Modelling for Science, for a better future - some recent outcomes
Earthquake Hazard and Risk Assessment Based on Unified Scaling Law for Earthquakes: State of Gujarat, India
by Imtiyaz A. Parvez, Anastasia Nekrasova and Vladimir Kossobokov
The Gujarat state of India is one of the most seismically active intercontinental regions of the world. Historically, it has experienced many damaging earthquakes including the devastating 1819 Rann of Kachchh and 2001 Bhuj earthquakes. The effect of the later one is grossly underestimated by the Global Seismic Hazard Assessment Program (GSHAP). To assess a more adequate earthquake hazard for the state of Gujarat, we apply Unified Scaling Law for Earthquakes (USLE), which generalizes the Gutenberg–Richter recurrence relation taking into account naturally fractal distribution of earthquake loci. USLE has evident implications since any estimate of seismic hazard depends on the size of the territory considered and, therefore, may differ dramatically from the actual one when scaled down to the proportion of the area of interest (e.g. of a city) from the enveloping area of investigation. We cross-compare the seismic hazard maps compiled for the same standard regular grid 0.2° × 0.2° (1) in terms of design ground acceleration based on the neo-deterministic approach, (2) in terms of probabilistic exceedance of peak ground acceleration by GSHAP, and (3) the one resulted from the USLE application. Finally, we present the maps of seismic risks for the state of Gujarat integrating the obtained seismic hazard, population density based on India’s Census 2011 data, and a few model assumptions of vulnerability.
Impact of Assimilation on Heavy Rainfall Simulations Using WRF Model: Sensitivity of Assimilation Results to Background Error Statistics
by V. Rakesh and B. Kantharao
Data assimilation is considered as one of the effective tools for improving forecast skill of mesoscale models. However, for optimum utilization and effective assimilation of observations, many factors need to be taken into account while designing data assimilation methodology. One of the critical components that determines the amount and propagation observation information into the analysis, is model background error statistics (BES). The objective of this study is to quantify how BES in data assimilation impacts on simulation of heavy rainfall events over a southern state in India, Karnataka. Simulations of 40 heavy rainfall events were carried out using Weather Research and Forecasting Model with and without data assimilation. The assimilation experiments were conducted using global and regional BES while the experiment with no assimilation was used as the baseline for assessing the impact of data assimilation. The simulated rainfall is verified against high-resolution rain-gage observations over Karnataka. Statistical evaluation using several accuracy and skill measures shows that data assimilation has improved the heavy rainfall simulation. Our results showed that the experiment using regional BES outperformed the one which used global BES. Critical thermo-dynamic variables conducive for heavy rainfall like convective available potential energy simulated using regional BES is more realistic compared to global BES. It is pointed out that these results have important practical implications in design of forecast platforms while decision-making during extreme weather events.
An evaluation strategy of skill of high-resolution rainfall forecast for specific agricultural applications
by V. Rakesh and Prashant Goswami
A strategy for validation of rainfall forecasts for specific agricultural applications is presented. The focus is mainly on the design of specific forecast advisories that are risk-free and useful in spite of their inherent errors. The strategy works for these specific applications because the forecast advisories are based on when NOT to irrigate or apply fertilizer/pesticide because rain is predicted (risk-free because wrong forecast only delays irrigation/application of fertilizer/pesticide within tolerance). Thus, unlike in conventional forecast evaluation, a forecast is considered as valid if the forecasted rain (or no rain) is correct for the day of the forecast (D0C) or the next day or the day after (designated D1C and D2C, respectively), as the farmer can afford to postpone the field application for a couple of days beyond the scheduled date. The methodology has been evaluated for rainfall forecasts over Karnataka (a state in southwest India with nearly 56% of the workforce engaged in agriculture). Here, forecast validation against rain gauge observations is presented at comparable resolutions for the southwest (June to September) and the northeast (October to December) monsoon seasons during 2011–2014. Analyses demonstrate that forecasts over several areas which may appear to be less reliable based on conventional evaluation (D0C) are found to have useful skill for the specific agro-applications as evident from evaluation based on D1C and D2C criteria. Our analysis shows that the evaluation strategy presented is effective during the non-rainy (January–May) season also. It is pointed out that such an approach can help to meet the challenges in designing and implementing best practices in agriculture by combining immediate gains for the end users with long-term sustainability.
- Reflection and Refraction of Attenuated Waves at the Interface Between Cracked Poroelastic Medium and Porous Solid Saturated with Two Immiscible Fluids
- Precipitation-aerosol relationship over the Indian region during drought and excess summer monsoon years
- Reduction of uncertainty associated with future changes in Indian summer monsoon projected by climate models and assessment of monsoon teleconnections
- Neo-deterministic Definition of Seismic and Tsunami Hazard Scenarios for the Territory of Gujarat (India)
- Probabilistic earthquake hazard assessment for Peninsular India