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Yanjuan Wu
,
Ivan D. Haigh
,
Chao Gao
,
Luke J. Jenkins
,
Joshua Green
,
Robert Jane
,
Yu Xu
,
Hengzhi Hu
, and
Naicheng Wu

Abstract

In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China’s Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall’s rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions.

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G. Cristina Recalde-Coronel
,
Benjamin Zaitchik
,
William K. Pan
,
Yifan Zhou
, and
Hamada Badr

Abstract

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March–May) using ensemble hindcasts for 2002–17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño–Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.

Open access
Habiba Kallel
,
Antoine Thiboult
,
Murray D. Mackay
,
Daniel F. Nadeau
, and
François Anctil

Abstract

Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multiyear direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model that cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error = −0.3 and −2.7 W m−2 for latent and sensible heat fluxes, respectively). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020.

Significance Statement

Freshwater bodies impact the regional climate through energy and water exchanges with the atmosphere. It is challenging to model surface energy fluxes over a northern lake due to the succession of stratification and mixing periods over a year. This study focuses on the interactions between the atmosphere of an irregular shaped northern hydropower reservoir. Direct measurements of turbulent fluxes using an eddy covariance system allowed the model assessment. Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement; however, the model overreacted to high wind episodes. Biases mostly occur during freeze-up and breakup, stressing the importance of a good representation of the ice cover processes.

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Wen-Shu Lin
,
Joel R. Norris
,
Michael J. DeFlorio
, and
F. Martin Ralph

Abstract

We apply the Ralph et al. scaling method to a reanalysis dataset to examine the climatology and variability of landfalling atmospheric rivers (ARs) along the western North American coastline during 1980–2019. The local perspective ranks AR intensity on a scale from 1 (weak) to 5 (strong) at each grid point along the coastline. The object-based perspective analyzes the characteristics of spatially independent and temporally coherent AR objects making landfall. The local perspective shows that the annual AR frequency of weak and strong ARs along the coast is highest in Oregon and Washington and lowest in Southern California. Strong ARs occur less frequently than weak ARs and have a more pronounced seasonal cycle. If those ARs with integrated water vapor transport (IVT) weaker than 250 kg m−1 s−1 are included, there is an enhanced seasonal cycle of AR frequency in Southern California and a seasonal cycle of AR intensity but not AR frequency in Alaska. The object-based analysis additionally indicates that strong ARs at lower latitudes are associated with stronger wind than weak ARs but similar moisture, whereas strong ARs at higher latitudes are associated with greater moisture than weak ARs but similar wind. For strong ARs, IVT at the core is largest for ARs in Oregon and Washington and smaller poleward and equatorward. Both IVT in the AR core and cumulative IVT along the coastline usually decrease after the first day of landfall for weak ARs but increase from the first to second day for strong ARs.

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Guo-Shiuan Lin
,
Ruben Imhoff
,
Marc Schleiss
, and
Remko Uijlenhoet

Abstract

Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains (e.g., river basins), although rainfall nowcasting in small urban areas is expected to be more challenging. Here, we selected 80 events with high rainfall intensities (at least one 1-km2 grid cell experiences precipitation >15 mm h−1 for 1-h events or 30 mm day−1 for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9060 probabilistic nowcasts with 20 ensemble members by applying the short-term ensemble prediction system (STEPS) from Pysteps to every 10-min issue time for the selected events. We found that nowcast errors increased with decreasing (urban) areas especially when below 100 km2. In addition, at 30-min lead time, the underestimation of nowcasts was 38% larger and the discrimination ability was 11% lower for 1-h events than for 24-h events. A set of gridded correction factors for the Netherlands, CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) could adjust the bias in real-time QPE and nowcasts by 70%. Yet, nowcasts were still found to underestimate rainfall more than 50% above 40-min lead time relative to the reference, which indicates that this error originates from the nowcasting model itself. Also, CARROTS did not adjust the rainfall spatial distribution in urban areas much. In summary, radar-based nowcasting for urban areas (between 67 and 213 km2) in the Netherlands exhibits a short skillful lead time of about 20 min, which can only be used for last-minute warning and preparation.

Open access
Yusen Yuan
,
Lixin Wang
,
Zhongwang Wei
,
Hoori Ajami
,
Honglang Wang
, and
Taisheng Du

Abstract

The isotopic composition of evapotranspiration δ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculate δ ET, though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested the δ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. The σ ET (mean) would be greater than σ ET (median) when the mean value of inverse vapor concentration ( 1 / C υ ¯ ) is greater than the median value of inverse vapor concentration [ 1 / C υ ( median ) ]. When applying the filter of r 2 > 0.8, around 70% of σ ET (mean) was greater than σ ET (median). This phenomenon might be due to the normality of the vapor concentration Cυ producing the asymmetric distribution of 1/Cυ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentration Cυ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

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Yuanyuan Zhou
and
Liang Gao

Abstract

The spatiotemporal variations of annual tropical-cyclone-induced rainfall (TCR) and non-tropical-cyclone-induced rainfall (NTCR) during 1960–2017 in Southeast China are investigated in this study. The teleconnections to sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are examined. A significant decrease in annual TCR in the Pearl River basin was detected, while an increase in annual TCR in rainstorms was observed in the northeast of the Pearl River basin and south of the Yangtze River basin. A northward migration of a TCR belt was identified, which was also indicated by the pronounced anomalies of annual TCR. There was in general an increasing trend of non-tropical-cyclone-induced moderate rain, heavy rain, and rainstorms in Southeast China. Compared with the non-tropical-cyclone-induced heavy rain, the abnormal non-tropical-cyclone-induced rainstorms are more northerly. Both monthly TCR and NTCR were remarkably affected by the Arctic Oscillation, Southern Oscillation, and Indian Ocean dipole. TCR was more easily affected by the Arctic Oscillation compared to NTCR.

Significance Statement

Tropical-cyclone- and non-tropical-cyclone-induced rainfall (TCR and NTCR) prevails in Southeast China, and their characteristics of spatiotemporal variability are of significance in predicting rainfall over the study area. Therefore, this study aims to detect the degree to which rainfall varies in time and space, respectively, using the Mann–Kendall test and the empirical orthogonal function method. Moreover, to explore which climatic factor contributes the most to the spatiotemporal variability of TCR and NTCR, the teleconnections to the large-scale climatic indices including sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are studied. The spatiotemporal variations of TCR and NTCR were affected by the sea surface temperature and the other three large-scale climatic indices. The findings in this study are expected to deepen the understanding of spatiotemporal variations of TCR and NTCR over Southeast China and the teleconnections to climatic indices.

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Zhehui Shen
,
Bin Yong
, and
Hao Wu

Abstract

Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

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Zhuoyong Xiao
,
Xinping Zhang
,
Xiong Xiao
,
Xin Chang
, and
Xinguang He

Abstract

Convective/advective precipitation partitions refer to the divisions of precipitation that are either convective or advective in nature, relative to the total precipitation amount. These distinct partitions can have a significant influence on the stable isotope composition of precipitation. This study analyzed and compared the effect of precipitation partitions on δ 18O in precipitation (δ 18O p ) by using daily precipitation stable isotope data from Changsha station and monthly precipitation stable isotope data from the Global Network of Isotopes in Precipitation (GNIP), under different time scales, time intervals (i.e., annual, warm season, and cold season), and precipitation intensities. The results showed that the correlation between the convective precipitation fraction (CPF) and total precipitation amount was influenced by the intensity of convection in different time intervals. On both the daily and monthly scales, the CPF decreased as the precipitation amount increased in the warm season, while it increased with increasing precipitation amount in the cold season. Regardless of the season, daily δ 18O p at Changsha station consistently increased with an increase in daily CPF. On a daily scale, the effect of convective activity on δ 18O p was stronger than that of the “precipitation amount effect” in the cold season, as compared to the situation in the warm season. As a result, the regression line slope between δ 18O p and CPF increased with increasing precipitation intensity in the warm season, meaning that as the CPF increased, the δ 18O p increased at a faster rate under higher precipitation intensity. Similarly, the slope increased with increasing precipitation intensity in the cold season. This suggests that precipitation intensity and convection intensity can affect the relationship between δ 18O p and CPF. Our findings shed light on how different precipitation partitions affect stable isotope composition of precipitation, thus enhancing our understanding of the variability of precipitation stable isotopes in the monsoon regions of China.

Significance Statement

This study aims to better elucidate the influence of different precipitation partitions on precipitation stable isotopes. In the eastern monsoon region of China, stable isotopes in precipitation showed a robust positive relationship with convective precipitation faction. On a daily scale, the convective activity enhanced the influences of the “precipitation amount effect” on precipitation stable isotopes in the warm season and reduced such influences in the cold season. These results improve our understanding of stable isotopic variability of precipitation in the eastern monsoon region, China.

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Nika Tsitelashvili
,
Trent Biggs
,
Ye Mu
, and
Vazha Trapaidze

Abstract

Analyzing water resources in areas with few hydrometeorological stations, such as those in post-Soviet countries, is difficult due to station closures after 1989. In Caucasus, evaluations often rely on outdated data from nearby rivers. We evaluated one national-level precipitation dataset, the Water Balance of Georgia (WBG) with two satellite-based precipitation products from 1981 to 2021, including the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data and CHIRPS blended with a dense rain gauge network (geoCHIRPS). We modeled mean annual precipitation from geoCHIRPS as a function of coastal distance and elevation. CHIRPS underestimated precipitation in the cold and wet seasons (R 2 = 0.74, r = 0.86) and overestimated dry season precipitation, while geoCHIRPS performed well in all seasons (R 2 = 0.86, r = 0.92). Distance from the coast was a more important predictor of precipitation than elevation in western Georgia, while precipitation correlated positively with elevation in the east. At four hydroelectric plants, the underperformance as a percentage of capacity (∼37%) corresponds with the percentage difference between differences in precipitation products (∼38%), suggesting that plants designed based on WBG may be systematically overdesigned, but further work is needed to determine the reasons for the underperformance of the plants and frequency. We conclude that 1) the existing WBG does not accurately reflect elevation–precipitation relationships near the coast, and 2) for accurate analysis of spatiotemporal precipitation variability and its impacts on hydropower generation and environmental and sustainable water resource management, it is essential to calibrate satellite-based precipitation estimates with additional rain gauge data.

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