Applicability of a CEEMD–ARIMA Combined Model for Drought Forecasting: A Case Study in the Ningxia Hui Autonomous Region by Dehe Xu, Yan Ding, Hui Liu, Qi Zhang, De Zhang

“…This study combined the strengths of autoregressive integrated moving average (ARIMA) and complementary ensemble empirical mode decomposition (CEEMD) to predict drought. …”
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Combining Standard Artificial Intelligence Models, Pre-Processing Techniques, and Post-Processing Methods to Improve the Accuracy of Monthly Runoff Predictions in Karst-Area Waters... by Chongxun Mo, Changhao Jiang, Xingbi Lei, Shufeng Lai, Yun Deng, Weiyan Cen, Guikai Sun, Zhenxiang Xing

“…Then, the performance of four coupled models—formed by combining two AI pre-processing techniques, Empirical Modal Decomposition (EMD) and Ensemble Empirical Modal Decomposition (EEMD), with the previously mentioned AI models—was investigated. …”
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Regional polar warming linked to poleward moisture transport variability by Richard Bintanja, Rune Grand Graversen, Marlen Kolbe

“…Geosci. 12 911–6), which, in turn, is linked to variability in poleward moisture transport (PMT) (Nash et al 2018 J. Geophys. Res. Atmos. 123 6804–21). …”
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Investigating Impacts of Climate Change on Runoff from the Qinhuai River by Using the SWAT Model and CMIP6 Scenarios by Jinqiu Sun, Haofang Yan, Zhenxin Bao, Guoqing Wang

“…The results show that the calibrated SWAT model is applicable to the Qinhuai River Basin and can accurately characterize the runoff process at daily and monthly scales with the Nash–Sutcliffe efficiency coefficients (NSE), correlation coefficients (R), and the Kling–Gupta efficiency (KGE) in calibration and validation periods being above 0.75 and relative errors (RE) are ±3.5%. …”
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Impact of Climate Change on Soil Water Content in Southern Saskatchewan, Canada by Mohammad Zare, Shahid Azam, David Sauchyn

“…The model was calibrated and validated using SUFI-2 in SWAT-CUP based on observations for streamflow and SWC, including measured data and Soil Moisture Active Passive (SMAP) Level 4 for surface (up to 5 cm deep) soil moisture. Values of the Nash–Sutcliffe model efficiency (NS) ranged from 0.616 and 0.784 and the coefficient of determination (<i>R</i>²) was 0.8 for calibration and 0.82 for validation. …”
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Rainfall and temperature changes under different climate scenarios at the watersheds surrounding the Ngorongoro Conservation Area in Tanzania by Mohamed Mwabumba, Brijesh K. Yadav, Mwemezi J. Rwiza, Isaac Larbi, Sam-Quarcoo Dotse, Andrew Manoba Limantol, Solomon Sarpong, Daniel Kwawuvi

“…The climate change analysis was performed at monthly and annual scale using outputs from a multi-model ensemble of Regional Climate Models (RCMs) and statistically downscaled Global Climate Models (GCMs). …”
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Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion by Vinh Ngoc Tran, Jongho Kim

“…More accurate quantification of these uncertainties using a large number of ensembles and model runs is hampered by the high computational burden. …”
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Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping by A. Annis, A. Annis, F. Nardi, F. Nardi, F. Castelli

“…Specifically, our findings reveal that the simultaneous assimilation of observations from static sensors and satellite images led to an overall improvement of the Nash–Sutcliffe efficiency (NSE) between 5 % and 40 %, the Pearson correlation up to 12 % and bias reduction up to 80 % with respect to the open-loop simulation. …”
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Enhanced forecasting of multi-step ahead daily soil temperature using advanced hybrid vote algorithm-based tree models by Javad Hatamiafkoueieh, Salim Heddam, Saeed Khoshtinat, Solmaz Khazaei, Abdol-Baset Osmani, Ebrahim Nohani, Mohammad Kiomarzi, Ehsan Sharafi, John Tiefenbacher

“…For 3-day ahead forecasting, V-RF was the superior model with Nash–Sutcliff efficiency (NSE) values of 0.85, compared to V-M5P's value of 0.81 and V-RT's value of 0.81. …”
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Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting by Adib, M. N. M., Harun, Sobri

“…The model fitted reasonably well, with Kling-Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976-1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995-2005). …”

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia by Ansoumana Bodian, Alain Dezetter, Lamine Diop, Abdoulaye Deme, Koffi Djaman, Aliou Diop

“…Model efficiency is evaluated using a multi-criteria function (Fagg) which aggregates Nash and Sutcliffe criteria, cumulative volume error, and mean volume error. …”
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Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling by Yiheng Xiang, Jie Chen, Lu Li, Tao Peng, Zhiyuan Yin

“…MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. …”
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On the choice of calibration metrics for “high-flow” estimation using hydrologic models by N. Mizukami, O. Rakovec, O. Rakovec, A. J. Newman, M. P. Clark, M. P. Clark, A. W. Wood, H. V. Gupta, R. Kumar

“…It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. …”
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Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling by D. Araya, P. A. Mendoza, P. A. Mendoza, E. Muñoz-Castro, J. McPhee, J. McPhee

“…Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. …”
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Simulating future flood risks under climate change in the source region of the Indus River by Muhammad Rizwan, Xin Li, Yingying Chen, Lubna Anjum, Shanawar Hamid, Muhammad Yamin, Junaid Nawaz Chauhdary, Muhammad Adnan Shahid, Qaisar Mehmood

“…Downscaled and bias corrected climatic data of six general circulation models and their ensemble were used in this study. The IFAS model simulated the stream flow efficiently (R2 = 0.86–0.93 and Nash–Sutcliffe efficiency = 0.72–0.92) in the Jhelum River basin (JRB), Kabul River basin (KRB), and upper Indus River basin (UIRB) during the calibration and validation periods. …”
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Assessing the Impact of Land Use and Climate Change on Surface Runoff Response Using Gridded Observations and SWAT+ by Paul Kiprotich, Xianhu Wei, Zongke Zhang, Thomas Ngigi, Fengting Qiu, Liuhao Wang

“…The gridded data predicted streamflow accurately with a Nash–Sutcliffe efficiency greater than 0.89 in both calibration and validation phases. …”
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Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain by R. A. Lane, G. Coxon, J. E. Freer, J. E. Freer, T. Wagener, T. Wagener, P. J. Johnes, P. J. Johnes, J. P. Bloomfield, S. Greene, C. J. A. Macleod, S. M. Reaney

“…</p> <p>Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a 0.5 Nash–Sutcliffe efficiency for at least 80&thinsp;% of catchments. …”
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Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression by M. C. Almeida, P. S. Coelho

“…The root mean square error (RMSE) and the Nash–Sutcliffe efficiency (NSE) values obtained for the ensemble of all model results were <span class="inline-formula">2.75±1.00</span> and <span class="inline-formula">0.56±0.48</span> <span class="inline-formula">^∘</span>C, respectively. …”
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Projection of Streamflow Changes Under CMIP6 Scenarios in the Urumqi River Head Watershed, Tianshan Mountain, China by Min Yang, Min Yang, Zhongqin Li, Muhammad Naveed Anjum, Rakesh Kayastha, Rijan Bhakta Kayastha, Mukesh Rai, Mukesh Rai, Xin Zhang, Xin Zhang, Chunhai Xu, Chunhai Xu

“…The GDM was calibrated and validated against in situ observed discharge data for the 2007–2011 and 2012–2018 periods. The resulting Nash–Sutcliffe efficiency (NSE) values were 0.82 and 0.81, respectively. …”
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A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+ by S. A. Abbas, R. T. Bailey, J. T. White, J. G. Arnold, M. J. White, N. Čerkasova, J. Gao

“…Furthermore, the iterative ensemble smoother (iES) is utilized as a technique for uncertainty quantification (UQ) and parameter estimation (PE) and to decrease the computational cost owing to the large number of parameters.…”
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