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Weiwei Lu, Huimin Lei, Wencong Yang, Jingjing Yang, and Dawen Yang

Abstract

Increasing evidence indicates that changes have occurred in heavy precipitation associated with tropical cyclone (TC) and local monsoon (non-TC) systems in the southeastern coastal region of China over recent decades. This leads to the following questions: what are the differences between TC and non-TC flooding, and how do TC and non-TC flooding events change over time? We applied an identification procedure for TC and non-TC floods by linking flooding to rainfall. This method identified TC and non-TC rainfall–flood events by the TC rainfall ratio (percentage of TC rainfall to total rainfall for rainfall–flood events). Our results indicated that 1) the TC rainfall–flood events presented a faster runoff generation process associated with larger flood peaks and rainfall intensities but smaller rainfall volumes, compared to that of non-TC rainfall–flood events, and 2) the magnitude of TC floods exhibited a decreasing trend, similar to the trend in the amount and frequency of TC extreme precipitation. However, the frequency of TC floods did not present obvious changes. In addition, non-TC floods decreased in magnitude and frequency while non-TC extreme precipitation showed an increase. Our results identified significantly different characteristics between TC and non-TC flood events, thus emphasizing the importance of considering different mechanisms of floods to explore the physical drivers of runoff response. Also, our results indicated that significant decreases occurred in the magnitude, but not the frequency, of floods induced by TC from the western North Pacific, which is the most active ocean basin for TC activity, and thus can provide useful information for future studies on the global pattern of TC-induced flooding.

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Ji-Qin Zhong, Bing Lu, Wei Wang, Cheng-Cheng Huang, and Yang Yang

Abstract

In this study, the causes of the underestimated diurnal 2-m temperature range and the overestimated 2-m specific humidity in the winter of northern China in the Rapid-Refresh Multiscale Analysis and Prediction System–Short Term (RMAPS-ST) are investigated. Three simulations based on RMAPS-ST are conducted from 1 November 2016 to 28 February 2017. Further analyses show that the partitioning of surface upward sensible heat fluxes and downward ground heat fluxes might be the main contributing factor to the 2-m temperature forecast bias. In this study, two simulations are conducted to examine the effect of soil moisture initialization and soil hydraulic property on the 2-m temperature and 2-m specific humidity forecasts. First, the High-Resolution Land Data Assimilation System (HRLDAS) is used to provide an alternative soil moisture initialization. The results show that the drier soil moisture could lead to noticeable change in energy partitioning at the land surface, which in turn results in improved prediction of the diurnal 2-m temperature range, although it also enlarges the 2-m specific humidity bias in some parts of the domain. Second, a soil texture dataset developed by Beijing Normal University and the revised hydraulic parameters are applied to provide a more detailed description of soil properties, which could further improve the 2-m specific humidity bias. In summary, the combination of using optimized soil moisture initialization, an updated soil map, and revised soil hydraulic parameters can help improve the 2-m temperature and 2-m specific humidity prediction in RMAPS-ST.

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Yang Lu, Jianzhi Dong, and Susan C. Steele-Dunne

Abstract

The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2–3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2–3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007 m3 m−3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar.

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Zhihua He, Long Yang, Fuqiang Tian, Guangheng Ni, Aizhong Hou, and Hui Lu

Abstract

The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.

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Yang Lu, Susan C. Steele-Dunne, and Gabriëlle J. M. De Lannoy

Abstract

Surface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.

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Wenli Wang, Kun Yang, Long Zhao, Ziyan Zheng, Hui Lu, Ali Mamtimin, Baohong Ding, Xin Li, Lin Zhao, Hongyi Li, Tao Che, and John C. Moore

Abstract

Snow depth on the interior of Tibetan Plateau (TP) in state-of-the-art reanalysis products is almost an order of magnitude higher than observed. This huge bias stems primarily from excessive snowfall, but inappropriate process representation of shallow snow also causes excessive snow depth and snow cover. This study investigated the issue with respect to the parameterization of fresh snow albedo. The characteristics of TP snowfall were investigated using ground truth data. Snow in the interior of the TP is usually only some centimeters in depth. The albedo of fresh snow depends on snow depth, and is frequently less than 0.4. Such low albedo values contrast with the high values (~0.8) used in the existing snow schemes of land surface models. The SNICAR radiative transfer model can reproduce the observations that fresh shallow snow has a low albedo value, based on which a fresh snow albedo scheme was derived in this study. Finally, the impact of the fresh snow albedo on snow ablation was examined at 45 meteorological stations on TP using the land surface model Noah-MP which incorporated the new scheme. Allowing albedo to change with snow depth can produce quite realistic snow depths compared with observations. In contrast, the typically assumed fresh snow albedo of 0.82 leads to too large snow depths in the snow ablation period averaged across 45 stations. The shallow snow transparency impact on snow ablation is therefore particularly important in the TP interior, where snow is rather thin and radiation is strong.

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