDVI) and precipitation. The proposed method uses the NARX networks to predict the temporal variations of the NDVI with respect to the precipitation. The grazing pressure can be thus calculated using the predicted values of the NDVI. For practical application, this study investigated an arid and semi‐arid grassland with heavy grazing pressure in Hulunbuir, China. The results demonstrate that the proposed method can provide an accurate prediction of the grazing pressure (mean absolute error 0.103, root‐mean‐square error 0.122, mean absolute percentage error 8.36% and coefficient of determination 0.899 at the confidence interval of 95%). In addition, the predicted values of the grazing pressure in the study area during the years from 2016 to 2020 can be obtained using the proposed method. The proposed method can obtain a good prediction of the grazing pressure, which can be further used as a guidance for the rangeland managers to reduce the occurrence of the overgrazing. [ABSTRACT FROM AUTHOR]

The intensity of interannual variation of spring precipitation over southern China during 1979–2014 and possible reasons for it are investigated in this paper. There is a significant interdecadal change in the intensity of interannual variation of spring precipitation over southern China around 1995/96. The intensity of interannual variation of spring rainfall over South China is stronger during 1979–95 than that during 1996–2014. The possible reason may be the larger amplitude of the sea surface temperature anomaly (SSTA) in the western Pacific Ocean (WP) before 1995/96. The cooler (warmer) SSTA in WP may trigger an abnormal local anticyclone (cyclone) at lower levels. The anomalous southwesterly (northeasterly) flow at the northwestern flank of the WP anticyclone (cyclone) covers South China, transporting more (less) moisture to South China. Meanwhile, the anomalous winds converge (diverge) in South China at lower levels and diverge (converge) at upper levels, which causes the anomalous ascent (descent) to enhance (reduce) the precipitation over there. However, during 1996–2014, the intensity of interannual variation of spring rainfall over the middle and lower reaches of the Yangtze River valley becomes much stronger than that during 1979–95, which is related to the intensified interannual variation of the atmospheric circulation in the middle and high latitudes over Eurasia. The weak (strong) Siberian high and East Asian trough may reduce (enhance) the northerly wind from the middle and high latitudes. As a result, the middle and lower reaches of the Yangtze River valley are subjected to the anomalous southerly wind, favoring more (less) precipitation over there. [ABSTRACT FROM AUTHOR]

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman-Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5-6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them. [ABSTRACT FROM AUTHOR]

Vegetation restoration in dryland regions may be a powerful way to control desertification and promote local habitat recovery. In these artificial revegetation systems, ecosystem processes and functioning are strongly determined by nitrogen (N) availability, while soil inorganic N (SIN) is the major available N form and is absorbed as a main N source for plants. However, SIN dynamics, their influencing factors, and their relative importance to the ecosystem of these N‐limited systems are not well understood. A field investigation was conducted to examine the monthly variations in SIN and its response to planting age and vegetation type in a typical artificial sand‐fixing system in northwestern China. The SIN content in topsoil (0–20 cm) during the growing season ranged from 7.10 to 95.65 mg kg−1, with a mean value of 27.14 mg kg−1. Soil nitrate N had a dominant role in determining SIN monthly dynamics, which showed a fluctuating trend with two peak values in early July and late August. SIN showed a significant increasing trend with the planting age of sand‐fixing vegetation, and it was highest for Nitraria sphaerocarpa (55.66 ± 5.76 mg kg−1), followed by Haloxylon ammodendron (29.81 ± 3.47 mg kg−1) and interspace (11.39 ± 1.13 mg kg−1). SIN displayed a hump‐shaped relationship with air temperature (R2 = 0.96, p < 0.01) and had a maximum at 19.88°C. The change rate of SIN was positively correlated with accumulated precipitation (R2 = 0.99, p < 0.05). SIN was correlated significantly with soil organic carbon (SOC), total nitrogen (TN), and soil clay content. Our results revealed that climatic (air temperature and precipitation), abiotic (soil texture), plant (planting age and vegetation type), and nutrient‐related (SOC and TN) factors regulate SIN dynamics in artificial sand‐fixing vegetation systems. Climate was the predominant factor affecting soil ammonium N, and soil nutrients (SOC and TN) were the predominant factor affecting soil nitrate N. Therefore, these factors should be integrated into optimizing regional vegetation establishment and improving ecosystem management practices in sand‐fixing lands. [ABSTRACT FROM AUTHOR]

China's Yellow River Basin (YRB) is sensitive to climate change due to its delicate ecosystem and complex geography. Water scarcity, soil erosion, and desertification are major challenges. To mitigate the YRB's ecological difficulties, climate change must be predicted. Based on the analysis of the evolution features of hydro-meteorological elements, the CMIP6 climate model dataset with Delta downscaling and the Empirical Orthogonal Function (EOF) is utilized to quantitatively explore the future variations in precipitation and temperature in the YRB. The following results are drawn: The spatial resolution of the CMIP6 climate model is less than 0.5° × 0.5° (i.e., about 55 km × 55 km), which is improved to 1 km × 1 km by the downscaling of Delta and has outstanding applicability to precipitation and temperature in the YRB. The most accurate models for monthly mean temperature are CESM2-WACCM, NorESM2-LM, and ACCESS-CM2, and for precipitation are ACCESS-ESM1-5, CESM2-WACCM, and IPSL-CM6A-LR. Between 2023 and 2100, annual precipitation increases by 6.89, 5.31, 7.02, and 10.18 mm/10a under the ssp126, ssp245, ssp370, and ssp585 climate scenarios, respectively, with considerable variability in precipitation in the YRB. The annual temperature shows a significant upward trend, and the change rates under the different climate scenarios are, respectively, 0.1 °C/10a, 0.3 °C/10a, 0.5 °C/10a, and 0.7 °C/10a. The increase is positively correlated with emission intensity. Based on the EOF analysis, temperature and precipitation mainly exhibit a consistent regional trend from 2023 to 2100, with the primary modal EOF1 of precipitation for each scenario exhibiting a clear spatial distribution in the southeast–northwest. [ABSTRACT FROM AUTHOR]

The impact of the directional propagation of mesoscale convective systems (MCSs) on precipitation structures during the pre-summer rainy season in South China remains unclear. Using multi-satellite datasets, this study aims to reveal the features and mechanisms of precipitation influenced by MCS propagation from the perspective of both cloud microphysics and diurnal forcing of land-atmospheric system. The study region mainly consists of three contiguous coastal regions (A1, B1, and C1 from southwest to northeast). Controlled by the steering flow, MCSs tend to move from region A1 to C1 with a direction parallel to the coastline with a speed of 50 km h-1. Although regions A1 and C1 are both hilly regions, the results show that region A1 is the only key region for initiation and development of MCS, while MCSs in region C1 mainly come from the upstream regions. The directional propagation of MCS causes the propagation of diurnal rainfall peaks, while strong precipitation may accelerate the dissipation of MCS in region C1. The activities of MCSs enhanced ice-phased precipitation processes by spreading more droplets and therefore near-surface rainfall in regions B1 and C1, whereas the hilly surface in region C1 further promoted liquid-phased processes by uplifting southerly low-level flow. Of all the thermodynamic parameters, the daytime vertically moistest layer above the boundary layer over the coastal regions plays a key role in the initiation and development of MCS. These results contribute to a deeper understanding of MCS-related precipitations over coastal regions. [ABSTRACT FROM AUTHOR]

Wang, X.; Jiang, R.; Xie, J.; Zhao, Y.; Li, F., and Zhu, J., 2019. Multiscale variability of precipitation and their teleconnection with large-scale climate anomalies: A case study of Xi'an city, China. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 417–426. Coconut Creek (Florida), ISSN 0749-0208. The changes of precipitation affect the urban rainstorm waterlogging and water resources management, which have important influences on the sustainable development of society. The primary objective of the paper is to provide theoretical basis for local water resources management and disaster prevention and reduction by analyzing the multiscale variability of precipitation and their teleconnection with large-scale climate anomalies in Xi'an city of China. The change characteristics of precipitation and extreme precipitation indices were investigated using Mann-Kendall test, Spearman's rank correlation and wavelet analysis. The cross wavelet transform and wavelet coherence were used to explore the correlations between the precipitation and large-scale teleconnection indices such as Arctic Oscillation (AO), Southern Oscillation Index (SOI), El Niño-Southern Oscillation (ENSO) and sunspot. Results indicate that: the precipitation was mainly concentrated in summer, followed by autumn. The annual, seasonal and monthly precipitation except June and August had decreasing trends, and the magnitudes of precipitation decrease were more pronounced in spring, April and November. The change patterns varied for extreme precipitation indices. The temporal variation in extreme precipitation indices indicated that the extreme precipitation events had mainly increased during 1971-1990 and 1991-2017, and decreased during 1951-1970 and 1951-2017, especially, increased more pronounced during 1991-2017 and decreased more pronounced during 1951-1970. The precipitation exhibited a fluctuant change from increase to decrease, then increase to decrease and finally increase trends, and there was an abrupt change point in 1965. The annual precipitation had statistically significant cycles of 3-5 year and 7-10 year at 5% significance level. The large-scale teleconnection indices affected the precipitation in Xi'an city. The phase differences between precipitation and teleconnection indices including AO and SOI were in-phase dominated, and anti-phase dominated between precipitation and teleconnection indices including ENSO and sunspot, indicating that the AO and SOI exerted positive climate impacts on the precipitation and vice-versa for ENSO and sunspot. [ABSTRACT FROM AUTHOR]

The changes of precipitation affect the urban rainstorm waterlogging and water resources management, which have important influences on the sustainable development of society. The primary objective of the paper is to provide theoretical basis for local water resources management and disaster prevention and reduction by analyzing the multiscale variability of precipitation and their teleconnection with large-scale climate anomalies in Xi'an city of China. The change characteristics of precipitation and extreme precipitation indices were investigated using Mann-Kendall test, Spearman's rank correlation and wavelet analysis. The cross wavelet transform and wavelet coherence were used to explore the correlations between the precipitation and large-scale teleconnection indices such as Arctic Oscillation (AO), Southern Oscillation Index (SOI), El Niño- Southern Oscillation (ENSO) and sunspot. Results indicate that: the precipitation was mainly concentrated in summer, followed by autumn. The annual, seasonal and monthly precipitation except June and August had decreasing trends, and the magnitudes of precipitation decrease were more pronounced in spring, April and November. The change patterns varied for extreme precipitation indices. The temporal variation in extreme precipitation indices indicated that the extreme precipitation events had mainly increased during 1971-1990 and 1991-2017, and decreased during 1951-1970 and 1951-2017, especially, increased more pronounced during 1991-2017 and decreased more pronounced during 1951-1970. The precipitation exhibited a fluctuant change from increase to decrease, then increase to decrease and finally increase trends, and there was an abrupt change point in 1965. The annual precipitation had statistically significant cycles of 3-5 year and 7-10 year at 5% significance level. The large-scale teleconnection indices affected the precipitation in Xi'an city. The phase differences between precipitation and teleconnection indices including AO and SOI were in-phase dominated, and anti-phase dominated between precipitation and teleconnection indices including ENSO and sunspot, indicating that the AO and SOI exerted positive climate impacts on the precipitation and vice-versa for ENSO and sunspot. [ABSTRACT FROM AUTHOR]

• Significantly changes of LAI and GPP mostly occurred in eastern and southern China. • Divergence between LAI and GPP were found in regions with dense vegetation coverage. • The importance of VPD, SM, and PT to vegetation indices increased as latitude decreases. Climate change notably boosts terrestrial hydraulic redistribution pattern, which could directly affect the spatiotemporal evolution characteristics of vegetation productivity. However, the spatiotemporal evolution pattern of long-term hydrometeorological controls of vegetation productivity is still not fully understood. This paper comprehensively explored the impacts of hydrometeorological controls on leaf area index (LAI) and gross primary productivity (GPP) in China during 1982–2015 using both satellite observations and reanalysis. Vapor pressure deficit (VPD), evapotranspiration (ET), precipitation (PT), and soil moisture (SM) were used as representative indicators during investigation. The dynamics of LAI and GPP displayed increasing trends on the whole. And significantly changes mostly occurred in eastern and southern China, accompanied by moderate to dense vegetation coverage. An accelerated increasing trend for LAI could be observed since September 2003 (+0.175 per decade), while the growth rate of GPP (+16.76 g C/m2 per decade) basically maintained stable. In terms of hydrometeorological controls, VPD and ET showed consistently increasing trend and SM presented falling trend in the context of warming climate. Through analysis of feature importance index established by Random Forest, the importance of VPD, SM, and PT obviously increased as latitude decreases, which implied the high sensitivity of vegetation in semi-humid\humid regions to water availability. Additionally, divergence between LAI and GPP can be found in southern Tibet Plateau region and coastal regions, which provides compelling evidence that in the case of high LAI, increasing LAI could weaken the improvement effect on GPP. Findings of this study is expected to promote the understanding of long-term vegetation responses to hydrometeorological variations. [ABSTRACT FROM AUTHOR]

Rainwater is the main water source in arid and semiarid areas of the Loess Plateau, where rainfall is generally insufficient, ineffective and underutilized during the growing season. Thus, improving rainwater utilization efficiency is essential for sustainable agricultural development. A new system composed of rainwater harvesting, an infiltrator bucket with multiple holes and mulching (RHM), was designed to maintain soil moisture at a proper level in rain-fed orchards in arid and semiarid areas of the Loess Region of China. However, there is a lack of clarity on the effectiveness of RHM. In this study, changes in the soil environment and the growth and physiology of apricot trees were monitored via two treatments: (1) Rain-harvesting irrigation system (RHM) treatment and (2) traditional orchard treatment (CK) as a baseline. The results showed that (1) RHM could effectively improve soil water storage at depths of 0–45 cm and at a horizontal distance of 40 cm from the trunk. For the 1.4 mm light rain event, the soil water content increased by 6.3–12%, and for the two moderate rains, the soil water content increased by 12–25%. The change in the soil relative water content predicted by the LSTM model is consistent with the overall trend of the measured value and gradually decreases, and the prediction accuracy is high, with an error of 0.65. (2) The average soil temperatures at 5 cm, 20 cm and 40 cm under RHM were 17.0% (2.4 °C), 13.6% (1.9 °C) and 7.5% (1 °C) greater than those under CK, respectively. (3) Compared with the control treatment, RHM improved the growth and WUEL of apricot trees. The results highlighted the efficiency of the RHM system in enhancing the soil environment and regulating the growth and physiology of apricot trees, which has greater popularization value in arid and semiarid areas. [ABSTRACT FROM AUTHOR]

Understanding runoff dynamics is vital for effective water management in climate-affected areas. This study focuses on the Dayekou basin in China's Qilian Mountains, known for their high climate variability. Using 25 years of data (1994–2018) on river runoff, precipitation, and temperature, statistical methods were applied to explore the annual variations and climate change impacts on these parameters. Results reveal a significant variability in the river runoff (132.27 to 225.03 mm), precipitation (340.19 to 433.29 mm), and average temperature (1.38 to 2.08 °C) over the period. Decadal rising rates average 17 mm for runoff, 17 mm for precipitation, and 0.25 °C for temperature, with the peak precipitation and runoff occurring in 1998–2000, 2008, and 2016. The annual runoff distribution also exhibited a unimodal pattern, peaking at 39.68 mm in July. The cumulative runoff during low periods constituted only 13.84% of the annual total, concentrated in the second half of the year, particularly during the June-October flood season. The correlation analysis underscored a strong relationship between river runoff and precipitation (correlation coefficient > 0.80), while the temperature correlation was weaker (correlation coefficient < 0.80). This 25-year analysis provides valuable insights into runoff variation, elucidating the interconnected effects of temperature and precipitation in the Dayekou basin, with substantial implications for sustainable development amid climate challenges. [ABSTRACT FROM AUTHOR]

The Qilian Mountains, located in northwest China and serving as a crucial water recharge area, have exhibited significant regional differences in precipitation patterns in recent decades. However, the limited temporal coverage of instrumental data has hindered a deep understanding of hydroclimate variations and regional differences. Further investigation into their long-term spatial and temporal precipitation characteristics is urgently needed. In this study, a new tree-ring-width chronology spanning 1743 years was established in the central Qilian Mountains using Qilian juniper (Juniperus przewalskii Kom.) samples. Significant correlations were found between the tree-ring indices and precipitation during both the growing and pre-growing seasons. Based on these correlations, annual precipitation from August of the previous year to July of the current year was reconstructed. The reconstruction model successfully explains 34.5% of the variation in precipitation during the calibration period. The analysis of the reconstructed series reveals notable interannual to multi-decadal dry–wet variability during the period from 614 AD to 2016 AD. The mid- to late-15th century emerges as the longest-lasting dry period, while the last decade stands out as the wettest. Comparative analysis with other precipitation reconstructions in the eastern and western Qilian Mountains reveals that regional drought events tend to be more pronounced and enduring. Low-frequency fluctuations on decadal to century scales show distinct wet and dry periods in the 12th–18th centuries in both the eastern and western parts of the Qilian Mountains, with weaker fluctuations in subsequent centuries. However, the central part of the Qilian Mountains exhibits opposite trends, possibly due to the complex interactions of multiple circulation systems. [ABSTRACT FROM AUTHOR]

As the upstream region of the Asian summer monsoon, the Bay of Bengal summer monsoon (BOBSM) system has impacts on rainfall patterns in East Asia. In this study, we investigate the impact of the interannual variability of the BOBSM retreat on China precipitation in early summer (June) of the following year. When the BOBSM retreat occurs earlier in the previous year, we find enhanced rainfall in both the northeastern and eastern parts of China. Conversely, when the retreat of the BOBSM is delayed in the previous year, there is a tendency for decreased rainfall in most of northeastern and eastern China, while rainfall in the northern part of the Taiwan island region tends to increase. Statistical analysis demonstrates the co-variability between China's June precipitation anomalies and preceding wind anomalies in the eastern Bay of Bengal. The results indicate a strong relationship between the preceding BOBSM retreat and China precipitation anomalies in the following June. Furthermore, the analysis suggests that the BOBSM retreat is more of an independent signal rather than modulated by an Indian Ocean Dipole event. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are important air water sources to the Three‐river‐source (TRS) region known as the "Chinese water tower." Using hourly equivalent blackbody temperature (TBB) data from geostationary satellites of Chinese Fengyun‐2 series during the warm season (May–August) in 2005–2020 and an objective algorithm, MCSs in the TRS are divided into meso‐α (MαCS), meso‐β (MβCS), and meso‐γ (MγCS), and MαCS and MβCS are subdivided into larger meso‐α (LMαCS), smaller meso‐α (SMαCS), larger meso‐β (LMβCS), and smaller meso‐β (SMβCS). Results show that a high‐frequency zone of MCSs in the TRS distributes along the source of the rivers. Most MCSs, except LMαCS, develop and dissipate in situ. The interannual variation in MCS frequency exhibits a decreasing trend, especially after 2013, mainly due to the decrease in MCSs in the source region of the Yellow–Lancang River. The occurrence of MCSs peaks in August, but MCSs are most likely to produce precipitation in July and usually generate between 1600–2200 h LST (UTC + 8). The precipitation caused by MCSs to the total precipitation (precipitation ratio, PR) accounts for about 40%; MCS PR is closely related to, and increases with, the horizontal scale of the MCS, with MαCS PR being the highest, exceeding 67%. The contribution of MCSs to precipitation is mainly reflected in weak precipitation, smaller than 10.0 mm/h. Most of the maximum precipitation of MCSs appears after MCSs reach their prime, with the maximum lag by MαCS up to 2 h. [ABSTRACT FROM AUTHOR]

Organic fertilizer is utilized to improve the organic carbon levels in arable soils, which is helpful for soil quality improvement and crop yield increase. However, the after-effect of organic fertilizer varies among regions with different temperature and precipitation conditions, and the extent of the impact remains unknown. This study aimed to investigate the impact of varying temperature and rainfall conditions on the accumulation of soil organic carbon after organic fertilizer application. A meta-analysis of 168 peer-reviewed studies published between 2005 and 2022 involving a total of 464 trials was conducted. The following was discovered: (1) In the major grain-producing areas of China, there was a significant positive correlation (p < 0.01) between latitude and soil organic carbon content. Meanwhile, temperature and precipitation had a significant negative correlation (p < 0.01) with soil organic carbon content. (2) The increase in temperature inhibited the increase in soil organic carbon storage. The improvement effect of organic fertilizer application in the low-temperature areas was significantly increased by 60.93% compared with the mid-temperature areas, and by 69.85% compared with the high-temperature areas. The average annual precipitation affected the after-effect of organic fertilizer as follows: 400–800 mm > 400 mm > more than 800 mm. (3) The influence of climatic conditions on the after-effect of organic fertilizer was more significant depending on the specific tillage practice. To increase organic fertilizer use efficiency and eliminate greenhouse gas emissions, liquid organic fertilizers with abundant trace nutrients and amino acids, which take advantage of releasing nutrients more swiftly and have a better fertilization effect, could be an alternative to traditional organic fertilizers. [ABSTRACT FROM AUTHOR]

Oscillations in global modes of variability (MoVs) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Understanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic oscillation (AO), Eurasian teleconnection, Indian Ocean dipole (IOD), North Atlantic oscillation (NAO), and El Niño southern oscillation (Nino34), with winter and summer climates in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2 m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, and a lagged correlation analysis is used to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and central China in both winter and summer and a positive correlation in western China in summer. PRECTOT has a positive correlation with all MoVs in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, especially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results identify different time windows and specific regions within HMA that exhibit high correlations between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe. [ABSTRACT FROM AUTHOR]

Middle and eastern routes of the South-North Water Diversion Project (SNWDP) of China, which are approximately located within the area 28°-42°N and 110°-122°E, are being constructed. This paper investigates the past climatic variations on various time scales using instrumental and proxy data. It is found that annual mean surface air temperature has increased significantly during the past 50-100 years, and winter and spring temperatures in the northern part of the region have undergone the most significant changes. A much more significant increase occurs for annual mean minimum temperature and extreme low temperature than for annual mean maximum temperature and extreme high temperature. No significant trend in annual precipitation is found for the region as a whole for the last 50 and 100 years, although obvious decadal and spatial variation is detectable. A seesaw pattern of annual and summer precipitation variability between the north and the south of the region is evident. Over the last 100 years, the Haihe River basin has witnessed a significant negative trend of annual precipitation, but no similar trend is detected for the Yangtze and Huaihe River basins. Pan evaporation has significantly decreased since the mid-1960s in the region in spite of the fact that the trend appears to have ended in the early 1990s. The negative trend of pan evaporation is very significant in the plain area between the Yangtze and Yellow Rivers. There was a notable series of dry intervals lasting decades in the north of the region. The northern drought of the past 30 years is not the most severe in view of the past 500 years; however, the southern drought during the period from the 1960s to the 1980s may have been unprecedented. The dryness-wetness index (DWI) shows significant oscillations with periodicities of 9.5 and 20 years in the south and 10.5 and 25 years in the north. Longer periodicities in the DWI series include 160-170- and 70-80-yr oscillations in the north, and 100-150-yr oscillations in the south. The observed climate change could have implications for the construction and management of the SNWDP. The official approval and start of the hydro project was catalyzed by the severe multiyear drought of 1997-2003 in the north, and the operation and management of the project in the future will also be influenced by climate change-in particular by precipitation variability. This paper provides a preliminary discussion of the potential implications of observed climate change for the SNWDP. [ABSTRACT FROM AUTHOR]

This paper matched the data for all non-financial listed firms in China from 1990 to 2017 with weather data and used temperature and precipitation to represent climate change. Using daily temperature and daily precipitation data, we constructed temperature bins and precipitation bins. This study presents a potential first empirical analysis that applies the risk perception theory to comprehensively examine the impacts of daily temperature and daily precipitation on firms' debt financing costs. Compared to a day with a suitable temperature, a day with temperatures above 30 °C increases firms' debt financing costs by 0.2411%. Compared to a sunny day, a day with torrential rain increases firms' financing costs by 0.2697%. Approximately $122.13 million and $20.49 million in extra costs were incurred across all firms in 2017. We conducted a series of heterogeneity tests. We found that the negative impacts of climate change on debt financing costs are greater for firms that investor considers more vulnerable. Finally, we found two possible mechanisms. First, from the firm's subjective initiative perspective, firms that respond proactively during seasons of high temperatures and heavy rains can reduce the negative impacts. Second, adverse weather caused by climate change may also increase firms' debt financing costs by blocking travel. These results enrich the literature on firms' debt financing costs and contribute to the understanding of climate change's impact on firms. [Display omitted] • An extra high-temperature day increases firms' debt financing cost by 0.2411%. • An extra heavy-rain day increases firms' debt financing costs by 0.2697%. • The negative impacts will be greater for those firms that investor considers more vulnerable. • Firms' response proactively can reduce the negative impacts. • Climate change also may increase firms' debt financing costs by blocking travel. [ABSTRACT FROM AUTHOR]

To achieve a holistic understanding of the intricate interactions among human activities, atmospheric chemistry, and acid rain in China, a rigorous analysis of rainwater chemistry was made using a dataset comprising 2656 data points from 24 sites. The main cation and anion in the chemical composition of precipitation were Ca2+ and SO42− in China, with an average concentration of 169.9 μeq/L and 135.4 μeq/L, respectively. Acid rain generally occurs in southern cities such as Shenzhen, Guangzhou, Zhuhai, Xiamen, and Chongqing. There were evident regional disparities in acidity and ion concentrations in rainwater, with an increase in acidity and a decrease in ion concentrations from north to south across China. Utilizing positive matrix factorization, the study found that NH4+, SO42−, and NO3− mainly originated from anthropogenic sources such as fossil fuel combustion, vehicle exhaust emissions, agricultural fertilization, and industrial emissions (as reflected by F3 and F4). Ca2+ mainly stems from crustal factors, including industrial dust and natural crust (as represented by F1 and F4). Na+ and Cl− were traceable from marine sources (as reflected by F5), while Mg2+ originated from crust origin (as presented by F1). K+ was mainly derived from a mixed source of crust, marine, and biomass burning (as indicated by F2 and F3). The correlation analyses showed that SO42− and NO3− showed significant correlations with GDP and population. F− was associated with wastewater, which may be linked to the production of brick and tiles from clay with high fluoride contents. The pH was negatively related to industrial wastewater. Long-term analysis of precipitation chemistry in four cities suggested a clear decrease in the proportion of SO42− but a considerable increase in the proportion of NO3− in anions in metropolitans of Shanghai and Chongqing due to the environmental measures that targeted reducing sulfur dioxide (SO2) emissions and increase of vehicles. This showed that pollution control strategies had an impact on precipitation ion concentrations. These results can conclude that economic and industrial growth, which will increase energy consumption, utilization of coal combustion, and a subsequent rise in pollutant emissions, can contribute to the change in the chemical compositions of rainwater and the exacerbation of acid rain. [ABSTRACT FROM AUTHOR]

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The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of "more in the east and less in the west" was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin. [ABSTRACT FROM AUTHOR]

Global warming substantially intensifies hydrologic cycles, causing increasing frequency and magnitude of catastrophic floods and droughts. Understanding the patterns and mechanisms of precipitation in historical periods is pivotal for regional disaster prevention and mitigation. Here, we analyzed the daily precipitation of six stations at the Shimantan Reservoir from 1952 to 2013 to examine precipitation characteristics at different time scales. The Mann–Kendall test, moving t-test, and Innovative Polygon Trend Analysis (IPTA) were employed to detect the trends and change points in total precipitation amount, frequency, and duration. Influences of atmospheric circulations on precipitation were then explored via cross-wavelet analysis. Our results showed increased average precipitation and decreased precipitation days annually at the Shimantan Reservoir in the past decades. Specifically, increased seasonal precipitation was only detected in summer, while precipitation days were mainly reduced in winter. There was a noticeable increasing to decreasing transition trend in precipitation from July to August, and a transition from decreasing to increasing from June to July in precipitation days. Summer rainfall was predominantly moderate and light, accompanied by shortening and highly fluctuating rainstorm durations. July exhibited the highest precipitation frequency and always experienced rainstorms. The Arctic Oscillation and East Asian summer monsoon showed positive and negative correlations, respectively, with the changes in precipitation at the Shimantan Reservoir. Our analyses provide a fine-scale portrait of precipitation patterns and mechanisms under a changing climate and benefit regional flood control and sustainable development. [ABSTRACT FROM AUTHOR]

Secondary ice productions (SIPs) can produce ice crystals with a number concentration much higher than that of ice nucleating particles in mixed-phase clouds and therefore influence cloud glaciation and precipitation. For midlatitude continental mesoscale convective systems (MCSs), how SIPs affect the microphysical properties and precipitation is still not clear. There are few studies of SIPs in midlatitude continental MCSs. This study investigates the roles of three SIPs (rime splintering, freezing drop shattering, and ice-ice collisional breakup) on a squall line case in North China on 18 August 2020 using the WRF model with a modified Morrison double-moment bulk microphysical scheme. Including SIPs, especially ice-ice collisional breakup, in the model simulations markedly improves the simulated convective area and convective precipitation rate of the squall line, while slightly improving the area and precipitation of the stratiform region. Within the mixed-phase layer in both the convective and stratiform regions of the squall line, ice-ice collisional breakup is the dominant process to generate ice crystals. In contrast, rime splintering generates an order of magnitude fewer ice crystals than ice-ice collisional breakup, while freezing drop shattering plays a negligible role due to the lack of large drops. Ice multiplication through ice-ice collisional breakup and rime splintering produces numerous snowflakes and graupel. This leads to enhanced depositional growth and weaker riming, which in turn weakens rime splintering. It is recommended to add SIP parameterization to the model. [ABSTRACT FROM AUTHOR]

The allocation of plant biomass above and below ground reflects their strategic resource utilization, crucial for understanding terrestrial carbon flux dynamics. In our comprehensive study, we analyzed biomass distribution patterns in 580 broadleaved and 345 coniferous forests across China from 2005 to 2020, aiming to discern spatial patterns and key drivers of belowground biomass proportion (BGBP) in these ecosystems. Our research revealed a consistent trend: BGBP decreases from northwest to southeast in both forest types. Importantly, coniferous forests exhibited significantly higher BGBP compared to broadleaved forests (p < 0.001). While precipitation and soil nutrients primarily influenced biomass allocation in broadleaved forests, temperature and soil composition played a pivotal role in coniferous forests. Surprisingly, leaf traits had a negligible impact on BGBP (p > 0.05). Climatic factors, such as temperature and rainfall, influenced biomass partitioning in both strata by altering soil nutrients, particularly soil pH. These findings provide valuable insights into understanding carbon sequestration dynamics in forest ecosystems and improving predictions of the future trajectory of this critical carbon cycle component. [ABSTRACT FROM AUTHOR]

Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one of the most widely used SPPs in the scientific community. However, there is a lack of comprehensive evaluation for the performance of the newly released IMERG Version 07, which is essential for determining its effectiveness and reliability in precipitation estimation. In this study, we compare the IMERG V07 Final Run (V07_FR) with its predecessor IMERG V06_FR across scales from January 2016 to December 2020 over the globe (cross-compare their similarities and differences) and a focused study on mainland China (validate against 2481 rain gauges). The results show that: (1) Globally, the annual mean precipitation of V07_FR increases 2.2% compared to V06_FR over land but decreases 5.8% over the ocean. The two SPPs further exhibit great differences as indicated by the Critical Success Index (CSI = 0.64) and the Root Mean Squared Difference (RMSD = 3.42 mm/day) as compared to V06_FR to V07_FR. (2) Over mainland China, V06_FR and V07_FR detect comparable precipitation annually. However, the Probability of Detection (POD) improves by 5.0%, and the RMSD decreases by 3.7% when analyzed by grid cells. Further, the POD (+0%~+6.1%) and CSI (+0%~+8.8%) increase and the RMSD (−11.1%~0%) decreases regardless of the sub-regions. (3) Under extreme rainfall rates, V07_FR measures 4.5% lower extreme rainfall rates than V06_FR across mainland China. But V07_FR tends to detect more accurate extreme precipitation at both daily and event scales. These results can be of value for further SPP development, application in climatological and hydrological modeling, and risk analysis. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are important tools for assessing the historical characteristics and predicting the future characteristics of precipitation, yet the quantitative understanding of how these land–atmospheric coupling models perform in simulating precipitation characteristics remains limited. This study conducts a comprehensive evaluation of precipitation changes simulated by 43 ESMs in CMIP5 and 32 ESMs in CMIP6 in Arid Central Asia (ALL) and its two sub-regions for 1959–2005 with reference to Climate Research Unit (CRU) data, and predicts precipitation changes for 2054–2100. Our analyses suggest the following: (a) no single model consistently outperformed the others in all aspects of simulated precipitation variability (annual averages, long-term trends, and climatological monthly patterns); (b) the CMIP5 and CMIP6 model simulations tended to overestimate average annual precipitation for most of the ALL region, especially in the Xinjiang Uygur Autonomous Region of China (XJ); (c) most model simulations projected a stronger increasing trend in average annual precipitation; (d) although all the model simulations reasonably captured the climatological monthly precipitation, there was an underestimation; (e) compared to CMIP5, most CMIP6 model simulations exhibited an enhanced capacity to simulate precipitation across all aspects, although discrepancies persisted in individual sub-regions; (f) it was confirmed that the multi-model ensemble mean (MME) provides a more accurate representation of the three aspects of precipitation compared to the majority of single-model simulations. Lastly, the values of precipitation predicted by the more efficient models across the ALL region and its sub-regions under the different scenarios showed an increasing trend in most seasons. Notably, the strongest increasing trend in precipitation was seen under the high-emission scenarios. [ABSTRACT FROM AUTHOR]

Wang, X.; Jiang, R.; Xie, J.; Zhao, Y.; Li, F., and Zhu, J., 2019. Multiscale variability of precipitation and their teleconnection with large-scale climate anomalies: A case study of Xi'an city, China. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 417–426. Coconut Creek (Florida), ISSN 0749-0208. The changes of precipitation affect the urban rainstorm waterlogging and water resources management, which have important influences on the sustainable development of society. The primary objective of the paper is to provide theoretical basis for local water resources management and disaster prevention and reduction by analyzing the multiscale variability of precipitation and their teleconnection with large-scale climate anomalies in Xi'an city of China. The change characteristics of precipitation and extreme precipitation indices were investigated using Mann-Kendall test, Spearman's rank correlation and wavelet analysis. The cross wavelet transform and wavelet coherence were used to explore the correlations between the precipitation and large-scale teleconnection indices such as Arctic Oscillation (AO), Southern Oscillation Index (SOI), El Niño-Southern Oscillation (ENSO) and sunspot. Results indicate that: the precipitation was mainly concentrated in summer, followed by autumn. The annual, seasonal and monthly precipitation except June and August had decreasing trends, and the magnitudes of precipitation decrease were more pronounced in spring, April and November. The change patterns varied for extreme precipitation indices. The temporal variation in extreme precipitation indices indicated that the extreme precipitation events had mainly increased during 1971-1990 and 1991-2017, and decreased during 1951-1970 and 1951-2017, especially, increased more pronounced during 1991-2017 and decreased more pronounced during 1951-1970. The precipitation exhibited a fluctuant change from increase to decrease, then increase to decrease and finally increase trends, and there was an abrupt change point in 1965. The annual precipitation had statistically significant cycles of 3-5 year and 7-10 year at 5% significance level. The large-scale teleconnection indices affected the precipitation in Xi'an city. The phase differences between precipitation and teleconnection indices including AO and SOI were in-phase dominated, and anti-phase dominated between precipitation and teleconnection indices including ENSO and sunspot, indicating that the AO and SOI exerted positive climate impacts on the precipitation and vice-versa for ENSO and sunspot. [ABSTRACT FROM AUTHOR]

The dry-wet abrupt transition (DWAT) is a typical anomaly of precipitation at the subseasonal scale and may result in a severer and greater hydro-meteorological hazard. This paper identified and examined the DWAT events in terms of precipitation in Poyang Lake basin, including both from dry to wet (DTW) and from wet to dry (WTD), during the period of 1960-2010 and analyzed its characteristics of temporal and spatial distribution and intra- and inter-annual tendencies based on the Mann-Kendall (M-K) test. The results revealed that the DTW was inclined to occur in March, but WTD in July and September; the inter-annual variation of the DWAT index showed a feeble long-term increasing trend in July and a decreasing trend in September with a Z-statistic of 1.81 and -1.44, respectively, although none of these trends achieved the statistical significance level (a = 0.05); the north parts of Poyang Lake basin experienced more DWAT events during the last 50 years in which the occurrence of WTD is more frequent than that of DTW. The outcomes of the study will help mitigate and regulate the flood and drought in Poyang Lake basin, as well as in other regions. [ABSTRACT FROM AUTHOR]

Obtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the entire country of China into eight vegetation zones based on different vegetation types. Within each vegetation zone, we employ a seasonally adjusted Extreme Random Trees approach to spatially downscale GPM (Global Precipitation Measurement) satellite monthly precipitation data. To validate the effectiveness of this method, we compare it with kriging interpolation and traditional global downscaling methods. By increasing the spatial resolution of the GPM monthly precipitation dataset from 0.1° to 0.01°, we evaluate the downscaled results and validate them against ground-level rain gauge data and GPM satellite precipitation data. The results indicate that the partitioned area prediction method outperforms other approaches, resulting in a precipitation dataset that not only achieves high accuracy but also offers finer spatial resolution compared to the original GPM precipitation dataset. Overall, this approach enhances the model's capability to capture complex spatial features and demonstrates excellent generalization. The resulting higher-resolution precipitation dataset enables the creation of more accurate precipitation distribution maps, providing data support for regions lacking hydrological information. These data can be used to analyze seasonal precipitation patterns and reveal differences in precipitation across different seasons and geographic regions. [ABSTRACT FROM AUTHOR]

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin. [ABSTRACT FROM AUTHOR]

Drought monitoring ensures the Yangtze River Basin's social economy and agricultural production. Developing a comprehensive index with high monitoring precision is essential to enhance the accuracy of drought management strategies. This study proposes the standardized comprehensive drought index (SCDI) using a novel approach that utilizes the joint distribution of C-vine copula to effectively combine three critical drought factors: precipitation, NDVI, and runoff. The study analyzes the reliability and effectiveness of the SCDI in detecting drought events through quantitative indicators and assesses its applicability in the Yangtze River Basin. The findings are as follows: (1) The SCDI is a highly reliable and applicable drought index. Compared to traditional indices like the SPI, VCI, and SRI, it has a consistency rate of over 67% and can detect drought events in more sensitive months by over 51%. It has a low false negative rate of only 2% and a false positive rate of 0%, making it highly accurate. The SCDI is also applicable to all the third-level sub-basins of the Yangtze River Basin, making it a valuable tool for regional drought monitoring. (2) The time lag effect of the NDVI can affect the sensitivity of the SCDI. When the NDVI time series data are shifted forward by one month, the sensitivity of the SCDI in detecting agricultural drought improves from 47.8% to 53%. (3) The SDCI can assist in monitoring drought patterns in the Yangtze River Basin. From 2001 to 2018, the basin saw fluctuations in drought intensity, with the worst in December 2008. The western region had less frequent but more intense and prolonged droughts, while the eastern part had more frequent yet less severe droughts. [ABSTRACT FROM AUTHOR]

Projecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K‐nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near‐term), 2043–2062 (Mid‐term), and 2081–2100 (Long‐term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5‐8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2‐4.5 with −30.48% to 44.67%, and the smallest under SSP1‐2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long‐term, followed by Mid‐term, and the smallest in the Near‐term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change. [ABSTRACT FROM AUTHOR]

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various "possible future" scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China's climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China's moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study's findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation. [ABSTRACT FROM AUTHOR]

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products. [ABSTRACT FROM AUTHOR]

This research investigates Southwest Vortex (SWV) events in China's Sichuan Basin using Fengyun-4A (FY-4A) and Global Precipitation Mission (GPM) observations. We selected representative cloud systems and precipitation cases, divided into developing, mature, and dissipating stages. Detailed analysis revealed critical characteristics of precipitation cloud systems at each stage. Our findings reveal that (1) during the SWV's developing and mature stages, a high concentration of water particles and ice crystals stimulates precipitation. In contrast, the dissipating stage is marked by fewer mixed-phase and ice particles, reducing precipitation area and intensity. (2) Near-surface precipitation in all stages is predominantly liquid, with a bright band of around 5.5 km. At the same time, stratiform precipitation is dominant in each life stage. Stratiform precipitation remains dominant throughout the life stages of the SWV, with localized convective activity evident in the developing and mature stages. (3) Mature stage particles, characterized by a configuration of 1.0–1.2 mm Dm and 31–35 dBNW (dBNW = 10log10NW), contribute significantly to near-surface precipitation. The Cloud Top Height (CTH) serves as an indicator of convective intensity and assists in characterizing raindrop concentration. These findings considerably enhance routine observations, advance our understanding of SWV events, and propose a novel approach for conducting refined observational experiments. [ABSTRACT FROM AUTHOR]

The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPMIMERG V06 Final Run products in different climatic and topographical regions of China for the 2014-20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R .0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April-September) (RBias 5 7.41%) than during the dry season (RBias 5 13.65%). 2) GPMIMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world. [ABSTRACT FROM AUTHOR]

Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°-50°N, 72°-107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash-Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale. [ABSTRACT FROM AUTHOR]