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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]

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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]

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]

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]

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]

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]

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]

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]

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]

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]

Conditions, distribution and development of vegetation in semi arid regions are highly variable. In this study we detected the temporal and spatial variability of vegetation in the Xilin river catchment from 2000 to 2008 by analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data products of that period. The study is based on LAI (Leaf area index) and supported by NDVI (Normalized difference vegetation index) and LST (Land surface temperature) data with a spatial resolution of 1 km. The mean LAI of the catchment from 2000 to 2008 is 0.59. Precipitation data of the study period governs the conditions and distribution of vegetation in the catchment. In dry years, e.g. 2001 and 2005, LAI was clearly lower (0.52) compared to 2003 (LAI = 0.72) which was a wet year. As precipitation generally decreases from south-east to north-west, LAI values decrease according to this gradient. The influence of heavy grazing in the vicinity of the Xilin river is obvious as LAI is low (0.4) in these areas. The high temporal variability of the LAI is displayed by its high mean CV (coefficient of variation) which is 48% for the observed years. The analysis of sample areas illustrates temporal and spatial differences in vegetation development within the catchment and shows a generally delayed growth start in the north-west of the catchment. [ABSTRACT FROM AUTHOR]

Spatial and temporal characteristics of precipitation trends in the Zhujiang River basin, South China, are analyzed. Nonparametric trend tests are applied to daily precipitation data from 192 weather stations between 1961 and 2007 for the following indices: annual, monthly, and daily precipitation; annual and monthly number of rain days and precipitation intensity; annual and monthly maximum precipitation; 5-day maximum precipitation, number of rainstorms with >50 mm day−−1, and peaks over thresholds (90th, 95th, and 99th percentile). The results show that few stations experienced trends in the precipitation indices on an annual basis. On a monthly basis, significant positive and negative trends above the 90%% confidence level appear in all months except December. Trends in the indices of monthly precipitation, rain intensity, rain days, and monthly maximum precipitation show very similar characteristics. They experience the most distinct negative (positive) trends in October (January). A change of the mean wind direction by 50°° from east-southeast to east-northeast explains the downward trend in precipitation in October. Dry October months (months with low precipitation indices) can be observed when the mean wind direction is east-northeast (arid) instead of the prevailing mean wind direction, east-southeast (moist). The former is typical for the East Asian winter monsoon (EAWM). Nearly 90%% of the driest October months can be explained by wind directions typical for the EAWM. The upward trend in precipitation indices in January cannot be explained by changes in large-scale circulation. The analysis of the precipitation indices delivers more detailed information on observed changes than other studies in the same area. This can be attributed to the higher station density, the quality of daily data, and the focus on monthly trends in the current study. [ABSTRACT FROM AUTHOR]

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon caused by the variability of climate. This study was conducted in the Songhua River Basin of China. The drought events were estimated by using the Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) which are based on precipitation (P) and potential evapotranspiration (PET) data. Furthermore, drought characteristics were identified for the assessment of drought trends in the study area. Short term (3 months) and long term (12 months) projected meteorological droughts were identified by using these drought indices. Future climate precipitation and temperature time series data (2021–2099) of various Representative Concentration Pathways (RCPs) were estimated by using outputs of the Global Circulation Model downscaled with a statistical methodology. The results showed that RCP 4.5 have a greater number of moderate drought events as compared to RCP 2.6 and RCP 8.5. Moreover, it was also noted that RCP 8.5 (40 events) and RCP 4.5 (38 events) showed a higher number of severe droughts on 12-month drought analysis in the study area. A severe drought conditions projected between 2073 and 2076 with drought severity (DS-1.66) and drought intensity (DI-0.42) while extreme drying trends were projected between 2097 and 2099 with drought severity (DS-1.85) and drought intensity (DI-0.62). It was also observed that Precipitation Decile predicted a greater number of years under deficit conditions under RCP 2.6. Overall results revealed that more severe droughts are expected to occur during the late phase (2050–2099) by using RDI and SPI. A comparative analysis of 3- and 12-month drying trends showed that RDI is prevailing during the 12-month drought analysis while almost both drought indices (RDI and SPI) indicated same behavior of drought identification at 3-month drought analysis between 2021 and 2099 in the research area. The results of study will help to evaluate the risk of future drought in the study area and be beneficial for the researcher to make an appropriate mitigation strategy. [ABSTRACT FROM AUTHOR]

The Heihe River Basin (HRB), located on the northeastern edge of the Tibetan Plateau, is the second-largest inland river basin in China, with an area of 140,000 km2. The HRB is a coupling area of the westerlies, the Qinghai–Tibet Plateau monsoon and the Southeast monsoon circulation system, and is a relatively independent land-surface water-circulating system. The refined characteristics of moisture recycling over the HRB was described by using the Weather Research and Forecasting (WRF) model for a long-term simulation, and the "finer box model" for calculating the net water-vapor flux. The following conclusions were drawn from the results of this study: (1) The water vapor of the HRB was dominantly transported by the wind from the west and from the north, and the west one was much larger than the north one. The net vapor transported by the west wind was positive, and by the north wind was negative. (2) The precipitation over the HRB was triggered mainly by the vapor from the west, which arose from the lower vertical layer to higher one during transporting from west to east. The vapor from the north sank from a higher layer to a lower one, and crossed the south edge of the HRB. (3) The moisture-recycling ratio of evapotranspiration to precipitation over the HRB was much higher than the other regions, which may be due to the strong land–atmosphere interaction in the arid inland river basin. [ABSTRACT FROM AUTHOR]

The lack of meteorological observation data limits the hydro-climatic analysis and modeling, especially for the ungauged or data-limited regions, while satellite and reanalysis products can provide potential data sources in these regions. In this study, three daily products, including two satellite products (Tropic Rainfall Measuring Mission Multi-Satellite Precipitation Analysis, TMPA 3B42 and 3B42RT) and one reanalysis product (China Meteorological Assimilation Driving Datasets for the SWAT Model, CMADS), were used to assess the capacity of hydro-climatic simulation based on the statistical method and hydrological model in Ganjiang River Basin (GRB), a humid basin of southern China. CAMDS, TMPA 3B42 and 3B42RT precipitation were evaluated against ground-based observation based on multiple statistical metrics at different temporal scales. The similar evaluation was carried out for CMADS temperature. Then, eight scenarios were constructed into calibrating the Soil and Water Assessment Tool (SWAT) model and simulating streamflow, to assess their capacity in hydrological simulation. The results showed that CMADS data performed better in precipitation estimation than TMPA 3B42 and 3B42RT at daily and monthly scales, while worse at the annual scale. In addition, CMADS can capture the spatial distribution of precipitation well. Moreover, the CMADS daily temperature data agreed well with observations at meteorological stations. For hydrological simulations, streamflow simulation results driven by eight input scenarios obtained acceptable performance according to model evaluation criteria. Compared with the simulation results, the models driven by ground-based observation precipitation obtained the most accurate streamflow simulation results, followed by CMADS, TMPA 3B42 and 3B42RT precipitation. Besides, CMADS temperature can capture the spatial distribution characteristics well and improve the streamflow simulations. This study provides valuable insights for hydro-climatic application of satellite and reanalysis meteorological products in the ungauged or data-limited regions. [ABSTRACT FROM AUTHOR]

As a key factor in the water cycle and climate change, the quality of precipitation data directly affects the hydrological processes of the river basin. Although many precipitation products with high spatial and temporal resolutions are now widely used, it is meaningful and necessary to investigate and evaluate their merits and demerits in hydrological applications. In this study, two satellite-based precipitation products (Tropical Rainfall Measurement Mission, TRMM; Integrated Multi-satellite Retrievals for GPM, IMERG) and one reanalysis precipitation product (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model, CMADS) are studied to compare their streamflow simulation performance in the Qujiang River Basin, China, using the SWAT model with gauged rainfall data as a reference. The main conclusions are as follows: (1) CMADS has stronger precipitation detection capabilities compared to gauged rainfall, while TRMM results in the most obvious overestimation in the four sub-basins. (2) In daily and monthly streamflow simulations, CMADS + SWAT mode offers the best performance. CMADS and IMERG can provide high quality precipitation data for data-scarce areas, and IMERG can effectively avoid the overestimation of streamflow caused by TRMM, especially on a daily scale. (3) The runoff projections of the three modes under RCP (Representative Concentration Pathway) 4.5 was higher than that of RCP 8.5 on the whole. IMERG + SWAT overestimates the surface water resources of the basin compared to CMADS + SWAT, while TRMM + SWAT provides the most stable uncertainty. These findings contribute to the comparison of the differences among the three precipitation products and provides a reference for the selection of precipitation data in similar regions. [ABSTRACT FROM AUTHOR]

Since other factors (soil properties, topography, etc.) under natural conditions are relatively invariant over one or two decades, climate variables (precipitation and temperature) and human activities are the two fundamental factors driving vegetation changes in global or large-scale areas. However, the combined effects of either single climatic factor and human activities on vegetation changes and the role of human activities itself in a specific region has not been fully discussed. In this study, the Hexi region, a typical dryland consisting of three inland river basins in northwest China was selected as a case area. A new classification framework combining Pearson correlation analysis and residual trend approach was proposed to assess their individual and conjoint contributions of climate variables and human activities in areas of significant vegetation changes. Our results indicated that most of vegetation covered areas in the Hexi region experienced significant changes during the period 2001−2017, and vegetation improvements were widespread except the interior of oases; significant changes in vegetation caused by human activities, precipitation, the interactions of precipitation and human activities, temperature, the interactions of temperature and human activities, the interactions of temperature and precipitation, and the interactions of the three factors accounted for 50.46%, 16.39%, 19.90%, 4.33%, 2.32%, 2.11%, and 4.49% of the total change areas, respectively. Generally, the influence of temperature was relatively weaker than that of precipitation, and the contributions of the interactions of climate variables and human activities on vegetation changes were greater than that of climate contributions alone. Moreover, the results of various investigations, according to the trends and the time of vegetation changes, indicate that decreasing trends of the normalized difference vegetation index (NDVI) in the Hexi region were chiefly attributed to the adjustments of agricultural planting structure while the comprehensive treatment programs implemented in river basins supported a large proportion of vegetation improvements. [ABSTRACT FROM AUTHOR]

Since there are many destructive effects caused by extreme climate events in the Yellow River, it is of great theoretical and practical significance to explore the variations of climatic extremes in this key basin. We used a meteorological dataset from 66 stations within the Yellow River basin (YRB) for the period 1960–2017 to calculate magnitude and frequency of precipitation/temperature extremes. We also analyzed the relationships between the main large-scale atmospheric circulation patterns (ACPs) and precipitation/temperature extremes. The trends in precipitation extremes were nonsignificant, only a few stations were characterized by significantly increasing or decreasing anomalies; this indicates the precipitation intensity may have been strengthened, and the extreme rainfall duration appears to have been reduced during 1960–2017. The trends of magnitudes for "cold" extremes were larger than those for "warm" extremes, changes of trends in frost days were higher than those for summer days, and the trends in increasing warm nights were higher than those of warm days. The influence of the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) on temperature extremes outweighed the influence of the North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) for the other extreme climate indices. The YRB might be at risk of increased extreme high temperature events, and more attention should be paid to this higher risk of extreme climatic events. [ABSTRACT FROM AUTHOR]

The purpose of this study is to quantitatively evaluate the accuracy of the GPM IMERG v5 and the TRMM 3B42 v7, with the reference of 224 rain gauge stations over the Yangtze River basin in China from April 2014 to December 2017. The results showed that: (1) The changing pattern of IMERG v5 was similar to the 3B42 v7, and higher correlations can be found between the satellite-based precipitation products (SPPs) and observed precipitation for the monthly and annual time scale; (2) the IMERG v5 tended to overestimate the distribution range of the main rain band while the 3B42 v7 underestimated the precipitation in Sichuan basin, and the largest differences were found for the precipitation less than 1 mm/d for two SPPs; (3) both of the IMERG v5 and 3B42 v7 overestimated the precipitation in the lower elevation areas (<3000 m), while the opposite was true for areas ≥ 3000 m (RBIMERG v5 = −5.42%, RB3B42 v7 = −1.87%), and the retrieved results of PPDFc index and average precipitation at different altitudes for IMERG v5 were better than 3B42 v7. This study highlighted that IMERG v5 performed generally better than 3B42 v7 in detecting precipitation, especially light precipitation in the Yangtze River basin, indicating the great potential utility in hydrological applications. However, its poor skills when retrieving data for high precipitation events and for detecting complex terrain environments remains, leaving room for IMERG v5 to improve its inversion algorithm. [ABSTRACT FROM AUTHOR]

To obtain independent, consecutive, and high-resolution precipitation data, the four-dimensional variational (4D-Var) method was applied to directly assimilate satellite precipitation products into the Weather Research and Forecasting (WRF) model. The precipitation products of the Tropical Rainfall Measuring Mission 3B42 (TRMM 3B42) and its successor, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) were assimilated in this study. Two heavy precipitation events that occurred over the Huaihe River basin in eastern China were studied. Before assimilation, the WRF model simulations were first performed with different forcing data to select more suitable forcing data and determine the control experiments for the subsequent assimilation experiments. Then, TRMM 3B42 and GPM IMERG were separately assimilated into the WRF. The simulated precipitation results in the outer domain (D01), with a 27-km resolution, and the inner domain (D02), with a 9-km resolution, were evaluated in detail. The assessments showed that (1) 4D-Var with TRMM 3B42 or GPM IMERG could both significantly improve WRF precipitation predictions at a time interval of approximately 12 h; (2) the WRF simulated precipitation assimilated with GPM IMERG outperformed the one with TRMM 3B42; (3) for the WRF output precipitation assimilated with GPM IMERG over D02, which has spatiotemporal resolutions of 9 km and 50 s, the correlation coefficients of the studied events in August and November were 0.74 and 0.51, respectively, at the point and daily scales, and the mean Heidke skill scores for the two studied events both reached 0.31 at the grid and hourly scales. This study can provide references for the assimilation of TRMM 3B42 or GPM IMERG into the WRF model using 4D-Var, which is especially valuable for hydrological applications of GPM IMERG during the transition period from the TRMM era into the GPM era. [ABSTRACT FROM AUTHOR]