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Extreme hydrometeorological events have an effect on the growth of vegetation worldwide. Here, we used China's Yellow River Basin to deeply explore the temporal and spatial trends of extreme hydrometeorological events and their impacts on vegetation. We identified extreme drought (Dry-PRE, Dry-VPD) and extreme wet (Wet-PRE, Wet-VPD) events based on precipitation and vapor pressure deficit (VPD). In this paper, the interannual variation of extreme hydrometeorological events in the Yellow River Basin, their temporal and spatial distribution patterns and their impacts on vegetation were analyzed by combining with normalized difference vegetation index and precipitation. The results indicated that Dry-VPD, Dry-PRE and Wet-PRE events showed an upward trend year by year, Wet-VPD events showed a downward trend year by year, and Dry-VPD and Dry-PRE events increased significantly. Overall, the environment of the Yellow River Basin continued to dry out. The sensitivity of vegetation in the Yellow River Basin to extreme drought events was higher compared with that to extreme wet events. Extreme drought events based on VPD might affect vegetation growth in the upstream western region and the downstream region of the Yellow River Basin, while extreme drought events based on PRE might affect vegetation growth in the central region of the Basin. This paper showed that, in addition to the impact of precipitation on vegetation, drought caused by VPD also had a significant impact on vegetation. Furthermore, we suggested strengthening the protection of vegetation where the trend was significant. [ABSTRACT FROM AUTHOR]

Moisture sources and transport processes play a critical part in hydrological cycle and determine regional precipitation. This paper utilizes the Water Accounting Model‐2layers (WAM‐2layers) and the ERA5 reanalysis data to track the sources of precipitation over the Pearl River Basin (PRB). The contribution of external moisture and the role of local recycling are investigated. The results show that during the period from 1980 to 2020, oceanic sources including the western North Pacific and Indian Oceans serve as the primary moisture sources of precipitation over the PRB. The contributions to total seasonal precipitation are respectively 62.57% in MAM, 54.79% in JJA, 43.70% in SON and 60.88% in DJF. By contrast, the contribution of local recycling is generally below 5.50%. In the dry years of 1994, 1997 and 2001, the contribution of terrestrial sources is about 19.22%; in the wet years of 1989, 2009 and 2011, the contribution is about 16.31%. The summer precipitation anomalies are mainly attributable to moisture anomalies from the Equatorial Indian Ocean in the wet years and from Southeast Asia in the dry years. Furthermore, vertically integrated moisture flux anomalies over the boundaries of the PRB are generally the result of anomalous wind rather than anomalous moisture. In the wet years, low‐pressure systems induce strong cyclonic moisture transports, increasing the PRB precipitation. In the dry years, high‐pressure anomalies over the PRB block the moisture transports from the Indian Ocean and western North Pacific. [ABSTRACT FROM AUTHOR]

Increasing variability of precipitation induced by climate change raises uncertainties for ecosystem services and agricultural production in the Poyang Lake Basin (PLB), China. Reanalysis precipitation datasets (RPDs) are attractive alternatives for monitoring the hydro‐climate cycles. However, their applicability in China has mainly been evaluated at the national scale, while less attention has been paid to the PLB or southeastern China. Here, we apply various metrics to evaluate the magnitude and spatial similarity of annual, monthly, different intensities and the spatial structure of precipitation from four reanalysis—the fifth global atmospheric analysis dataset of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), Interim ECMWF Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis project (JRA55) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2), against the rain‐gauges over the PLB during 1980–2018. The results showed that the detection capacity of ERA5 and MERRA2 shifted around 2002, whereas that of ERA‐Interim and JRA55 did not substantially change. Before 2002, ERA‐Interim had the lowest biases for the magnitude of annual, monthly and different intensities of precipitation (except for heavy rain), while ERA5 and MERRA2 had strengths in capturing spatial correlation. Notably, ERA5 and MERRA2 had region‐wide wet and dry biases, respectively, before 2002, which can be attributed mainly to the estimation of flood season precipitation (from April to June) and heavy rain. In contrast, the fractional contribution of light–moderate rain was underestimated and overestimated by ERA5 and MERRA2, respectively. However, after 2002, because of the improved reproducibility of flood season precipitation and heavy rain magnitude, the systematic biases of ERA5 and MERRA2 significantly decreased, especially for ERA5, which outperformed other RPDs on all aspects of precipitation. The conclusions of this paper suggest that the variation of applicability over time should be seriously tested before the utilization of RPDs in hydro‐climate analyses. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [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]

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 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]

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]

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]

• The proposed model improves the forecast skill for sub-daily precipitation. • The model was improved by treating precipitation forecasts as censored data. • The proposed model achieves similar forecast skill with an EMOS model. Statistical post-processing methods have been applied in hydrometeorological forecasting to correct the bias and spread error in raw forecasts. Among various post-processing methods, the meta-Gaussian distribution model (MGD) is one of the early successful methods for post-processing of precipitation forecasts and has been applied in the National Weather Service's Hydrologic Ensemble Forecast System (HEFS), together with the mix-type meta-Gaussian distribution model (MMGD). However, recent studies have shown that the original MGD cannot yield reliable forecasts especially for sub-daily precipitation forecasts (e.g., 6-hourly). In this paper, we improved the MGD model by applying the censored maximum likelihood estimation (CMLE) method. We conducted experiments using GEFS reforecasts in Huai river basin in China to evaluate its performance. The results show that the proposed method performs better than the original MGD for sub-daily precipitation forecasts. The proposed method also achieves similar forecast skill with the state-of-the-art censored, shifted Gamma distribution-based ensemble MOS (CSGD-EMOS) if both use ensemble mean as the only predictor. The results indicate that the proposed CMLE-MGD can be useful for further applications such as flood forecasting that needs forecasts of high temporal resolution. [ABSTRACT FROM AUTHOR]

The magnitude-frequency (MF) relationship of debris flows is the basis for engineering designs and risk quantification. However, because of the lack of debris flow monitoring data, research progress in this area has been relatively slow. The MF relationship of debris flows in Jiangjia Gully, Yunnan Province was evaluated based on a regression analysis of 178 debris flow events that occurred from 1987–2004. The magnitude-cumulative frequency (MCF) relationship of the debris flows in the Jiangjia Gully is consistent with the linear logarithmic transformation function. Moreover, observed data for debris flows in Hunshui Gully of Yunnan Province and Huoshao Gully, Liuwan Gully, and Niwan Gully of Gansu Province were used to verify the function. The results showed that the MCF relationship of high-frequency debris flows is consistent with the power law equation, although the regression coefficients in the equation are considerably different. Further analysis showed a strong correlation between the differences in the constants and the drainage area and daily maximum precipitation. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the trends of the annual streamflow and precipitation and cross correlations between them were analyzed in nine large river basins of China during 1956–2005. The results indicate that: (1) the annual mean streamflow decreases in arid and semi-arid regions of north China; however, increasing trends occur in south and Southwest China; (2) the annual streamflow and precipitation exhibit reasonable correlation in nine large river basins except those located in inland areas. The annual streamflow over most areas of China is fed by precipitation; however, the decline in streamflow is faster than the decreases of precipitation since 1970s in the arid and semi-arid regions of north China. The relationship between the annual precipitation and streamflow presents a non-stationary state in north China. This non-stationary relationship is strongly influenced by both human activities and precipitation changes; (3) a significant increase of water use might be the major factor responsible for the steeper decline in streamflow than in precipitation in Haihe River, Yellow River and Songliao River basins in north China. In inland river areas, increase of water use and actual evapotranspiration might result in decline in streamflow although precipitation has an increase tendency. This paper sheds light on the non-stationary relationship between annual precipitation and streamflow and possible underlying causes, which will be helpful for a better understanding of the changes of precipitation and streamflow in China at large scale and in other regions of the world. [Copyright &y& Elsevier]

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This paper investigates the annual and seasonal precipitation variability at 20 stations over upper Indus River basin (UIRB) by using an innovative trend analysis (ITA) method, Mann-Kendall (MK) and Sen's slope estimator tests. Moreover, the annual precipitation time series is investigated for five precipitation intensities i.e. light, low, moderate, high and heavy; and on a seasonal scale for extreme values i.e. light and heavy precipitation intensities. Annual precipitation showed an increasing trend at four stations in the northeast region and a decreasing trend at two stations in the southeast region of UIRB. On a seasonal scale, the results showed that Garidopatta, Peshawar, Astore and Gupis stations are more sensitive to seasonal precipitation variabilities. Changes in heavy precipitation (> 90th percentile) are found more severe in winter and summer seasons in the northern regions suggesting a probable flooding aggravation. However, a reduction in flooding event observed during the spring season in the southern regions of the UIRB indicating a shift in the precipitation regime from south to north. The results of light precipitation (< 10th percentile) intensity showed that winter and spring seasons are more vulnerable to the occurrence of prolonged drought events. Moreover, ITA method has the advantage of detecting the sub-trends in the precipitation series because of its ability to present the results in graphical format. The results of this study could help to understand seasonal and annual precipitation variability over UIRB and will have imperative insinuations for further studies. [ABSTRACT FROM AUTHOR]

Locally evaporated water vapor is an important source of precipitation in China. The spatiotemporal variation characteristics of the precipitation recycling ratio (ρ) in the Yangtze River Basin (YRB) in 1979–2020 were calculated and analyzed, and the contribution of internal and external cycling precipitation changes to the total precipitation changes in YRB under climate change was studied. The results show that the annual average ρ in YRB is approximately 10.3%, with the highest value of 21.8% in summer, lower than 10% in spring and autumn, and the lowest in winter, with only approximately 3.5%. Over the past 40 years, the annual average ρ in YRB has shown an increasing trend, with an increased rate of 0.4%/10a, especially in summer, with an increasing rate of 1.2%/10a. In terms of spatial distribution, ρ in YRB shows an obvious difference between the eastern and western regions, with that in the upstream western region being significantly higher than that in the downstream eastern region. The annual average ρ in the upstream region was 15–35% and can reach 20–50% in summer. The annual average ρ in the downstream region is below 10%. In general, precipitation formed by advection moisture accounts for the majority of the total precipitation in YRB. From 1979 to 2020, the annual precipitation in YRB showed an increasing trend. The cumulative increase is about 47.4 mm, of which 68.9% was contributed by local evaporation, and 31.1% was contributed by external moisture. [ABSTRACT FROM AUTHOR]

Obtaining precipitation estimates with high resolution and high accuracy is critically important for regional meteorological, hydrological, and other applications. Although satellite precipitation products can provide precipitation fields at various scales, their applications are limited by the relatively coarse spatial resolution and low accuracy. In this study, we propose a multi-source merging approach for generating accurate and high-resolution precipitation fields on a daily time scale. Specifically, a random effects eigenvector spatial filtering (RESF) method was first applied to downscale satellite precipitation datasets. The RESF method, together with Kriging, was then applied to merge the downscaled satellite precipitation products with station observations. The results were compared against observations and a data fusion dataset, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). It was shown that the estimates of the proposed method significantly outperformed the individual satellite precipitation product, reducing the average value of mean absolute error (MAE) by 52%, root mean square error (RMSE) by 63%, and improving the mean value of Kling–Gupta efficiency (KGE) by 157%, respectively. Daily precipitation estimates exhibited similar spatial patterns to the MSWEP products, and were more accurate in almost all cases, with a 42% reduction in MAE, 46% reduction in RMSE, and 79% improvement in KGE. The proposed approach provides a promising solution to generate accurate daily precipitation fields with high spatial resolution. [ABSTRACT FROM AUTHOR]

Understanding and exploring hydrological (H) or soil moisture (SM) drought due to meteorological (M) drought under changing climate is crucial for drought early warning. Previously, different methods were used to calculate drought propagation from one state (M to another [H or SM]). However, each method has its pros and cons and thus cannot describe appropriate propagation attributes. In this study, a time series analysis is carried out to explore the propagation process in the Yellow River Basin (YRB) and Hai River Basin (HRB) of China. Standardized Streamflow Index (SSI), Standardized SM Index (SSMI), and Standardized Precipitation Index (SPI) 1‐month time series were calculated using streamflow, satellite‐based SM, and precipitation from 1979 to 2016. The time series framework identifies the average drought propagation as 3.4–8.3 months (M–H) and 2.3–5 months (M–SM) in YRB, while 2.8–7.7 months (M–H) and 2.3–8.5 months (M–SM) in HRB. Cross‐wavelet analysis indicated that periodic characteristics of M drought are responsible for these droughts. Overall, the findings of this study may help to minimize the drought hazards posed by M droughts. [ABSTRACT FROM AUTHOR]

The change in natural runoff is highly relevant to total river flow dispatch and water resource utilization in the Yellow River Basin (YRB). Based on the annual mean temperature and total precipitation records from 70 meteorological stations from 1961–2020, the impact of climate change on the natural runoff of the YRB is investigated using the Mann-Kendall (M-K) test and Bivariate Wavelet analysis methods. Results show that the annual mean temperature over the YRB increased by 0.33 °C decade−1 during 1961–2020, with a warming rate of more than 0.40 °C decade−1 observed in its northern part. The annual total precipitation increases by 10–20 mm decade−1 in the northwest YRB, while it decreases by 20–30 mm decade−1 in the southeast YRB. The result of the M-K test shows abrupt variations in temperature and natural runoff, especially in the 1980s and 1990s. The decrease in natural runoff is closely tied to the increase (decrease) in temperature (precipitation), especially for the period 1993–2020. The bivariate wavelet coherence analysis further suggests that the decrease in the natural runoff, which has persisted over the past 60 years, is primarily driven by precipitation reduction rather than regional warming. In the stage of rapid warming, the inter-decadal influence of precipitation on natural runoff gradually changes to the influence of inter-annual fluctuation. The finding contributes to providing an important scientific basis for evaluating the optimal allocation of water resources in arid and semi-arid areas against the background of climate change. [ABSTRACT FROM AUTHOR]

Climate change is a global scientific problem, and its impact on the spatiotemporal variations of precipitation has been a crucial research topic. Although previous studies have used different methods to evaluate the precipitation characteristics of the Huaihe River Basin, the time series were short and the few stations could not fully and accurately represent the precipitation characteristics. In this study, daily temperature and precipitation data were collected from 233 meteorological stations in the Huaihe River Basin from 1960–2020. The Mann–Kendall test was used to analyze the trend and significance of interannual and interseasonal scale changes in temperature and precipitation in the Basin, respectively. The correlation between temperature and precipitation was analyzed using the Pearson correlation coefficient method. The spatial distribution of the significance of temperature and precipitation changes and the spatial distribution of the correlation between temperature and precipitation in the basin were plotted. The temperature in the basin tended to increase on interannual and interseasonal scales, with more noticeable changes in spring and winter. Precipitation showed an overall decreasing trend but an increasing trend in localized areas in the south. A decreasing trend in the interseasonal variation scale was observed in spring, an increasing trend in winter, a decreasing trend in the northeastern region in summer, an increasing trend in the southwestern region, and an increasing trend in the northern and southern parts in autumn were observed. The correlation between average temperature and precipitation on interannual and interseasonal scales was analyzed using Pearson's correlation coefficient method, and the annual average temperature and annual average precipitation in the Huaihe River basin were found to be negatively correlated, except for sporadic areas that showed extremely weakly positive correlations or no correlations. [ABSTRACT FROM AUTHOR]

This study investigated the combined impacts of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on streamflow under four scenarios: neutral, pure ENSO, pure IOD, and a combination of ENSO and IOD. The Jinsha River Basin (JRB), at the head of the Yangtze River, was used as a case study. By using statistical methods such as coherent wavelet analysis (WTC), we are committed to studying what kind of impact the IOD will have, the difference in impact between ENSO and IOD at different stages, and the difference in impact between ENSO and IOD on the mean and extreme values of runoff, compared with traditional single ENSO event, to provide support for water resource management, especially for reservoir operation. The key results are as follows. (a) Both ENSO and IOD events affect annual and seasonal streamflow in the JRB. (b) The impact of pure IOD events on annual streamflow in the JRB was twice as great as that of pure ENSO events in developing years, whereas the opposite was true in decaying years. (c) The combined impact of ENSO and IOD led to a higher streamflow maximum than the annual or seasonal average streamflow. Conversely, their impact on the streamflow minima was less than 10% during both developing and decaying years, except at Zhimenda Station. (d) Overall, water shortages could be more serious in developing years than in neutral years, and much more attention should be given to flooding control in decaying years. These results can be used as a reference for water resource management concerning agricultural planning and ecological protection in the JRB. [ABSTRACT FROM AUTHOR]

The Shiyang River Basin is located at the edge of the monsoon wind system of South and Southeast Asia. The hydrochemical characteristics of precipitation are influenced by both monsoon and arid regions. The regression analysis method, comparative analysis, neutralization factor (NF), enrichment factor (EF) and HYSPLIT4 were used to analyze the precipitation samples collected from the upper reaches of the Shiyang River from October 2016 to October 2017. In order to study the hydrochemical characteristics and ion sources of precipitation in this basin. The results, as discussed in this paper, show that the precipitation in the upper reaches of the Shiyang River is mildly alkaline all year round while the neutralization ability of Ca2+ and NH4+ in precipitation is strong. The ion concentration was higher in the dry season than that in the wet season, but the concentration of NH4+ was higher in summer. Furthermore, as the altitude increased, the electrical conductivity (EC) of the precipitation decreased gradually. Influenced by precipitation and rainy days, the wet deposition of nitrogen (N) and sulfur (S) was higher in the wet season than that during the dry season, and the wet deposition gradually increased with the elevation. In precipitation, the earth's crust is a major source of Ca2+, K+ and Mg2+, the ocean is a major source of Cl−, the ocean and the earth's crust are the sources of Na+, human activities are the main sources of SO42−, NO3− and NH4+, the amount of F− is very small, its sources are natural and human activities. Water vapor in precipitation mainly comes from westerly air mass circulation and monsoon circulation while the particles come mainly from the earth's crust. [ABSTRACT FROM AUTHOR]

Satellite-based and reanalysis precipitation estimates are an alternative and important supplement to rain gauge data. However, performance of China's Fengyun (FY) satellite precipitation product and how it compares with other mainstream satellite and reanalysis precipitation products over China remain largely unknown. Here five satellite-based precipitation products (i.e., FY-2 precipitation product, IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and one reanalysis product (i.e., ERA5) are intercompared and evaluated based on in situ daily precipitation measurements over mainland China during 2007–17. Results show that the performance of these precipitation products varies with regions and seasons, with better statistical metrics over wet regions and during warm seasons. The infrared–microwave combined precipitation [i.e., IMERG, GSMaP, and CMORPH, with median KGE (Kling–Gupta efficiency) values of 0.53, 0.52, 0.59, respectively] reveals better performance than the infrared-based only product (i.e., PERSIANN-CDR, with a median KGE of 0.31) and the reanalysis product (i.e., ERA5, with a median KGE of 0.43). IMERG performs well in retrieving precipitation intensity and occurrence over China, while GSMaP performs well in the middle to low reaches of the Yangtze River basin but poorly over sparsely gauged regions, e.g., Xinjiang in northwest China and the Tibetan Plateau. CMORPH performs well over most regions and has a greater ability to detect precipitation events than GSMaP. The FY-2 precipitation product can capture the overall spatial distribution of precipitation in terms of both precipitation intensity and occurrence (median KGE and CSI of 0.54 and 0.55), and shows better performance than other satellite precipitation products in winter and over sparsely gauged regions. Annual precipitation from different products is generally consistent, though underestimation exists in the FY-2 precipitation product during 2015–17. Significance Statement: Intercomparison between the FY-2 precipitation product and mainstream precipitation products is valuable to guide applications of satellite precipitation products to China and its subregions. This study illustrates uncertainties in various satellite precipitation products, and could guide optimization of algorithms of precipitation retrieval and data fusion/merging to improve the accuracy and resolution of satellite precipitation products. [ABSTRACT FROM AUTHOR]

Summary The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n , and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1–2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1–4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1–3.1%/a) occurring in the Northwest, the largest negative values (−1.0 to −0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation ( P ) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of ∼3% and a maximum of ∼20% when 10% precipitations change in those catchments of China. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products are commonly evaluated using gauge measurement, yet their regional evaluation and hydrological applicability have not been sufficiently studied, especially for dry basins. In this study, we evaluated the performance of four state-of-the-art remotely sensed precipitation products (CMORPH, GSMaP, IMERG, and PERSIANN-CDR) and their ensemble products (the reliability ensemble averaging and three-cornered hat methods) over the Heihe River basin, northwest China. Both direct evaluation using gauge measurement during 2001-19 and indirect evaluation using the Soil andWater Assessment Tool (SWAT) model during 2001-10 were conducted. Our results showed that 1) for point-to-pixel evaluation, GSMaP and IMERG products with high spatial resolution effectively captured the quantile distribution of gauge data; 2) compared to the spatially interpolated gauge data, all products underestimated the precipitation, among which GSMaP provided the closest interannual variability to the observations; 3) these products had better detection abilities upstream and during the rainy season, indicating that their performance was affected by the rain intensity-in particular, GSMaP exhibited the best ability; 4) the spatial patterns of individual products were inconsistent, while the ensemble products could reduce the bias with the gauge data; and 5) for hydrological modeling, streamflow simulation driven by GSMaP had the best performance, and the ensemble precipitation using the three-cornered hat method was better than that using the reliability ensemble averaging method. Collectively, these findings illustrated the reliability of GSMaP in representing the precipitation characteristics in similar arid areas and elucidated the advantages of using the three-cornered hat method. [ABSTRACT FROM AUTHOR]

Interactions between precipitation, vegetation, and erosion are crucial and not fully solved issues in the area of earth surface processes. The Qingshui River Basin (QRB), as the main sediment source tributary of the upper reaches of the Yellow River, is characterized by spatial heterogeneity of rainfall, vegetation, and soil erosion. In this study, we investigated the spatiotemporal variations of sediment yields within the QRB and further identified the coupling effects of precipitation and vegetation on soil erosion. We collected annual (1955 to 2016) and daily (2006 to 2016) hydrological and sediment series from six hydrological stations, which subdivided the whole basin into six different sub-basins with heterogeneity in climate and landscape. Variations in parameter a of the sediment rating curves among the six sub-basins continuously declined, showing the continuously increasing effect of vegetation coverage on reducing soil erosion. The unique combination of relationships between precipitation characteristics and vegetation patterns in six sub-basins and these coupling effects resulted in different precipitation–vegetation–erosion patterns in six sub-basins. Sediment yield followed bell-shaped relationships with vegetation and precipitation, with a clear critical threshold at normalized difference vegetation index (NDVI) = 0.36/precipitation = 100 mm at a monthly scale. Based on these thresholds, the non-linear relationships between precipitation, vegetation, and erosion were also explained. We also found that reducing the time lags in which vegetation follows precipitation may be effective in suppressing sediment yield. These findings could provide a quantitative approach to estimating the potential changes in sediment yield associated with proposed ecological rehabilitation schemes in this region. [ABSTRACT FROM AUTHOR]

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area. [ABSTRACT FROM AUTHOR]

This paper explored the projected changes in precipitation, maximum temperature (T max), and minimum temperature (T min) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multi-model ensemble (MME) of the bias-corrected dataset during 2025–2044 (near-term), 2045–2064 (mid-term), and 2081–2100 (long-term) periods, with reference to the baseline 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). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/decade, and 21.01 mm/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (3) T max (T min) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, T max (T min) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °C under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections. • Projected changes were explored by 23 CMIP6 GCMs under the three SSP-RCP scenarios. • Biases in the GCMs were corrected by Empirical Quantile Mapping (EQM) method. • Multi-model ensemble (MME) was used to reduce the uncertainties. [ABSTRACT FROM AUTHOR]

Changes in precipitation seasonality or the distribution of precipitation have important impacts on hydrological extremes (e.g., floods or droughts). Precipitation extremes have been widely reported to increase with global warming; however, the variability and mechanism of precipitation seasonality have not been well quantified in China. Here, we explore the multiscale variability in precipitation seasonality from 1960 to 2018 in China. A seasonality index of precipitation is defined to quantify the precipitation seasonality with a lower value indicating a more even distribution throughout a year. The seasonality index increases from southeastern to northwestern China, with a decrease in the annual mean precipitation, a later timing of the wet season, and a shorter wet season duration. The seasonality index decreases from 1960 to 2018 in China, accompanied by the increasing duration of wet season, especially in northern climate-sensitive basins, such as the Northwest River, Hai River, and Songliao River basins. In the Northwest River basin, for example, the observed significant decrease in the seasonality index (~0.02 decade−1) from 1960 to 2018 is consistent with a significant decrease in the ratio of annual maximum 10-day precipitation to annual precipitation, which is confirmed by their significant positive correlation (R = 0.72; p = 0). El Niño–Southern Oscillation (ENSO) dominates interannual fluctuations and spatial patterns of precipitation seasonality in China. In El Niño years, the precipitation seasonality index decreases across China except for the Yangtze River basin, with broad increases in annual precipitation. [ABSTRACT FROM AUTHOR]

Acid rain is a global environmental issue and causes serious adverse impacts on natural ecosystems and human health. The acid rain and its subsequent impacts have been extensively studied in temperate regions and in big cities globally, but the monitoring of acid rain in non-urban areas in subtropical region is still limited. Here, we analyze 18 years of water acidity observations from a non-urban site in western Pearl River Delta region, south China. From 2000 to 2018, annual mean precipitation pH values in the study site showed a marked increase (P<0.0001) from 4.96 in 2000 to 6.88 in 2018. This 18-year dataset of precipitation acidity shows a clear recovery from acid deposition over the annual period, dry seasons and wet seasons. Within a year, precipitation pH was relatively lower in dry seasons (5.46±0.95) than in wet seasons (5.80±0.89) during the whole study period, and the seasonal pattern of precipitation pH was amplified during drier years as a strong dilution effect was detected between precipitation amount and its acidity. The pH of surface runoff water (4.74±0.70) was significantly lower than that of precipitation as a result of the regulating effects of the highly acidified soil and the rich humus on the forest floor. The groundwater pH (6.32±0.63) was significantly higher than the precipitation pH especially during dry seasons. The results of the present study indicated that the groundwater has a strong acid buffer and neutralizing effects and thus plays an important role in supplies of clean water. [ABSTRACT FROM AUTHOR]

Determining the contributions of climate change and human activities to runoff variation is important for water resource management. An improved double mass curve method (IDMC), which can distinguish the individual impact of each climatic factor (precipitation and potential evapotranspiration), is proposed in this study to attribute runoff variation to climate change and human activities. The runoff variation characteristics of four subregions of the Jinsha River Basin (JRB) were detected using statistical methods, and the contributions of climate change and human activities to runoff variation were comprehensively assessed using IDMC, Budyko-based elasticity method, and slope change ratio of cumulative quantity analysis. Results show that annual runoff had an increasing trend in the upstream, midstream, and downstream of JRB and a decreasing trend in Yalong River Basin (YRB) of JRB during 1966–2016. Changed points in 2004, 1986, 1996, and 2005 were detected for the upstream, midstream, and downstream of JRB and YRB, respectively; the study period was, therefore, partitioned into the base period and impacted period for each subregion. Suggested attribution methods gave consistent conclusions that climate change dominated the runoff variation in the upstream of JRB with an average contribution rate of 86.5%, whereas human activities were the major force for the runoff variation in the midstream and downstream of JRB and YRB with average contribution rates of 73.0%, 113.6% and 75.8%, respectively. Furthermore, precipitation caused the increase of runoff in the upstream and midstream of JRB and the reduction of runoff in the downstream of JRB and YRB. Potential evapotranspiration caused the reduction of runoff in the subregions of JRB except for the midstream of JRB. The findings of this study are meaningful for water resources management in the JRB. [ABSTRACT FROM AUTHOR]

The precipitation pattern in the Taihu Lake basin in East China has significantly changed over the last decades, perhaps due to intensive urbanization. However, there is limited understanding of the effect of large-scale expansion of city groups on the precipitation variability, which causes a challenge for flood control in the basin. Considering the process of urbanization, we investigated in this study the variability of precipitation in different urban zones and characteristic scales and explored the influence of urban development on the rain island effect in the basin. The basin was divided into three zones (old urban area, new urban area, and suburbs) considering different degrees of urbanization. Results indicated that precipitation change and its response to urbanization had spatial differences at various characteristic scales. Urbanization exhibited a significant "precipitation enhancement effect" at the characteristic scales including annual, flood season, summer, autumn, and winter but indicated a "precipitation reduction effect" in spring. Annual (flood season) precipitation in 1990–2013 increased by 30.04 mm (16.94 mm), due to urbanization, compared with that in the preceding period. The rate of contribution of urbanization to precipitation increase was 15.6%, 41.1%, and 14.4% in summer, autumn, and winter. The enhancement effect of short duration rainfall extremes in old urban area was also discernible, and that in new urban areas would enhance along with urban expansion, complicating urban flood and waterlogging control. More effective adaptation strategies should be implemented to handle the unfavorable situation. [ABSTRACT FROM AUTHOR]

Many studies found that land use change (LUC) had great impacts on regional precipitation, due to thermodynamic and dynamic responses. However, the relative contributions of these two factors to changes in precipitation due to LUC are rarely investigated. This study quantifies the relative contributions of thermodynamic and dynamic to the changes of mean and extreme precipitation due to LUC based on simulations of the Weather Research and Forecasting (WRF) model. The Yangtze River Basin (YRB) is used as a case study, as it has experienced great LUC during the past decades. Four land use scenarios (two factual cases and two hypothetical reforestation scenarios) were used to test the sensitivity of precipitation changes to LUC. Changes in mean and extreme precipitation over the YRB for all seasons are attributed to thermodynamic and dynamic changes using 500‐hPa geopotential height and precipitation data derived from the simulations of WRF. The results show that the factual process of LUC results in a decrease in summer mean daily precipitation and precipitation extremes in most areas of the YRB (except for the middle reaches of the YRB), while the hypothetical reforestation contributes to an increase in summer mean and extreme precipitation, and the impacts of reforestation on increasing precipitation are limited. The thermodynamic change contributes to an increase in seasonal mean daily precipitation, extreme precipitation totals and occurrence frequency of precipitation extremes with contributions ranging from 94 to 102%, which is the main contributor of changes in precipitation due to LUC. The dynamic change only makes a small contribution to the change of precipitation. Because the contributions of thermodynamic can be offset by dynamic changes to changes in precipitation, it could result in minimal changes in precipitation and occurrences of precipitation extremes. [ABSTRACT FROM AUTHOR]

We evaluated 24-h control forecast products from The International GrandGlobal Ensemble center over the 10 first-class water resource regions of Mainland China in 2013-18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conductmeteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average formoderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based onBMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability. [ABSTRACT FROM AUTHOR]

Precipitation fluctuations are continuously threatening the environment and may cause huge economic losses. In present study, the precipitation over China has been evaluated under five principal shared socioeconomic pathways (SSPs) scenarios during 2015–2099 based on eight CMIP6 models bias-corrected by the method of Equidistant Cumulative Distribution Functions. The results showed that (1) the simulated precipitation in China was in good agreement with observed precipitation for the eight CMIP6 models during 1961–2014, especially for the UKESM1-0-LL and MIROC6. However, the simulated annual mean precipitation has been significantly overvalued in the Southwest River basin (> 50%), while it was undervalued in the higher elevations of the Northwest River basin (< − 60%); (2) the annual mean precipitation will show a fluctuating upward trend during 2015–2099 over China under all the SSPs scenarios for the eight CMIP6 models. The rate of precipitation increase over north China will be higher than that in south China, especially in the Northwest River basin (reach 57.44% in the 2090s under the SSP5-8.5 for the ensemble mean). This increase of the precipitation in north China might alleviate the shortage of water there, but will not change the pattern of more rain in the south and less in the north; (3) in the southeastern basins, the precipitation of the multi-model ensemble (MME) and MIROC6 during 2011–2020 will be lower than that of 1961–2010 (− 6.53 to − 0.06%) under all SSPs scenarios. While the precipitation will increase obviously under all the SSPs scenarios, especially for the SSP5-8.5 scenario after the year of 2060; (4) the bias of the MME was much lower than that of individual CMIP6 models, and the bias of lower SSPs scenarios will be relatively lower. Generally, uncertainty ranges of precipitation fluctuations in north China (15.31–79.26%) will be higher than those in south China (16.06–7.55%). These findings revealed the projections and uncertainties of CMIP6 precipitation over China, which will be helpful for a better understanding of the future evolution of precipitation in China at large scale and in other regions of the world. [ABSTRACT FROM AUTHOR]

Urban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation are still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008-15 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran's I are 0.49, 0.47, and 0.37 for the intensity percentiles of ≥95%, 75%-95%, and < 75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%),mainly occurring in the Haihe River basin, the Yangtze River basin, and the Pearl River basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran's I = 0.47) and the cold frontal system in the Pearl River delta (Moran's I = 0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions [e.g., relative humidity (RH) > 75% over Beijing and RH > 85% over the Pearl River delta] and warmer conditions (T > 258C over Beijing and T > 288C over the Pearl River delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events. [ABSTRACT FROM AUTHOR]

This study investigated the attribution of terrestrial water storage (TWS) variations across China to changes in groundwater and human water use. As one vital storage component, the groundwater storage (GWS) derived from the Jet Propulsion Laboratory's GRACE (Gravity Recovery and Climate Experiment) mass concentration solution compared reasonably well with the in situ groundwater table depth, with the correlation coefficients ranging from −0.83 to −0.18, all of which were statistically significant at the 95% confidence level. About 71% of the trends in derived GWS had the same sign as those of observations, without systematic deviation, across China. The GWS variation contributed a large portion of the TWS trend in most regions of China, and the majority of contribution values reached 50%–150% in the Hai River basin, the Loess Plateau, and the middle portion of the Yangtze River basin. The dominant role of GWS is closely related to the detected long-term "memories" in both TWS and GWS. The increase of irrigation consumption accelerated the TWS depletion trend by 13.4% in the Huai River basin, while the decrease of consumptive agricultural water use alleviated the TWS decline rate by 4.1% in the Hai River basin. Importantly, the correlation coefficients reached 0.74–0.95 between the TWS change and the residual of precipitation, evapotranspiration, flow into the sea, and irrigation consumption in the four river basins of particular interest. The findings of this study are helpful for understanding regional water cycles in China. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) play an important role in modulating the global water cycle and energy balance and frequently generate high-impact weather events. The majority of existing literature studying MCS activity over East Asia is based on specific case studies and more climatological investigations revealing the precipitation characteristics of MCSs over eastern China are keenly needed. In this study, we use an iterative rain cell tracking method to identify and track MCS precipitation during 2008-16 to investigate regional differences and seasonal variations of MCS precipitation characteristics. Our results show that the middle-to-lower reaches of the Yangtze River basin (YRB-ML) receive the largest amount and exhibit the most pronounced seasonal cycle of MCS precipitation in eastern China. MCS precipitation over YRB-ML can exceed 2.6mmday-1 in June, contributing over 30.0% of April-July total rainfall. Particularly long-lived MCSs occur over the eastern periphery of the Tibetan Plateau (ETP), with 25% of MCSs over the ETP persisting for more than 18 h in spring. In addition, spring MCSs feature larger rainfall areas, longer durations, and faster propagation speeds. Summer MCSs have a higher precipitation intensity and a more pronounced diurnal cycle except for southeastern China, where MCSs have similar precipitation intensity in spring and summer. There is less MCS precipitation in autumn, but an MCSprecipitation center over the ETP still persists. MCSs reach peak hourly rainfall intensities during the time of maximum growth (a few hours after genesis), reach their maximum size around 5 h after genesis, and start decaying thereafter. [ABSTRACT FROM AUTHOR]

Understanding of the vegetation dynamics is essential for addressing the potential threats of terrestrial ecosystem. In recent years, the vegetation coverage of the Yangtze River Basin (YRB) has increased significantly, yet the spatio-temporal variations and potential driving meteorological factors of carbon use efficiency (CUE) under the context of global warming are still not clear. In this study, MODIS-based public-domain data during 2000–2015 was used to analyze these aspects in the YRB, a large river basin with powerful ecological functions in China. Spatio-temporal variations of CUE in different sub-basins and land cover types were investigated and the correlations with potential driving meteorological factors were examined. Results revealed that CUE in the YRB had strong spatiotemporal variability and varied remarkably in different land cover types. For the whole YRB, the average CUE of vegetated land was 0.519, while the long-term change trend of CUE was obscure. Along the rising altitude, CUE generally showed an increasing trend until the altitude of 3900 m and then followed by a decreasing trend. CUE of grasslands was generally higher than that of croplands, and then forest lands. The inter-annual variation of CUE in the YRB is likely to be driven by precipitation as a strong positive partial correlation between the inter-annual variability of CUE and precipitation was observed in most of sub-basins and land cover types in the YRB. The influence of temperature and relative humidity is also outstanding in certain regions and land cover types. Our findings are useful from the view point of carbon cycle and reasonable land cover management under the context of global warming. [ABSTRACT FROM AUTHOR]

As one of the most influential oceanic and atmospheric oscillations in the Earth system, El Niño‐Southern Oscillation (ENSO) has modulated numerous geophysical processes. This is particularly true for the Yangtze River Basin (YRB), which is vulnerable to Asian Monsoon and faces serious hydrological hazards. In this study, the co‐variability between lag–lead precipitation and sea surface temperature anomalies was evaluated utilizing singular value decomposition (SVD) method. Moreover, certain teleconnections between ENSO and streamflow were identified by wavelet methods. In addition, the contribution of related atmospheric variables was revealed by composite analysis. Results indicate that there are strong associations in lag–lead seasons between the wet condition (dry condition) and September–November (December–February) mature ENSO phase. Significant common power and coherence signals between the ENSO indices and the streamflow occur in the 4–8, 8–16 and 16–32 seasonal scales. Meanwhile, the activity cycle of the ENSO indices ahead of streamflow increases from the mid‐lower reaches to the source region. In addition, the Western Pacific Subtropical High is strengthened during the mature ENSO phase. Anomalous sinking motions and divergent water vapour flux occupy the YRB, reducing the precipitation and leading to the dry condition in the source region until the following March–May. On the other hand, ascending movements and abundant water vapour flux coming from northern Pacific, equatorial western Pacific and the Bay of Bengal result in the wet condition in the mid‐lower reaches. [ABSTRACT FROM AUTHOR]

There is a well-known seesaw pattern of precipitation between the tropical western North Pacific (WNP) and the Yangtze River basin (YRB) during summer. This study identified that this out-of-phase relationship experiences a subseasonal change; that is, the relationship is strong during early summer but much weaker during mid-summer. We investigated the large-scale circulation anomalies responsible for the YRB rainfall anomalies on the subseasonal timescale. It was found that the YRB rainfall is mainly affected by the tropical circulation anomalies during early summer, i.e., the anticyclonic or cyclonic anomaly over the subtropical WNP associated with the precipitation anomalies over the tropical WNP. During mid-summer, the YRB rainfall is mainly affected by the extratropical circulation anomalies in both the lower and upper troposphere. In the lower troposphere, the northeasterly anomaly north of the YRB favors heavier rainfall over the YRB by intensifying the meridional gradient of the equivalent potential temperature over the YRB. In the upper troposphere, the meridional displacement of the Asian westerly jet and the zonally oriented teleconnection pattern along the jet also affect the YRB rainfall. The subseasonal change in the WNP-YRB precipitation relationship illustrated by this study has important implications for the subseasonalto- seasonal forecasting of the YRB rainfall. [ABSTRACT FROM AUTHOR]

Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS) rainfall dataset was early evaluated and compared with 29 meteorological stations over the Haihe River basin in China, for the period 1981-2015. Seven statistical and categorical metrics were applied to evaluate the performance of CHIRPS with gauge measurements at multi-time scales (monthly, seasonally and annually). Using the Standardized Precipitation Index (SPI) as the drought indicator, the applicability of this new long-term satellite precipitation product for drought monitoring was investigated in this study. Results indicate that the good performances were performed at multiple temporal scales (monthly, seasonally and annually). Although it tends to overestimate the higher precipitation in this region, CHIRPS demonstrated good agreement (R2 > 0.70) with gauge observations at monthly scale and greater agreements (R2 > 0.78) at seasonal and annual scales. Meanwhile, CHIRPS performed a good score of BIAS and lower error in a majority of months at multi-time scales. Because of its good performance at multi-time scales and the advantages of high spatial resolution and long-time record, CHIRPS was applied to derive the SPI over the Haihe River basin. It is evaluated and compared with stations observations to derive SPI at time scale of 1, 3 and 6 months. The results indicate that it performed good ability to monitor drought (R2 > 0.70) and successfully captured the historical drought years (1981, 1999, 2001 and 2012). Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and drought monitoring in this region. [ABSTRACT FROM AUTHOR]

The impacts of climate change on hydrological cycles and water resource distribution is particularly concerned with environmentally vulnerable areas, such as the Loess Plateau, where precipitation scarcity leads to or intensifies serious water related problems including water resource shortages, land degradation, and serious soil erosion. Based on a geographical information system (GIS), and using gauged hydrological data from 2001 to 2010, digital land-use and soil maps from 2005, a Soil and Water Assessment Tool (SWAT) model was applied to the Xichuan Watershed, a typical hilly-gullied area in the Loess Plateau, China. The relative error, coefficient of determination, and Nash-Sutcliffe coefficient were used to analyze the accuracy of runoffs and sediment yields simulated by the model. Runoff and sediment yield variations were analyzed under different precipitation scenarios. The increases in runoff and sediment with increased precipitation were greater than their decreases with reduced precipitation, and runoff was more sensitive to the variations of precipitation than was sediment yield. The coefficients of variation (CVs) of the runoff and sediment yield increased with increasing precipitation, and the CV of the sediment yield was more sensitive to small rainfall. The annual runoff and sediment yield fluctuated greatly, and their variation ranges and CVs were large when precipitation increased by 20%. The results provide local decision makers with scientific references for water resource utilization and soil and water conservation. [ABSTRACT FROM AUTHOR]

Drought/wetness conditions are fundamental not only for agricultural production but also ecology, human health, and economic activity. Dryness/wetness is a function of precipitation, temperature, vegetation and potential evapotranspiration. Regions with low moisture are often characterized by aridity which, in turn, reflects the degree of meteorological drought. Observed climatic data from eleven meteorological stations in and around Shiyang River basin, China, were used to calculate the aridity index (AI) which was defined as the ratio of potential evapotranspiration (ET) to precipitation (P). ET was calculated using the Penman-Monteith method. The ordinary kriging method was used to interpolate the spatial variability of ET, P and AI. The Mann-Kendall test with a pre-whitening method was employed using the Yue and Wang autocorrelation correction to detect temporal trends. The Theil-Sen estimator was used to estimate the slopes of trend lines. Results showed a higher AI in the north basin and a lower AI in the Qilian Mountain region. Annual ET and P had increasing trends with a slope of 0.672 and 0.459 mm per year, respectively, but trends were not statistically significant for most stations. While annual AI had a slight decreasing trend with a slope of −0.01 per year, the trend was not statistically significant for all stations. The decreasing trends in winter AI (at a rate of −0.313/a) was more significant than that in other seasons. The study indicates that the Shiyang River basin is getting slightly wetter, especially in winter. [ABSTRACT FROM AUTHOR]

Much attention has recently been focused on the effects of climate variability and human activities on the runoff. In this study, we analyzed 56-yr (1957-2012) runoff change and patterns in the Jinghe River Basin (JRB) in the arid region of northwest China. The nonparametric Mann-Kendall test and the precipitation-runoff double cumulative curve (PRDCC) were used to identify change trend and abrupt change points in the annual runoff. It was found that the runoff in the JRB has periodically fluctuated in the past 56 yr. Abrupt change point in annual runoff was identified in the JRB, which occurred in the years around 1964 and 1996 dividing the long-term hydrologic series into a natural period (1957-1964) and a climate and man-induced period (1965-1996 and 1997-2012). In the 1965-1996 period, human activities were the main factor that decreased runoff with contribution of 88.9%, while climate variability only accounted for 11.1%. However, the impact of climate variability has been increased from 11.1% to 47.5% during 1997-2012, showing that runoff in JRB is more sensitive to climate variability during global warming. This study distinguishes the effect of climate variability from human activities on runoff, which can do duty for a reference for regional water resources assessment and management. [ABSTRACT FROM AUTHOR]

Development of an accurate precipitation dataset is of primary importance for regional hydrological process studies and water resources management. Here, four regional precipitation products are evaluated for the Heihe River basin (HRB): 1) a spatially and temporally disaggregated Climate Prediction Center Merged Analysis of Precipitation (CMAP) at 0.25° spatial resolution (DCMAP); 2) a fusion product obtained by merging China Meteorological Administration station data and Tropical Rainfall Measuring Mission precipitation data at 0.1° spatial resolution supported by the Institute of Tibetan Plateau Research (ITP), Chinese Academy of Sciences (ITP-F); 3) a disaggregated CMAP downscaled by a statistical meteorological model tool at 1-km spatial resolution (DCMAP-MicroMet); and 4) a Weather Research and Forecasting (WRF) Model simulation with 5-km resolution (WRF-P). The validation metrics include spatial pattern, temporal pattern, error analysis with respect to observation data, and precipitation event verification indicators. The results indicate that 1) precipitation from the DCMAP product may not be suitable for water cycle studies at the watershed scale because of its coarser spatial resolution and 2) ITP-F, WRF-P, and DCMAP-MicroMet precipitation products generally show similar spatial-temporal patterns in HRB but have varying performances between different subbasins. [ABSTRACT FROM AUTHOR]

The current study investigates the variability of vertical structure of precipitation in the Huaihe River Basin (HRB) of China. The precipitation characteristics have been revealed by the long-term observations of vertical profile of reflectivity (VPR) from the first spaceborne precipitation radar (PR) onboard the National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) satellite. This study has statistically analyzed the latest TRMM 2A-23 and 2A-25 products (version 7, released in 2012) with ∼15 years time span (from 11 December 1997 to 19 August 2012). First, the spatial and seasonal variations of storm height and freezing level have been investigated. The results show a climatological relation connecting the storm height with the rainfall rate in HRB. Second, mean VPRs have been studied for the stratiform and convective precipitation. The VPR variability has been analyzed for different seasons and rain intensities. Third, the characteristics of rain intensification and weakening in the vertical direction have been examined by the statistical analysis of VPR slope below the melting layer. The results show that the rainfall tends to be reduced (or intensified) with the height changing downward in the light (or moderate and heavy) precipitating clouds, no matter stratiform or convection. Finally, the S-band climatological VPRs have been characterized by converting the VPR from Ku-band to S-band. Considering the wide application of national radar network for weather surveillance in China, the developed S-band climatological VPRs can be potentially applied in a VPR correction scheme to improve the ground radar-based quantitative precipitation estimation (QPE) in this river basin. [ABSTRACT FROM AUTHOR]

Carbonate rocks are widely distributed in southwest China, forming a unique karst landscape. The Lijiang River Basin provides a typical example of an area with concentrated karst. Research on the laws of hydrology and water quality migration in the Lijiang River Basin is important for the management of the water resources of Guilin City and similar areas. In this study, we combined three meteorological data with the soil and water assessment tool (SWAT) model and the hydrological simulation program-Fortran (HSPF) model to simulate the hydrological and water quality processes in the Lijiang River Basin separately. We chose the Nash–Sutcliffe efficiency (NSE) coefficient, coefficient of determination (R2), root mean square error-observations standard deviation ratio (RSR), and mean absolute error (MAE) as the metrics used to evaluate the models. The results, combined with the time-series process lines, indicated that the SWAT model provides a more accurate performance than the HSPF model in streamflow, ammonia nitrogen (NH3-N), and dissolved oxygen (DO) simulations. In addition, we divided the karst and non-karst areas, and we analyzed the differences between them in water balance, sediment transport, and pollution load. We further identified the key source areas of pollution load in the Lijiang River Basin, evaluated the pollution reduction effect of best management practices (BMPs) on surface source pollution, and proposed some pollution control countermeasures. Each scenario, especially returning farmland to forest and creating vegetation buffer zones, reduces the NH3-N and DO pollution load. [ABSTRACT FROM AUTHOR]