Changes in LULC

During the 1980s, the dominant LULC type in India was woody savanna, which was mostly forest land and this type was especially dominant over Central India, the majority of Peninsular India and Northeast India (Fig. 1). Because of development and agricultural intensification, these regions were largely deforested and woody savanna was converted to cropland. LULC data from 2005 show that cropland is the dominant LULC type in the country and it is important to understand the effect of this change on monsoon precipitation. For example, in Northeast India, the dominant LULC changed from woody savanna to evergreen broadleaf because of increased tea plantations. In general, approximately 20% of leaf area index (LAI) has been reduced in the core monsoon zone (Fig. 1(c)). To understand the effects of such changes, we simulated the regional monsoon with WRF coupled (details in supplementary) with Community Land Model (CLM4.0), forced with different LULC types. However, significant uncertainty resides in the estimation of LULC changes, specifically because of the following (details in Methods):

1 The historical satellite images are of coarse resolution. 2 The LULC from the two periods were obtained from two different sources. 3 Differences exist in the classification schemes and methods between the data extraction procedures for the two periods.

Figure 1 Land Use and Land Cover (LULC) changes in India in recent decades. (a) LULC map for 1987 (b) and 2005 (c) Changes (%) in Leaf Area Index in 2005 from 1987. Maps are prepared with ArcGIS10.0 (http://www.esri.com/software/arcgis/arcgis-for-desktop). The LULC from 1987 is obtained from AVHRR and from 2005 is obtained from MODIS. Full size image

Evaluation of WRF

First, we evaluated WRF simulations for 2000–2010 while imposing 2005 LULC and large-scale forcing provided by ECMWF (ERA-Interim) as boundary conditions [details in Methods]. Details of the LULC datasets used and their application in the WRF model, along with complete information on the model setup, model resolution and rainfall observations used are given in the section ‘Methods’.

Figure 2(a) presents the domain used for the study. We considered a slightly larger domain than the Indian subcontinent to ensure that specific monsoon features were properly incorporated, specifically those associated with the variabilities of SST in the Indian Ocean and temperature over the Tibetan plateau. The meteorologically homogeneous regions as mentioned by India Meteorological Department (IMD) are presented in Fig. 2(b).

Figure 2 Evaluation of regional simulations by Weather Research and Forecasting (WRF) model. (a) The domain considered for WRF simulations, (b) IMD meteorologically homogeneous regions, (c) Bias in regional model simulation without nudging and (d) with spectral nudging. (e–l) The scatter plots between WRF simulations (with and without nudging) and observed precipitation for all India and its different homogeneous regions (e–l). The period considered here is 2000–2010 and the LULC considered is for 2005. Maps are prepared with MATLAB R2012b (http://in.mathworks.com/products/new_products/release2012b.html). The observed precipitation data are obtained from the India Meteorological Department. Full size image

We performed regional simulations for 2000–2010 with and without spectral nudging to understand whether nudging could provide a better simulation of monsoon rainfall. Nudging is not performed at surface level (below planetary boundary layer); hence, the low-level variables are not forced to match with those from reanalysis. We compared the results with the observed gridded precipitation data from India Meteorological Department (IMD) [details in Methods]. Bias in simulated precipitation was reduced when the model was nudged spectrally (Fig. 2(c,d)), specifically in regions of high precipitation such as Northeast India and the West Coast region. This pattern was also evident from the scatter plots for all of India as well for its homogeneous regions (Fig. 2(e–l)). Each point in the scatter plot represents a grid point with its mean observed or simulated precipitation during the summer monsoon from 2000 to 2010. North and Northeast India showed better agreement. Spatially averaged seasonal precipitations (JJAS) for all of India and its homogeneous regions, as simulated by WRF with nudging, have higher correlations with observed JJAS than WRF without nudging, except in Jammu and Kashmir (Supplementary Fig. S1). This finding also indicates that WRF with nudging improves the simulation of interannual variation of monsoon precipitation. We also evaluated the regional runs in terms of spatial variability, represented by Probability Density Functions (PDF). PDFs show better agreement for nudged simulations with respect to the free runs when evaluated with observed data in the entire country as well as for different regions, specifically North India, Western India and the northeastern hills (Supplementary Fig. S2).

Impacts of Changes in LULC on Monsoon Rainfall

We performed WRF-CLM simulations with nudging and imposed LULC for both the 1980s (1987) and the 2000s (2005). The duration of the run was 2000–2010. The mean monsoon rainfall (JJAS) obtained with both the LULCs is presented in Fig. 3(a,b), with their differences shown in Fig. 3(d). The differences are negative when the 1980s LULC run is subtracted from the 2000s LULC run. Negative differences denote the changes in monsoon rainfall due to large-scale conversion of woody savannah to crop land as obtained from remote sensing images from the 1980s and the 2000s. These negative differences are larger and more prevalent in Northeast India and the Ganga Basin, where Pathak et al.18 found higher contributions of recycled precipitation to monsoons. We also plotted the observed difference in monsoon rainfall between the 2000s and the 1980s (Fig. 3(c)). The observed data show a strong decline in monsoon rainfall in Northeast India, the Ganga Basin and some regions of Central India. Hence, similarities are observed between the observed changes and the differences obtained from the LULC runs. This observation indicates that the effects of an experimental large-scale LULC change in India—specifically, the change from forest land to crop land—may lead to a significant decline in ISMR, similar in extent to the observed changes. We further plotted the PDF representing the spatial variability of observed changes and WRF simulated changes in India as well as its regions due to deforestation (Fig. 3(e–l)). The similarities in the PDFs showing changes between the “observed” and “simulated LULC effects” for Northeast India may indicate that the role of deforestation on precipitation will be similar to observed changes in the high-recycling precipitation zone. For all regions, except the northeastern hills and Central India, the PDFs agreed closely. We also tested the statistical significance of the similarities between the PDFs. It is very unlikely that the changes in observed and simulated precipitation (due to LULC change) will follow exactly the same distribution; hence, the Kolmogorov-Smirnov test fails. We also performed a t-test to determine whether both of the changes have a similar mean and standard deviation. We found that the similarities in changes between the “observed” and “simulated LULC effects” are statistically significant for Northeast India, South India and North India. The significance level is 0.05. This further strengthens our hypotheses that changes in LULC play a major role in the changing patterns of Indian monsoons and that the impacts of large-scale deforestation are similar in extent to the observed recent decline in monsoons. Poor agreement in the northeastern hills can be attributed to: first, the poor quality of gridded data because the region lacks stations and second, the model is unable to produce the fine-resolution precipitation required for hilly regions. The resolution considered here is 36 km, which is not sufficient to simulate the spatial variability of precipitation in mountainous regions. However, the simulated differences do not explain the changes in monsoon precipitation fully and significant uncertainty is associated with the detected LULC changes. The unexplained parts of the monsoon decline are due to changes in SST, aerosol emissions and other large-scale circulations.

Figure 3 Changes in Monsoon Precipitation due to changes in LULC and their consistency with the declining patterns of ISMR. Simulated average monsoon precipitation with (a) 1987 and (b) 2005 LULC and (d) their differences, which are consistent with observed changes in ISMR (c) from the 1980s to the 2000s. PDFs representing the spatial variability of changes or differences with different LULC are presented for all India and its different homogeneous regions (e–l). Maps are prepared with MATLAB R2012b (http://in.mathworks.com/products/new_products/release2012b.html). Full size image

We also computed monthly changes or differences in precipitation during monsoon months separately (Supplementary Fig. S3). The observed monthly changes in precipitation are not uniform across months (Supplementary Fig. S3(a–d)) and show greater decline during August, when the amount of recycled precipitation is higher18. The differences in rainfall due to changes in LULC are greatest during August and September, with the WRF simulations when ERA-interim reanalysis (from ECMWF) is used. We also test our hypothesis with WRF simulations forced with CFSR reanalysis and find decline in monsoon precipitation due to LULC changes (Supplementary Fig. S3(i–l)).

We further investigated the mechanism through which the changes in LULC affected monsoon precipitation. We found decrease in ET (Fig. 4(a)) due to deforestation and conversion of woody savanna to crop land. Compared to cropland, woody savanna has higher root depth; hence, it also has higher water intake resulting in higher ET. To understand the impacts of ET, we applied Dynamic Recycling Model (DRM)22 to quantify the amount of precipitation ET generated. DRM is applied to both the WRF simulations with 1980s and 2005 LULC. The differences in recycled precipitation as obtained from the two simulations (2005 LULC- 1980 LULC) are presented in Fig. 4(b) and the results are consistent with those of ET, as expected. The results show a decrease in recycled precipitation due to a decrease in ET. We also found that this decrease is higher at the Ganga Basin and Northeast India as expected, where the observed recycling ratio (ratio of recycled to total precipitation) is also high18. This higher decrease of recycled precipitation is consistent with the overall decline of the Indian monsoon. We also computed the convective available potential energy (CAPE) from both the simulations and found a very modest decrease in the number of days with CAPE>0 (Fig. 4(c)). A decrease in convective precipitation suggests an increase in atmospheric stability, a finding that is consistent with reduced total precipitation. Low CAPE is presumably due to a reduced supply of moisture, which is due to a decline in ET because of the LULC changes. The decline of ET and recycled precipitation is larger during the end of a monsoon (Supplementary Fig. S4) and this is consistent with overall declines in precipitation during August and September. We also present the simulated differences in wind fields and surface temperature (Fig. 4(e,f)). We find that impact of changes in LULC over surface temperature is prominent in Central and some part of Northern India (Fig. 4(f)). The simulated changes in wind field show a slight weakening of both south westerlies from the Arabian Sea to central India and winds from the Bay of Bengal to the Ganga Basin. Such changes result into lower moisture fluxes from moisture sources to sinks with weakening monsoon precipitation. However, the observed changes in wind fields as obtained from ERA-interim (Supplementary Fig. S5) shows a strengthening of westerlies from the Arabian Sea to west-central India but a declining of the same to South India. The observed data show the weakening of the wind from the Bay of Bengal to North India and the Ganga Basin. The simulated temperature remains unaltered over the Ganga Basin and Northeast India with the changes in LULC, where maximum changes in recycled precipitation are simulated. We also compute the tropospheric temperature gradient (∆TT) to understand the synoptic scale processes. The tropospheric temperature (TT) is defined as the average of temperature vertically between 200 hPa and 700 hPa. ∆TT is defined as the difference between TT spatially averaged over two boxes: 64°E-108° E, 10°N-35°N and 64°E-108° E, 7.8°S-10°N. The extent of the boxes is limited by the extent of WRF domain. No significant changes (Fig. 4(g)) are observed in simulated ∆TT due to LULC changes. This is expected and consistent with our conclusion, suggesting that changes in LULC lead to a decrease in recycled precipitation; however, the large-scale circulation affecting moisture transport from distant oceanic moisture sources remains the same. ∆TT is a proxy for large-scale circulation, which does not change when LULC changes. We also plotted the ∆TT from ERA-interim reanalysis dataset. Significant differences are observed between the simulated and reanalyzed ∆TT (Fig. 4(g)). In spectral nudging23,24, the nudging term is spectrally expanded in both the zonal and meridional directions over the entire domain for the waves under selected wave numbers (in our case 3). All the other waves are filtered out. The deviation of ∆TT as simulated from reanalysis probably originates from the selection of the threshold wave number. Further decrease in the threshold wave number would probably result in merging simulated ∆TT to the reanalysis.

Figure 4 Differences in (a) ET, (b) recycled precipitation, (c) number of days with CAPE>0 and (d) convective precipitation between the regional simulations with 1987 and 2005 LULC, during the period 2000–10. Maps are prepared with MATLAB R2012b (http://in.mathworks.com/products/new_products/release2012b.html). Full size image

Discussions and Conclusion

Here, we performed a sensitivity analysis to quantify the impacts of large-scale conversion from woody savannah to crop land in India on monsoon precipitation. We found such a change results in decreased ET and subsequently a decrease in recycled precipitation leading to a decline in monsoon precipitation. This decline is similar in extent to the observed recent decadal weakening of monsoon precipitation. However, other reasons may account for this observed weakening, such as the warming of Indian Ocean SST. We further experimented with WRF simulations, in which we conducted regional runs forced with 1980s SST and 2000s SST data with the same LULC (Supplementary Fig. S6). When WRF is forced with ERA-Interim SST, the decline in precipitation is limited to peninsular India (Supplementary Fig. S6(c)). However, when WRF is forced with CFSR SST, the area-wide decline in precipitation is quite prominent. This is possibly because the warming of WIO and SIO is not as prominent in ERA-Interim when compared to CFSR. This also agrees with findings obtained by Roxy et al.11 and Rao et al.8,9 that warming of SIO and WIO resulted in a decline in monsoon precipitation. The future scope of this present work is to perform detection and attribution studies for potential declines of Indian monsoons with model runs forced with SST warming only, aerosol forcing only, LULC changes only and all controlling factors together.

The limitations of the study are as follows:

1 The LULC map for 1987, as obtained from the University of Maryland (UMD), presents the dominant LULC of a selected grid25; hence, the final LULC map may have resulted in a small number of misclassifications due to merged classes. Some of the misclassifications may be attributed to noisy pixels, inconsistency in the ancillary data sources during training and improper choice of the metrics during classification26. However, the overall accuracy of the final LULC map was approximately 81%26 and it is widely used in various similar studies for analyzing changes in vegetation patterns and biophysical properties27,28,29, projecting global water resources30 and global data assimilation systems31. 2 The LULC map of 2005, as obtained from MODIS, may have misclassified some land types such as savannahs and woody savannahs, open shrub lands and grasslands due to similar biophysical and spectro-thermal properties32. 3 Changes in LULC might have resulted in changes in emissions of absorbing and scattering aerosols33. These changes would have affected the monsoon circulation, which has not been considered here. 4 The land surface model considered here uses natural hydrological processes without human interventions, such as irrigation through ground water pumping and reservoir operation, which may have significant feedback to monsoon circulation. 5 The dynamic recycling model considered here is based on the assumption of well-mixed atmospheric conditions.

Nevertheless, our findings highlight the need to consider changes in LULC in climate model projections for the Indian monsoon, because such changes have a significant role in precipitation trends. We also argue that land surface representation in climate models used for simulation of monsoon at different scales needs to be improved to consider the significant role of LULC and land surface ET on the changing behavior of the Indian monsoon.