Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2969-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2969-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2022
Research article |
| 15 Jun 2022
| 15 Jun 2022
A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and regression over China
Huajin Lei
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
Hongyu Zhao
State Key Laboratory of Earth Surface Processes and Resource
Ecology, Beijing Normal University, Beijing 100875, China
Tianqi Ao
CORRESPONDING AUTHOR
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
Related authors
No articles found.
Xingyu Zhou, Lunwu Mou, Tianqi Ao, Xiaorong Huang, and Haiyang Yang
EGUsphere, https://doi.org/10.5194/egusphere-2024-404, https://doi.org/10.5194/egusphere-2024-404, 2024
Short summary
Short summary
According to the analysis of terrain attributes, using the DSM obtained by drone to conduct a mountainous urban fluvial flood modelling, the resolution of the DSM used should be kept within 1 m to 5 m. However, if larger mountainous cities were involved, in the case of non-extreme discharges, considering the cost of processing, using a resolution of 5 m to 10 m could also meet requirements in terms of inundation area drawing, but there could be a possibility of overestimation of flood depth.
Lingxue Liu, Tianqi Ao, and Li Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2022-452, https://doi.org/10.5194/egusphere-2022-452, 2022
Preprint archived
Short summary
Short summary
Soil moisture (SM) initial conditions of the hydrological model, usually obtained from the model warm-up, significantly impact the simulation efficiency. In the case of no warm-up phase, this paper proposes a methodology to fill the gap via obtaining the SM initial conditions using an alternative global dataset. It validates that warm-up is necessary for the model calibration with default initial conditions, and well-utilization of the processed ERA5-Land could skip warm-up effectively.
Xiaohua Hao, Guanghui Huang, Zhaojun Zheng, Xingliang Sun, Wenzheng Ji, Hongyu Zhao, Jian Wang, Hongyi Li, and Xiaoyan Wang
Hydrol. Earth Syst. Sci., 26, 1937–1952, https://doi.org/10.5194/hess-26-1937-2022, https://doi.org/10.5194/hess-26-1937-2022, 2022
Short summary
Short summary
We develop and validate a new 20-year MODIS snow-cover-extent product over China, which is dedicated to addressing known problems of the standard snow products. As expected, the new product significantly outperforms the state-of-the-art MODIS C6.1 products; improvements are particularly clear in forests and for the daily cloud-free product. Our product has provided more reliable snow knowledge over China and can be accessible freely https://dx.doi.org/10.11888/Snow.tpdc.271387.
Xiaohua Hao, Guanghui Huang, Tao Che, Wenzheng Ji, Xingliang Sun, Qin Zhao, Hongyu Zhao, Jian Wang, Hongyi Li, and Qian Yang
Earth Syst. Sci. Data, 13, 4711–4726, https://doi.org/10.5194/essd-13-4711-2021, https://doi.org/10.5194/essd-13-4711-2021, 2021
Short summary
Short summary
Long-term snow cover data are not only of importance for climate research. Currently China still lacks a high-quality snow cover extent (SCE) product for climate research. This study develops a multi-level decision tree algorithm for cloud and snow discrimination and gap-filled technique based on AVHRR surface reflectance data. We generate a daily 5 km SCE product across China from 1981 to 2019. It has high accuracy and will serve as baseline data for climate and other applications.
Hongbo Zhang, Tianqi Ao, Maksym Gusyev, Hiroshi Ishidaira, Jun Magome, and Kuniyoshi Takeuchi
Proc. IAHS, 379, 323–333, https://doi.org/10.5194/piahs-379-323-2018, https://doi.org/10.5194/piahs-379-323-2018, 2018
Short summary
Short summary
During my study period from master to doctor, I have been studying in the field of water resources and hydrology and water quality. Especially in my doctor-study period, I would like to develop a non-point source pollution model based on the BTOPMC model and I did it successfully although the model is so simple that its performance is not good. This experience makes me start my period of combining the hydrological model with water quality and I think I will continue the work in this area.
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Assessing rainfall radar errors with an inverse stochastic modelling framework
Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution
Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy
An increase in the spatial extent of European floods over the last 70 years
140-year daily ensemble streamflow reconstructions over 661 catchments in France
The agricultural expansion in South America's Dry Chaco: regional hydroclimate effects
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Improving runoff simulation in the Western United States with Noah-MP and variable infiltration capacity
Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France
Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)
Global total precipitable water variations and trends over the period 1958–2021
Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends
Identification of compound drought and heatwave events on a daily scale and across four seasons
Potential for historically unprecedented Australian droughts from natural variability and climate change
Review of Gridded Climate Products and Their Use in Hydrological Analyses Reveals Overlaps, Gaps, and Need for More Objective Approach to Model Forcings
Flood risk assessment for Indian sub-continental river basins
Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities
Divergent future drought projections in UK river flows and groundwater levels
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
Hydroclimatic processes as the primary drivers of the Early Khvalynian transgression of the Caspian Sea: new developments
Accounting for hydroclimatic properties in flood frequency analysis procedures
Understanding the influence of “hot” models in climate impact studies: a hydrological perspective
Downscaling precipitation over High Mountain Asia using Multi-Fidelity Gaussian Processes: Improved estimates from ERA5
A semi-parametric hourly space–time weather generator
A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics
Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model
Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely-sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates
Investigating the response of land–atmosphere interactions and feedbacks to spatial representation of irrigation in a coupled modeling framework
Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia
Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks
Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US
Hybrid forecasting: blending climate predictions with AI models
Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin
Local moisture recycling across the globe
How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?
Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany
The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System
Spatial distribution of oceanic moisture contributions to precipitation over the Tibetan Plateau
Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India
Declining water resources in response to global warming and changes in atmospheric circulation patterns over southern Mediterranean France
Linking the complementary evaporation relationship with the Budyko framework for ungauged areas in Australia
Risks of seasonal extreme rainfall events in Bangladesh under 1.5 and 2.0 °C warmer worlds – how anthropogenic aerosols change the story
Pan evaporation is increased by submerged macrophytes
Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
Short summary
Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024, https://doi.org/10.5194/hess-28-4455-2024, 2024
Short summary
Short summary
We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, https://doi.org/10.5194/hess-28-4361-2024, 2024
Short summary
Short summary
Our findings show that runoff in the Yarlung Zangbo (YZ) basin is primarily driven by rainfall, with the largest glacier runoff contribution in the downstream sub-basin. Annual runoff increased in the upper stream but decreased downstream due to varying precipitation patterns. It is expected to rise throughout the 21st century, mainly driven by increased rainfall.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
Short summary
Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
Short summary
Short summary
The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
Short summary
Short summary
We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
Short summary
Short summary
Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
María Agostina Bracalenti, Omar V. Müller, Miguel A. Lovino, and Ernesto Hugo Berbery
Hydrol. Earth Syst. Sci., 28, 3281–3303, https://doi.org/10.5194/hess-28-3281-2024, https://doi.org/10.5194/hess-28-3281-2024, 2024
Short summary
Short summary
The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
Short summary
Short summary
Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
Short summary
Short summary
We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
Short summary
Short summary
The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
Short summary
Short summary
Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
Short summary
Short summary
Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
Short summary
Short summary
This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
Short summary
Short summary
This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
Short summary
Short summary
Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-58, https://doi.org/10.5194/hess-2024-58, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We assess 60 gridded climate datasets [ground- (G), satellite- (S), reanalysis-based (R)]. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; but R outperformed G when underlying data had low station density. G outperformed S or R datasets, though better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
Short summary
Short summary
A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
Short summary
Short summary
We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
Short summary
Short summary
We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
Short summary
Short summary
Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
Short summary
Short summary
Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
Short summary
Short summary
Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2145, https://doi.org/10.5194/egusphere-2023-2145, 2023
Short summary
Short summary
This work aims to improve the understanding of precipitation patterns in High Mountain Asia, a crucial water source for around 2 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows comparable or better accuracy to existing benchmark datasets.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
Short summary
Short summary
Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
Short summary
Short summary
Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
Short summary
Short summary
High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
Peter E. Levy and the COSMOS-UK team
EGUsphere, https://doi.org/10.5194/egusphere-2023-2041, https://doi.org/10.5194/egusphere-2023-2041, 2023
Short summary
Short summary
Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely-sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach which is closely linked to direct measurements of soil moisture at a network sites across the UK, and to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212), as well as to remotely-sensed satellite estimates.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
Short summary
Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu
Hydrol. Earth Syst. Sci., 27, 2919–2933, https://doi.org/10.5194/hess-27-2919-2023, https://doi.org/10.5194/hess-27-2919-2023, 2023
Short summary
Short summary
This study aims to investigate the performance of a genetic particle filter which was used as a snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
Short summary
Short summary
Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Marcos Julien Alexopoulos, Hannes Müller-Thomy, Patrick Nistahl, Mojca Šraj, and Nejc Bezak
Hydrol. Earth Syst. Sci., 27, 2559–2578, https://doi.org/10.5194/hess-27-2559-2023, https://doi.org/10.5194/hess-27-2559-2023, 2023
Short summary
Short summary
For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li
Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, https://doi.org/10.5194/hess-27-1945-2023, 2023
Short summary
Short summary
We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
Short summary
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
Short summary
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
Hydrol. Earth Syst. Sci., 27, 1771–1789, https://doi.org/10.5194/hess-27-1771-2023, https://doi.org/10.5194/hess-27-1771-2023, 2023
Short summary
Short summary
The goal of this study is to understand the uncertainties of different modeling configurations for simulating hydroclimate responses in the mountainous watershed. We run a group of climate models with various configurations and evaluate them against various reference datasets. This paper integrates a climate model and a hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
Jolanda J. E. Theeuwen, Arie Staal, Obbe A. Tuinenburg, Bert V. M. Hamelers, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 27, 1457–1476, https://doi.org/10.5194/hess-27-1457-2023, https://doi.org/10.5194/hess-27-1457-2023, 2023
Short summary
Short summary
Evaporation changes over land affect rainfall over land via moisture recycling. We calculated the local moisture recycling ratio globally, which describes the fraction of evaporated moisture that rains out within approx. 50 km of its source location. This recycling peaks in summer as well as over wet and elevated regions. Local moisture recycling provides insight into the local impacts of evaporation changes and can be used to study the influence of regreening on local rainfall.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
Short summary
Short summary
Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
Short summary
Short summary
Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, https://doi.org/10.5194/hess-27-501-2023, 2023
Short summary
Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
Short summary
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Ying Li, Chenghao Wang, Ru Huang, Denghua Yan, Hui Peng, and Shangbin Xiao
Hydrol. Earth Syst. Sci., 26, 6413–6426, https://doi.org/10.5194/hess-26-6413-2022, https://doi.org/10.5194/hess-26-6413-2022, 2022
Short summary
Short summary
Spatial quantification of oceanic moisture contribution to the precipitation over the Tibetan Plateau (TP) contributes to the reliable assessments of regional water resources and the interpretation of paleo archives in the region. Based on atmospheric reanalysis datasets and numerical moisture tracking, this work reveals the previously underestimated oceanic moisture contributions brought by the westerlies in winter and the overestimated moisture contributions from the Indian Ocean in summer.
Urmin Vegad and Vimal Mishra
Hydrol. Earth Syst. Sci., 26, 6361–6378, https://doi.org/10.5194/hess-26-6361-2022, https://doi.org/10.5194/hess-26-6361-2022, 2022
Short summary
Short summary
Floods cause enormous damage to infrastructure and agriculture in India. However, the utility of ensemble meteorological forecast for hydrologic prediction has not been examined. Moreover, Indian river basins have a considerable influence of reservoirs that alter the natural flow variability. We developed a hydrologic modelling-based streamflow prediction considering the influence of reservoirs in India.
Camille Labrousse, Wolfgang Ludwig, Sébastien Pinel, Mahrez Sadaoui, Andrea Toreti, and Guillaume Lacquement
Hydrol. Earth Syst. Sci., 26, 6055–6071, https://doi.org/10.5194/hess-26-6055-2022, https://doi.org/10.5194/hess-26-6055-2022, 2022
Short summary
Short summary
The interest of this study is to demonstrate that we identify two zones in our study area whose hydroclimatic behaviours are uneven. By investigating relationships between the hydroclimatic conditions in both clusters for past observations with the overall atmospheric functioning, we show that the inequalities are mainly driven by a different control of the atmospheric teleconnection patterns over the area.
Daeha Kim, Minha Choi, and Jong Ahn Chun
Hydrol. Earth Syst. Sci., 26, 5955–5969, https://doi.org/10.5194/hess-26-5955-2022, https://doi.org/10.5194/hess-26-5955-2022, 2022
Short summary
Short summary
We proposed a practical method that predicts the evaporation rates on land surfaces (ET) where only atmospheric data are available. Using a traditional equation that describes partitioning of precipitation into ET and streamflow, we could approximately identify the key parameter of the predicting formulation based on land–atmosphere interactions. The simple method conditioned by local climates outperformed sophisticated models in reproducing water-balance estimates across Australia.
Ruksana H. Rimi, Karsten Haustein, Emily J. Barbour, Sarah N. Sparrow, Sihan Li, David C. H. Wallom, and Myles R. Allen
Hydrol. Earth Syst. Sci., 26, 5737–5756, https://doi.org/10.5194/hess-26-5737-2022, https://doi.org/10.5194/hess-26-5737-2022, 2022
Short summary
Short summary
Extreme rainfall events are major concerns in Bangladesh. Heavy downpours can cause flash floods and damage nearly harvestable crops in pre-monsoon season. While in monsoon season, the impacts can range from widespread agricultural loss, huge property damage, to loss of lives and livelihoods. This paper assesses the role of anthropogenic climate change drivers in changing risks of extreme rainfall events during pre-monsoon and monsoon seasons at local sub-regional-scale within Bangladesh.
Brigitta Simon-Gáspár, Gábor Soós, and Angela Anda
Hydrol. Earth Syst. Sci., 26, 4741–4756, https://doi.org/10.5194/hess-26-4741-2022, https://doi.org/10.5194/hess-26-4741-2022, 2022
Short summary
Short summary
Due to climate change, it is extremely important to determine evaporation as accurately as possible. In nature, there are sediments and macrophytes in the open waters; thus, one of the aims was to investigate their effect on evaporation. The second aim of this paper was to estimate daily evaporation by using different models, which, according to results, have high priority in the evaporation prediction. Water management can obtain useful information from the results of the current research.
Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Hydrol. Earth Syst. Sci., 26, 4603–4618, https://doi.org/10.5194/hess-26-4603-2022, https://doi.org/10.5194/hess-26-4603-2022, 2022
Short summary
Short summary
There have been many machine learning simulation studies based on eddy-covariance observations for water flux and evapotranspiration. We performed a meta-analysis of such studies to clarify the impact of different algorithms and predictors, etc., on the reported prediction accuracy. It can, to some extent, guide future global water flux modeling studies and help us better understand the terrestrial ecosystem water cycle.
Cited articles
Ajaaj, A. A., Mishra, A., and Khan, A. A.: Comparison of BIAS correction
techniques for GPCC rainfall data in semi-arid climate, Stoch. Environ. Res. Risk A., 30, 1659–1675, 2016.
Arshad, A., Zhang, W., Zhang, Z., Wang, S., and Shalamzari, M. J.: Reconstructing high-resolution gridded precipitation data using an improved
downscaling approach over the high altitude mountain regions of upper Indus
basin (UIB), Sci. Total Environ., 784, 147140, https://doi.org/10.1016/j.scitotenv.2021.147140, 2021.
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R.,
Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies, B. Am. Meteorol. Soc., 96, 69–83,
https://doi.org/10.1175/BAMS-D-13-00068.1, 2015.
Awange, J. L., Hu, K. X., and Khaki, M.: The newly merged satellite remotely sensed, gauge and reanalysis-based multi-source weighted-ensemble
precipitation: evaluation over Australia and Africa (1981–2016), Sci. Total Environ., 670, 448–465, https://doi.org/10.1016/j.scitotenv.2019.03.148, 2019.
Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Beck, H. E., McNamara, I.,
Ribbe, L., Nauditt, A., Birkel, C., Verbist, K., Giraldo-Osorio, J. D., and Xuan Thinh, N.: RF-MEP: A novel Random Forest method for merging gridded
precipitation products and ground-based measurements, Remote Sens. Environ.,
239, 111606, https://doi.org/10.1016/j.rse.2019.111606, 2020.
Beaudoing, H. and Rodell, M.: NASA/GSFC/HSL, GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25 degree V2.1, GES DISC – Goddard Earth Sciences Data and Information Services Center, Greenbelt, Maryland, USA [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2022.
Bhuiyan, E., Abul, M., Nikolopoulos, E. I., and Anagnostou, E. N.: Machine
learning-based blending of satellite and reanalysis precipitation datasets:
A multiregional tropical complex terrain evaluation, J. Hydrometeorol., 20, 2147–2161, 2019.
Bhuiyan, M., Nikolopoulos, E. I., Anagnostou, E. N., P Quintana-Seguí,
and Barella-Ortiz, A.: A nonparametric statistical technique for combining
global precipitation datasets: development and hydrological evaluation over
the Iberian Peninsula, Hydrol. Earth Syst. Sci., 22, 1371–1389,
https://doi.org/10.5194/hess-22-1371-2018, 2018.
Breiman, L.: Arcing the edge, Tech. Rep. 486, Statistics Department, University of California at Berkeley, Berkely, http://www.stat.Berkeley.EDU/users/breiman/ (last access: 12 June 2022), 1997.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001.
Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J., and Yu, Z.: Geographically
weighted regression based methods for merging satellite and gauge precipitation, J. Hydrol., 558, 275–289, https://doi.org/10.1016/j.jhydrol.2018.01.042, 2018.
Chen, C., Hu, B., and Li, Y.: Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy, Hydrol. Earth Syst. Sci., 25, 5667–5682, https://doi.org/10.5194/hess-25-5667-2021, 2021.
Chen, S., Xiong, L., Ma, Q., Kim, J., Chen, J., and Xu, C.: Improving daily
spatial precipitation estimates by merging gauge observation with multiple
satellite-based precipitation products based on the geographically weighted
ridge regression method, J. Hydrol., 589, 125156, https://doi.org/10.1016/j.jhydrol.2020.125156, 2020.
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in:
Proceedings of the ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, ACM, August 2016, Washington, USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Chen, Y., Huang, J., Sheng, S., Mansaray, L. R., Liu, Z., Wu, H., and Wang, X.: A new downscaling-integration framework for high-resolution monthly
precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data, Remote Sens. Environ., 214, 154–172, 2018.
Climate Prediction Center: NOAA CPC Morphing Technique (CMORPH) Global Precipitation Analyses, Climate Prediction Center [data set], https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/CRT/, last access: 13 June 2022.
CMA: China Meteorological Administration, http://data.cma.cn (last access: 12 June 2022), 2018.
Collischonn, B., Collischonn, W., Carlos, E., and Morelli, T.: Daily
hydrological modeling in the Amazon basin using TRMM rainfall estimates, J.
Hydrol., 360, 207–216, https://doi.org/10.1016/j.jhydrol.2008.07.032, 2008.
Duan, Z. and Bastiaanssen, W. G. M.: First results from Version 7 TRMM 3B43
precipitation product in combination with a new downscaling–calibration
procedure, Remote Sens. Environ., 131, 1–13, 2013.
Friedman, J. H.: Stochastic gradient boosting, Comput. Stat. Data An., 38,
367–378, https://doi.org/10.1016/S0167-9473(01)00065-2, 2002.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 1–21,
https://doi.org/10.1038/sdata.2015.66, 2015.
Funk, C. C., Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., Romero, B. E., Husak, G. J., Michaelsen, J. C., and Verdin, A. P.: A quasi-global precipitation time series for drought monitoring, US Geological Survey Data Series 832, p. 4, US Geological Survey [data set], https://data.chc.ucsb.edu/products/CHIRPS-2.0/ (last access: 13 June 2022), 2014.
Ghorbanpour, A. K., Hessels, T., Moghim, S., and Afshar, A.: Comparison and
assessment of spatial downscaling methods for enhancing the accuracy of
satellite-based precipitation over Lake Urmia Basin, J. Hydrol., 596, 126055, https://doi.org/10.1016/j.jhydrol.2021.126055, 2021.
He, X., Chaney, N., Schleiss, M., and Sheffield, J.: Spatial downscaling of
precipitation using adaptable random forests, Water Resour. Res., 52,
8217–8237, https://doi.org/10.1002/2016WR019034, 2016.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, 2020.
Ho, T. K.: The Random Subspace Method for Constructing Decision Forests (PDF), IEEE. T. Pattern. Anal., 20, 832–844, https://doi.org/10.1109/34.709601, 1998.
Hong, Z., Han, Z., Li, X., Long, D., and Wang, J.: Generation of an improved precipitation data set from multisource information over the Tibetan plateau, J. Hydrometeorol., 22, 1275–1295, https://doi.org/10.1175/JHM-D-20-0252.1, 2021.
Hsu, K. L., Gao, X., Sorooshian, S., and Gupta, H.: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks, J. Appl. Meteorol., 36, 1176–1190.
https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2, 1997.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Tan, J. K.: Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation, NASA [data set],
https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDF.06/ (last access: 12 June 2022), 2019.
JAXA: GSMaP (Global Satellite Mapping of Precipitation), JAXA [data set], http://sharaku.eorc.jaxa.jp/GSMaP/index.htm, last access: 13 June 2022.
Jia, S., Zhu, W., Lu, A., and Yan, T.: A statistical spatial downscaling
algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of
China, Remote Sens. Environ., 115, 3069–3079, https://doi.org/10.1016/j.rse.2011.06.009, 2011.
Jiang, Q., Li, W., Fan, Z., He, X., Sun, W., Chen, S., Wen, J., Gao, J.,
and Wang, J.: Evaluation of the ERA5 reanalysis precipitation dataset over
Chinese Mainland, J. Hydrol., 595, 125660, https://doi.org/10.1016/j.jhydrol.2020.125660, 2021.
Jiang, S., Ren, L., Yang, H., Yong, B., Yang, X., Fei, Y., and Ma, M.:
Comprehensive evaluation of multi-satellite precipitation products with a
dense rain gauge network and optimally merging their simulated hydrological
flows using the Bayesian model averaging method, J. Hydrol., 452–453,
213–225, https://doi.org/10.1016/j.jhydrol.2012.05.055, 2012.
Joyce, R., Janowiak, J., Arkin, P., and Xie, P.: CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution, J. Hydrometeorol., 5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
Katiraie-Boroujerdy, P. S., Rahnamay Naeini, M., Akbari Asanjan, A.,
Chavoshian, A., Hsu, K., and Sorooshian, S.: Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran, Remote Sens., 12, 2102, https://doi.org/10.3390/rs12132102, 2020.
Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S.,
Hirose, M., Takayabu, Y.N., Ushio, T., Nakagawa, K., Iwanami, K., Kachi, M.,
and Okamoto, K.: Global precipitation map using satellite-borne microwave
radiometers by the GSMaP project: production and validation, IEEE T. Geosci. Remote, 45, 2259–2275, https://doi.org/10.1109/TGRS.2007.895337, 2007.
Kumar, A., Ramsankaran, R., Brocca, L., and Munoz-Arriola, F.: A machine
learning approach for improving near-real-time satellite-based rainfall
estimates by integrating soil moisture, Remote Sens., 11, 2221,
https://doi.org/10.3390/rs11192221, 2019.
Le, X. H., Lee, G., Jung, K., An, H. U., Lee, S., and Jung, Y.: Application of convolutional neural network for spatiotemporal bias correction of daily
satellite-based precipitation, Remote Sens., 12, 2731, https://doi.org/10.3390/rs12172731, 2020.
Lei, H., Li, H., Zhao, H., Ao, T., and Li, X.: Comprehensive evaluation of
satellite and reanalysis precipitation products over the eastern Tibetan
plateau characterized by a high diversity of topographies, Atmos. Res., 259,
105661, https://doi.org/10.1016/j.atmosres.2021.105661, 2021.
Lei, H., Zhao, H., and Ao, T.: Ground validation and error decomposition for six state-of-the-art satellite precipitation products over mainland China, Atmos. Res., 269, 106017, https://doi.org/10.1016/j.atmosres.2022.106017, 2022.
Lu, X., Tang, G., Wang, X., Liu, Y., Wei, M., and Zhang, Y.: The development of a two-step merging and downscaling method for satellite precipitation products, Remote Sens., 12, 398, https://doi.org/10.3390/rs12030398, 2020.
Ma, Y., Zhang, Y., Yang, D., and Farhan, S.: Precipitation bias variability
versus various gauges under different climatic conditions over the Third
Pole Environment (TPE) region. Int. J. Climatol., 35, 1201–1211, https://doi.org/10.1002/joc.4045, 2015.
Ma, Y., Yang, H., Yang, C., Yuan, Y., Tang, G., Yao, Y., Di, L., Li, C.,
Han, Z., and Liu, R.: Performance of optimally merged multisatellite
precipitation products using the dynamic Bayesian model averaging scheme
over the Tibetan plateau. J. Geophys. Res.-Atmos., 123, 814–834, https://doi.org/10.1002/2017JD026648, 2017.
Ma, Y., Sun, X., Chen, H., Hong, Y., and Zhang, Y.: A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau, Hydrol. Earth Syst. Sci., 25, 359–374, https://doi.org/10.5194/hess-25-359-2021, 2021.
Manz, B., Buytaert, W., Zulkafli, Z., Lavado, W., Willems, B., Robles, L.
A., and Rodríguez-Sánchez, J. P.: High-resolution satellite-gauge
merged precipitation climatologies of the Tropical Andes, J. Geophys.
Res.-Atmos., 121, 1190–1207, 2016.
NCEP/NCAR – National Centers for Environment Prediction and National Center for Atmospheric Research – and ECMWF – European Centre for Medium-Range Weather Forecasts: ERA5-Land monthly averaged data from 1950 to present, NCEP/NCAR and ECMWF [data set], https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.68d2bb30, last access: 13 June 2022.
Nerini, D., Zulkafli, Z., Wang, L. P., Onof, C., Buytaert, W.,
Lavadocasimiro, W., and Guyot, J.: A comparative analysis of TRMM-rain gauge data merging techniques at the daily time scale for distributed rainfall-runoff modeling applications, J. Hydrometeorol., 16, 2153–2168,
https://doi.org/10.1175/JHM-D-14-0197.1, 2015.
Nguyen, G. V., Le, X. H., Van, L. N., Jung, S., Yeon, M., and Lee, G.:
Application of Random Forest Algorithm for Merging Multiple Satellite
Precipitation Products across South Korea, Remote Sens., 13, 4033,
https://doi.org/10.3390/rs13204033, 2021.
Nie, S., Luo, Y., Wu, T., Shi, X., and Wang, Z.: A merging scheme for
constructing daily precipitation analyses based on objective bias-correction
and error estimation techniques, J. Geophys. Res.-Atmos., 120, 8671–8692,
2015.
Pham, Q. B., Yang, T. C., Kuo, C. M., Tseng, H. W., and Yu, P. S.: Combing
random forest and least square support vector regression for improving
extreme rainfall downscaling, Water, 11, 451, https://doi.org/10.3390/w11030451, 2019.
Piani, C., Haerter, J., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, 2010a.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and Rigol-Sanchez, J. P.: An assessment of the effectiveness of a random forest
classifier for land-cover classification, ISPRS J. Photogram., 67, 93–104,
https://doi.org/10.1016/j.isprsjprs.2011.11.002, 2012.
Sharifi, E., Steinacker, R., and Saghafian, B.: Assessment of GPM-IMERG and
other precipitation products against gauge data under different topographic
and climatic conditions in Iran: Preliminary results, Remote Sens., 8, 135, https://doi.org/10.3390/rs8020135, 2016.
Sharifi, E., Saghafian, B., and Steinacker, R.: Downscaling satellite
precipitation estimates with multiple linear regression, artificial neural
networks, and spline interpolation techniques, J. Geophys. Res.-Atmos., 124, 789–805, https://doi.org/10.1029/2018JD028795, 2019.
Shen, Y., Xiong, A., Wang, Y., and Xie, P.: Performance of high resolution
satellite precipitation products over China. J. Geophys. Res.-Atmos., 115,
D02114, https://doi.org/10.1029/2009JD012097, 2010.
Shen, Y., Pan, Y., Yu, J., Zhao, P., and Zhou, Z.: Quality assessment of hourly merged precipitation product over China, Trans. Atmos. Sci., 36, 37–46, https://doi.org/10.13878/j.cnki.dqkxxb.2013.01.005, 2013.
Shen, Y., Xiong, A., Hong, Y., Yu, J., Pan, Y., Chen, Z., and Saharia, M.:
Uncertainty analysis of five satellite-based precipitation products and
evaluation of three optimally merged multi-algorithm products over the
Tibetan Plateau, Int. J. Remote. Sens., 35, 6843–6858, 2014.
Shen, Z. and Yong, B.: Downscaling the GPM-based satellite precipitation
retrievals using gradient boosting decision tree approach over Mainland
China, J. Hydrol., 602, 126803, https://doi.org/10.1016/j.jhydrol.2021.126803, 2021.
Tan, J., Xie, X., Zuo, J., Xing, X., Liu, B., and Xia, Q.: Coupling random
forest and inverse distance weighting to generate climate surfaces of
precipitation and temperature with multiple-covariates, J. Hydrol., 598,
126270, https://doi.org/10.1016/j.jhydrol.2021.126270, 2021.
Tang, X., Yin, Z., Qin, G., Guo, L., and Li, H.: Integration of Satellite
Precipitation Data and Deep Learning for Improving Flash Flood Simulation in
a Poor-Gauged Mountainous Catchment, Remote Sens., 13, 5083, https://doi.org/10.3390/rs13245083, 2021.
Tao, Y., Gao, X., Hsu, K., Sorooshian, S., and Ihler, A.: A deep neural network modeling framework to reduce bias in satellite precipitation products, J. Hydrometeorol., 17, 160114111258006, https://doi.org/10.1175/JHM-D-15-0075.1, 2016.
Tong, Y., Gao, X., Han, Z., Xu, Y., Xu, X., and Giorgi, F.: Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods, Clim. Dynam., 57, 1425–1443, 2021.
UC-IRVINE/CHRS – Center for Hydrometeorology and Remote Sensing, University of California, Irvine: NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1, Revision 1, UC-IRVINE/CHRS [data set], https://www.ncei.noaa.gov/data/precipitation-persiann/access/, last access: 13 June 2022.
Wang, R., Lu, S., and Li, Q.: Multi-criteria comprehensive study on predictive algorithm of hourly heating energy consumption for residential buildings, Sustain. Cities Soc., 49, 101623, https://doi.org/10.1016/j.scs.2019.101623, 2019.
Wang, Y., Wang, L., Li, X., Zhou, J., and Hu, Z.: An integration of gauge, satellite, and reanalysis precipitation datasets for the largest river basin of the Tibetan Plateau, Earth Syst. Sci. Data, 12, 1789–1803, https://doi.org/10.5194/essd-12-1789-2020, 2020.
Wehbe, Y., Temimi, M., and Adler, R. F.: Enhancing precipitation estimates
through the fusion of weather radar, satellite retrievals, and surface
parameters, Remote Sens., 12, 1342, https://doi.org/10.3390/rs12081342, 2020.
WMO: Guide to Hydrological Practice, Volume I. Hydrology – From Measurement to Hydrological Information, WMO-No. 168, p. 48,
https://library.wmo.int/doc_num.php?explnum_id=10473 (last access: 12 June 2022), 2008.
Wu, H., Yang, Q., Liu, J., and Wang, G.: A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China, J. Hydrol., 584, 124664, https://doi.org/10.1016/j.jhydrol.2020.124664, 2020.
Wu, Z., Zhang, Y., Sun, Z., Lin, Q., and He, H.: Improvement of a combination of TMPA (or IMERG) and ground-based precipitation and application to a typical region of the east China plain, Sci. Total Environ., 640–641, 1165–1175, 2018.
Xiao, S., Zou, L., and Xia, J.: Bias correction framework for satellite precipitation products using a rain/no rain discriminative model, Sci. Total Environ., 818, 151679, https://doi.org/10.1016/j.scitotenv.2021.151679, 2022.
Xie, P. and Arkin, P. A.: Global precipitation: A 17-year monthly analysis
based on gauge observations, satellite estimates and numerical model outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997.
Xie, P. and Xiong, A. Y.: A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses, J. Geophys. Res.-Atmos., 116, D21106, https://doi.org/10.1029/2011JD016118, 2011.
Xin, Y., Lu, N., Jiang, H., Liu, Y., and Yao, L.: Performance of ERA5 reanalysis precipitation products in the Guangdong-Hong Kong-Macao greater Bay Area, China, J. Hydrol., 602, 126791, https://doi.org/10.1016/j.jhydrol.2021.126791, 2021.
Xu, J., Ma, Z., Yan, S., and Peng, J.: Do ERA5 and ERA5-land precipitation
estimates outperform satellite-based precipitation products? A comprehensive
comparison between state-of-the-art model-based and satellite-based
precipitation products over mainland China, J. Hydrol., 605, 127353, https://doi.org/10.1016/j.jhydrol.2021.127353, 2022.
Xu, Q., Chen, J., Peart, M. R., Ng, C. N., Hau, B. C., and Law, W. W.: Exploration of severities of rainfall and runoff extremes in ungauged catchments: a case study of Lai Chi Wo in Hong Kong, China, Sci. Total Environ., 634, 640–649. https://doi.org/10.1016/j.scitotenv.2018.04.024, 2018.
Yang, X., Yang, S., Tan, M. L., Pan, H., Zhang, H., Wang, G., He, R., and Wang, Z.: Correcting the Bias of Daily Satellite Precipitation Estimates in
Tropical Regions Using Deep Neural Network, J. Hydrol., 608, 127656, https://doi.org/10.1016/j.jhydrol.2022.127656, 2022.
Yang, Z., Hsu, K., Sorooshian, S., Xu, X., Dan, B., Yuan, Z., and Koen, M. J.: Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements - a case study in Chile, J. Geophys. Res.-Atmos., 122, 5267–5284, https://doi.org/10.1002/2016JD026177, 2017.
Yilmaz, K. K., Hogue, T. S., Hsu, K. L., Sorooshian, S., Gupta, H. V., and Wagener, T.: Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting, J. Hydrometeorol., 6, 497–517, https://doi.org/10.1175/JHM431.1, 2005.
Yin, J., Guo, S., Gu, L., Zeng, Z., and Xu, C. Y.: Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling, J. Hydrol., 593, 125878, https://doi.org/10.1016/j.jhydrol.2020.125878, 2021.
Yu, C., Hu, D., Liu, M., Wang, S., and Di, Y.: Spatio-temporal accuracy
evaluation of three high-resolution satellite precipitation products in
China area, Atmos. Res., 241, 104952, https://doi.org/10.1016/j.atmosres.2020.104952, 2020.
Yumnam, K., Guntu, R. K., Rathinasamy, M., and Agarwal, A.: Quantile-based
Bayesian Model Averaging approach towards merging of precipitation products,
J. Hydrol., 604, 127206, https://doi.org/10.1016/j.jhydrol.2021.127206, 2022.
Zhang, L., Li, X., Zheng, D., Zhang, K., and Ge, Y.: Merging multiple
satellite-based precipitation products and gauge observations using a novel
double machine learning approach, J. Hydrol., 594, 125969,
https://doi.org/10.1016/j.jhydrol.2021.125969, 2021.
Short summary
How to combine multi-source precipitation data effectively is one of the hot topics in hydrometeorological research. This study presents a two-step merging strategy based on machine learning for multi-source precipitation merging over China. The results demonstrate that the proposed method effectively distinguishes the occurrence of precipitation events and reduces the error in precipitation estimation. This method is robust and may be successfully applied to other areas even with scarce data.
How to combine multi-source precipitation data effectively is one of the hot topics in...
