•Four latest satellite–gauge QPEs and their hydrologic applications are evaluated.•Gauge adjustment procedures and the gauge density greatly affect the QPE quality.•The error characteristics of ...rainfall are propagated into hydrologic simulations.•CMORPH CMA can serve as an alternative high quality QPE in China.
Satellite–gauge quantitative precipitation estimate (QPE) products may reduce the errors in near real-time satellite precipitation estimates by combining rain gauge data, which provides great potential to hydrometeorological applications. This study aims to comprehensively evaluate four of the latest satellite–gauge QPEs, including NASA’s Tropical Rainfall Measuring Mission (TRMM) 3B42V7 product, NOAA’s Climate Prediction Center (CPC) MORPHing technique (CMORPH) bias-corrected product (CMORPH CRT), CMORPH satellite–gauge merged product (CMORPH BLD) and CMORPH satellite–gauge merged product developed at the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) (CMORPH CMA). These four satellite–gauge QPEs are statistically evaluated over the Huaihe River basin during 2003–2012 and applied into the distributed Variable Infiltration Capacity (VIC) model to assess hydrologic utilities.
Compared to the China Gauge-based Daily Precipitation Analysis (CGDPA) newly developed at CMA/NMIC, the four satellite–gauge QPEs generally depict the spatial distribution well, with the underestimation in the southern mountains and overestimation in the northern plain of the Huaihe River basin. Specifically, both TRMM and CMORPH CRT adopt simple gauge adjustment algorithms and exhibit relatively poor performance, with evidently deteriorated quality in winter. In contrast, the probability density function-optimal interpolation (PDF-OI) gauge adjustment procedure has been applied in CMORPH BLD and CMORPH CMA, resulting in higher quality and more stable performance. CMORPH CMA further benefits from a merged dense gauge observation network and outperforms the other QPEs with significant improvements in rainfall amount and spatial/temporal distributions. Due to the insufficient gauge observations in the merging process, CMORPH BLD features the similar error characteristics of CMORPH CRT with a positive bias of light precipitation and a negative bias of heavy precipitation, in contrast to the overall large overestimation by TRMM. The quality of QPEs directly impacts streamflow simulations, as the precipitation biases are propagated into simulated streamflow through interaction with hydrologic processes. The general streamflow pattern is well captured at multiple time scales by the simulations using the four satellite–gauge QPEs as the input forcing. CMORPH CRT shows the worst simulations in both long-term streamflow and extreme flood events, while CMORPH CMA forced streamflow simulations even outperform that forced by CGDPA. CMORPH CMA is able to reproduce the July 2003 flood event, while the other three QPEs fail to generate such extreme flood. Overall, CMORPH CMA shows great potential to improve the precipitation distribution and hydrometeorological simulations, and can serve as an alternative high quality QPE in China.
Storm nowcasting is critical and urgently needed. Recent advances in deep learning (DL) have shown potential for improving nowcasting accuracy and predicting general low‐intensity precipitation ...events. However, DL models yield poor performance on high‐impact storms due to insufficient extraction and characterization of complex multi‐scale spatiotemporal variations of storms. To tackle this challenge, we propose a novel customized multi‐scale (CM) DL framework, including a flexible attention module capturing scale variations and a customized loss function ensuring multi‐scale spatiotemporal consistency. The CM framework was applied to the storm event imagery data set (SEVIR). Representative cases indicate that the CM framework preserves the shape of storms and adequately forecasts intense storms even for longer predictions. The quantitative evaluation shows that all models applying our framework can improve skill scores by 8.5%–42.6% for 1‐hr nowcasting. This work highlights the importance of modeling multi‐scale spatiotemporal characteristics of meteorological variables when using DL.
Plain Language Summary
Severe storms such as heavy rain, hail, and thunderstorm may cause damage and sometimes life‐threatening hazards, and thus storm nowcasting (such as 0–1 hr forecasts) is a socioeconomic imperative. While deep learning (DL) methods start to become more popular for nowcasting, they cannot meet the demand of accurate warning required for high‐impact storms because they have a weak ability to extract and characterize spatiotemporal features of storms at different scales. To fill this gap, here we introduce a flexible customized multi‐scale (CM) DL framework, which modifies the model structure for enhancing multi‐scale features and proposes a loss function for capturing spatiotemporal changes. Several experiments in the storm event imagery data set (SEVIR) show that the CM framework can promote the DL model to make better forecasts of storm shape and rainfall area, especially for heavy rainfall.
Key Points
A flexible attention module is proposed to capture complex multi‐scale variations from storms in deep learning models
The customized loss function facilitates models to learn the spatial and temporal variation of storms simultaneously
The framework can be flexibly applied in various models and significantly improve forecast quality, particularly for heavy rain
•Evaluate uncertainties of hydrological parameters in WRF-Hydro simulations.•The pedo-transfer function (PTF) based ensemble is used to estimate 3D SHPs.•The 3D soil hydraulic parameters (SHP) are ...important as scaling parameters.
Parameter calibration and uncertainty estimation are crucial for hydrological simulations in the distributed land surface-hydrological model. To investigate soil properties impacting hydrological processes, five conventional pedo-transfer functions (PTFs) are applied to create a 3D soil hydraulic parameter (SHP) ensemble in the Weather Research and Forecasting-Hydrological extension (WRF-Hydro), a distributed, multi-physics land surface hydrological model. The SHPs are generated, based on a high-resolution Chinese soil property dataset, over the heterogeneous Upper Huaihe River basin. The results show that the SHPs can influence the streamflow in WRF-Hydro, which is similar to the impact of the scaling parameters on the streamflow over the study basin. Analyses of the uncertainty in the SHP ensemble reveal that SHPs mainly constrain the peak flow during the flood rise and impact the baseflow during the flood recession. A hydrological Bayesian model average (BMA) method is constructed to postprocess the streamflow ensemble based on the 3D SHPs. Probabilistic streamflow estimations by the BMA method are more skillful than the simulations using the individual 3D SHP ensemble members for all five studied hydrological stations, especially for high flows. Therefore, improved estimation of the uncertainty in the 3D SHPs may enhance the spatial representation of flood processes, resulting in more accurate estimates of the streamflow in the main streams in a heterogeneous basin.
Increasing spatial and temporal resolution of numerical models continues to propel progress in hydrological sciences, but, at the same time, it has strained the ability of modern automatic ...calibration methods to produce realistic model parameter combinations for these models. This paper presents a new reliable and fast automatic calibration framework to address this issue. In essence, the proposed framework, adopting a divide and conquer strategy, first partitions the parameters into groups of different resolutions based on their sensitivity or importance, in which the most sensitive parameters are prioritized with the highest resolution in the parameter search space, while the least sensitive ones are initially explored with the coarsest resolution. This is followed by an optimization‐based iterative calibration procedure consisting of a series of subtasks or runs. Between consecutive runs, the setup configuration is heterogeneous with parameter search ranges and resolutions varying among groups. At the completion of each subtask, the parameter ranges within each group are systematically refined from their previously estimated ranges, which are initially based on a priori information. Parameters attain stable convergence progressively with each run. A comparison of this new calibration framework with a traditional optimization‐based approach was performed using a quasi‐synthetic double‐model setup experiment to calibrate 134 parameters and two well‐known distributed hydrological models: the Variable Infiltration Capacity (VIC) model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The results demonstrate statistically that the proposed framework can better mitigate equifinality problem, yields more realistic model parameter estimates, and is computationally more efficient.
Key Points
A novel framework is developed for automatic calibration of computationally demanding hydrological models with a large number of parameters
The framework alleviates equifinality, and calibrated parameters achieve more reasonable values that better correspond to physical reality
The framework leads to faster improvement and smoother convergence to optimal objective values and is tested with 134 calibration parameters
The summer precipitation over the southeastern Tibetan Plateau (SETP) exhibits significant diurnal variability, with a notable peak before midnight. This study investigates how thermally forced ...circulations regulate the diurnal precipitation over the SETP, using surface rain‐gauge observations and high‐resolution reanalysis data from June to August during 2015–2019. Water vapor supply and precipitation onset in the afternoon are associated with the upslope winds and upward motions of the mountain–plains solenoid (MPS) between the SETP and nearby lowlands (the Sichuan Basin and Yunnan–Guizhou Plateau). After sunset, the sustained easterly and stronger southerly winds below 500 hPa transport abundant moisture from the rainy lowlands to the SETP. The findings suggest that these winds are mainly due to the larger zonal continental‐scale thermally forced circulation combined with the meridional large‐scale MPS before midnight and the meridional small‐scale MPS after midnight.
Plain Language Summary
The summer rainfall over the southeastern Tibetan Plateau (SETP) is crucial to the hydrological cycle of both the SETP and nearby regions. However, it remains unclear why the rainfall over the SETP mostly occurs at night due to limited observations. This study aims to investigate the role of thermally driven winds in diurnal rainfall during summer (June–August) over the SETP, using high‐resolution reanalysis data and surface rain‐gauge observations. The results show that the strong upward motions and low‐level upslope winds of the thermally forced mountain‐valley wind systems (MVWS) between the SETP and the lowlands (Sichuan Basin and Yunnan–Guizhou Plateau) help to trigger the precipitation and transport moisture in the afternoon. After sunset, thermally forced circulations, including the east–west continental‐scale thermal circulation between the Asian continent and its adjacent ocean, the north–south large‐scale MVWS between the Yunnan–Guizhou Plateau and its southern plain, and the north–south small‐scale MVWS between the SETP and its southern slope, help maintain horizontal winds from the rainy lowlands, transporting abundant moisture to facilitate the rainfall over the SETP at night.
Key Points
Thermally forced circulations greatly impact the diurnal cycle of summer precipitation over the southeastern Tibetan Plateau (SETP)
Daytime rainfall over this region is associated with the upward motions and upslope winds of the mountain–plains solenoid
Multi‐scale thermal circulations are responsible for moisture transport of nighttime rainfall over the SETP
Abstract
As offshore wind power is continuously integrated into the electric power systems in around the world, it is critical to understand its variability. Weather regimes (WRs) can provide ...meteorological explanations for fluctuations in wind power. Instead of relying on traditional large-scale circulation WRs, this study focuses on assessing the dependency of wind resources on WRs in the tailored region clustered based on the finer spatial scale. For this purpose, we have applied self-organizing map algorithm to cluster atmospheric circulations over the South China Sea (SCS) and characterized wind resources for the classified WRs. Results show that WRs at mesoscale can effectively capture weather systems driving wind power production variability, especially on multi-day timescale. Capacity factor reconstruction during four seasons illustrates that WRs highly influence most areas in winter and southern part of SCS in summer, and WRs can serve as a critical source of predicting the potential of wind resources. In addition, we further qualify the wind power intermittency and complementarity under different WRs, which have not been assessed associated with WRs. During WRs with changeable atmosphere conditions, the high complementarity over coastal areas can reduce the impact of intermittency on wind power generation. The proposed approach is able to be implemented in any region and may benefit wind resource evaluation and characterization.
•Heat convection by liquid water and latent heat is added into the soil model.•Grass growth can alter soil thermal conductivity and ground heat flux.•Thermal conductivity is significantly vertically ...heterogeneous for 0–5 cm soil.•Considering vertical heterogeneity and water vapor improves ground heat flux.•Change in ground cover influences spatial representativeness of ground heat flux.
Near-surface soil hydro-thermodynamics plays an important role in heat and water transfer between land and atmosphere. However, it is difficult to quantify these processes in field campaigns, such as movement of liquid water and water vapor. This has affected determination of ground heat flux, a key component of surface energy budget. This study aims to quantify soil heat and water processes utilizing in situ measurements, and to improve ground heat flux estimation and surface energy imbalance. A new model has been proposed based on three main physical processes, including thermal conduction, convection of heat by moving water, and convection of latent heat. The model was tested at a grassland site in Colorado, USA. Results show that the model can capture high values of soil water content during rain events, low values under intense solar radiation, and high-frequency fluctuations under intermittent cloudy conditions. Also, the model produces reasonable vertical velocity and mass flux of water vapor. For the estimation of ground heat flux, the method that considers vertical variation of soil thermal conductivity and the contribution of water vapor gives better energy balance ratio than methods that ignore these conditions. In spite of this, the energy balance ratio is still low. Footprint analysis further shows the seasonal variation of ground cover is a potential reason responsible for this.
The Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China.
This study evaluates three quantitative ...precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model.
APHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.
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•Compare quantitative precipitation estimates (QPEs) over the Ganjiang River basin.•Quantitatively evaluate hydrological ensemble simulations forced by three QPEs.•QPEs based on high-density gauge stations improve hydrological simulations.•Satellite-gauge merged QPEs provide valuable inputs over sparsely gauged basins.
•Three traditional methods to address heteroscedasticity are compared.•A combined approach to address the complicated heteroscedasticity is proposed.•The combined approach suits the basin and can ...effectively avoid the negative flows.
The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.
The study analyzes the consistency of future precipitation extremes projected over China under moderate climate scenarios (RCP4.5 and SSP2-4.5) and extreme climate scenarios (RCP8.5 and SSP5-8.5), ...based on best models' ensembles for each extreme index (best-MME), best models' ensembles regarding all indices (best-rankMME), and multi-model ensembles (MME). To select the best models, the integrated quadratic distance (IQD) combined with the Taylor skill score are used. This evaluation is a practical example of the benefits of using IQD to evaluate the models compared with root mean square error since the former is not sensitive to the similarity of the distribution's averages. All ensembles in each scenario consistently project the decrease of consecutive dry days (CDD) periods in western and northeast China, the increase of the number of days with heavy precipitation (R10mm) over the Qinghai-Tibet Plateau, and the increase of the precipitation intensity (SDII) in most of the Chinese territory. The moderate and extreme scenarios project very similar climate change patterns but more extreme scenarios show greater climate change signal magnitudes. In the case of the moderate scenarios, the most significant uncertainty relies on the CDD projections over southeastern China, where best-rankMME and MME project increasing CDD in the future whilst best-MME project decreasing CDD, although these changes are not statistically significant. Regarding extreme scenarios, the most significant uncertainty relies on the R10mm projections over southeastern China, where the best-MME CMIP5 projects decreasing R10mm (not statistically significant), whilst all the remaining ensembles project increasing values.
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