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Hong Wang
and
Fubao Sun

Abstract

Stationarity is an assumption that permeates training and practice in water-resource engineering. However, with global change, the validity of stationarity as well as uncertainty of nonstationarity in water-resource planning are being questioned; thus, it is critical to evaluate the stationarity of climate variables, especially precipitation. Based on the continuous observation data of precipitation from 1427 stations across China, 593 efficient grid cells (1° × 1°) are constructed, and the annual precipitation stationarities from 1959 to 2018 are analyzed. The evaluated autocorrelation stationarity indicates that 92.24%–96.12% of the grid cells for an autocorrelation coefficient of lag 1–8 years of precipitation are indistinguishable from 0 [90% confidence level (CL)]. The mean stationarity indicates that 97.47% of the grid cells have a stable mean for 30 years (90% CL); beyond the confidence limits, they are mainly located in the northwest of China, where annual precipitation is less, and the average exceeding range is ±3.78 mm. The long-term observation of annual precipitation in Beijing (1819–2018) and Shanghai (1879–2018) also yields autocorrelation and mean stationarities. There is no significant difference in the annual precipitations between the past 20 years (1999–2018) and the past 60 years (1959–2018) over China. Therefore, the annual precipitation in China exhibits a weak stationary behavior that is indistinguishable from the stationary stochastic process. The average variation in precipitation is ±9.55% between 30 successive years and 16.53% between 10 successive years. Therefore, it is valuable and feasible to utilize the historical data of annual precipitation as the basis of water-resources application.

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Haixu Hong
,
Jianqi Sun
, and
Huijun Wang

Abstract

This study investigates the spatial–temporal variations in summer extreme precipitation event (EPE) frequency over northern Asia and related atmospheric circulations. The division analysis indicates that three subregions of western Siberia (WS), eastern Siberia (ES), and eastern Mongolia–northeastern China can be identified, and the EPE variations over WS and ES are focused on here. On an interannual time scale, higher EPE frequencies are related to a similar dipole pattern in the upper troposphere [anomalous cyclone (anticyclone) to the west (southeast) of these two subregions] and a local anomalous cyclone in the lower troposphere. The dipole pattern leads to anomalous air divergence in the upper troposphere and compensating ascending motion over the subregions; the local anomalous cyclone in the lower troposphere leads to water vapor convergence. These anomalous atmospheric circulations therefore provide favorable dynamic and moisture conditions for higher EPE frequencies. Further analysis indicates that the WS EPE frequency is influenced by the combination of polar–Eurasian (POL) and North Atlantic Oscillation (NAO) patterns, while the ES EPE frequency is influenced by Scandinavian (SCAND) [British–Baikal Corridor (BBC)] pattern over 1987–2004 (2005–15). The alternate influence on the ES EPE frequency may result from the interdecadal change in the structure of SCAND and BBC patterns. In addition, the East Asian summer monsoon (EASM) shows enhanced influence on ES EPE frequency after the late 1990s, which could be due to interdecadal strengthening and extending of the anomalous cyclone around Lake Baikal. This cyclone is concurrent with EASM, and its changes favor water vapor transported by EASM to ES after the late 1990s.

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Haixu Hong
,
Jianqi Sun
, and
Huijun Wang

Abstract

This study investigates the spatial–temporal variations in summer extreme precipitation event (EPE) frequency over northern Asia and related atmospheric circulations. The division analysis indicates that three subregions of western Siberia (WS), eastern Siberia (ES), and eastern Mongolia–northeastern China can be identified, and the EPE variations over WS and ES are focused on here. On an interannual time scale, higher EPE frequencies are related to a similar dipole pattern in the upper troposphere [anomalous cyclone (anticyclone) to the west (southeast) of these two subregions] and a local anomalous cyclone in the lower troposphere. The dipole pattern leads to anomalous air divergence in the upper troposphere and compensating ascending motion over the subregions; the local anomalous cyclone in the lower troposphere leads to water vapor convergence. These anomalous atmospheric circulations therefore provide favorable dynamic and moisture conditions for higher EPE frequencies. Further analysis indicates that the WS EPE frequency is influenced by the combination of polar–Eurasian (POL) and North Atlantic Oscillation (NAO) patterns, while the ES EPE frequency is influenced by Scandinavian (SCAND) [British–Baikal Corridor (BBC)] pattern over 1987–2004 (2005–15). The alternate influence on the ES EPE frequency may result from the interdecadal change in the structure of SCAND and BBC patterns. In addition, the East Asian summer monsoon (EASM) shows enhanced influence on ES EPE frequency after the late 1990s, which could be due to interdecadal strengthening and extending of the anomalous cyclone around Lake Baikal. This cyclone is concurrent with EASM, and its changes favor water vapor transported by EASM to ES after the late 1990s.

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Hong Wang
,
Fubao Sun
,
Fa Liu
,
Tingting Wang
,
Yao Feng
, and
Wenbin Liu

Abstract

The most basic features of climatological normals and variability are useful for describing observed or likely future climate fluctuations. Pan evaporation (E pan) is an important indicator of climate change; however, current research on E pan has focused on its change in mean rather than its variability. The variability of monthly E pan from 1961 to 2020 at 969 stations in China was analyzed using a theoretical framework that can distinguish changes in E pan variance between space and time. The E pan variance was decomposed into spatial and temporal components, and the temporal component was further decomposed into interannual and intra-annual components. The results show that the variance in E pan was mainly controlled by the temporal component. The time variance was mainly controlled by intra-annual variance, decreasing continuously in the first 30 years, and slightly increasing after the 1990s. This is mainly due to the fact that the decrease of wind speed and the increase of water vapor pressure deficit with the temperature increase offset each other and inhibit the variability of E pan. The variance decreased more in the northern region, whereas it exhibited a small decrease or slight increase in the southern region. The reduction in seasonality was dominated by spring, followed by summer. The differences in E pan variability in space and season were mainly caused by the differing rates of change in evaporation driving forces, such as a greater reduction in wind speed in the northern region and spring.

Significance Statement

The purpose of this study is to better understand how the variability of evaporation changes rather than in mean under climate change. This is important because the variability is useful to describe the observed or likely future fluctuations, and a small fluctuation may have large impacts on water practices, such as agricultural production. Our findings showed that the temporal and spatial variability of evaporation decreased due to its drivers offsetting each other. However, because the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to its mean and variability for serving water resources practice.

Restricted access
Hong Wang
,
Fubao Sun
,
Tingting Wang
,
Yao Feng
,
Fa Liu
, and
Wenbin Liu

Abstract

Pan evaporation (E pan) serves as a monitorable method for estimating potential evaporation, evapotranspiration, and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, the monthly E pan was calculated over China during 1951–2021, resulting in an average R 2 of 0.93 ± 0.045 and an RMSE of 21.48 ± 6.06 mm month−1. The trend of E pan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of E pan trends, the E pan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of E pan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted E pan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in E pan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in E pan. These results show the complex and dynamic nature of E pan and underscore the need for continued monitoring and in-depth analysis of its drivers.

Significance Statement

The primary objective of this study is to explore the spatiotemporal patterns and potential driving factors of pan evaporation in China based on constructing a comprehensive dataset of pan evaporation. This is important because pan evaporation is an important indicator of the water cycle, which is currently undergoing modifications and is expected to become more pronounced as the climate continues to warm. Our findings showed that the patterns of pan evaporation were characterized by its drivers. As the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to the pattern and attribution of pan evaporation.

Restricted access
Tingting Wang
,
Fubao Sun
,
Wee Ho Lim
,
Hong Wang
,
Wenbin Liu
, and
Changming Liu

Abstract

Climate change and its potential threats on water security call for reliable predictions of evapotranspiration (ET) and runoff Q at different time scales, but current knowledge of the differences in their predictability between humid and nonhumid regions is limited. Based on spatially distributed catchments in China, the authors characterized their predictability and provided plausible explanations. Using the Budyko framework, it was confirmed that annual ET is predictable in nonhumid regions but less predictable in humid regions, and annual Q is predictable in humid regions but less reliable in nonhumid regions. The main cause of the varied predictability lies in the variation of water storage change ΔS in the water balance equation. It affects both the estimation and the variability of Q in nonhumid catchments more than that in humid catchments, which increases the challenge of predicting annual Q in nonhumid regions, while the opposite effect occurs in annual ET prediction between humid and nonhumid catchments. Moreover, the differences between the controlling factors of ET variability in different regions add more differences in their predictability. The dominant control of precipitation makes it easy to predict annual ET in nonhumid regions. By contrast, precipitation, potential evaporation, and their covariance take considerable effort to determine annual ET variations, which leads to less reliable ET estimation and predictability in humid catchments. Therefore, one can accurately predict annual ET in nonhumid catchments and Q in humid catchments based on commonly used hydrological models. With proper consideration of ΔS, the predictability of annual ET and Q in both humid and nonhumid catchments can be improved.

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Chengzu Bai
,
Mei Hong
,
Dong Wang
,
Ren Zhang
, and
Longxia Qian

Abstract

The identification of the rainfall–runoff relationship is a significant precondition for surface–atmosphere process research and operational flood forecasting, especially in inadequately monitored basins. Based on an information diffusion model (IDM) improved by a genetic algorithm, a new algorithm (GIDM) is established for interpolating and forecasting monthly discharge time series; the input variables are the rainfall and runoff values observed during the previous time period. The genetic operators are carefully designed to avoid premature convergence and “local optima” problems while searching for the optimal window width (a parameter of the IDM). In combination with fuzzy inference, the effectiveness of the GIDM is validated using long-term observations. Conventional IDMs are also included for comparison. On the Yellow River or Yangtze River, twelve gauging stations are discussed, and the results show that the new method can simulate the observations more accurately than traditional IDMs, using only 50% or 33.33% of the total data for training. The low density of observations and the difficulties in information extraction are key problems for hydrometeorological research. Therefore, the GIDM may be a valuable tool for improving water management and providing the acceptable input data for hydrological models when available measurements are insufficient.

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Zhangli Sun
,
Di Long
,
Zhongkun Hong
,
Mohamed A. Hamouda
,
Mohamed M. Mohamed
, and
Jianhua Wang

Abstract

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

Significance Statement

Intercomparison between the FY-2 precipitation product and mainstream precipitation products is valuable to guide applications of satellite precipitation products to China and its subregions. This study illustrates uncertainties in various satellite precipitation products, and could guide optimization of algorithms of precipitation retrieval and data fusion/merging to improve the accuracy and resolution of satellite precipitation products.

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Bin Yong
,
Jingjing Wang
,
Liliang Ren
,
Yalei You
,
Pingping Xie
, and
Yang Hong

Abstract

The Diaoyu Islands are a group of uninhabited islets located in the East China Sea between Japan, China, and Taiwan. Here, four mainstream gauge-adjusted multisatellite precipitation estimates [TRMM Multisatellite Precipitation Analysis, version 7 (TMPA-V7); CPC morphing technique–bias-corrected product (CMORPH-CRT); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR); and Global Satellite Mapping of Precipitation–gauge adjusted (GSMaP_Gauge)] are adopted to detect the rainfall characteristics of the Diaoyu Islands area with a particular focus on typhoon contribution. Out of the four products, CMORPH-CRT and GSMaP_Gauge show much more similarity both in terms of the spatial patterns and error structures because of their use of the same morphing technique. Overall, GSMaP_Gauge performs better than the other three products, likely because of denser in situ observations integrated in its retrieval algorithms over East Asia. All rainfall products indicate that an apparent rain belt exists along the northeastern 45° direction of Taiwan extending to Kyushu of Japan, which is physically associated with the Kuroshio. The Diaoyu Islands are located on the central axis of this rain belt. During the period 2001–09, typhoon-induced rainfall accounted for 530 mm yr−1, and typhoons contributed on average approximately 30% of the annual precipitation budget over the Diaoyu Islands. Higher typhoon contribution was found over the southern warmer water of the Diaoyu Islands, while the northern cooler water presented less contribution ratio. Supertyphoon Chaba, the largest typhoon of 2004, recorded 53 h of rainfall accumulation totaling 235 mm on the Diaoyu Islands, and this event caused severe property damage and human casualties for Japan. Hence, the Diaoyu Islands play an important role in weather monitoring and forecasting for the neighboring countries and regions.

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Jonathan J. Gourley
,
Yang Hong
,
Zachary L. Flamig
,
Jiahu Wang
,
Humberto Vergara
, and
Emmanouil N. Anagnostou

Abstract

This study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration.

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