Multi-model evaluation of catchment- and global-scale hydrological model simulations of drought characteristics across eight large river catchments

Kumar, Amit; Gosling, Simon N.; Johnson, Matthew F.; Jones, Matthew D.; Zaherpour, Jamal; Kumar, Rohini; Leng, Guoyong; Schmied, Hannes Müller; Kupzig, Jenny; Hanasaki, Naota; Tang, Qiuhong; Ostberg, Sebastian; Stacke, Tobias; Pokhrel, Yadu; Wada, Yoshihide; Masaki, Yoshimitsu

doi:10.1016/j.advwatres.2022.104212

Cited by 13 publications

(6 citation statements)

References 55 publications

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“…Data were examined for homogeneity using the double mass curve, linear regression, and Mann-Whitney test procedures to ensure quality. The technique found a few inhomogeneities, and the irregular data were corrected using data from nearby dependable stations [23].…”

Section: Study Area Descriptionmentioning

confidence: 99%

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Achite,

Katipoğlu,

Jehanzaib

et al. 2023

Atmosphere

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Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.

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Section: Study Area Descriptionmentioning

confidence: 99%

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Achite,

Katipoğlu,

Jehanzaib

et al. 2023

Atmosphere

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“…Li, Rodell, Sheffield, et al, 2019). However, due to the coarse grid size of these models, incomplete representation of physical processes and the use of different meteorological forcings (Kumar et al, 2022;Moradkhani et al, 2005;Scanlon et al, 2018) introduce uncertainties in the output storage components, particularly in smaller basins (Bolaños Chavarría et al, 2021;Scanlon et al, 2019;Shrestha et al, 2020). Combining storage compartments from these models with GRACE can pose challenges in reliably estimating climatic and anthropogenic TWS changes.…”

Section: Introductionmentioning

confidence: 99%

Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin

Arshad,

Mirchi,

Taghvaeian

et al. 2024

Water Resources Research

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GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities.

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“…Climate impact assessments [6,7,8], drought monitoring [9,10,11] and forecasting systems [12,13,14], and drought studies in general [15,16,17] widely use these models today. Yet, some studies revealed poor model performances when simulating streamflow droughts [18] and their generating processes [19]. Furthermore, human activities heavily modify the hydrological cycle [20] and streamflow droughts [21] at present, but their representation in hydrological models remains challenging [22].…”

Section: Introductionmentioning

confidence: 99%

Hydrological model skills change with drought severity; insights from multi-variable evaluation

Bruno,

Avanzi,

Alfieri

et al. 2023

Preprint

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Hydrological models often do not properly simulate streamflow (Q) during extreme events, including droughts. Limited abilities in simulating Q during droughts may arise from a misrepresentation of Q generating processes during these periods, but little research has focused on distributed, process-based models over human-affected areas and extreme events. To shed more light into model consistency during these periods, we evaluated the ability of the hydrological model Continuum in simulating Q over the human-affected Po river basin in Italy during droughts of different severity over the last 13 years, including the severe 2022 event. To investigate the causes for potential model deterioration during severe droughts, we assessed the simulation of evapotranspiration (ET) and Terrestrial Water Storage (TWS) against independent remote sensing-based benchmarks, and possible inconsistencies in forcing and benchmark data. Finally, we included a moderate drought in the calibration period, as potential strategy to improve model performances during severe droughts. The model represented well Q (KGE = 0.81 for the outlet of the basin), ET (r = 0.94) and TWS (r = 0.76) over the whole study period. Focusing on Q and specific sub-periods, model performances were comparable during wet years (2014 and 2020) and moderate droughts (2012 and 2017), with KGE across the 38 study sub-catchments of 0.59±0.32 (mean ± standard deviation) during wet years and 0.55±0.25 during moderate droughts. The model simulated Q well for the outlet section of the basin also during the severe 2022 drought (KGE = 0.82). However, performances across the subcatchments declined in 2022 (KGE = 0.18±0.69). For the severe drought, we detected a decrease in model performances for ET, in particular over human-affected croplands (mean decrease in r by 105% and mean increase in nRMSE by 86%). Furthermore, calibrating during a moderate drought did not improve model performances in 2022 (KGE = 0.18±0.63), pointing to the fairly unique conditions of this period in terms of hydrological processes and human interference on them. Our study highlighted decreased model skills specifically during a severe drought and identified the neglection of irrigation as the most plausible cause for this. Given projected increases in severe droughts and the frequent modelling simplification of human activities, despite their heavy interference in many regions, our findings are highly relevant to move towards more robust hydrological modelling in a changing climate and the Anthropogenic era, to support management and adaptation strategies.

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Multi-model evaluation of catchment- and global-scale hydrological model simulations of drought characteristics across eight large river catchments

Cited by 13 publications

References 55 publications

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin

Hydrological model skills change with drought severity; insights from multi-variable evaluation

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