Urmin Vegad - Academia.edu (original) (raw)

Papers by Urmin Vegad

Hydrology and Earth System Sciences, Dec 1, 2022

Developing an ensemble hydrological prediction system is essential for reservoir operations and f... more Developing an ensemble hydrological prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrological ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River Basin as a test bed. We use the variable infiltration capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada Basin. Streamflow prediction skills of the ERFS forecast were examined for the period 2003-2018 at 1-32 d lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1-10 d lead for the summer monsoon (June-September) 2019-2020. The ERFS forecast underestimates extreme precipitation against the observations compared to the GEFS forecast during the summer monsoon of 2019-2020. However, both forecast products show better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performs better than the ERFS during 2019-2020. The performance of GEFS-based ensemble streamflow forecast declines after 5 days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1-5 d lead, which can be utilized in streamflow prediction. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India.

Floods are among India's most frequently occurring natural disasters, which disrupt all aspects o... more Floods are among India's most frequently occurring natural disasters, which disrupt all aspects of socioeconomic well-being. A large population is affected by floods during almost every summer monsoon season in India, leaving its footprint through human mortality, migration, and damage to agriculture and infrastructure. Despite the massive imprints of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamical models, we reconstructed sub-basin level observed floods for the 1901-2020 period. Our modelling framework includes the influence of 51 major reservoirs that affect flow variability and flood inundation. Sub-basins in the Ganga and Brahmaputra River basins witnessed the greatest flood extent during the worst flood in the observational record. Major floods in the sub-basins of the Ganga and Brahmaputra occur during the late summer monsoon season (August-September). Beas, Brahmani, upper Satluj, Upper Godavari, Middle and Lower Krishna, and Vashishti sub-basins are among the most influenced by the dams, while Beas, Brahmani, Ravi, and Lower Satluj are among the most impacted by floods and the presence of dams. Bhagirathi, Gandak, Kosi, lower Brahmaputra, and Ghaghara are India's sub-basins with the highest flood risk. Our findings have implications for flood mitigation in India. globally are exposed to a 100-year return period flood (Koks et al., 2019). In Asia, about 75% of the population is exposed to riverine floods (Varis et al., 2022). India falls among the top ten most flood-affected countries in Asia and the Pacific (Kimuli et al., 2021). In addition, India is also among the top-ten countries that experienced the highest human mortality due to floods. Considerable population exposure, climate change, and rapid growth and development in flood-prone areas contribute to increased losses from floods. In India, state administration takes decisions to mitigate floods while the central government provides financial aid under severe conditions (Jain et al., 2017). The state authorities develop action plans to minimize flood damage. Therefore, identifying the regions with higher flood risk is essential for planning and mitigation. Flood impacts can be quantified according to the affected population, gross domestic product (GDP), and agricultural practices (Ward et al., 2013). The flood risk assessment framework suggested by the Intergovernmental Panel on Climate Change (IPCC) has been extensively applied at the regional and global scales (Allen et al., 2016; IPCC, 2014; Roy et al., 2021). The risk can be quantified as a function of vulnerability, hazard, and exposure (IPCC, 2014). To control the risk, reducing vulnerability is considered a short to the mid-term goal (Mishra et al., 2022), while reducing hazards and exposure are long-term goals (Birkmann & Welle, 2015). Flood risk assessment can assist in identifying the regions at high risk due to higher vulnerability, hazard, and exposure, which can be used for developing a framework, methodology, and guidelines for flood mitigation and damage assessment. A flood risk assessment performed on a global scale may not help in identifying the flood risk-prone regions at a country scale due to the coarser spatial resolution (Bernhofen et al., 2022). Due to complex geomorphological characteristics and diverse climatic conditions, India is considered a relatively high flood-risk region (Hochrainer

Environmental Research: Climate

Pakistan suffered from severe floods in the past, but in August 2022, the country experienced exc... more Pakistan suffered from severe floods in the past, but in August 2022, the country experienced exceptional extreme rainfall events that caused widespread and catastrophic flooding. The 2022 flood affected all aspects of socio-economic lives including agriculture, infrastructure, and mortality of humans and livestock. The two-day accumulated extreme rainfall on 17–18 August was anomalous and contributed the most to the flood in the southern provinces of Pakistan. The damage caused by extreme rainfall and the subsequent flooding has raised questions regarding the predictability of extreme rainfall by the existing weather forecasting models. Here, we use ensemble forecasts from four numerical weather prediction models under THORPEX Interactive Grand Global Ensemble datasets to examine the predictability of extreme rainfall at a six-day lead. The extreme precipitation during 17–18 August 2022 was predictable a week before the event that contributed the most to the flooding. All the forec...

WIREs. Water, May 26, 2024

Fig. S1-Evaluation of ERFS meteorological forecast minimum temperature data for period of 2003-20... more Fig. S1-Evaluation of ERFS meteorological forecast minimum temperature data for period of 2003-2018. The median of all members' skill at a grid is represented.

Developing an ensemble hydrologic prediction system is essential for reservoir operations and flo... more Developing an ensemble hydrologic prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrologic ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River basin as a testbed. We use the Variable Infiltration Capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada basin. We examined the streamflow forecast skills of the ERFS forecast for the period 2003-2018 at 1-32 day lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1-10 day lead for the summer monsoon (June-September) 2019-2020. The ERFS forecast underestimated extreme precipitation against the observations compared to the GEFS during the summer monsoon of 2019-2020. However, both the forecast products showed better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performed better than the ERFS during 2019-2020. The performance of the GEFS based ensemble streamflow forecast declines after five days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1-5 day lead. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India. billion dollars (USD) and resulted in the loss of lives (Joshi, 2020). In addition, climate warming is projected to increase the frequency and intensity of riverine floods (Field et al., 2011; Luo et al., 2015). Preparedness for disasters like floods can help mitigate economic loss and human lives (Jain et al., 2018). While financial loss due to floods is projected to rise under the warming climate, human mortality can be reduced with the flood early warning systems and effective communication (Dipti, 2017). Developing a flood prediction system is necessary for early warning and preparedness. Streamflow prediction is an essential component of flood forecasting, which helps in planning and decision-making (Georgakakos et al., 2012; Alfieri et al., 2013). Most of the streamflow prediction systems in India are based on the deterministic approach (Harsha, 2020a; Todini, 2017), which does not account for perturbations in initial conditions to quantify the uncertainty (Bowler et al., 2008). Uncertainty quantification in streamflow prediction can reduce the risk of false alarms (Todini, 2017). In addition, ensemble streamflow prediction is essential for the probabilistic flood forecast. The probabilistic approach performs better than the deterministic approach by quantifying uncertainties associated with flood prediction and early warning system (Krzysztofowicz, 2001). Previous studies used ensemble streamflow prediction in flood forecasting (Cloke and Pappenberger, 2009; Nanditha and Mishra, 2021; Wu et al., 2020)using ensemble meteorological forecast and hydrologic models (Zhang et al., 2020). Ensemble weather forecast provides multiple members at the same location and time that can be used for probabilistic hydrologic prediction. However, several challenges are associated with the operational ensemble streamflow forecast, including computational limitations, explanation of ensemble forecasts to non-experts, and up-gradation in the policy to use the forecast for decision making (Demeritt et al., 2010; Arnal et al., 2020). Despite these challenges, the advantages of ensemble flood forecasts have been reported in previous studies (Pappenberger et al., 2012; Cloke and Pappenberger, 2009). The Central Water Commission (CWC) manages flood forecast systems in India. The flood forecast network monitors 325 stations covering low lying areas and towns close to reservoirs. CWC observes real-time water level and discharge along the major rivers of India during the designated flood period. The flood forecast is performed using statistical correlation methods from gauge to gauge. Moreover, Quantitative Precipitation Forecast (QPF) from the India Meteorological Department (IMD) is used to forecast floods at a 3-day lead time (Teja and Umamahesh, 2020). The current flood forecast approach used by CWC is deterministic, which lacks incorporating uncertainties in the forecast and early warning system. An ensemble forecast system can help in the flood early warning and decision making (Harsha, 2020b; Nanditha and Mishra, 2021). Moreover, river basins in India are considerably influenced by reservoirs' presence, and incorporating the influence of reservoirs in streamflow prediction remains a challenge. Incorporating reservoir influence in hydrologic models is essential as reservoirs significantly affect the magnitude and timing of streamflow (Zajac et al., 2017; Yassin et al., 2019; Dang et al., 2019a). However, most of the previous studies on flood forecasts and early warnings in India did not consider the

&amp;amp;amp;amp;lt;p&amp;amp;amp;amp;gt;Ensemble Streamflow Prediction (ESP) is ... more &amp;amp;amp;amp;lt;p&amp;amp;amp;amp;gt;Ensemble Streamflow Prediction (ESP) is a widely used method in forecasting streamflow, particularly for extremely low or high flows. However, the incorporation of reservoir operations in using ensemble streamflow prediction has not been investigated till yet. We calibrated Variable Infiltration Capacity (VIC) model for daily streamflow for Narmada river basin at four stations (Sandia, Handia, Mandleshwar and Garudeshwar) considering the effect of four reservoirs (Bargi, Tawa, Indira Sagar and Sardar Sarovar). The model is well-calibrated for the selected river basin (R2&amp;amp;amp;amp;gt;0.55) at all locations. Further, routing of streamflow is done considering the reservoir storage dynamics and operating rules. Input data for ensemble prediction is taken from all 16 members of the Extended Range Forecast System (ERFS) developed by Indian Institute of Tropical Meteorology (IITM) and implemented by India Meteorological Department (IMD). Post-processing of the results gave us probabilities of uncertainties associated with streamflow prediction using ERFS members. This study provides key information in predictions of streamflow by incorporating the reservoirs based on the ERFS ensemble members, which can be used to effectively mitigate life and property losses associated with extreme flows in rivers.&amp;amp;amp;amp;lt;/p&amp;amp;amp;amp;gt;

Hydrology and Earth System Sciences, Dec 1, 2022

Environmental Research: Climate

WIREs. Water, May 26, 2024