Papers by Amar Deep Tiwari
Scientific Data
The Mekong River basin (MRB) is a transboundary basin that supports livelihoods of over 70 millio... more The Mekong River basin (MRB) is a transboundary basin that supports livelihoods of over 70 million inhabitants and diverse terrestrial-aquatic ecosystems. This critical lifeline for people and ecosystems is under transformation due to climatic stressors and human activities (e.g., land use change and dam construction). Thus, there is an urgent need to better understand the changing hydrological and ecological systems in the MRB and develop improved adaptation strategies. This, however, is hampered partly by lack of sufficient, reliable, and accessible observational data across the basin. Here, we fill this long-standing gap for MRB by synthesizing climate, hydrological, ecological, and socioeconomic data from various disparate sources. The data— including groundwater records digitized from the literature—provide crucial insights into surface water systems, groundwater dynamics, land use patterns, and socioeconomic changes. The analyses presented also shed light on uncertainties asso...
A Bayesian Hierarchical Model Combination Framework for Real‐Time Daily Ensemble Streamflow Forecasting Across a Rainfed River Basin
Earth's Future
Warming climate and ENSO variability enhance the risk of sequential extremes in India
One Earth
Re-operating dams in the Mekong
Nature Sustainability
Short to sub-seasonal streamflow forecast in India�, in IUKWC Workshop: Science and Innovation for Catchment Management, University of Warwick, UK, May 08-10, 2019
by Amar Deep Tiwari, Atul Kumar Sahai and Vimal Mishr
On the occurrence of the observed worst flood in Mahanadi River basin under the warming climate
Weather and Climate Extremes
Remotely-sensed near real-time monitoring of reservoir storage in India
by Amar Deep Tiwari and Vimal Mishr
Short to Sub-seasonal Streamflow Forecasts for Reservoir Operations in India
A framework to incorporate spatiotemporal variability of rainfall extremes in summer monsoon declaration in India
Environmental Research Letters
The Indian summer monsoon rainfall is a lifeline for agricultural activities and the socio-econom... more The Indian summer monsoon rainfall is a lifeline for agricultural activities and the socio-economic development of more than one billion people. All-India averaged summer monsoon rainfall has about 10% variability from its long-term mean. A departure of all-India averaged precipitation within ±10% is declared a normal summer monsoon. Using the long-term (1901-2021) gridded rainfall observations, we highlight the limitations in the current approach to the declaration of the normal summer monsoon, which ignores the role of spatiotemporal variability of rainfall. Dry and wet extremes within the same monsoon season can lead to a normal monsoon. Moreover, different parts of the country face drought and wet extremes, while the summer monsoon can be declared normal. Considering the profound implications of dry and wet extremes on agricultural activities, we propose a novel framework to account for the rainfall variability in the declaration of the summer monsoon. The proposed framework acc...
Experimental daily ensemble streamflow forecasting system using physical model output in a Bayesian hierarchical framework
Utility of Sub-seasonal Experiment (SubX) forecast for hydrologic prediction in India
Data contains the forecast skill of the meteorological and hydrological forecast from 5 SubX mode... more Data contains the forecast skill of the meteorological and hydrological forecast from 5 SubX models, ERFS model, and GEFS model generated using RAW and bias-corrected data.
CMIP6_BiasCorrectedData_PrecipTMaxTMin_Countrywise
Here we have put the bias-corrected data of precipitation, maximum temperature, and minimum tempe... more Here we have put the bias-corrected data of precipitation, maximum temperature, and minimum temperature data in country wise format. Each zipped country file contains 13 models, and each model includes five scenarios (historical, ssp126, ssp245, ssp370, and ssp585). Inside a scenario folder, a file named PrecipData can be read as the first three columns from the 3rd row contain year month and day numbers. 1st two rows from the 3rd column represent the longitude and latitude.
CMIP6_BiasCorrectedData_PrecipTMaxTMin_Basinwise
Here we have put the bias-corrected data of precipitation, maximum temperature, and minimum tempe... more Here we have put the bias-corrected data of precipitation, maximum temperature, and minimum temperature data in basin wise format. Each zipped basin file contains 13 models, and each model includes five scenarios (historical, ssp126, ssp245, ssp370, and ssp585). Inside a scenario folder, a file named PrecipData can be read as the first three columns from the 3rd row contain year month and day numbers. 1st two rows from the 3rd column represent the longitude and latitude.
Bias Corrected Climate Projections from CMIP6 Models for South Asia
Bias-corrected data of precipitation, maximum temperature, and minimum temperature are developed ... more Bias-corrected data of precipitation, maximum temperature, and minimum temperature are developed for six countries in South Asia. Each zipped country file contains 13 models, and each model includes five scenarios (historical, ssp126, ssp245, ssp370, and ssp585). Inside a scenario folder, a file named PrecipData can be read as the first three columns from the 3rd row contain year month and day numbers. 1st two rows from the 3rd column represent the longitude and latitude.
Sub‐seasonal prediction of drought and streamflow anomalies for water management in India
Journal of Geophysical Research: Atmospheres
This is the confidential data related to the article "Sub-seasonal prediction of drought and... more This is the confidential data related to the article "Sub-seasonal prediction of drought and streamflow anomalies for water management in India".
Reservoir storage forecasting and monitoring major reservoirs in India
Climate change is likely to pose enormous challenges for agriculture, water resources, infrastruc... more Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951-2014) and projected (2015-2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 CMIP6-GCMs. The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3-5°C) and wetter (13-30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate ...
Monitoring and forecasting of reservoir storage in India
MAUSAM
The evolution of operational extended range forecast (ERF) system of IMD starting from use of emp... more The evolution of operational extended range forecast (ERF) system of IMD starting from use of empirical models, dynamic models and the Multi-model Ensemble (MME) from 2009 to 2016 till the operational implementation of fully coupled model in 2016 is discussed. The coupled model implemented in IMD is the Climate Forecast System version 2 (CFSv2). The performance of ERF for southwest monsoon, northeast monsoon, cyclogenesis over the Bay of Bengal (BoB) and maximum and minimum temperature during the period from 2009 to 2018 have been discussed along with its prospects of its application in different sectors like, agriculture, hydrology, health, power etc. are also analysed. The performance of extended range forecasts for the southwest monsoon seasons clearly captured the intraseasonal variability of monsoon including delay/early onset of monsoon, active/break spells of monsoon and also withdrawal of monsoon in the real time in providing guidance for various applications. The MME based ERF also provides encouraging results to provide useful guidance upto 2/3 weeks about northeast monsoon and cyclogenesis potential during October to December (OND) over the north Indian Ocean, heat wave, cold wave during summer and winter with statistically significant correlation coefficient (CC) upto two weeks. For applications in agriculture sector meteorological subdivision level forecasts are prepared for two weeks for the purpose of agro advisory. In addition to the regular ERF products for application in agriculture and hydrology, additional products are being prepared like, Standarised Precipitation Index (SPI), land-surface hydrology products like soil moisture and runoff change, transmission windows products for vector borne diseases etc for applications in agriculture, hydrology and health sectors.
Influence of bias correction of meteorological and streamflow forecast on hydrological prediction in India
Journal of Hydrometeorology
The efforts to develop a hydrologic model-based operational streamflow forecast in India are limi... more The efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecast and streamflow post-processing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root zone soil moisture in the Narmada basin (drainage area: 97,410 km2), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June-September) season for 2000-2018 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1-3 day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root mean squared error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1-3-day lead time. We used Empirical Quantile Mapping (EQM) to bia...
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Papers by Amar Deep Tiwari