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In this paper, we estimate the resource costs of agricultural water use and simulate the environmental and economic impacts of their recovery. To this end, we develop a socio-hydrology-inspired, dynamic, protocol-based modular approach that interconnects economic and hydrologic modeling via two-way feedback protocols. The hydrologic module is populated with the AQUATOOL model, the Decision Support System used in Spanish river basins; while the economic module is populated with an ensemble of four Mathematical Programming Models (MPMs) that capture human agency and responses. This allows us to sample uncertainty and provide a range for resource costs estimates and the environmental and economic impacts of their recovery, rather than a point estimate. Methods are illustrated with an application to the Órbigo Catchment, a sub-basin of the Douro River Basin in Spain. Our results suggest significant resource costs (a 34–62% increase in existing charges, depending on the model) with non-trivial impacts on income (2–27% reduction) and the environment (water savings range between 6% and 69%), while the impact on tax revenue is ambiguous yet potentially significant (between $-$2.3 million EUR/year and 5 million EUR/year).

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20mm/day and an RMSE up to 15mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to $\pm 1.5$mm/day and RMSE up to 2mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

As the global mean sea surface temperature (SST) increases, the frequency and intensity of marine heatwaves (MHWs) are also increasing. A model evaluation is needed to better understand future projections for MHWs. In this study, we evaluated MHWs in a historical simulation of 14 CMIP6 (Coupled Model Project Intercomparison Phase 6) models in the North Pacific Ocean (NPO) where MHW occurs frequently by comparing the OISST (Optimum Interpolation Sea Surface Temperature) reanalysis data for 33 years (1982∼2014). The CMIP6 models overestimated the annual mean cumulative duration of MHWs in the NPO by approximately 25 days compared to the OISST while the frequency was underestimated by 0.3∼0.6 events per year. Over 80% of CMIP6 models underestimated the spatial mean of the mean and maximum intensities, although a majority (60%) of the models overestimated the intensity north of the Kuroshio extension region, presumably by Kuroshio overshooting. Furthermore, because the CMIP6 multi-model ensemble underestimated the increasing trend regarding MHW characteristics (duration, frequency, and intensity) according to warming, it is necessary to investigate the influences of trend underestimation in the characteristics of MHWs on future changes in MHWs. Our results suggest that the MHW biases are still significant in CMIP6 models, demanding an increase in model horizontal resolution.