Climate change scenario analysis for Baro-Akobo river basin, Southwestern Ethiopia

Background Several water resources projects are under planning and implementation in the Baro-Akobo basin. Currently, the planning and management of these projects is relied on historical data. So far, hardly any study has addressed water resources management and adaptation measures in the face of changing water balances due to climate change in the basin. The main bottleneck to this has been lack of future climate change scenario base data over the basin. The current study is aimed at developing future climate change scenario for the basin. To this end, Regional Climate Model (RCM) downscaled data for A1B emission scenario was employed and bias corrected at basin level using observed data. Future climate change scenario was developed using the bias corrected RCM output data with the basic objective of producing baseline data for sustainable water resources development and management in the basin. Result The projected future climate shows an increasing trend for both maximum and minimum temperatures; however, for the case of precipitation it does not manifest a systematic increasing or decreasing trend in the next century. The projected mean annual temperature increases from the baseline period by an amount of 1 °C and 3.5 °C respectively, in 2040s and 2090s. Similarly, evapotranspiration has been found to increase to an extent of 25% over the basin. The precipitation is predicted to experience a mean annual decrease of 1.8% in 2040s and an increase of 1.8% in 2090s over the basin for the A1B emission scenario. Conclusion The study resulted in a considerable future change in climatic variables (temperature, precipitation, and evapotranspiration) on the monthly and seasonal basis. These have an implication on hydrologic extremes-drought and flooding, and demands dynamic water resources management. Hence the study gives a valuable base information for water resources planning and managers, particularly for modeling reservoir inflow-climate change relations, to adapt reservoir operation rules to the real-time changing climate.

ASSESSING THE IMPACTS OF CLIMATE CHANGE ON THE HYDROLOGY OF THE INDRAWATI RIVER BASIN, NEPAL

This study details climate change assessment of the hydrological regime of Indrawati basin of Nepal. The study uses Soil and Water Assessment Tool (SWAT) model to delineate, discretize and parameterize the Indrawati basin to compute model’s input parameters. The model was then run for 1990–2014 to simulate the discharge at the outlet (Dholalghat). The coefficient of determination R2 and Nash-Sutcliffe (ENS) were used to evaluate model calibration and validation. The results found were satisfactory for the gauging station R2 = 0.951 and ENS = 0.901 for calibration and R2 = 0.937 and. ENS = 0.906 for validation. The calibrated hydrological model was run for the future climate change scenario using the RegCM4-LMDZ4 data and the relative changes with the baseline scenarios were analyzed. The comparison suggests that the historical trend of flow is decreasing at the rate of 0.55 m3/s per year. According to RegCM4-LMDZ4 simulations, the trend is going to continue but at a flatter rate. The decreasing trend is observed to be very less. The characteristic peak flow month in the historical scenario is August but the RegCM4-LMDZ4 led simulated flows suggest a shift in monthly peak to October suggesting decrease in monsoon flows and a subsequent significant increase in flows from October to January.

Evaluation of long term forest fires in India with respect to state administrative boundary, forest category of LULC and future climate change scenario: A Geospatial Perspective

Analysing the forest fires events in climate change scenario is essential for protecting the forest from further degradation. Geospatial technology is one of the advanced tools that has enormous capacity to evaluate the number of data sets simultaneously and to analyse the hidden relationships and trends. This study has evaluated the long term forest fire events with respect to India’s state boundary, its seasonal monthly trend, all forest categories of LULC and future climate anomalies datasets over the Indian region. Furthermore, the spatial analysis revealed the trend and their relationship. The state wise evaluation of forest fire events reflects that the state of Mizoram has the highest forest fire frequency percentage (11.33%) followed by Chhattisgarh (9.39%), Orissa (9.18%), Madhya Pradesh (8.56%), Assam (8.45%), Maharashtra (7.35%), Manipur (6.94%), Andhra Pradesh (5.49%), Meghalaya (4.86%) and Telangana (4.23%) when compared to the total country’s forest fire counts. The various LULC categories which represent the forest show some notable forest fire trends. The category ‘Deciduous Broadleaf Forest’ retain the highest fire frequency equivalent to 38.1% followed by ‘Mixed Forest’ (25.6%), ‘Evergreen Broadleaf Forest’ (16.5%), ‘Deciduous Needle leaf Forest’ (11.5%), ‘Shrub land’ (5.5%), ‘Evergreen Needle leaf Forest’ (1.5%) and ‘Plantations’ (1.2%). Monthly seasonal variation of forest fire events reveal the highest forest fire frequency percentage in the month of ‘March’ (55.4%) followed by ‘April’ (28.2%), ‘February’ (8.1%), ‘May’ (6.7%), ‘June’ (0.9%) and ‘January’ (0.7%). The evaluation of future climate data for the year 2030 shows significant increase in forest fire seasonal temperature and abrupt annual rainfall pattern; therefore, future forest fires will be more intensified in large parts of India, whereas it will be more crucial for some of the states such as Orissa, Chhattisgarh, Mizoram, Assam and in the lower Sivalik range of Himalaya. The deciduous forests will further degrade in future. The highlight/results of this study have very high importance because such spatial relationship among the various datasets is analysed at the country level in view of the future climate scenario. Such analysis gives insight to the policymakers to make sustainable future plans for prioritization of the various state forests suffering from forest fire keeping in mind the future climate change scenario.