Complementary use of multi-model climate ensemble and Bayesian model averaging for projecting river hydrology in the Himalaya

Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia

The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1-5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021-2060 and far-future period 2061-2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.

Earth observation satellite data-based assessment of wetland dynamics in the Kashmir Himalaya

Earth observation (EO) technology offers enormous opportunities to assess the magnitude and patterns of spatial variability in wetlands over time. This study aims to assess the spatial and temporal changes in the wetlands of the Kashmir valley using multiple remote sensing satellite data products, Geographic Information System (GIS), and field observations. Moreover, role of major factors operating at different time scales including regional geology, climate, and human activities in driving the wetland change is presented. The dynamics of the wetlands are illustrated in the occurrence, seasonality, and recurrence of surface water, land cover transitions and loss patterns particularly for the period from 1984 to 2021. Constituting about 3% (495 Km2) of the total area, substantial and variable patterns of seasonal and annual changes are exhibited by the wetlands. The main transitions of the water surface reveal that 2% of the area has changed from permanent to seasonal; 8% is lost; 15% is new seasonal; 0.12% is permanently lost; and 0.3% is new permanent. About 22% of the area reveals increase in the intensity of water surface occurrence, whereas 44% shows no change, and 34% exhibits decrease. Bathymetric analysis suggests that the average depth of the wetlands ranges between 0.6 and 16.6 m. In general, alpine wetlands are relatively deeper and mostly static in their structure whereas those in the floodplain are shallow, fragmented, and showing signs of depletion during the assessment period. The results of this assessment will inform the policy on conservation and sustainability of wetlands in the Kashmir Himalaya.

Development of multi-model ensembles using tree-based machine learning methods to assess the future renewable energy potential: case of the East Thrace, Turkey

Since investigating the long-term trends of the renewable energy potential may help in planning sustainable energy systems, this study intends to forecast the renewable energy potential of the East Thrace, Turkey region, in the future based on CMIP6 Global Circulation Models data using the ensemble mean output of the best-performed tree-based machine learning method. To evaluate the accuracy of global circulation models, Kling-Gupta efficiency, modified index of agreement, and normalized root-mean-square error are applied. The best four global circulation models are detected as a result of the comprehensive rating metric, which combines all accuracy performance results into a single metric. Three different machine learning methods, random forest, gradient boosting regression tree, and extreme gradient boosting, are trained using the historical data of the top-four global circulation models and the ERA5 dataset to calculate the multi-model ensembles of each climate variable, and then, the future trends of those variables are forecasted based on the output of ensemble means of best-performed machine learning methods with the lowest out-of-bag root-mean-square error. It is foreseen that there will not be a significant change in the wind power density. The annual average solar energy output potential is found to be between 237.8 and 240.7 kWh/m2/year depending on the shared socioeconomic pathway scenario. Under the forecasted precipitation scenarios, 356-362 l/m2/year of irrigation water could be harvested from agrivoltaic systems. Thereby, it would be possible to grow crops, generate electricity, and harvest rainwater on the same area. Furthermore, tree-based machine learning methods provide much lower error compared to simple mean methods.