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

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

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

In this paper, we describe and analyze two datasets entitled "Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021" and "Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010". These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

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]

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

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

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

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]

Abstract: Beijing’s mountainous areas have an important ecological significance because they are ecological conservation and water source protection areas of Beijing. The impact of precipitation on the vegetation coverage of Beijing’s mountainous areas is qualitatively analyzed using multi-temporal Landsat images obtained during the last 20 years. Firstly, the influence of external factors, such as phenology and relative radiometric correction, on the normalized difference vegetation index (NDVI) are removed by the normalization of remote sensing images for different periods. Then, the vegetation coverage is calculated using the method of the dimidiate pixel model. Finally, based on that, the relationship between precipitation in different seasons and change of vegetation coverage is discussed. The results indicate that the mean vegetation coverage has the same change trends with winter and summer precipitation, but an opposite change trend with spring precipitation. The paper provides a theoretical basis and data support for further research on driving models of natural vegetation restoration in Beijing’s mountainous areas. [Copyright &y& Elsevier]

Evapotranspiration (ET) is a fundamental component of the hydrological cycle. It is also the main mode of irrigation and precipitation loss in cultivated lands. Accurate estimation of ET is therefore important for sustainable water resources management, including monitoring droughts and simulating crop production. Whereas precipitation and runoff are measured with reasonable accuracy, ET is difficult to quantify especially at the basin/regional scale. Remote sensing (RS) energy balance models are therefore the commonly preferred methods for estimating ET on large spatial scales. The ETWatch model is one such RS-based model that interactively links several standard RS-algorithms to estimate ET. Another promising alternative method for estimating ET is the terrestrial-based water balance approach. Observations of the Gravity Recovery and Climate Experiment (GRACE) allow sufficient closure of terrestrial water balance. In combination with measured precipitation and runoff data, GRACE data can be used to estimate ET. This paper compares ET estimated by the RS-based ETWatch model with that derived as a residual of the terrestrial-based water balance technique (GRACE plus measured precipitation data) for the approx. 319 000-km2 Hai River Basin (HRB) in North China. About 63 consecutive months of data, spanning December 2002 to February 2008, are used in the study. The amplitudes and phases of the RS-based ETWatch-estimated ET match favourably those of the terrestrial-based water balance technique. The average seasonal (12.7 mm) and annual (16.6 mm) RMSE is less than 10% of the estimated ET. This implies that the RS-based ETWatch model reliably estimates ET at large spatial scales. Time series plots show that the amplitudes of the hydrological components of precipitation, ET and total water storage change are highest in summer and lowest in winter. The GRACE-derived total water storage change shows overall storage loss in the basin. This method could be used not only to fine-tune RS-based ET models, but also to monitor basin/regional water storage change in real-time conditions. Citation Moiwo, J. P., Yang, Y., Yan, N. & Wu, B. (2011) Comparison of evapotranspiration estimated by ETWatch with that derived from combined GRACE and measured precipitation data in Hai River Basin, North China. Hydrol. Sci. J. 56(2), 249-267. [ABSTRACT FROM AUTHOR]

Vegetation around the Daihai Lake, northern China, is very sensitive to climate changes. In this paper, pollen-based quantitative climate reconstructions using three methods [weighted averaging partial least squares method (WAPLS), modern analog technique (MAT), and pollen response surface method (PRS)] were conducted to obtain robust reconstructions of Holocene climate changes in the Daihai Lake area. The result obtained by the three methods all consistently show the annual precipitation to have been 50–100 mm lower in the early Holocene, 100–200 mm higher in the Mid-Holocene, and 50–100 mm lower again in the late Holocene than at present. The WAPLS and the MAT methods also show quasi-synchronous oscillations of the mean annual temperature (Ta); 1°–2°C lower in the Early Holocene and 1°–3°C higher in the Mid-Holocene than today. The time period from 6200 to 5100 cal yr BP was the wettest and the warmest interval, with an annual precipitation (Pa) greater than 550 mm and mean annual temperature Ta higher than 6.5°C. Several cold and dry events can be identified to occur about 8200, 6000, and 4400 cal yr BP, with an annual precipitation less than 400 mm and a mean annual temperature colder than 4.5°C, respectively. The mean temperature of the warmest month (Tw) as reconstructed using both WAPLS and MAT methods was relatively stable during the Holocene, fluctuating about ±2°C relative to the present level, but the PRS method suggests more varied Tw values in both amplitude and frequency. After 1500 cal yr BP, no consistent pattern can be observed from these three different analyses, probably because of the impact of intensified human disturbances on the natural vegetation. The fluctuations of annual precipitation (Pa) correspond to that observed in Dongge Cave in southern China. The differences might be linked to Indian monsoon and East Asia monsoon climates or caused by the different degree of dating precision, different temporal resolution, and different sensitive response of climate proxies to the climate variations. [ABSTRACT FROM AUTHOR]

Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB > EBK > OK > RBF > DIB > IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions. [ABSTRACT FROM AUTHOR]

• Dynamic yield prediction with the integral regression model at any time intervals. • Remote sensing-based TVDI replaces precipitation to represent the actual amount of water available to crops. • We couple the three types of factors (sunshine hours, temperature, and precipitation/TVDI) into a comprehensive factor for yield prediction. • The combination of sunshine hours, temperature, and TVDI has better dynamic yield prediction results. This paper describes integrating remote sensing and meteorological data for dynamic winter wheat yield prediction at fixed date intervals with an integral regression model. In most cases, both rainfall and irrigation supply the water needs of crops. We postulated that meteorological precipitation could not fully represent the actual amount of water available to crops, whereas the temperature vegetation dryness index (TVDI) can represent actual water, which monitors soil moisture. According to this, meteorological and remote-sensing data based on sunshine hours, temperature, and TVDI formed a light–temperature–water data combination, which are the three most fundamental and critical factors related to crop growth. The new data combination was used as the input variables of the integral regression model, which could predict the crop yield at a fixed date interval of any length. The dynamic results were then compared with that of the meteorology data combination (sunshine hours, temperature, and precipitation). We selected a total of seven county-level areas in semi-humid and arid regions in China. The proposed dynamic prediction approach achieved good results at the county level from March 21 to maturity at 16-day intervals with predicted R2 > 0.94 and root mean squared error range of 15.54–121.01 kg ha−1 between predicted and actual yield with leave-one-year-out cross-validation for each prediction date. The trend of dynamic prediction accuracy had a relatively stable state. In addition, TVDI contained more crop water information, and prediction results using TVDI and meteorology were generally superior to that using only meteorology for semi-humid and arid areas, with the best results for arid regions. Thus, integrating meteorology and TVDI could effectively dynamically predict yield at county level for most agroclimatic regions, and particularly for arid regions. The work has implications for dynamic yield prediction and improving prediction accuracy and can be extended to other areas. [ABSTRACT FROM AUTHOR]

This paper presents a large data set on carbon isotope composition (δ13C) of modern soils which were collected under the main vegetation communities along an altitude of 1250–5500 m above sea level in the Qinghai-Tibetan Plateau. The δ13C values of 198 samples range from -28.6 to -15.1‰ versus PDB and exhibit a clean relation to different vegetation communities from forest (-25.9 ± 1.2‰) to shrub (-24.7 ± 1.4‰), steppe (-23.1 ± 1.3‰), alpine meadow (-23.6 ± 0.7‰), alpine desert steppe (-21.3 ± 1.6‰), and alpine desert (-18.9 ± 2.5‰). We attributed the observed variability in δ13C values to that the mean annual precipitation (MAP) and the mean annual temperature (MAT) are the main factors controlling the distribution of vegetation types in the Tibetan Plateau, which causes the change in carbon isotope composition of modern soils at any given altitude. The result of both linear and nonlinear regression analyses also confirms that MAP and MAT are the major factors affecting the δ13C values of surface soils. In the absence of favorable moisture and temperature conditions, low pCO2 alone is not sufficient to cause the distinct changes in carbon isotope composition of modern soils in the Tibetan Plateau. This study provides some fundamental information on the carbon isotope composition of terrestrial carbon pools and bears some practical significance for the use of carbon isotope data to document vegetation changes and environmental conditions of the high plateau in the past. [ABSTRACT FROM AUTHOR]

Xinjiang is located in an arid and semi-arid climate region in China, but Xinjiang Ili river valley is more humid, with higher precipitation intensity and precipitation, which is closely related to the role of the Tianshan Mountains. In this paper, through the NCRP 1° × 1° reanalysis data and the conventional observation data of the Ili River Valley in Xinjiang, the terrain sensitivity experiment conducted by the WRF model is used to analyze the short-term extreme precipitation event of the Ili River Valley from 18–19 of May 2017, to reveal the influence of Tianshan Mountains on the extreme precipitation event of the Ili River Valley. The results show that: (1) The reduction or removal of the terrain will cause a wide range of wind field changes, weaken the vertical upward movement of the windward slope, and the accumulation of water vapor before the windward slope will also be reduced; a large-scale change of the terrain will also affect the direction of water vapor transportation. These effects together lead to a decrease or increase in regional precipitation. (2) "Fuzzy" (smooth) terrain will affect the precipitation simulated by changing the local vertical movement and water vapor transport, which shows that the WRF model's accurate description of the terrain structure characteristics of mountainous areas is beneficial to accurately simulate the precipitation process on the windward slope area. [ABSTRACT FROM AUTHOR]

To clarify the precipitation and evolution law of the Laves phase in China low-activation martensitic (CLAM) steel during long-term aging at high temperature, this paper carried out an aging treatment of CLAM steel at 550 °C for up to 30,000 h. The segregation behavior of alloy elements and the precipitation amount and average size of the Laves phase were quantitatively characterized by transmission electron microscopy (TEM) and scanning electron microscopy (SEM), and the precipitation and coarsening behavior of the Laves phase were obtained. The results show that the Laves phase begins to precipitate within 5000 h after aging and mainly depends on M23C6 carbides to nucleate and grow at the grain boundary and subgrain boundary. During the aging process, the average size of the Laves phase grows continuously. After more than 25,000 h, the growth rate of the Laves phase decreases. After 30,000 h of aging, the average size reaches 439.9 nm, and the maximum size exceeds 800 nm. The area fraction of the Laves phase increases continuously during the 20,000 h aging process and tends to be stable after aging for 20,000 h. The area fraction is approximately 1.85%. [ABSTRACT FROM AUTHOR]

The influence of urbanization on regional precipitation is one of the most important issues in hydrology. This paper selected the North Haihe Basin as the study area and explored the connection between summer precipitation and urbanization index (URBI) based on satellite precipitation and nighttime lights datasets. A moving spatial percentage anomaly method (MSP) was used to signify the local induced enhancement of precipitation (LIEP). Local indicators of spatial association (LISA) clustering for LIEP and URBI based on Bivariate Moran's I coupled with digital elevation model (DEM) segmentation was used to separate the impacts caused by urbanization and terrain uplifting, and thus, the regions of interest (ROI) where the urban induced enhancement of precipitation (UIEP) plays a dominant role were located. A geographically weighted regression (GWR) model was used to reveal the spatial variation of the sensitivity of UIEP to URBI within the ROI. Pearson correlation and cross-wavelet analyses (XWT) were adopted to investigate the teleconnection between UIEP and climate anomalies, using Niño3.4 and SOI as indices. The results indicated that: (1) Urbanization effects on precipitation spatial variation in the upstream mountainous area would be hidden away by topographic factors. (2) From the perspective of the basin, the highest urbanization level areas have the Lowest LIEP, while the suburban areas have the highest LIEP, and the rural areas are in the middle. (3) The UIEP and the URBI are generally negatively correlated within ROI. (4) UIEP versus Niño3.4 and SOI both show a significant common high-power period at a time scale of 2 years. This work can help comprehensively understand the hydrological response to urbanization. [ABSTRACT FROM AUTHOR]

In this paper, the diurnal variations of various clouds are analyzed using hourly cloud observations at weather stations in China from 1985 to 2011. In combination with merged hourly precipitation data, the relationship between the diurnal variations of clouds and precipitation in the summers from 2008 to 2011 are studied. The results show that the occurrence frequencies of total cloud and various cloud types exhibit significant diurnal variations. The diurnal variations of the occurrence frequencies of altocumulus and stratocumulus show a bimodal pattern, with peaks appearing in the early morning and late afternoon. The early morning peaks of altocumulus and stratocumulus appear earlier in the summer than in the other seasons, while the late afternoon maxima show an opposite trend. The occurrence frequency of nimbostratus peaks in the morning between 07 and 12 LST (local solar time), and the peak value lags 2 to 3 h from west to east along the Yangtze River valley; meanwhile, the diurnal variation shows no clear differences caused by changes in the latitude or seasons. Cumulus shows an afternoon (14 LST) maximum, while cumulonimbus peaks in the late afternoon during 16–20 LST, and both of them present a great diurnal range. Cirrus usually reaches its peak at 17–18 LST, and it differs by 1 to 2 h with a change in the latitude. The results of the study first show that the diurnal variations of precipitation among different regions are dominated by different clouds. The upper reaches of the Yangtze River valley present a midnight precipitation maximum that is mainly dominated by cumulonimbus. For the middle reaches of the Yangtze River valley impacted by nimbostratus, the precipitation peaks in the early morning. In South and Northeast China, the precipitation peaks in the afternoon and is determined by the diurnal variations of convective clouds. In the region between the Yangtze River valley and Yellow River valley, the precipitation peaks in the early morning and afternoon; the early morning peak is mainly determined by stratiform clouds, while the afternoon peak is closely related to convective clouds. [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]