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Nergui Nanding, Huan Wu, Jing Tao, Viviana Maggioni, Hylke E. Beck, Naijun Zhou, Maoyi Huang, and Zhijun Huang

Abstract

This study characterizes precipitation error propagation through a distributed hydrological model based on the river basins across the contiguous United States (CONUS), to better understand the relationship between errors in precipitation inputs and simulated discharge (i.e., PQ error relationship). The NLDAS-2 precipitation and its simulated discharge are used as the reference to compare with TMPA-3B42 V7, TMPA-3B42RT V7, Stage IV, CPC-U, MERRA-2, and MSWEP V2.2 for 1548 well-gauged river basins. The relative errors in multiple conventional precipitation products and their corresponding discharges are analyzed for the period of 2002–13. The results reveal positive linear PQ error relationships at annual and monthly time scales, and the stronger linearity for larger temporal accumulations. Precipitation errors can be doubled in simulated annual accumulated discharge. Moreover, precipitation errors are strongly dampened in basins characterized by temperate and continental climate regimes, particularly for peak discharges, showing highly nonlinear relationships. Radar-based precipitation product consistently shows dampening effects on error propagation through discharge simulations at different accumulation time scales compared to the other precipitation products. Although basin size and topography also influence the PQ error relationship and propagation of precipitation errors, their roles depend largely on precipitation products, seasons, and climate regimes.

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Ryan A. Zamora, Benjamin F. Zaitchik, Matthew Rodell, Augusto Getirana, Sujay Kumar, Kristi Arsenault, and Ethan Gutmann

Abstract

Research in meteorological prediction on subseasonal to seasonal (S2S) time scales has seen growth in recent years. Concurrent with this growth, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low-resolution global models, whereas the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the generalized analog regression downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model to forecast U.S. Drought Monitor–style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts with hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies but that the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the contiguous United States, and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

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Yanni Zhao, Rensheng Chen, Chuntan Han, Lei Wang, Shuhai Guo, and Junfeng Liu

Abstract

A precipitation observation network was constructed in a high-altitude area of the Qilian Mountains in northwest China, which contained 23 sets of instruments. Because 21 sets of instruments were surrounded by protective fences, a precipitation intercomparison experiment was carried out at the highest station (4651 m) of the observation network with the same configuration to study the impact of wind on precipitation measurements under this configuration and to develop a correction method suitable for the entire network. The 30-min measured precipitation from June 2018 to October 2019 was corrected by the World Meteorological Organization Solid Precipitation Intercomparison Experiment transfer functions, and their parameters were recalibrated with a local dataset. The results showed that the transfer functions fitted to the local dataset had a better performance than those using original parameters. Because of the influence of the experimental configuration on wind speed and direction, the root-mean-square error (RMSE) corrected with the original parameters increased by an average of 86%, but the RMSE adjusted by the new transfer functions decreased by 9%. Moreover, the resultant biases using new transfer functions were close to 0. The new coefficients of snowfall were derived by local measurements and applied to datasets from other sites within the Qilian Mountains observation network to evaluate their performances. The corrected snowfall at 21 stations increased by an average of 32.6 mm, and the relative precipitation increment ranged from 6% to 26%. This method can be used to correct snowfall in the observation network under the nonstandard site configuration.

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Fidele Karamage, Yuanbo Liu, and Yongwei Liu

Abstract

The availability of streamflow records in Africa has been declining since the 1980s due to malfunctioning gauging stations and data collection failures. Africa also has insufficient hydrological information owing to the allocation of few resources to research efforts. Unreliable runoff datasets and large uncertainties in runoff trends due to climate change patterns and human activities are major challenges to water resource management in Africa. Therefore, this study aimed to improve runoff estimates and to assess runoff trend responses to climate change and human activities in Africa during 1981–2016. Using statistical methods, monthly gridded runoff datasets were generated for the period of 1981–2016 from a modified runoff curve number method calibrated with river discharge data from 535 gauging stations. According to the cross-validation results, the constructed runoff datasets comprised the Nash and Sutcliffe coefficients ranging from 0.5 to 1, coefficients of determination ranging from 0.5 to 1, and percent biases between ±25% for a large number of stations up to 73%, 80%, and 91% of the 535 gauged catchments used as references. Analysis of runoff trend responses to climate change and human activities revealed that land cover change contributed more (72%) to the observed net runoff change (0.30% a−1) than continental climate changes (28%). These contributions were results of cropland expansion rate of 0.46% a−1 and a precipitation increase of 0.07% a−1. The performance and simplicity of the statistical methods used in this study could be useful for improving runoff estimations in other regions with limited streamflow data. The results of the current study could be important to natural resource managers and decision-makers in terms of raising awareness of climate change adaptation strategies and agricultural land-use policies in Africa.

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Ryann A. Wakefield, David D. Turner, and Jeffrey B. Basara

Abstract

Land–atmosphere feedbacks are a critical component of the hydrologic cycle. Vertical profiles of boundary layer temperature and moisture, together with information about the land surface, are used to compute land–atmosphere coupling metrics. Ground-based remote sensing platforms, such as the Atmospheric Emitted Radiance Interferometer (AERI), can provide high-temporal-resolution vertical profiles of temperature and moisture. When collocated with soil moisture, surface flux, and surface meteorological observations such as at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site, it is possible to observe both the terrestrial and atmospheric legs of land–atmosphere feedbacks. In this study, we compare a commonly used coupling metric computed from radiosonde-based data with that obtained from the AERI to characterize the accuracy and uncertainty in the metric derived from the two distinct platforms. This approach demonstrates the AERI’s utility where radiosonde observations are absent in time and/or space. Radiosonde- and AERI-based observations of the convective triggering potential and low-level humidity index (collectively referred to as CTP-HIlow) were computed during the 1200 UTC observation time and displayed good agreement during both the 2017 and 2019 warm seasons. Radiosonde- and AERI-derived metrics diagnosed the same atmospheric preconditioning based upon the CTP-HIlow framework a majority of the time. When retrieval uncertainty was considered, even greater agreement was found between radiosonde- and AERI-derived classification. The AERI’s ability to represent this coupling metric well enabled novel exploration of temporal variability within the overnight period in CTP and HIlow. Observations of CTP-HIlow computed within a few hours of 1200 UTC were essentially equivalent; however, with greater differences in time there arose greater differences in CTP and HIlow.

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He Sun, Fengge Su, Zhihua He, Tinghai Ou, Deliang Chen, Zhenhua Li, and Yanping Li

Abstract

In this study, two sets of precipitation estimates that are based on the regional Weather Research and Forecasting (WRF) Model—the high Asia refined analysis (HAR) and outputs with a 9-km resolution from WRF (WRF-9km)—are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20%–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias from −11% to 3%), whereas the HAR performs well in the upper Indus and upper Brahmaputra basins (with NSE of 0.6 and bias from −15% to −9%). Both of the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.

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Dayal Wijayarathne, Paulin Coulibaly, Sudesh Boodoo, and David Sills

Abstract

Flood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar quantitative precipitation estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. First, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Second, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE’s effects on streamflow simulation accuracy. Third, flood extent maps were produced using coupled hydrological–hydraulic models integrated within the Hydrologic Engineering Center Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar–gauge merging obtained a Kling–Gupta efficiency, modified peak flow criterion, Nash–Sutcliffe efficiency, and volume error improvement of about +0.42, +0.12, +0.78, and −0.23, respectively, for S-band and +0.64, +0.36, +1.12, and −0.34, respectively, for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

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Yalei You, Christa Peters-Lidard, S. Joseph Munchak, Jackson Tan, Scott Braun, Sarah Ringerud, William Blackwell, John Xun Yang, Eric Nelkin, and Jun Dong

Abstract

Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) onboard the NPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective vs. stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multi-satellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers onboard.

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Guofeng Zhu, Zhuanxia Zhang, Huiwen Guo, Yu Zhang, Leilei Yong, Qiaozhuo Wan, Zhigang Sun, and Huiying Ma

Abstract

As raindrops fall from the cloud base to the ground, evaporation below those clouds affects the rain’s isotope ratio, reduces precipitation in arid areas and impacts the local climate. Therefore, in arid areas with scarce water resources and fragile ecological environments, the below-cloud evaporation is an issue of great concern. Based on 406 event-based precipitation samples collected from 9 stations in the Shiyang river basin (SRB) in the northwest arid area, GMWL and LMWL are compared and the Stewart model is used to study the effect of spatial and temporal variation of below-cloud evaporation on isotope values in different geomorphic units at the SRB. Furthermore, factors influencing below-cloud evaporation are analyzed. The results show that (1) the change of d-excess (Δd) in precipitation at the SRB and the residual ratio of raindrop evaporation (f) vary in time and space. With regards to temporal variation, the intensity of below-cloud evaporation is described by: summer < autumn < winter < spring. Regarding spatial variation, the below-cloud evaporation in mountain areas is weaker than in oases and deserts. The intensity of below-cloud evaporation in mountain areas increases with decreasing altitude, and the below-cloud evaporation in oasis and desert areas is affected by local climatic conditions. (2) Below-cloud evaporation is also affected by local transpiration evaporation, especially around reservoirs. Reservoirs increase the relative humidity of the air nearby, weakening below-cloud evaporation. This study deepens our understanding of the water cycle process in arid areas.

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Yanzhong Li, Di Tian, and Hanoi Medina

Abstract

This study assessed multi-model subseasonal precipitation forecasts (SPFs) from eight subseasonal experiment (SubX) models over the contiguous United States (CONUS) and explored the generalized extreme value distribution (GEV)-based ensemble model output statistics (EMOS) framework for postprocessing multi-model ensemble SPF. The results showed that the SubX SPF skill varied by location and season, and the skill were relatively high in the western coastal region, north-central region, and Florida peninsula. The forecast skill was higher during winter than summer seasons, especially for lead week 3 in the northwest region. While no individual model consistently outperformed the others, the simple multi-model ensemble (MME) demonstrated a higher skill than any individual model. The GEV-based EMOS approach dramatically improved the MME subseasonal precipitation forecast skill at long lead times. The continuous ranked probability score (CRPS) was improved by approximately 20% in week 3 and 43% in lead week 4; the 5-mm Brier skill score (BSS) was improved by 59.2% in lead week 3 and 50.9% in lead week 4, with the largest improvements occurring in the northwestern, north-central, and southeastern CONUS. Regarding the relative contributions of the individual SubX model to the predictive skill, the NCEP model was given the highest weight at the shortest lead time, but the weight decreased dramatically with the increase in lead time, while the CESM, EMC, NCEP, and GMAO models were given approximately equal weights for lead weeks 2-4. The presence of active MJO conditions notably increased the forecast skill in the north-central region during weeks 3-4, while the ENSO phases influenced the skill mostly in the southern regions.

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