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Paolo Reggiani
and
Oleksiy Boyko

Abstract

We study the impact of uncertain precipitation estimates on simulated streamflows for the poorly gauged Yarlung Tsangpo basin (YTB), High Mountain Asia (HMA). A process-based hydrological model at 0.5 km resolution is driven by an ensemble of precipitation estimation products (PEPs), including analyzed ground observations, high-resolution precipitation estimates, climate data records and reanalyses over the 2008-2015 control period. The model is then forced retrospectively from 1983 onward to obtain seamless discharge estimates till 2007, a period for which there is very sparse flow data coverage. Whereas temperature forcing is considered deterministic, precipitation is sampled from the predictive distribution, which is obtained through processing PEPs by means of a probabilisitc processor of uncertainty. The employed Bayesian processor combines the PEPs and outputs the predictive densities of daily precipitation depth accumulation as well as the probability of precipitation occurrence, from which random precipitation fields for probabilistic model forcing are sampled. The predictive density of precipitation is conditional on the precipitation estimation predictors that are bias-corrected and variance adjusted. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at three flow gauging points, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed observed precipitation and merged products, which were corrected against ground observations. The applied methodology indicates how missing flows for poorly gauged sites can be retrieved and is further extendable to hydrological projections of climate.

Open access
Jiayi Lu
,
Kaicun Wang
,
Guocan Wu
, and
Yuna Mao

Abstract

The spatiotemporal characteristics of extreme precipitation intensity are crucial for hydroclimatic studies. This study delineates the spatiotemporal distribution features of extreme precipitation intensity across China from 2001 to 2019 using the gridded daily precipitation dataset CN05.1, constructed from an observation network of over 2400 stations. Furthermore, we evaluate the reliability of 12 widely used precipitation datasets (including gauge-based, satellite retrieval, reanalysis, and fusion products) in monitoring extreme precipitation events. Our findings indicate the following: 1) CN05.1 reveals a consistent spatial distribution characterized by a decline in extreme precipitation intensity from the southeastern coastal regions toward the northwestern inland areas of China. From 2001 to 2019, more pronounced declining intensity trends are discernible in the northern and southwestern regions of China, whereas marked increasing trends manifest in the northeastern and the Yangtze River plain regions. National mean extreme precipitation indices consistently exhibit significant increasing trends throughout China. 2) Datasets based on station observations generally exhibit superior applicability concerning spatiotemporal distribution. 3) Multisource weighted precipitation fusion products effectively capture the temporal variability of extreme precipitation indices. 4) Satellite retrieval datasets exhibit notable performance disparities in representing various intensity indices. Most products tend to overestimate the increasing trends of national mean intensity indices. 5) Reanalysis datasets tend to overestimate extreme precipitation indices, and inadequately capture the trends. ERA5 and JRA-55 underestimate trends, while CFSR and MERRA-2 significantly overestimate the trends. These findings serve as a basis for selecting reliable precipitation datasets for extreme precipitation and hydrological simulation research in China.

Significance Statement

Extreme precipitation events have increasingly become more widespread, posing significant threats to human lives and property. Accurately understanding the spatiotemporal patterns of these events is imperative for effective mitigation. Despite the proliferation of precipitation products, their capacity to faithfully represent extreme events remains inadequately validated. In this study, we utilize a gauge-based dataset derived from over 2400 gauge stations across China to investigate the spatiotemporal changes in extreme precipitation events from 2001 to 2019. Subsequently, we conduct a rigorous evaluation of 12 widely used precipitation datasets to assess their efficacy in depicting extreme events. The results of this research offer valuable insights into the strengths and weaknesses of various precipitation products in depicting extreme events.

Restricted access
R. D. Koster
,
A. F. Feldman
,
T. R. H. Holmes
,
M. C. Anderson
,
W. T. Crow
, and
C. Hain

Abstract

Evapotranspiration has long been understood to vary with soil moisture in drier regions and to be relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability.

Significance Statement

When a soil is somewhat dry, an increase in soil moisture can lead to an increase in evapotranspiration E. In contrast, when a soil is wet, E is limited instead by the availability of energy. Determining where E is water limited, energy limited, or some combination of both is important because it tells us where accurate soil moisture initialization in a forecast system might contribute to more accurate forecasts of E and thus air temperature. Here we use a combination of independent datasets (satellite-derived estimates of soil moisture and E as well as air temperature measurements from weather stations) to provide new monthly maps of the water-limited, energy-limited, and combination regimes over the continental United States and across the world.

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Xiaodong Hong
and
Qingfang Jiang

Abstract

The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

Open access
Yiping Yu
,
Ling Zhang
,
Liuxian Song
,
Wei Li
,
Lu Zhou
, and
Lin Ouyang

Abstract

Using high-resolution hourly precipitation data and ERA5 reanalysis data, this study employs the K-means method to categorize 32 cases of warm-sector heavy rainfall events accompanied by a warm-type shear line (WSWR) along the Yangtze–Huaihe coastal region (YHCR) from April to September during 2010–17. Considering the synoptic system features of WSWR by K means, the result reveals 15 southwest type (SW-type) and 17 south-biased type (S-type) WSWR events. Composite analysis illuminates the distinct dynamic and thermodynamic features of each type. For the SW-type WSWR, the maximum value of water vapor is concentrated around 850 hPa in the lower troposphere. The YHCR is located at the intersection of the exit area of the 850-hPa synoptic low-level jet (LLJ) and the entrance area of the 600-hPa jet. The suction effects, combined with the location of YHCR on the left side of the boundary layer jet (BLJ), facilitate the triggering of local convection. Conversely, the S-type WSWR shows peak water vapor in the boundary layer. Before the onset of WSWR events, a warm, humid tongue indicated by pseudoequivalent potential temperature θ se is present in the boundary layer, signified by substantial unstable energy. The BLJ aids mesoscale ascent on its terminus, enhancing convergence along the coastline. The BLJ also channels unstable energy and water vapor to the YHCR, causing significant rainfall. Typical case studies of both types show similar environmental backgrounds. The scale analysis shows mesoscales of dynamic field are crucial in shaping both types of WSWR, while the large-scale and meso-α-scale dynamic field facilitate the transportation of moist and warm airflow.

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Anju Vijayan Nair
,
Sungwook Wi
,
Rijan Bhakta Kayastha
,
Colin Gleason
,
Ishrat Dollan
,
Viviana Maggioni
, and
Efthymios I. Nikolopoulos

Abstract

Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets (IMERG, CHIRPS, ERA5 Land, and APHRODITE) and two glaciohydrological models (GDM and HYMOD_DS) are evaluated for the Marsyangdi and Budhigandaki river basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5 Land overestimate the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimates by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, icemelt, rainfallrunoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments.

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Shanshan Li
,
Xiaofang Wang
,
Jianhua Sun
,
Zheng Ma
,
Yuanchun Zhang
,
Yuan Gao
,
Yang Hu
, and
Wengang Zhang

Abstract

Convection initiations (CIs) observed using the advanced geosynchronous radiation imager on the Chinese Fengyun-4A satellite were identified over the middle reaches of the Yangtze River basin during warm season (May–September) of 2018–21. A hybrid objective tracking algorithm combining the conventional area overlapping with the Kalman filter method was applied. Subsequently, spatial and temporal variations in the identified CIs and their synoptic circulation patterns were analyzed. The frequency of CIs was highest in August and lowest in May. Nearly 81% of CIs occurred during noon–afternoon (1100–1859 LST), with the highest frequency in the southern mountains of the study region, whereas the CIs with relatively low frequency moved to the plains from afternoon to morning (1700–1059 LST). The diurnal variation of CIs throughout the study region exhibited a unimodal structure, with a peak appearing at noon (1200–1259 LST). CIs during noon–afternoon in July and August had faster cloud-top cooling rates. The synoptic circulations without tropical cyclones during noon–afternoon hours were classified into four patterns by hierarchical clustering; two dominant patterns (i.e., SW-Flows and S-Flows) had broader areas of higher most unstable convective available potential energy (MUCAPE), whereas the 0–3-km shear (SHR3) was the weakest in the S-Flows pattern. It was clear that the high-frequency areas of CIs were most likely to occur in stronger MUCAPE and weaker SHR3 environments, and CIs were more controlled by thermally unstable environments. We further illustrated that CIs tend to concentrate in unstable and moisture flux convergence areas affected by mountains.

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Hui Guo
,
Ying Hou
,
Yuting Yang
, and
Tim R. McVicar

Abstract

Macroscale hydrological/land surface models are important tools for assessing historical and predicting future characteristics of extreme hydrological events, yet quantitative understandings of how these large-scale models perform in simulating extreme hydrological characteristics remain limited. Here we evaluate simulated high and low flows from 23 macroscale models within three modeling experiments (i.e., 14 climate models from CMIP6, 6 global hydrological models from ISIMIP2a, and 3 land surface models from GLDAS) against observation in 633 unimpaired catchments globally over 1971–2010. Our findings reveal limitations in simulating extreme flow characteristics by these models. Specifically, we find that (i) most models overestimate high-flow magnitudes (bias range: from +15% to +70%) and underestimate low-flow magnitudes (bias range: from −80% to −20%); (ii) interannual variability in high and low flows is reasonably reproduced by ISIMIP2a and GLDAS models but poorly reproduced by CMIP6 models; (iii) no model consistently replicates the observed trend direction in high and low flows in over two-thirds of the catchments, and most models overestimate high-flow trends and underestimate low-flow trends; and (iv) CMIP6 and GLDAS models show timing biases, with early high flows and late low flows, while ISIMIP2a models exhibit the opposite pattern. Furthermore, all models performed better in more humid environments and noncold regions, with model structure and parameterization contributing more to uncertainties than climatic forcings. Overall, our results demonstrate that extreme flow characteristics simulated from current state-of-the-art macroscale models still contain large uncertainties and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs.

Significance Statement

Macroscale hydrological and land surface models represent crucial tools for assessing historical trends and making predictions about future hydrological changes. Nevertheless, our current understanding of the quantitative performance of these large-scale models in simulating extreme hydrological characteristics remains limited. Here, we evaluate simulated high and low flows from 23 state-of-the-art macroscale models against observation in 633 unimpaired catchments globally over 1971–2010. Our results reveal important limitations in the extreme flow characteristics simulated from these models and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. The model evaluation performed herein serves as a pivotal, offering valuable insights to inform the development of the next generation of macroscale hydrological and land surface models.

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Adrien Pierre
,
Daniel F. Nadeau
,
Antoine Thiboult
,
Alain N. Rousseau
,
François Anctil
,
Charles P. Deblois
,
Maud Demarty
,
Pierre-Erik Isabelle
, and
Alain Tremblay

Abstract

The hydrological processes of cascading hydroelectric reservoirs differ from those of lakes, due to the importance of the inflows and outflows that vary with energy demand. These heat and water advection terms are rarely considered in water body energy balance analyses even though reservoirs are common man-made structures, especially in North America, and thus may affect the regional climate. This study provides a comprehensive assessment of the water and energy balance of the 85-km2 Romaine-2 northern reservoir (50.69°N, 63.24°W), mean depth of 44 m, highlighting the significant contribution of the advection heat fluxes. The water balance input was primarily controlled by upstream (turbine) inflows (77.6%), while lateral (natural) inflows and direct precipitation represented 21.2% and 1.2%, respectively. As for the reservoir’s heat budget, the net advection of heat accounted on average for 25.0% of the input, of which net radiation was the largest component (73.3%). After accounting for the absence of energy balance closure, latent heat and sensible heat fluxes represented 73.2% and 25.1% of total energy output from the reservoir, respectively. The thermal regime was influenced by the hydrological flow conditions, which were regulated by reservoir management. This played a major role in the evolution of the thermocline and the temperature of the epilimnion, and ultimately, in the dynamics of the turbulent heat fluxes. This study suggests that the heat advection term represents a large fraction of the heat budget of northern reservoirs and should be properly considered.

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Haochen Tan
,
Rao Kotamarthi
, and
Pallav Ray

Abstract

The surface sensible heat flux induced by precipitation (QP ) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components.

Open access