Rainfall Variability Analysis In The Nira River Basin Using Multi-Model GCM Ensemble

Observed daily rainfall data during baseline period i.e. 1961-1990 of four raingauge stations namely Akluj, Baramati, Bhor and Malsiras located in the Nira River basin in Central India were analyzed to study the impact of climate change on rainfall. LARS-WG incorporating 15 GCM’s from the CMIP3 predictions for A1B, A2 and B1 emission scenarios was used to statistically downscale the daily rainfall data during three time spans centred at 2020’s, 2055’s and 2090’s. Uncertainty in GCMs rainfall predictions was analyzed on monthly, seasonal and annual scales. Kolmogorov-Smirnov test, Student’s t-test, and Fisher test have shown average to good performance during synthetic rainfall data generation for all the stations. The analysis of the data shows that the uncertainty in the prediction increases with the timescale. Also, the variability in the predictions is smaller in annual values followed by seasonal and monthly values. Maximum uncertainty is observed in A2 scenario followed by A1B and B1 Scenarios. Monsoon months show minimum uncertainty in all the scenarios. The rainfall of Dec, Mar, Apr and May months are expected to increase in first two spans while expected to decrease in the last time span 2080 -2099 under all the scenarios. The monsoon month’s rainfall is expected to increase slightly in future for all the scenarios. Baramati shows maximum increase in annual rainfall for all scenarios while rainfall at Malsiras is expected to decrease only during third time span for all three scenarios

Snowmelt Modelling Of Dhauliganga River Using Snowmelt Runoff Model

Snowmelt modeling was attempted in this study using Snowmelt Runoff Model to simulate streamflow in Tamak Lata Catchment located in the Indian Himalayas. The daily snow cover data was generated using the depletion curves prepared using snow cover information obtained from MODIS remote sensing images during May 2009 to August 2012. Discharge, temperature and rainfall data observed at Tamak Lata during May 2009 to August 2012 were used for calibration and validation of the model. The characteristics of snow cover in the basin shows that the accumulation of snow at higher altitude starts from the second week of October and the snowline comes down to lower elevation up to lower zone. By the end of March, the snowmelt begins and the snowline recedes up to elevation of 5200 m by the end of the melt season. Till the start of the melt season, more than 75% of the basin area is covered with snow and it reduces to approximately 25% at the end of the melt season. The calibration of the model in terms of stream flow has indicated that the low flows and the peaks in the stream flow are well produced. Statistical evaluation of the model performance during calibration period, in the form of efficiency varied from 0.74 to 0.90 with an average value of 0.812 indicating a good model fit. The model performance during validation period was also found to be very good with efficiency with 0.8. The modeling of the snowmelt shows that snow and glacier runoff contribution in Tamak Lata catchment were 63.81% on annual basis and 65.34%, 52.64%, 73.4% for monsoon, post monsoon and, winter and pre-monsoon seasons

Using a Multi-Institutional Ensemble of Watershed Models to Assess Agricultural Conservation Effectiveness in a Future Climate

This study investigates the combined impacts of climate change and agricultural conservation on the magnitude and uncertainty of nutrient loadings in the Maumee River Watershed, the second-largest watershed of the Laurentian Great Lakes. Two scenarios — baseline agricultural management and increased agricultural conservation — were assessed using an ensemble of five Soil and Water Assessment Tools driven by six climate models. The increased conservation scenario included raising conservation adoption rates from a baseline of existing conservation practices to feasible rates in the near future based on farmer surveys. This increased adoption of winter cover crops on 6%–10% to 60% of cultivated cropland; subsurface placement of phosphorus fertilizers on 35%–60% to 68% of cultivated cropland; and buffer strips intercepting runoff from 29%–34% to 50% of cultivated cropland. Increased conservation resulted in statistically significant (p ≤ 0.05) reductions in annual loads of total phosphorus (41%), dissolved reactive phosphorus (18%), and total nitrogen (14%) under the highest emission climate scenario (RCP 8.5). While nutrient loads decreased with increased conservation relative to baseline management for all watershed models, different conclusions on the true effectiveness of conservation under climate change may be drawn if only one watershed model was used.publishedVersio

Impact Of El Niño Southern Oscillation On Monsoon Rainfall In Bhima Basin, Central India

The analysis of the effects of ENSO phenomenon on monsoon rainfall of the Bhima River basin located in Central India was investigated using 10x10 degree gridded monsoon rainfall during 1951-2003 and data of four ENSO indices (SOI, MEI, Niño 3.4 and DMI). Data of ENSO indices were divided into three seasons and only statistically significantly correlation was found between monsoon rainfall and monsoon season ENSO indices. Analysis also shows that the monsoon rainfall is positively correlated with SOI index whereas negatively correlated with all other indices. A significant change in the regional climate after 1976 has been reported in many studies. Thus, monsoon rainfall data and ENSO indices were also analyzed in two different duration i.e before and after the climate shift. Correlation between monsoon rainfall and ENSO indices increases after the climate shift year. ENSO phase wise analysis show that a strong/weak monsoon rainfall is associated with La Niña phase / El Niño phase except for the grids that are located on higher elevation where orography plays an important role in rainfall occurrences. Analysis of monsoon rainfall data of La Niña and El Niño phases describes that more rainfall is received at most of the grids during the La Niña phases which becomes higher after the climate shift year; during El Niño phase less rainfall is received which becomes lesser after the climate shift year