Vimal Mishra

A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions*

AbstractThis paper describes a publicly available, long-term (1915–2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset h...

Assessment of Drought due to Historic Climate Variability and Projected Future Climate Change in the Midwestern United States

Understanding the occurrence and variability of drought events in historic and projected future climate is essential to managing natural resources and setting policy. The Midwest region is a key contributor in corn and soybean production, and the occurrence of droughts may affect both quantity and quality of these crops. Soil moisture observations play an essential role in understanding the severity and persistence of drought. Considering the scarcity of the long-term soil moisture datasets, soil moisture observations in Illinois have been one of the best datasets for studies of soil moisture. In the present study, the authors use the existing observational dataset and then reconstruct long-term historic time series (1916‐2007) of soil moisture data using a land surface model to study the effects of historic climate variability and projected future climate change on regional-scale (Illinois and Indiana) drought. The objectives of this study are to (i) estimate changes and trends associated with climate variables in historic climate variability (1916‐2007) and in projected future climate change (2009‐99) and (ii) identify regional-scale droughts and associated severity, areal extent, and temporal extent under historic and projected future climate using reconstructed soil moisture data and gridded climatology for the period 1916‐2007 using the Variable Infiltration Capacity (VIC) model. The authors reconstructed the soil moisture for a long-term (1916‐2007) historic time series using the VIC model, which was calibrated for monthly streamflow and soil moisture at eight U.S. Geological Survey (USGS) gauge stations and Illinois Climate Network’s (ICN) soil moisture stations, respectively, and then it was evaluated for soil moisture, persistence of soil moisture, and soil temperature and heat fluxes. After calibration and evaluation, the VIC model was implemented for historic (1916‐2007) and projected future climate (2009‐99) periods across the study domain. The nonparametric Mann‐Kendall test was used to estimate trends using the gridded climatology of precipitation and air temperature variables. Trends were also estimated for annual anomalies of soil moisture variables, snow water equivalent, and total runoff using a long-term time series of the historic period. Results indicate that precipitation, minimum air temperature, total column soil moisture, and runoff have experienced upward trends, whereas maximum air temperature, frozen soil moisture, and snow water equivalent experienced downward trends. Furthermore, the decreasing trends were significant for the frozen soil moisture in the study domain. The results demonstrate that retrospective drought periods and their severity were reconstructed using model-simulated data. Results also indicate that the study region is experiencing reduced extreme and exceptional droughts with lesser areal extent in recent decades.

A prominent pattern of year-to-year variability in Indian Summer Monsoon Rainfall

The dominant patterns of Indian Summer Monsoon Rainfall (ISMR) and their relationships with the sea surface temperature and 850-hPa wind fields are examined using gridded datasets from 1900 on. The two leading empirical orthogonal functions (EOFs) of ISMR over India are used as basis functions for elucidating these relationships. EOF1 is highly correlated with all India rainfall and El Niño–Southern Oscillation indices. EOF2 involves rainfall anomalies of opposing polarity over the Gangetic Plain and peninsular India. The spatial pattern of the trends in ISMR from 1950 on shows drying over the Gangetic Plain projects onto EOF2, with an expansion coefficient that exhibits a pronounced trend during this period. EOF2 is coupled with the dominant pattern of sea surface temperature variability over the Indian Ocean sector, which involves in-phase fluctuations over the Arabian Sea, the Bay of Bengal, and the South China Sea, and it is correlated with the previous winter’s El Niño–Southern Oscillation indices. The circulation anomalies observed in association with fluctuations in the time-varying indices of EOF1 and EOF2 both involve distortions of the low-level monsoon flow. EOF1 in its positive polarity represents a southward deflection of moist, westerly monsoon flow from the Arabian Sea across India, resulting in a smaller flux of moisture to the Himalayas. EOF2 in its positive polarity represents a weakening of the monsoon trough over northeastern India and the westerly monsoon flow across southern India, reminiscent of the circulation anomalies observed during break periods within the monsoon season.

Changes in observed climate extremes in global urban areas

Climate extremes have profound implications for urban infrastructure and human society, but studies of observed changes in climate extremes over the global urban areas are few, even though more than half of the global population now resides in urban areas. Here, using observed station data for 217 urban areas across the globe, we show that these urban areas have experienced significant increases (p-value <0.05) in the number of heat waves during the period 1973–2012, while the frequency of cold waves has declined. Almost half of the urban areas experienced significant increases in the number of extreme hot days, while almost 2/3 showed significant increases in the frequency of extreme hot nights. Extreme windy days declined substantially during the last four decades with statistically significant declines in about 60% in the urban areas. Significant increases (p-value <0.05) in the frequency of daily precipitation extremes and in annual maximum precipitation occurred at smaller fractions (17 and 10% respectively) of the total urban areas, with about half as many urban areas showing statistically significant downtrends as uptrends. Changes in temperature and wind extremes, estimated as the result of a 40 year linear trend, differed for urban and non-urban pairs, while changes in indices of extreme precipitation showed no clear differentiation for urban and selected non-urban stations.

Evaluation of the Reanalysis Products for the Monsoon Season Droughts in India

AbstractDrought monitoring in near–real time is essential for management of water resources, irrigation planning, and food security. However, lack of availability of quality real-time observations leads to slow decision making and relatively poor natural resources management, especially during and after severe and prolonged droughts. The global reanalysis products that are available in near–real time could be valuable for drought monitoring and assessment. Three high-resolution reanalysis products—the Modern-Era Retrospective Analysis for Research and Applications (MERRA), the Interim ECMWF Re-Analysis (ERA-Interim), and the NCEP Climate Forecast System Reanalysis (CFSR)—are examined for their effectiveness in reproducing retrospective droughts during the period 1980–2005. All the selected reanalysis products show biases in the monsoon season precipitation and temperature. MERRA, ERA-Interim, and CFSR showed median bias in the monsoon season precipitation (temperature) of 10% (−0.39°C), 34% (−0.21°C), and...

Climatic uncertainty in Himalayan water towers

The Himalayan water towers (e.g., Indus, Ganges, and Brahmaputra) play a major role in water resource availability and affect a significant population in the South Asia region. Climatic uncertainty in the region not only hampers progress toward process understanding but also decision making. Observational data sets show uncertainty (standard deviation in mean temperature in data sets) of 0.2 to 0.5°C in winter (January-February-March-April) and the monsoon season (June-July-August-September) air temperature. Observational uncertainty in precipitation in the winter and monsoon seasons ranged between 6 and 17% (with respect to ensemble mean seasonal precipitation) in the Indus, Ganges, and Brahmaputra river basins. The Coordinated Regional Climate Downscaling Experiment (CORDEX) South Asia regional climate models (CORDEX-RCMs) show larger uncertainty (1–3.6°C in temperature and 18–60% in precipitation) than that of the observations. Moreover, RCMs exhibit a large cold bias (6–8°C) and are not able to reproduce the observed warming in the Himalayan water towers. In fact, the CORDEX-RCMs overestimate observed warming by threefold in Ganges and Brahmaputra basins, which raises a question on their reliability on future warming trends in the region. The CORDEX-RCMs overestimate the area that experienced significant warming and fail to reproduce precipitation trends in both magnitude as well as direction. In observational data sets, uncertainty in precipitation and air temperature increases with elevation, which may be associated with sparse observations. However, the CORDEX-RCMs showed larger uncertainty at the lower elevations in both precipitation as well as temperature. The host general circulation models show a better performance in simulating winter climate than the CORDEX-RCMs, which suggest that an improved representation of elevation may not necessarily improve the models performance. While observations show significant warming in the Indus basin and decline in the monsoon season precipitation in the Ganges basin, reliability of future climate projections and their impacts on water resources in the region will depend on improvements in the models and observations in coming years.

Soil Moisture Droughts under the Retrospective and Projected Climate in India

AbstractChanges in precipitation, air temperature, and model-simulated soil moisture were examined for the observed (1950–2008) and projected (2010–99) climate for the sowing period of Kharif and Rabi [KHARIF_SOW (May–July) and RABI_SOW (October–December)] and the entire Kharif and Rabi [KHARIF (May–October) and RABI (October–April)] crop-growing periods in India. During the KHARIF_SOW and KHARIF periods, precipitation declined significantly in the Gangetic Plain, which in turn resulted in declines in soil moisture. Statistically significant warming trends were noticed as all-India-averaged air temperature increased by 0.40°, 0.90°, and 0.70°C in the KHARIF, RABI_SOW, and RABI periods, respectively, during 1950–2008. Frequency and areal extent of soil moisture–based droughts increased substantially during the latter half (1980–2008) of the observed period. Under the projected climate (2010–99), precipitation, air temperature, and soil moisture are projected to increase in all four crop-growing seasons. In...

Impacts of Historic Climate Variability on Seasonal Soil Frost in the Midwestern United States

Abstract The present study examines the effects of historic climate variability on cold-season processes, including soil temperature, frost depth, and the number of frost days and freeze–thaw cycles. Considering the importance of spatial and temporal variability in cold-season processes, the study was conducted in the midwestern United States using both observations and model simulations. Model simulations used the Variable Infiltration Capacity (VIC) land surface model (LSM) to reconstruct and to analyze changes in the long-term (i.e., 1917–2006) means of soil frost variables. The VIC model was calibrated using observed streamflow records and near-surface soil temperatures and then evaluated for streamflow, soil temperature, frost depth, and soil moisture before its application at the regional scale. Soil frost indicators—such as the number of frost days and freeze–thaw cycles—were determined from observed records and were tested for the presence of significant trends. Overall trends in extreme and mean ...

Parameterization of Lakes and Wetlands for Energy and Water Balance Studies in the Great Lakes Region

Abstract Lakes and wetlands are prevalent around the Great Lakes and play an important role in the regional water and energy cycle. However, simulating their impacts on regional-scale hydrology is still a major challenge and not widely attempted. In the present study, the Variable Infiltration Capacity (VIC) model is applied and evaluated with a physically based lake and wetland algorithm, which can simulate the effect of lakes and wetlands on the grid cell energy and water balance. The VIC model was calibrated at 10 U.S. Geological Survey (USGS) stream gauging stations against daily streamflow records for the period of 1985–95, and successfully evaluated for the period of 1996–2005. Single-grid sensitivity experiments showed that runoff, baseflow, and inundation area were sensitive to the lake model parameters. Simulations were also conducted to analyze the spatial and temporal variability of inundation area for the period of 1985–2005. Results indicated that water and energy fluxes were substantially af...

Development of an Experimental Near-Real-Time Drought Monitor for India*

AbstractPersistent and widespread drought hampers water resources management and crop production. India has faced frequent droughts over the last few decades. Despite the detrimental impacts of droughts in India, a real-time monitoring system at appropriate spatial and temporal resolution has been lacking. Here, an experimental drought monitor for India is developed that operates daily at a spatial resolution of 0.25° and provides near-real-time information on droughts. The real-time daily precipitation data are obtained from the Tropical Rainfall Measurement Mission (TRMM) while daily temperatures are obtained from the Global Ensemble Forecast System (GEFS), version 2. Near-real-time precipitation and temperatures are bias corrected using the historic precipitation and temperature data from the India Meteorological Department (IMD). Data extending from the past to near present were reconstructed by combining IMD (1969–2010) with real-time, bias-corrected TRMM and GEFS datasets (2010 onward). The experime...