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.
The essence of propagation from meteorological to hydrological drought is the cause‐effect relationship between precipitation and runoff. This study challenged the reliability of applying linear or ...non‐linear correlation (i.e., closeness/similarity, a non‐directional scalar) to study drought propagation (i.e., causality, a directional vector). Meanwhile, in the field of hydrometeorology, causality analysis is burgeoning in model simulations, but still rare in analyzing the observations. Therefore, this study aims to provide a new perspective on drought propagation (i.e., causality) using convergent cross mapping (CCM) based on pure observations. Compared with the results in previous studies, the effectiveness of applying causality analysis in drought propagation study was proven, indicating that causality analysis would be more powerful than correlation analysis, especially for detecting drought propagation direction.
Plain Language Summary
The cause‐effect relationship (causality) between meteorological and hydrological drought is clear, that is, precipitation deficit can lead to decrease in runoff. Naturally, a significant correlation between the behaviors of precipitation and runoff is expected, based on which the drought propagation time can be calculated. This study is inspired by the existence of spurious correlation, which implies that correlation is commonly confused with causality. Though the causality in drought propagation study is physically logical, spurious correlation accentuates the irrationality of applying correlation analysis wherever causality concerns. Hence, this study applied causality analysis to explore drought propagation, and the results were satisfying especially in detecting two main characteristics of drought propagation: direction and time. Nevertheless, this study not only aims to apply a new method in detecting drought propagation direction and time, but also to reassess the nature of drought propagation. We advocate to reject the overuse of correlation analysis and embraces the true causality analysis in the field of hydrometeorology.
Key Points
Causality analysis, instead of correlation analysis, was introduced in drought propagation study for the first time
Convergent cross mapping (CCM) was proven to be powerful in detecting drought propagation direction and time based on pure observations
This study can fill the gap of using real‐world observations (rather than model simulations) in the causality analysis to a large extent
Abstract
Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and ...food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative precipitation predictions over 1–30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7–0.8 after 3–5 days, then gradually decreases and settles at approximately 0.2–0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model’s prediction is 0.2–0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables.
Significance Statement
The daily and cumulative precipitation prediction skills of three subseasonal prediction products were evaluated over China in this study. Our results reveal the spatiotemporal variations in prediction skill, especially with respect to time scale. Compared to daily precipitation predictions, cumulative precipitation predictions are more skillful, with correlation coefficients peaking at 0.7–0.8 after 3–5 days. These results can provide valuable information for water resource managers who are more concerned with the general conditions over a period than with hydrometeorological events occurring on a particular day. This study can guide end users in applying appropriate time scales to fully exploit numerical weather prediction information and satisfy their specific needs.
Abstract
The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface ...latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables.
Significance Statement
A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.
Abstract
Root-zone soil moisture (RZSM) is an important variable in land–atmosphere interactions, notably affecting the global climate system. Contrary to satellite-based acquisition of surface soil ...moisture, RZSM is generally obtained from model-based simulations. In this study, in situ observations from the Naqu and Pali networks that represent different climatic conditions over the Tibetan Plateau (TP) and a triple collocation (TC) method are used to evaluate model-based RZSM products, including Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.5a and 3.5b), Global Land Data Assimilation System (GLDAS) (versions 2.1 and 2.2), and the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5). The evaluation results based on in situ observations indicate that all products tend to overestimate but could generally capture the temporal variation, and ERA5 exhibits the best performance with the highest
R
(0.875) and the lowest unbiased RMSE (ubRMSE; 0.015 m
3
m
−3
) against in situ observations in the Naqu network. In the TC analysis, similar results are obtained: ERA5 has the best performance with the highest TC-derived
R
(0.785) over the entire TP, followed by GLEAM v3.5a (0.746) and GLDAS-2.1 (0.682). Meanwhile, GLEAM v3.5a and GLDAS-2.1 outperform GLEAM v3.5b and GLDAS-2.2 over the entire TP, respectively. Besides, possible error causes in evaluating these RZSM products are summarized, and the effectiveness of TC method is also evaluated with two dense networks, finding that TC method is reliable since TC-derived
R
is close to ground-derived
R
, with only 6.85% mean relative differences. These results using both in situ observations and TC method may provide a new perspective for the soil moisture product developers to further enhance the accuracy of model-based RZSM over the TP.
Significance Statement
The purpose of this study is to better understand the quality and applicability of GLEAM, GLDAS, and ERA5 RZSM products over the TP using both in situ observations and the triple collocation (TC) method, making it better applied to climate and hydrological research. This study provides four standard statistical metrics evaluation based on in situ observations, as well as the reliable metric, that is, correlation coefficient (
R
) derived from TC method, and highlights that TC-based evaluation could supplement the ground-based validation, especially over the data-scarce TP region.
Category 4 landfalling hurricane Harvey poured more than a metre of rainfall across the heavily populated Houston area, leading to unprecedented flooding and damage. Although studies have focused on ...the contribution of anthropogenic climate change to this extreme rainfall event
, limited attention has been paid to the potential effects of urbanization on the hydrometeorology associated with hurricane Harvey. Here we find that urbanization exacerbated not only the flood response but also the storm total rainfall. Using the Weather Research and Forecast model-a numerical model for simulating weather and climate at regional scales-and statistical models, we quantify the contribution of urbanization to rainfall and flooding. Overall, we find that the probability of such extreme flood events across the studied basins increased on average by about 21 times in the period 25-30 August 2017 because of urbanization. The effect of urbanization on storm-induced extreme precipitation and flooding should be more explicitly included in global climate models, and this study highlights its importance when assessing the future risk of such extreme events in highly urbanized coastal areas.
The complementary operation is a mode to effectively ensure power benefits for a hydro-photovoltaic (PV) hybrid power system. Operating rules is a classical management tool to guide the system ...operation that generalizes the intrinsic dynamics relationship among optimal decision making, hydrometeorology variables and hybrid power system characteristics. Conventional rules for hydro-PV hybrid power system are often established in a subjective manner. It is a challenge in their applications because the operating rules are sensitive to climatic conditions. For dealing this challenge, dominant hydro-meteorological factors for the operation decisioning in different climatic conditions are eliminated by using the variance-based sensitivity analysis method, and further identify effective operating rules form to obtain robust operation benefits. The hydro-PV hybrid power system in Longyangxia, China is used as a case study. The result shows that the major factors in decisioning are the initial total potential stored energy (SE) and the input of potential energy (IE), whereas the interaction between SE and IE is the minor factor. It is also found that higher performance is achieved when SE and IE are considered separately instead of the product factor available energy (AE). Compared to the traditional complementary operating rules, the annual power generation and reliability of the system are increased by 1.59% and 1.71% under the newly derived rules, respectively. These findings are meaningful for robustly operating hybrid power systems under changing environments.
•Optimal trajectories of the hybrid energy system operation model are taken as samples for the sensitivity analysis.•The influence of hydrometeorology variables on the optimal decision-making was qualified.•The input of potential energy should be explicitly expressed in the complementary operating rules.•The performance of the proposed complementary operating rules is superior to the traditional operating rules.
The Water and Global Change (WATCH) project evaluation of the terrestrial water cycle involves using land surface models and general hydrological models to assess hydrologically important variables ...including evaporation, soil moisture, and runoff. Such models require meteorological forcing data, and this paper describes the creation of the WATCH Forcing Data for 1958–2001 based on the 40-yr ECMWF Re-Analysis (ERA-40) and for 1901–57 based on reordered reanalysis data. It also discusses and analyses model-independent estimates of reference crop evaporation. Global average annual cumulative reference crop evaporation was selected as a widely adopted measure of potential evapotranspiration. It exhibits no significant trend from 1979 to 2001 although there are significant long-term increases in global average vapor pressure deficit and concurrent significant decreases in global average net radiation and wind speed. The near-constant global average of annual reference crop evaporation in the late twentieth century masks significant decreases in some regions (e.g., the Murray–Darling basin) with significant increases in others.
Comprehensive assessments on the reliability of remotely sensed soil moisture products are undeniably essential for their advancement and application. With the establishment of extensive dense ...networks across the globe, mismatches between satellite footprints and ground single-point observations can be feasibly relieved. In this study, five remotely sensed soil moisture products, namely, the Soil Moisture Active Passive (SMAP), two Soil Moisture and Ocean Salinity (SMOS) products, the Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2) and the European Space Agency (ESA) Climate Change Initiative (CCI), were systematically investigated by utilizing in-situ soil moisture observations from global dense and sparse networks. Distinguished from previous studies, several perturbing factors comprising the surface temperature, vegetation optical depth (VOD), surface roughness and spatial heterogeneity were taken into account in this investigation. Furthermore, products' skills under various climate regions were also evaluated.
Through the results, the SMAP product captures temporal trends of ground soil moisture, exhibiting an averaged R of 0.729, whereas for overall accuracy, ESA CCI outperformed other products with a slightly smaller ubRMSE of 0.041 m3 m−3 and a bias of −0.005 m3 m−3. This complementarity between SMAP and ESA CCI was further demonstrated under different climate conditions and can afford the reference of their integration for a more reliable global soil moisture product. Though some underestimations still exist, the newly developed SMOS- INRA-CESBIO (SMOS-IC) was illustrated to gain considerable upgrades with regard to R and ubRMSE compared to SMOS-L3 product, especially in dense VOD conditions achieving the highest R compared to other products.
Generally, the underestimations of the European Centre for Medium-Range-Weather Forecasts (ECMWF) surface temperature used for SMOS under moderate or high VOD, heterogeneity, and most surface roughness conditions were consistent with the underestimations of the soil moisture product and provide the directions of product promotions. As for LPRM surface temperature, the worse skills can partially explain the unsatisfactory performances for LPRM soil moisture products. In spite of relatively acceptable skills of SMAP and SMOS-IC soil moisture products concerning R under moderate or dense VOD, small surface roughness, low heterogeneity conditions and temperate and cold climate types, advances in soil moisture products under high or even slightly low VOD, high roughness or topography complexity and heterogeneity, as well as in tropical or desert regions, remain challenging. It is expected that these findings can contribute to algorithm refinements, product enhancements (e.g., fusion and disaggregation) and hydrometeorological usages.
•The impacts of perturbing factors, heterogeneity and climate types were assessed.•SMOS-IC showed better performance concerning R and ubRMSE.•The complementarity between SMAP and ESA CCI was observed.•The underestimation of SMOS surface temperature contributed to the dry bias in SMOS soil moisture products.
Abstract
In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate ...a catalog of 52 flash-flood-producing storms over the 1996–2021 period for the arid Las Vegas Wash watershed using rain gauge observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration, high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are “intensity driven” in the warm season but “volume driven” in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality.
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