Identification of homogeneous regions of near surface air temperature lapse rates across India

Abstract

Spatial variation of near surface temperature lapse rate is usually analysed by subjectively dividing the study area in to different sub-regions based on political or physiographic boundaries. This can often lead to unreliable results as near surface temperature lapse rate largely depends on regional conditions. In this study, homogeneous regions of near surface air temperature lapse rate across India have been identified for monthly mean minimum, mean, and mean maximum temperature using a probabilistic Gaussian mixture model clustering approach. For the delineated regions, lapse rates are estimated using weighted linear regression model. The weights corresponding to each station were obtained from the clustering approach. Results indicate that regions with uniform lapse rate vary across different months and a minimum of three regions is obtained for maximum temperature in April month. Further, temperature lapse rate estimates exhibit spatial and temporal variability, and are less steep than the Environmental lapse rate (−6.5 C/km). The maximum spatial variability in lapse rate is observed in May for both maximum and minimum temperature. The delineation of entire India into homogeneous regions improves temperature interpolation when compared to results obtained by considering entire India as one region. A maximum improvement of 34% in root mean square error is observed in prediction of monthly mean temperature in March. The identified regions and the associated lapse rates are expected to improve the prediction of near surface temperature at ungauged locations across India. K E Y W O R D Scluster analysis, India, lapse rate, regions, temperature | INTRODUCTIONTemperature is an important variable in hydrological, agricultural and climate model studies, as it strongly influences the partitioning of energy and water fluxes between the land and the atmosphere. The estimates of hydrological processes such as evaporation and evapotranspiration that depend on temperature can improve streamflow prediction in a river basin. In agricultural models, the estimates of these processes can assist in modelling soil moisture dynamics, determining crop water requirement and irrigation scheduling. In climate change impact assessment studies, temperature is an important indicator of global warming (Bharath et al., 2016). Therefore, it is important for societal planning in water resources, hydropower, agriculture, ecology, and other areas, to accurately quantify temperature distribution over the region of interest (Minder et al., 2010).Numerous studies have focused on modelling spatial and/or temporal variation of temperature (e.g., Luterbacher et al., 2004;Toreti and Desiato, 2008;Bharath et al., 2016). However, modelling spatial-temporal variability of temperature remains a challenging task, particularly over large geographical areas with complex topography and varying climate. Dense networks of sensors with high temporal

Maximum and minimum air temperature lapse rates in the Andean region of Ecuador and Peru

“…Even under stable atmospheric conditions, LRs can vary considerably from one season to another for maximum and minimum air temperature (Pepin, 2001; Marshall et al ., 2007; Kattel et al ., 2013). This notion has been confirmed across the global mountain systems, including the Rocky Mountains (Kunkel, 1989; Pepin and Losleben, 2002), the Himalayas (Immerzeel et al ., 2014; Romshoo et al ., 2018; Ojha, 2019) and the Alps (Dumas, 2013; Nigrelli et al ., 2017), along with other regions (e.g., the Iberian Peninsula; (Navarro‐Serrano et al ., 2018), as well as in the Antarctic Peninsula (Ambrozova et al ., 2019). In each region, the vertical distribution of air temperature varies between nighttime and daytime due to variations in heat fluxes between the atmosphere and the surface (Duane et al ., 2008; Gardner et al ., 2009).…”

Daily and seasonal variation of the surface temperature lapse rate and 0°C isotherm height in the western subtropical Andes

“…Surface air temperature (SAT, normally measured at 2 m above the surface) and its spatial distribution are key climatic features in mountainous regions (Yang et al ., 2011; Wang et al ., 2017), essential for understanding and modelling a wide range of processes in high‐elevation environments (Minder et al ., 2010; Pages et al ., 2017; Ojha, 2019). In particular, the relationship between SAT and altitude, that is, the surface temperature lapse rate (STLR), controls some relevant processes such as rainfall‐runoff transformation, snow accumulation and snowmelt, ecosystems distributions and glacier mass variations.…”

“…Hydrological modelling and other applications commonly use STLR to extrapolate SAT from a low‐lying base station to different elevations with no observations (Blandford et al ., 2008; Ojha, 2019). Typical STLR values range between −7.0 and −6.0°C/km (Prentice et al ., 1992; Hamlet and Lettenmaier, 2005; Otto‐Bliesner et al ., 2006; Livneh et al ., 2013; Ojha, 2019), with −6.5°C/km being most commonly used (Blandford et al ., 2008; López‐Moreno et al ., 2018). This value corresponds to the standard atmospheric lapse rate representative of the theoretical pseudoadiabatic lapse rate (Brunt, 1933).…”

Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

“…Gaussian mixture model (GMM) algorithm (Ahani et al, 2020;Bishop, 2006;Ojha, 2019) involves maximization of the following log-likelihood function. The notation is the same as that considered in Section 2.…”