The identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a novel multimodel ensemble mean bias-correction technique which uses the ensemble empirical mode decomposition method to correct the bias of ensemble mean of seven CMIP6 GCMs outputs with reference to the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have a nonlinear trend of seven GCMs but have interannual variance and mean climate of ERA5 dataset. The dataset spans from 1985 to 2100 for historical and future climate scenarios (SSP126, SSP245, SSP370, and SSP585) at daily time intervals with a 1 km grid resolution. The result of different scenarios indicates that the increase in maximum (Tmax) and minimum temperature (Tmin) ranging from 1.5 to 5.4 °C and 1.8 to 6.8 °C from 2015 to 2100, respectively. Similarly, elevation-dependent warming is identified in Tmin from 1.7 to 7.0 °C at elevations <2,000 to 6,000 m asl, while the contrary relationship in Tmax is projected under different scenarios from 2015 to 2100. This study provides an insight on how to improve the GCMs projections and can be helpful for further climate change impact studies.
Streamflow forecasting is highly crucial in the domain of water resources. For this study, we coupled the Wavelet Transform (WT) and Artificial Neural Network (ANN) to forecast Gilgit streamflow at short-term (T0.33 and T0.66), intermediate-term (T1), and long-term (T2, T4, and T8) monthly intervals. Streamflow forecasts are uncertain due to stochastic disturbances caused by variations in snow-melting routines and local orography. To remedy this situation, decomposition by WT was undertaken to enhance the associative relation between the input and target sets for ANN to process. For ANN modeling, cross-correlation was used to guide input selection. Corresponding to six intervals, nine configurations were developed. Short-term intervals performed best, especially for T0.33; intermediate intervals showed decreasing performance. However, interestingly, performance regains back to a decent level for long-term forecasting. Almost all the models underestimate high flows and slightly overestimate low- to intermediate-flow conditions. At last, inference implicitly implies that shorter forecasting benefits from extrapolated trends, while the good results of long-term forecasting is associated to a larger recurrent pattern of the Gilgit River. In this way, weak performance for intermediate forecasting could be attributed to the insufficient ability of the model to capture either one of these patterns.
Accurate hydrological simulations comply with the water (sixth) Sustainable Development Goals (SDGs). The study investigates the utility of artificial neural networks and support vector regressors, as well as the post-simulation bias treatment of these simulations in the Swat River basin, Pakistan. For this, climate variables were lag adjusted for the first time, then cross-correlated with the flow to identify the most associative delay time. In sensitivity analysis, seven combinations were selected as input with suitable hyperparameters. For SVR, grid search cross-validation was used for determining the optimal set of hyperparameters, while for ANN, neurons and hidden layers were optimized by trial and error. We ran the model by using optimized hyperparameter configurations and input combinations. In comparison to SVRs (Root mean square error (RMSE) 34.2; mean absolute error (MAE) 3.0; CC 0.91) values, respectively, ANN fits the observations better than SVR with (RMSE 11.9; MAE 1.14; CC 0.99). Linear bias-corrected simulations greatly improved ANN performance (RMSE 3.98; MAE 0.625; CC 0.99), while the improvement was slight in the case of SVR (RMSE 35; MAE 0.58; CC 0.92). On a seasonal scale, bias-corrected simulations remedy the low- and high-flow seasonal discrepancies. Flow duration analysis results reveal deviation at low- and high-flow conditions by models, which were then reconciled by applying bias corrections.
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