Hydrological impacts of climate change are assessed by downscaling the General Circulation Model (GCM) outputs of predictor variables to local or regional scale hydrologic variables (predictand). ...Support Vector Machine (SVM) is a machine learning technique which is capable of capturing highly nonlinear relationship between predictor and predictand and thus performs better than conventional linear regression in transfer function‐based downscaling modeling. SVM has certain parameters the values of which need to be fixed appropriately for controlling undertraining and overtraining. In this study, an optimization model is proposed to estimate the values of these parameters. As the optimization model, for selection of parameters, contains SVM as one of its constraints, analytical solution techniques are difficult to use in solving it. Probabilistic Global Search Algorithm (PGSL), a probabilistic search technique, is used to compute the optimum parameters of SVM. With these optimum parameters, training of SVM is performed for statistical downscaling. The obtained relationship between large‐scale atmospheric variables and local‐scale hydrologic variables (e.g., rainfall) is used to compute the hydrologic scenarios for multiple GCMs. The uncertainty resulting from the use of multiple GCMs is further modeled with a modified reliability ensemble averaging method. The proposed methodology is demonstrated with the prediction of monsoon rainfall of Assam and Meghalaya meteorological subdivision of northeastern India. The results obtained from the proposed model are compared with earlier developed SVM‐based downscaling models, and improved performance is observed.
Urban areas have different climatology with respect to their rural surroundings. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced ...an unprecedented rate of growth over the last 30 years. Here we take up an observational study to understand the influence of urbanization on the characteristics of precipitation (specifically extremes) in India. We identify 42 urban regions and compare their extreme rainfall characteristics with those of surrounding rural areas. We observe that, on an overall scale, the urban signatures on extreme rainfall are not prominently and consistently visible, but they are spatially nonuniform. Zonal analysis reveals significant impacts of urbanization on extreme rainfall in central and western regions of India. An additional examination, to understand the influences of urbanization on heavy rainfall climatology, is carried with station level data using a statistical method, quantile regression. This is performed for the most populated city of India, Mumbai, in pair with a nearby nonurban area, Alibaug; both having similar geographic location. The derived extreme rainfall regression quantiles reveal the sensitivity of extreme rainfall events to the increased urbanization. Overall the study identifies the climatological zones in India, where increased urbanization affects regional rainfall pattern and extremes, with a detailed case study of Mumbai. This also calls attention to the need of further experimental investigation, for the identification of the key climatological processes, in different regions of India, affected by increased urbanization.
Key Points
Regional versus urbanization‐induced precipitation changes in India
Urbanization‐induced precipitation extremes changes in Mumbai, India
Quantile regression model for understanding impacts of local changes
Abstract
Atmospheric aridity (vapor pressure deficit, VPD) and soil moisture (SM) deficit limit plant photosynthesis and, thus, affect vegetation carbon uptake. The strong correlation between SM and ...VPD makes it challenging to delineate their relative contributions to regional vegetation productivity. Addressing this gap is vital to understand the future trajectory of plant productivity in India—the second-highest contributor to global greening. Here, we separate the controls of SM and VPD on the Indian vegetation using statistical and causal analysis. We found that vegetation productivity in India is primarily controlled by SM limitation (87.66% of grids) than VPD limitation (12.34% of grids). Vegetation has a varying association with SM and VPD across different agroecological regions in India. The negative impact of VPD on vegetation carbon uptake is not visible in high-rainfall areas of India. These findings advance our understanding of vegetation dynamics under regional dryness stress and can enhance dynamic vegetation model estimates for India under changing climate scenarios.
Higher warming will affect more regions globally with intensified agricultural and ecological droughts. Higher CO2 concentration improves vegetation’s water use efficiency (WUE), but its potential to ...alleviate extreme agricultural and ecological droughts is unclear. India is the second-highest contributor to global greening, having two of the eight global hottest biodiversity hotspots. Here, for the first time, using the CMIP6 earth system models (ESMs), we found an increase in the net vegetation productivity in India at the rate of 10.552 TgC year−1 with 1% per year increase in atmospheric CO2 concentration from 285 ppm to 1140 ppm, contrary to global trends. The improved WUE resulting from carbon fertilization and higher rain under warming will supersede the increased evapotranspiration water loss due to radiative effects. We found that the substantial increase in vegetation productivity in India attributes to plant physiology, and such factor needs to be considered in the drought projections.
Abstract
Increased occurrence of heatwaves across different parts of the world is one of the characteristic signatures of anthropogenic warming. With a 1.3 billion population, India is one of the hot ...spots that experience deadly heatwaves during May-June – yet the large-scale physical mechanism and teleconnection patterns driving such events remain poorly understood. Here using observations and controlled climate model experiments, we demonstrate a significant footprint of the far-reaching Pacific Meridional Mode (PMM) on the heatwave intensity (and duration) across North Central India (NCI) – the high risk region prone to heatwaves. A strong positive phase of PMM leads to a significant increase in heatwave intensity and duration over NCI (0.8-2 °C and 3–6 days;
p
< 0.05) and vice-versa. The current generation (CMIP6) climate models that adequately capture the PMM and their responses to NCI heatwaves, project significantly higher intensities of future heatwaves (0.5-1 °C;
p
< 0.05) compared to all model ensembles. These differences in the intensities of heatwaves could significantly increase the mortality (by ≈150%) and therefore can have substantial implications on designing the mitigation and adaptation strategies.
Socioeconomic challenges continue to mount for half a billion residents of central India because of a decline in the total rainfall and a concurrent rise in the magnitude and frequency of extreme ...rainfall events. Alongside a weakening monsoon circulation, the locally available moisture and the frequency of moisture-laden depressions from the Bay of Bengal have also declined. Here we show that despite these negative trends, there is a threefold increase in widespread extreme rain events over central India during 1950-2015. The rise in these events is due to an increasing variability of the low-level monsoon westerlies over the Arabian Sea, driving surges of moisture supply, leading to extreme rainfall episodes across the entire central subcontinent. The homogeneity of these severe weather events and their association with the ocean temperatures underscores the potential predictability of these events by two-to-three weeks, which offers hope in mitigating their catastrophic impact on life, agriculture and property.Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India. Here the authors identify a threefold increase in widespread extreme rains over the region during 1950-2015, driven by an increasing variability of the low-level westerlies over the Arabian Sea.
There is an emerging understanding toward the importance of land‐atmosphere interactions in the monsoon system, but the effects of specific land and water management practices remain unclear. Here, ...using regional process‐based experiments, we demonstrate that monsoon precipitation is sensitive to the choice of irrigation practices in South Asia. Experiments with realistic representation of unmanaged irrigation and paddy cultivation over north‐northwest India exhibit an increase in the late season terrestrial monsoon precipitation and intensification of widespread extreme events over Central India, consistent with changes in observations. Such precipitation changes exhibit substantially different spatial patterns in experiments with a well‐managed irrigation system, indicating that increase in unmanaged irrigation might be a factor driving the observed changes in the intraseasonal monsoon characteristics. Our findings stress the need for accurate representation of irrigation practices to improve the reliability of earth system modeling over South Asia.
Plain Language Summary
South Asia is one of the most heavily irrigated regions of the world, and a large proportion of the water for irrigation is extracted through groundwater pumping. The major summer crop of the region is paddy, which is cultivated in fields flooded with water. Here we study the impact of this agricultural water use on the Indian Summer Monsoon using a climate model. We find that the excess irrigation over northern India shifts the September month monsoon rainfall toward the northwestern part of the subcontinent. This pattern of change is also visible in recorded rainfall over the region in the recent past. Another major impact we see is on the intensity of extreme rainfall events over the central part of the country. Central India has witnessed an increase in extreme rainfall in recent decades, and through this study, we find that irrigation increases the rainfall intensity over Central India during these events. These findings indicate that it is important to represent irrigation practices more accurately in climate models.
Key Points
Representation of irrigation and paddy fields is added to a regional climate model to study that impacts on Indian Summer Monsoon
Excess irrigation over northern India causes northwestward shift in September month monsoon rainfall over the land mass
Irrigation intensifies widespread extreme events over Central India
Global and local environmental changes are likely to introduce nonstationarity in the characteristics of Indian Summer Monsoon Rainfall (ISMR) extremes. Here we perform a nonstationary frequency ...analysis on ISMR extremes in a Generalized Additive Model for Location, Scale and Shape framework with a cluster of 74 models, considering nonstationarity in different possible combinations. Interestingly, we observe significant nonstationarity in ISMR extremes in urbanizing/developing‐urban areas (transitioning from rural to urban), compared to completely urbanized or rural areas. This presents a postulation that the extent of urbanization plays a significant role in introducing nonstationarity in ISMR extremes. We emphasize the effect of urbanization in changing the character of ISMR extremes, which further needs a scientific re‐evaluation by implementing physics‐based modeling. The impact of these observational studies will be critical in correcting the bias of model projections of ISMR.
Key Points
Signature of nonstationarity in rainfall extremes is more significant in the developing‐urban areas than in the urbanized and rural areas
Extent of urbanization plays a significant role in introducing nonstationarity in Indian Summer Monsoon Rainfall extremes
A framework with a cluster of 74 nonstationary models is proposed to capture both linear and nonlinear changing characteristics of rainfall
This study investigated the impacts of changes in land cover and climate on runoff and sediment yield in a river basin in India. Land Change Modeler was used to derive the future land cover and its ...changes using the Sankey diagram approach. The future climatic parameters were derived from five general circulation models for two emission scenarios with representative concentration pathways (RCPs) 4.5 and 8.5. The land cover and climate change impacts on runoff and sediment yield were estimated using SWAT model. The results show important changes in land cover and indicate that urban and agricultural areas strongly influence the runoff and sediment yield. Among the land cover and climate change impacts, climate has more predominant (70%-95%) impact. Runoff and sediment yield are likely to decrease in both RCP scenarios in the future period. The impacts of land cover changes are more prominent on sediment yield than runoff.
The South Asian summer monsoon is a complex coupled human‐natural system that poses unique challenges for understanding its evolution alongside increasing anthropogenic activities. Rapid and ...substantial changes in land‐use, land‐management and industrial activities over the subcontinent, and warming in the Indian Ocean, have influenced the South Asian summer monsoon. These might continue to be significant drivers in the near‐term along with rising global greenhouse gas emissions. Deciphering the region's vulnerability to climate change requires an understanding of how these anthropogenic activities, acting on a range of spatial scales, have shaped the monsoon spatially and temporally. This review summarizes historical changes in monsoon rainfall characteristics, associated mechanisms, and the role of anthropogenic forcings, focusing on subseasonal variability and extreme events. Several studies have found intensified subseasonal extremes across parts of India and an increase in spatial variability of rainfall despite an overall weakening of seasonal rainfall in the monsoon core. However, understanding these changes remains challenging because of uncertainties in observations and climate models. The mechanisms and relative influences of various anthropogenic activities, particularly on subseasonal extremes, remain relatively underexplored. Large biases in the representation of relevant processes in global climate models limit the ability to attribute historical changes and make reliable projections. Nevertheless, recent advances in modeling these processes using higher‐resolution modeling frameworks provide new tools to investigate the Indian summer monsoon's response to various anthropogenic forcings. There is an urgent need to understand how these forcings interact to shape climate variability and change in this vulnerable region.
This article is categorized under:
Paleoclimates and Current Trends > Earth System Behavior
Influence of anthropogenic forcings on rainfall variability.
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