Deep Dive into Global Hydrologic Simulations: Harnessing the Power of Deep Learning and Physics-informed Differentiable Models (δHBV-globe1.0-hydroDL) DOI Creative Commons
Dapeng Feng, Hylke E. Beck, Jens de Bruijn

et al.

Published: Oct. 5, 2023

Abstract. Accurate hydrological modeling is vital to characterizing how the terrestrial water cycle responds climate change. Pure deep learning (DL) models have shown outperform process-based ones while remaining difficult interpret. More recently, differentiable, physics-informed machine with a physical backbone can systematically integrate equations and DL, predicting untrained variables processes high performance. However, it was unclear if such are competitive for global-scale applications simple backbone. Therefore, we use – first time at this scale differentiable hydrologic (fullname δHBV-globe1.0-hydroDL shorthanded δHBV) simulate rainfall-runoff 3753 basins around world. Moreover, compare δHBV purely data-driven long short-term memory (LSTM) model examine their strengths limitations. Both LSTM provide competent daily simulation capabilities in global basins, median Kling-Gupta efficiency values close or higher than 0.7 (and 0.78 subset of 1675 long-term records), significantly outperforming traditional models. regionalized demonstrated stronger spatial generalization ability (median KGE 0.64) parameter regionalization approach 0.46) even ungauged region tests Europe South America. Nevertheless, relative LSTM, hampered by structural deficiencies cold polar regions, highly arid significant human impacts. This study also sets benchmark estimates world builds foundations improving simulations.

Language: Английский

Climate Change and Hydrological Extremes DOI
Jinghua Xiong, Yuting Yang

Current Climate Change Reports, Journal Year: 2024, Volume and Issue: 11(1)

Published: Oct. 2, 2024

Language: Английский

Citations

24
Scaling from global to regional river flow with global hydrological models: Choice matters DOI
Tongbi Tu, Jiahao Wang, Gang Zhao

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 130960 - 130960

Published: Feb. 25, 2024

Language: Английский

Citations

9
Amplified future risk of compound droughts and hot events from a hydrological perspective DOI Creative Commons

Sifang Feng,

Zengchao Hao, Yitong Zhang

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 617, P. 129143 - 129143

Published: Jan. 19, 2023

Language: Английский

Citations

22
Water-carbon-sediment synergies and trade-offs: Multi-faceted impacts of large-scale ecological restoration in the Middle Yellow River Basin DOI
Zihan Yan, Taihua Wang, T. Ma

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 634, P. 131099 - 131099

Published: March 24, 2024

Language: Английский

Citations

8
Global hydrological models continue to overestimate river discharge DOI Creative Commons
Stefanie Heinicke, Jan Volkholz, Jacob Schewe

et al.

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(7), P. 074005 - 074005

Published: May 31, 2024

Abstract Global hydrological models (GHMs) are widely used to assess the impact of climate change on streamflow, floods, and droughts. For ‘model evaluation attribution’ part current round Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), modelling teams generated historical simulations based observed direct human forcings with updated model versions. Here we provide a comprehensive daily maximum annual discharge ISIMIP3a from nine GHMs by comparing observational data 644 river gauge stations. We also low flows effects different routing schemes. find that can reproduce variability in discharge, but tend overestimate both quantities, as well flows. Models perform better at stations wetter areas lower elevations. Discharge routed CaMa-Flood improve performance some models, for others, is overestimated, leading reduced performance. This study indicates future development include improving simulation processes arid regions cold dynamics high further suggest studies attributing changes using ensemble will be most meaningful humid areas, elevations, places regular seasonal these where underlying seem best represented.

Language: Английский

Citations

8
A holistic approach to projecting streamflow and analyzing changes in ecologically relevant hydrological indicators under climate and land use/cover change DOI
Yang Liu, Feng Liu, Cheng Chen

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130863 - 130863

Published: Feb. 15, 2024

Language: Английский

Citations

6
Estimates of the Land Surface Hydrology from the Community Land Model Version 5 (CLM5) with Three Meteorological Forcing Datasets over China DOI Creative Commons
Dayang Wang, Dagang Wang, Yiwen Mei

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(3), P. 550 - 550

Published: Jan. 31, 2024

The land surface model (LSM) is extensively utilized to simulate terrestrial processes between and atmosphere in the Earth system. Hydrology simulation key component of model, which can directly reflect capability LSM. In this study, three offline LSM simulations were conducted over China using Community Land Model version 5.0 (CLM5) driven by different meteorological forcing datasets, namely Meteorological Forcing Dataset (CMFD), Global Soil Wetness Project Phase 3 (GSWP3), bias-adjusted ERA5 reanalysis (WFDE5), respectively. Both gridded situ reference data, including evapotranspiration (ET), soil moisture (SM), runoff, employed evaluate performance levels CLM5-based across its ten basins. general, all realistically replicate magnitudes, spatial patterns, seasonal cycles ET when compared with remote-sensing-based observations. Among basins, Yellow River Basin (YRB) basin where are best, supported higher KGE value 0.79. However, substantial biases occur Northwest Rivers (NWRB) significant overestimation for CMFD WFDE5 underestimation GSWP3. addition, both grid-based or site-based evaluations SM indicate that systematic wet exist CLM5 shallower layer nine basins China. Comparatively, simulating deeper slightly better. Moreover, types reasonable runoff among capture more detailed information, but GSWP3 presents comparable change trends data. summary, study explored capacity assessment results may provide important insights future developments applications

Language: Английский

Citations

5
Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data DOI
Tongbi Tu,

Yilan Li,

Kai Duan

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 359, P. 121044 - 121044

Published: May 1, 2024

Language: Английский

Citations

5
Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions DOI Creative Commons
George P. Petropoulos, Christina Lekka

Sensors, Journal Year: 2024, Volume and Issue: 24(10), P. 3024 - 3024

Published: May 10, 2024

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic research community is SimSphere SVAT model, popular software toolkit employed simulating among layers of vegetation, soil, atmosphere on surface. The aim present review two-fold: (1) to deliver critical assessment model’s usage by scientific wider over last 15 years, (2) provide information current developments implemented in model. From conducted herein, it clearly evident that from models’ inception day, has received notable interest worldwide, dissemination continuously grown years. been used so far several applications study interactions. validation performed worldwide shown able produce realistic estimates parameters have validated, whereas detailed sensitivity analysis experiments with further confirmed its structure architectural coherence. Furthermore, recent inclusion novel functionalities as outlined review, resulted improving capabilities opening up new opportunities use community. also ongoing different aspects, advancing our understanding both educational points view anticipated grow coming

Language: Английский

Citations

5
Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations DOI
Fan Bai, Zhongwang Wei, Nan Wei

et al.

Journal of Geophysical Research Atmospheres, Journal Year: 2024, Volume and Issue: 129(23)

Published: Nov. 28, 2024

Abstract Offline land surface models (LSMs) require atmospheric forcing data sets for simulating water, energy, and biogeochemical fluxes. However, available remain highly uncertain can introduce additional differences in LSM simulations. This study explored the impact of various sets, ranging from widely used to newly developed, on hydrological simulations using Common Land Model 2024 (CoLM2024). We conducted 12 global experiments different force CoLM2024. evaluated model's performance against plot‐scale observations globally gridded reference data. examined uncertainties forcings their output variables such as latent heat, sensible net radiation, total runoff. Globally, precipitation has highest degree uncertainty at 4.4%. The propagate model cause significant simulated variables. Runoff is about 15.7% globally, with a greater low latitudes. Our evaluation shows that developed CRUJRA ERA5LAND, generally outperform others. optimal set varies depending variable interest targeted region. Partial Least Squares Regression analysis reveals are associated dominant variables, highlighting importance selecting specific applications regions. confirms improving quality consistency meteorological would help reduce simulation biases guide improvement structure parameterization

Language: Английский

Citations

4