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1. Assessing the Influence of a Bias Correction Method on Future Climate Scenarios Using SWAT as an Impact Model Indicator

   https://doi.org/10.3390/w15040750  

   Brighenti, Tássia Mattos; Gassman, Philip W.; Gutowski, William J.; Thompson, Janette R. (February 2023, Water)

   In this study, we evaluate the implications of a bias correction method on a combination of Global/Regional Climate Models (GCM and RCM) for simulating precipitation and, subsequently, streamflow, surface runoff, and water yield in the Soil and Water Assessment Tool (SWAT). The study area is the Des Moines River Basin, U.S.A. The climate projections are two RCMs driven by two GCMs for historical simulations (1981–2005) and future projections (2030–2050). Bias correction improves historical precipitation for annual volumes, seasonality, spatial distribution, and mean error. Simulated monthly historical streamflow was compared across 26 monitoring stations with mostly satisfactory results for percent bias (Pbias). There were no changes in annual trends for future scenarios except for raw WRF models. Seasonal variability remained the same; however, most models predicted an increase in monthly precipitation from January to March and a reduction for June and July. Meanwhile, the bias-corrected models showed changes in prediction signals. In some cases, raw models projected an increase in surface runoff and water yield, but the bias-corrected models projected a reduction in these variables. This suggests the bias correction may be larger than the climate-change signal and indicates the procedure is not a small correction but a major factor. 

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2. Temperature and precipitation projections for the Antarctic Peninsula over the next two decades: contrasting global and regional climate model simulations

   https://doi.org/https://doi.org/10.1007/s00382-021-05667-2  

   Bozkurt, D. (February 2021, Climate dynamics)

   null (Ed.)

   This study presents near future (2020–2044) temperature and precipitation changes over the Antarctic Peninsula under the high-emission scenario (RCP8.5). We make use of historical and projected simulations from 19 global climate models (GCMs) participating in Coupled Model Intercomparison Project phase 5 (CMIP5). We compare and contrast GCMs projections with two groups of regional climate model simulations (RCMs): (1) high resolution (15-km) simulations performed with Polar-WRF model forced with bias-corrected NCAR-CESM1 (NC-CORR) over the Antarctic Peninsula, (2) medium resolution (50-km) simulations of KNMI-RACMO21P forced with EC-EARTH (EC) obtained from the CORDEX-Antarctica. A further comparison of historical simulations (1981–2005) with respect to ERA5 reanalysis is also included for circulation patterns and near-surface temperature climatology. In general, both RCM boundary conditions represent well the main circulation patterns of the historical period. Nonetheless, there are important differences in projections such as a notable deepening and weakening of the Amundsen Sea Low in EC and NC-CORR, respectively. Mean annual near-surface temperatures are projected to increase by about 0.5–1.5 ◦ C across the entire peninsula. Temperature increase is more substantial in autumn and winter ( ∼ 2 ◦C). Following opposite circulation pattern changes, both EC and NC-CORR exhibit different warming rates, indicating a possible continuation of natural decadal variability. Although generally showing similar temperature changes, RCM projections show less warming and a smaller increase in melt days in the Larsen Ice Shelf compared to their respective driving fields. Regarding precipitation, there is a broad agreement among the simulations, indicating an increase in mean annual precipitation ( ∼ 5 to 10%). However, RCMs show some notable differences over the Larsen Ice Shelf where total precipitation decreases (for RACMO) and shows a small increase in rain frequency. We conclude that it seems still difficult to get consistent projections from GCMs for the Antarctic Peninsula as depicted in both RCM boundary conditions. In addition, dominant and common changes from the boundary conditions are largely evident in the RCM simulations. We argue that added value of RCM projections is driven by processes shaped by finer local details and different physics schemes that are introduced by RCMs, particularly over the Larsen Ice Shelf. 

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3. An Alternative Ensemble Streamflow Prediction Approach Using Improved Subseasonal Precipitation Forecasts from the North America Multi-Model Ensemble Phase II

   https://doi.org/10.1175/JHM-D-24-0048.1  

   Zhang, Lujun; Yang, Tiantian; Gao, Shang; Fan, Ming; Lu, Dan; Xu, Hongmei; Xiao, Chan (March 2025, Journal of Hydrometeorology)

   Abstract Streamflow forecasting at a subseasonal time scale (10–30 days into the future) is important for various human activities. The ensemble streamflow prediction (ESP) is a widely applied technique for subseasonal streamflow forecasting. However, ESP’s reliance on the randomly resampled historical precipitation limits its predictive capability. Available dynamical subseasonal precipitation forecasts provide an alternative to the randomly resampled precipitation in ESP. Prior studies found the predictive performance of raw subseasonal precipitation forecast is limited in many regions such as the central south of the United States, which raises questions about its effectiveness in assisting streamflow forecasting. To further assess the hydrologic applicability of dynamical subseasonal precipitation forecasts, we test the subseasonal precipitation forecast from North America Multi-Model Ensemble Phase II (NMME-2) at four watersheds in the central south region of the United States. The subseasonal precipitation forecasts are postprocessed with bias correction and spatial disaggregation (BCSD) to correct bias and improve spatial resolution before replacing the randomly resampled precipitation in ESP for streamflow predictions. The performance of the resulting streamflow predictions is benchmarked with ESP. Evaluation is conducted using Kling–Gupta Efficiency (KGE), continuous ranked probability score (CRPS), probability of detection (POD), false alarm ratios (FARs), as well as reliability diagrams. Our results suggest that BCSD-corrected subseasonal precipitation forecasts lead to overall improved streamflow predictions due to added skills in winter and spring. Our results also suggest that BCSD-corrected subseasonal precipitation forecasts lead to improved predictions on the occurrence of high-percentile streamflow values above 75%. Overall, BCSD-corrected subseasonal precipitation has shown promising performance, highlighting its potential broader applications for river and flood forecasting. 

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4. Steric Sea Level Rise and Relationships with Model Drift and Water Mass Representation in GFDL CM4 and ESM4

   https://doi.org/10.1175/JCLI-D-23-0591.1  

   Krasting, John P; Griffies, Stephen M; Tesdal, Jan-Erik; MacGilchrist, Graeme; Beadling, Rebecca L; Little, Christopher M (December 2024, Journal of Climate)

   Abstract Density-driven steric seawater changes are a leading-order contributor to global mean sea level rise. However, intermodel differences in the magnitude and spatial patterns of steric sea level rise exist at regional scales and often emerge during the spinup and preindustrial control integrations of climate models. Steric sea level results from an eddy-permitting climate model, GFDL CM4, are compared with a lower-resolution counterpart, GFDL-ESM4. The results from both models are examined through basin-scale heat budgets and watermass analysis, and we compare the patterns of ocean heat uptake, redistribution, and sea level differ in ocean-only [i.e., Ocean Model Intercomparison Project (OMIP)] and coupled climate configurations. After correcting for model drift, both GFDL CM4 and GFDL-ESM4 simulate nearly equivalent ocean heat content change and global sea level rise during the historical period. However, the GFDL CM4 model exhibits as much as a 40% increase in surface ocean heat uptake in the Southern Ocean and subsequent increases in horizontal export to other ocean basins after bias correction. The results suggest regional differences in the processes governing Southern Ocean heat export, such as the formation of Antarctic Intermediate Water (AAIW), Subpolar Mode Water (SPMW), and gyre transport between the two models, and that sea level changes in these models cannot be fully bias-corrected. Since the process-level differences between the two models are evident in the preindustrial control simulations of both models, these results suggest that the control simulations are important for identifying and correcting sea level–related model biases. 

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5. Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin

   https://doi.org/10.3390/hydrology11050066  

   Thota, Saichand; Nassar, Ayman; Filali_Boubrahimi, Soukaina; Hamdi, Shah Muhammad; Hosseinzadeh, Pouya (May 2024, Hydrology)

   Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using 30 years of monthly data (1991–2020), split into 80% for training (1991–2014) and 20% for testing (2015–2020). Initially, only historical streamflow data were used for predictions, followed by including meteorological factors to assess their impact on streamflow. Subsequently, sequence analysis was conducted to explore various input-output sequence window combinations. We then evaluated the influence of each factor on streamflow by testing all possible combinations to identify the optimal feature combination for prediction. Our results indicate that the Random Forest Regression model consistently outperformed others, especially after integrating all meteorological factors with historical streamflow data. The best performance was achieved with a 24-month look-back period to predict 12 months of streamflow, yielding a Root Mean Square Error of 2.25 and R-squared (R2) of 0.80. Finally, to assess model generalizability, we tested the best model at other locations—Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan) in the basin. 

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