Response Evaluation of Environmental Flow Indicators to Land use Land Cover and Climate Change Over Three Humid Tropical River Basins

Abstract

Climate change and Land Use Land Cover (LULC) change are two major factors influencing river basin hydrology. The present research explored the isolated and combined impacts of these drivers on the ecologically relevant flow in three humid tropical basins, Meenachil, Manimala, and Achencoil, located in Kerala, India. Climate change and associated extreme events have a critical influence on maintaining the socioeconomic stability of society. The study examined the trend in climate variables, precipitation, and temperature over historical and future time steps. The distribution of historic precipitation is assessed with Precipitation Concentration Indices (PCI). The trend analysis of the climatic variables is evaluated using Mann Kendall test and Sen’s slope at annual and seasonal time steps. The historical precipitation showed the predominance of an insignificant declining trend in annual, winter, pre-monsoon, and monsoon time steps. The post-monsoon rainfall showed a positive trend in the area. The maximum and minimum temperatures showed a prominent rising trend in annual and seasonal time steps. The nature of extreme events is evaluated with extreme climate indices. The trend exhibited by ten precipitation indices: Maximum daily rainfall (Rx1day), Maximum 5-day rainfall (Rx5day), Number of heavy rainfall days (R10), Number of very heavy rainfall days (R20), Consecutive Wet Days (CWD), Consecutive Dry Days (CDD), Annual wet day rainfall total (PRCPTOT), Simple Daily Intensity Index (SDII), Precipitation from very wet days (R95p), Extremely wet days (R99p) and 4 temperature indices: Warmest day (TXx), Coldest day (TXn), Warmest night (TNx), Coldest night (TNn) over the area are determined. There is a general trend in the area of a considerable decrease in the number of CWD and a significant increase in the absolute extremes, Rx1day and Rx5day. The results revealed the possibility of rainfall getting concentrated in fewer days. The extreme temperature indices showed rising trend with significant rise for TXx and TXn, which can be a signal of the climate warming in the region. The future climate changes in the study basins are analysed with the statistically downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs, NEX-GDDP, and Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset. The Multi-Criteria Decision Making (MCDM) approaches Compromise Programming ii (CP), and PROMETHE-2 are considered for selecting a suitable subset of GCMs in the research. The ensemble mean of the top four models for the considered scenarios is subjected to bias correction using the Delta Change and Distribution Mapping techniques. The bias-corrected future projections under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios and Shared Socioeconomic Pathways (SSP) 126, 245, 370, and 585 scenarios are further considered for the trend test for the period 2020-2099. The analyses reveal a decreasing trend in average annual precipitation under the RCP 4.5 scenario, while an increasing trend is observed under the RCP 8.5 scenario. The SSP scenarios showed a significant rising trend in annual rainfall for end-century time slices. However, the projected maximum and minimum temperatures showed a significant rise under all the scenarios. The extreme precipitation indices are noticed with a rising trend under the considered emission scenarios, which points out the possibility of more extreme rainfall events in these basins. The rising temperature extremes and frequent rainfall extremes emphasize the need for suitable adaptation strategies and mitigation measures in these basins. The LULC is crucial in influencing the processes that shape the Earth's surface. It is essential to comprehend the spatio-temporal dynamics of the LULC within a certain location. Thus, the LULC maps for the basins are studied with a Land Change Modeler (LCM) and utilised to predict future LULC. The Random Forest (RF) classifier in Google Earth Engine (GEE) was utilised for LULC classification for the years 1990, 2000, 2008, 2018 and 2021. The overall accuracy obtained for the years 1990, 2000, 2008, 2018, and 2021 were 0.92, 0.91, 0.97, 0.88, and 0.95, respectively, followed by a Kappa coefficient of 0.91, 0.89, 0.96, 0.85, and 0.94. LCM was explored for LULC change detection, the model was validated successfully in predicting the LULC distribution in 2021, and the results were comparable with the actual 2021 LULC. Results of the analysis revealed the changes undergone by various LULC classes in both basins. Historical LULC analysis showed the expansion of the Built-up area and Barren land in these basins. The study then utilised LCM to predict future LULC up to the year 2050 at decadal intervals. The predicted future LULC maps revealed the drastic expansion of built-up that these basins might witness in the coming decades. iii The NEX-GDDP and CMIP6 datasets, along with the projected LULC, are considered for impact assessments. The Soil and Water Assessment Tool (SWAT) is employed to simulate streamflow under LULC and climate change scenarios. The LULC 2050 scenario shows the most significant rise in average annual streamflow, at 5.9%, 6.7%, and 7.5%, respectively, in the Meenachil, Manimala, and Achencoil basins. The average monthly flow, as well as the extreme flow indices, is also expected to increase with the predicted LULC in the study basins. Meanwhile, in climate change scenarios, the response of the average annual flow varies according to the selected emission scenarios and time slices. It is observed that there is a decrease in average annual flow under RCP 4.5 and an increase under RCP 8.5. However, according to the SSP 126 and SSP 245 scenarios, the flow is projected to decrease in the near future and then increase towards the end of this century. The SSP 370 and SSP 585 showed an increase in flow in most of the time slices. The combined impacts of climate change and LULC change are found to be relatively higher than the isolated effect of these drivers in the basins. The study outcomes are expected to help policymakers in assessing the impact of climate change and LULC change on the hydrology of the river. This will enable the adoption of management measures that take the riverine ecosystem into account.