Cooperative Institute for Research in Environmental Sciences

Balaji Rajagopalan

Balaji Rajagopalan

Research Interests

Stochastic Hydrology and Hydroclimatology; Nonparametric functional estimation techniques (probability density Functions, regression, scenarios generation, forecasting); Understanding low frequency climate variability and its signatures on regional hydrology; Incorporating climate information in water resources/hydrologic decision making; Understanding spatio-temporal variability in Indian summer monsoon;Nonlinear Dynamics - recovering dynamics from data;Bayesian techniques for optimal combination of information from multiple sources and decision making;Statistical climate modeling and its application to hydrology, water resources eng. related issues; Stochastic modeling of rainfall and other weather variables; scaling issues in rainfall; Spatial estimation of hydro-climate variables; nonparametric estimation of density and regression functions for Multivariate Time series analysis of climate data; Identifying inter-annual variability in hydro climate variables and nonlinear dynamical modeling and forecasting; Inferring long range climate variability through statistical analysis of paleo proxy data.


Current Research

Predicting early- and late-season Indian monsoon rainfall

India’s vital dependence on its summer monsoon rainfall for agriculture and other water resources makes improving forecasting of rainfall amount a continual challenge. Variability and predictability hinge on two widely regarded influences: local land warming and distant El Niño Southern Oscillation (ENSO). Most statistical methods for forecasting monsoon rainfall, used by the India Meteorological Department, use predictors related to these influences. Although a combination of such predictors once engendered optimism, marked variations in correlations with ENSO indices has cast doubt on such predictors. When applied to the entire monsoon season, such predictors generally account for less than 20 percent of variance in observed rainfall over India. Similarly, predictions exploiting general circulation models and surface boundary conditions account for less than 20 percent of observed monsoon variance.

We exploit ENSO indices and moist static energy (MSE) of surface air over the Indian subcontinent and surroundings as predictors of monsoon rainfall over India (Figure 1) during early and late seasons (May 20-June 15 and September 20-October 15). Although these seasons only contribute about 22 percent of the entire seasonal rainfall, they affect planning of agriculture and water resources. A simple, nonlinear statistical model applied to these predictors accounts for about 40 and 45 percent of observed variance of early and late-season rainfall, respectively, and similar fractions of three day maximum rainfall intensity (Figure 2). Forecasted average and three day maximum rainfall at grid points covering India show greatest success over Central India during the early season and over West-Central, Northwestern, and Northern India during the late season, regions where agriculture dominates land use. But these predictors offer virtually no predictability of peak season rainfall, at least with ENSO and MSE. This is noteworthy because the target, if not holy grail, of Indian monsoon prediction has been the seasonal total rainfall, which apart from being less predictable might also ultimately be of limited use. This research prompts a re-thinking of monsoon forecasting to attributes that not only are more predictable but also of value for agriculture and water-resources planning.

Rajagopalan, B, and P Molnar. 2014. Combining regional moist static energy and ENSO for forecasting of early and late season Indian monsoon rainfall and its extremes. Geophys. Res. Lett. 41.


View Publications

  • Mendoza, PA, MP Clark, N Mizukami, ED Gutmann, JR Arnold, LD Brekke and B Rajagopalan (2016), How do hydrologic modeling decisions affect the portrayal of climate change impacts?. Hydrol. Process. Version: 1 30 (7) 1071-1095, issn: 0885-6087, ids: DH8CR, doi: 10.1002/hyp.10684
  • Mendoza, PA, MP Clark, N Mizukami, AJ Newman, M Barlage, ED Gutmann, RM Rasmussen, B Rajagopalan, LD Brekke and JR Arnold (2015), Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts. J. Hydrometeorol. Version: 1 16 (2) 762-780, issn: 1525-755X, ids: CF7KM, doi: 10.1175/JHM-D-14-0104.1
  • Verdin, A, B Rajagopalan, W Kleiber and RW Katz (2015), Coupled stochastic weather generation using spatial and generalized linear models. Stoch. Env. Res. Risk A Version: 1 29 (2) 347-356, issn: 1436-3240, ids: CA5DL, doi: 10.1007/s00477-014-0911-6
  • Mendoza, PA, MP Clark, M Barlage, B Rajagopalan, L Samaniego, G Abramowitz and H Gupta (2015), Are we unnecessarily constraining the agility of complex process-based models?. Water Resour. Res. Version: 1 51 (1) 716-728, issn: 0043-1397, ids: CB8OO, doi: 10.1002/2014WR015820
  • Weirich, SR, J Silverstein and B Rajagopalan (2015), Resilience of Secondary Wastewater Treatment Plants: Prior Performance Is Predictive of Future Process Failure and Recovery Time. Environ. Eng. Sci. Version: 1 32 (3) 222-231, issn: 1092-8758, ids: CD1AD, doi: 10.1089/ees.2014.0406
  • Weirich, SR, J Silverstein and B Rajagopalan (2015), Simulation of Effluent Biological Oxygen Demand and Ammonia for Increasingly Decentralized Networks of Wastewater Treatment Facilities. Environ. Eng. Sci. Version: 1 32 (3) 232-239, issn: 1092-8758, ids: CD1AD, doi: 10.1089/ees.2014.0407
  • Verdin, A, B Rajagopalan, W Kleiber and C Funk (2015), A Bayesian kriging approach for blending satellite and ground precipitation observations. Water Resour. Res. Version: 1 51 (2) 908-921, issn: 0043-1397, ids: CD9ET, doi: 10.1002/2014WR015963
  • Caldwell, J, B Rajagopalan and E Danner (2015), Statistical Modeling of Daily Water Temperature Attributes on the Sacramento River. J. Hydrol. Eng. Version: 1 20 (5) , Art. No. 4014065, issn: 1084-0699, ids: CH4HU, doi: 10.1061/(ASCE)HE.1943-5584.0001023
  • Mendoza, PA, B Rajagopalan, MP Clark, K Ikeda and RM Rasmussen (2015), Statistical Postprocessing of High-Resolution Regional Climate Model Output. Mon. Weather Rev. Version: 1 143 (5) 1533-1553, issn: 0027-0644, ids: CH5RW, doi: 10.1175/MWR-D-14-00159.1
  • Yoo, J, HH Kwon, BJ So, B Rajagopalan and TW Kim (2015), Identifying the role of typhoons as drought busters in South Korea based on hidden Markov chain models. Geophys. Res. Lett. Version: 1 42 (8) 2797-2804, issn: 0094-8276, ids: CI2FN, doi: 10.1002/2015GL063753
  • Krishnaswamy, J, S Vaidyanathan, B Rajagopalan, M Bonell, M Sankaran, RS Bhalla and S Badiger (2015), Non-stationary and non-linear influence of ENSO and Indian Ocean Dipole on the variability of Indian monsoon rainfall and extreme rain events. Clim. Dyn. Version: 1 45 (2-Jan) 175-184, issn: 0930-7575, ids: CI4IS, doi: 10.1007/s00382-014-2288-0
  • Gill, EC, B Rajagopalan and PH Molnar (2015), An assessment of the mean annual precipitation needed to sustain Lake Sambhar in Rajasthan, India, during mid-Holocene time. Holocene Version: 1 25 (12) 1923-1934, issn: 0959-6836, ids: CW8NI, doi: 10.1177/0959683615596817
  • Gill, EC, B Rajagopalan and P Molnar (2015), Subseasonal variations in spatial signatures of ENSO on the Indian summer monsoon from 1901 to 2009. J. Geophys. Res.-Atmos. Version: 1 120 (16) 8165-8185, issn: 2169-897X, ids: CU3KX, doi: 10.1002/2015JD023184
  • Vogel, RM, U Lall, XM Cai, B Rajagopalan, PK Weiskel, RP Hooper and NC Matalas (2015), Hydrology: The interdisciplinary science of water. Water Resour. Res. Version: 1 51 (6) 4409-4430, issn: 0043-1397, ids: CN3CK, doi: 10.1002/2015WR017049
  • Esmaeili, B, MR Hallowell and B Rajagopalan (2015), Attribute-Based Safety Risk Assessment. II: Predicting Safety Outcomes Using Generalized Linear Models. J. Constr. Eng. Manage. Version: 1 141 (8) , Art. No. 4015022, issn: 0733-9364, ids: CN1LP, doi: 10.1061/(ASCE)CO.1943-7862.0000981
  • Esmaeili, B, MR Hallowell and B Rajagopalan (2015), Attribute-Based Safety Risk Assessment. I: Analysis at the Fundamental Level. J. Constr. Eng. Manage. Version: 1 141 (8) , Art. No. 4015021, issn: 0733-9364, ids: CN1LP, doi: 10.1061/(ASCE)CO.1943-7862.0000980
  • Nakamura, J, U Lall, Y Kushnir and B Rajagopalan (2015), HITS: Hurricane Intensity and Track Simulator with North Atlantic Ocean Applications for Risk Assessment. J. Appl. Meteor. Clim. Version: 1 54 (7) 1620-1636, issn: 1558-8424, ids: CM9AT, doi: 10.1175/JAMC-D-14-0141.1
  • Bracken, C, B Rajagopalan, M Alexander and S Gangopadhyay (2015), Spatial variability of seasonal extreme precipitation in the western United States. J. Geophys. Res.-Atmos. Version: 1 120 (10) 4522-4533, issn: 2169-897X, ids: CL1IT, doi: 10.1002/2015JD023205
  • McCreight, JL, AG Slater, HP Marshall and B Rajagopalan (2014), Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size, random sampling, predictor quality, and validation procedures. Hydrol. Process. Version: 1 28 (3) 933-957, issn: 0885-6087, ids: 284WN, doi: 10.1002/hyp.9618
  • Ballard, T, R Seager, JE Smerdon, BI Cook, AJ Ray, B Rajagopalan, Y Kushnir, J Nakamura and N Henderson (2014), Hydroclimate Variability and Change in the Prairie Pothole Region, the "Duck Factory'' of North America. Earth Interact. Version: 1 18 , Art. No. 14, issn: 1087-3562, ids: AP1GI, doi: 10.1175/EI-D-14-0004.1
  • Caraway, NM, JL McCreight and B Rajagopalan (2014), Multisite stochastic weather generation using cluster analysis and k-nearest neighbor time series resampling. J. Hydrol. Version: 1 508 197-213, issn: 0022-1694, ids: AB9SI, doi: 10.1016/j.jhydrol.2013.10.054
  • Broman, D, B Rajagopalan and T Hopson (2014), Spatiotemporal Variability and Predictability of Relative Humidity over West African Monsoon Region. J. Clim. Version: 1 27 (14) 5346-5363, issn: 0894-8755, ids: AL4WJ, doi: 10.1175/JCLI-D-13-00414.1
  • Rajagopalan, B and P Molnar (2014), Combining regional moist static energy and ENSO for forecasting of early and late season Indian monsoon rainfall and its extremes. Geophys. Res. Lett. Version: 1 41 (12) 4323-4331, issn: 0094-8276, ids: AN0SM, doi: 10.1002/2014GL060429
  • Mendoza, PA, B Rajagopalan, MP Clark, G Cortes and J McPhee (2014), A robust multimodel framework for ensemble seasonal hydroclimatic forecasts. Water Resour. Res. Version: 1 50 (7) 6030-6052, issn: 0043-1397, ids: AQ2QG, doi: 10.1002/2014WR015426
  • Bracken, C, B Rajagopalan and E Zagona (2014), A hidden Markov model combined with climate indices for multidecadal streamflow simulation. Water Resour. Res. Version: 1 50 (10) 7836-7846, issn: 0043-1397, ids: AT2RX, doi: 10.1002/2014WR015567
  • May-Ostendorp, PT, GP Henze, B Rajagopalan and CD Corbin (2013), Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building. J. Build. Perf. Simul. Version: 1 6 (3) 199-219, issn: 1940-1493, ids: 122IV, doi: 10.1080/19401493.2012.665481
  • Rajagopalan, B and P Molnar (2013), Signatures of Tibetan Plateau heating on Indian summer monsoon rainfall variability. J. Geophys. Res.-Atmos. Version: 1 118 (3) 1170-1178, issn: 2169-897X, ids: 129LC, doi: 10.1002/jgrd.50124
  • May-Ostendorp, PT, GP Henze, B Rajagopalan and D Kalz (2013), Experimental investigation of model predictive control-based rules for a radiantly cooled office. HVAC&R Res. Version: 1 19 (5) 602-615, issn: 1078-9669, ids: 187GH, doi: 10.1080/10789669.2013.801303
  • Kishtawal, CM, D Niyogi, B Rajagopalan, M Rajeevan, N Jaiswal and UC Mohanty (2013), Enhancement of inland penetration of monsoon depressions in the Bay of Bengal due to prestorm ground wetness. Water Resour. Res. Version: 1 49 (6) 3589-3600, issn: 0043-1397, ids: 189BF, doi: 10.1002/wrcr.20301
  • Towler, E, M Roberts, B Rajagopalan and RS Sojda (2013), Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework. Water Resour. Res. Version: 1 49 (8) 4997-5008, issn: 0043-1397, ids: 223VA, doi: 10.1002/wrcr.20378
  • Stevenson, S, B Rajagopalan and B Fox-Kemper (2013), Generalized linear modeling of the El Nino/Southern Oscillation with application to seasonal forecasting and climate change projections. J. Geophys. Res.-Oceans Version: 1 118 (8) 3764-3781, issn: 2169-9275, ids: 224XX, doi: 10.1002/jgrc.20260
  • Pike, A, E Danner, D Boughton, F Melton, R Nemani, B Rajagopalan and S Lindley (2013), Forecasting river temperatures in real time using a stochastic dynamics approach. Water Resour. Res. Version: 1 49 (9) 5168-5182, issn: 0043-1397, ids: 238ZO, doi: 10.1002/wrcr.20389
  • Towler, E, B Rajagopalan, D Yates, A Rodriguez and RS Summers (2013), Integrated Approach to Simulate Stream Water Quality for Municipal Supply under a Changing Climate. J. Environ. Eng.-ASCE Version: 1 139 (12) 1432-1440, issn: 0733-9372, ids: 292JC, doi: 10.1061/(ASCE)EE.1943-7870.0000766
  • Cayan, DR, M Tyree, KE Kunkel, C Castro, A Gershunov, J Barsugli, AJ Ray, J Overpeck, M Anderson, J Russell, B Rajagopalan, I Rangwala and P Duffy (2013), Future Climate: Projected Average. Version: 1 ASSESSMENT OF CLIMATE CHANGE IN THE SOUTHWEST UNITED STATES: A REPORT PREPARED FOR THE NATIONAL CLIMATE ASSESSMENT 101-125, Ed. Garfin, G; Jardine, A; Merideth, R; Black, M; LeRoy, S, ids: BA8DA, isbn: 978-1-61091-484-0; 978-1-59726-420-4, doi: 10.5822/978-1-61091-484-0_6
  • Gershunov, A, B Rajagopalan, J Overpeck, K Guirguis, D Cayan, M Hughes, M Dettinger, C Castro, RE Schwartz, M Anderson, AJ Ray, J Barsugli, T Cavazos and M Alexander (2013), Future Climate: Projected Extremes. Version: 1 ASSESSMENT OF CLIMATE CHANGE IN THE SOUTHWEST UNITED STATES: A REPORT PREPARED FOR THE NATIONAL CLIMATE ASSESSMENT 126-147, Ed. Garfin, G; Jardine, A; Merideth, R; Black, M; LeRoy, S, ids: BA8DA, isbn: 978-1-61091-484-0; 978-1-59726-420-4, doi: 10.5822/978-1-61091-484-0_7
  • Hwang, Y, M Clark, B Rajagopalan and G Leavesley (2012), Spatial interpolation schemes of daily precipitation for hydrologic modeling. Stoch. Environ. Res. Risk Assess. Version: 1 26 (2) 295-320, issn: 1436-3240, ids: 876PQ, doi: 10.1007/s00477-011-0509-1
  • Kleiber, W, RW Katz and B Rajagopalan (2012), Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes. Water Resour. Res. Version: 1 48 , Art. No. W01523, issn: 0043-1397, ids: 880DS, doi: 10.1029/2011WR011105
  • Molnar, P and B Rajagopalan (2012), Late Miocene upward and outward growth of eastern Tibet and decreasing monsoon rainfall over the northwestern Indian subcontinent since similar to 10 Ma. Geophys. Res. Lett. Version: 1 39 , Art. No. L09702, issn: 0094-8276, ids: 937RE, doi: 10.1029/2012GL051305
  • Nowak, K, M Hoerling, B Rajagopalan and E Zagona (2012), Colorado River Basin Hydroclimatic Variability. J. Clim. Version: 1 25 (12) 4389-4403, issn: 0894-8755, ids: 969GL, doi: 10.1175/JCLI-D-11-00406.1
  • Kim, Y, RW Katz, B Rajagopalan, GP Podesta and EM Furrer (2012), Reducing overdispersion in stochastic weather generators using a generalized linear modeling approach. Clim. Res. Version: 1 53 (1) 13-24, issn: 0936-577X, ids: 947KL, doi: 10.3354/cr01071
  • Phillips, T, RS Nerem, B Fox-Kemper, JS Famiglietti and B Rajagopalan (2012), The influence of ENSO on global terrestrial water storage using GRACE. Geophys. Res. Lett. Version: 1 39 , Art. No. L16705, issn: 0094-8276, ids: 995JM, doi: 10.1029/2012GL052495
  • Rajagopalan, B and P Molnar (2012), Pacific Ocean sea-surface temperature variability and predictability of rainfall in the early and late parts of the Indian summer monsoon season. Clim. Dyn. Version: 1 39 (6) 1543-1557, issn: 0930-7575, ids: 005AO, doi: 10.1007/s00382-011-1194-y
  • Danner, EM, FS Melton, A Pike, H Hashimoto, A Michaelis, B Rajagopalan, J Caldwell, L DeWitt, S Lindley and RR Nemani (2012), River Temperature Forecasting: A Coupled-Modeling Framework for Management of River Habitat. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. Version: 1 5 SI (6) 1752-1760, issn: 1939-1404, ids: 063OL, doi: 10.1109/JSTARS.2012.2229968
  • Towler, E, B Raucher, B Rajagopalan, A Rodriguez, D Yates and RS Summers (2012), Incorporating Climate Uncertainty in a Cost Assessment for New Municipal Source Water. J. Water Resour. Plan. Manage.-ASCE Version: 1 138 (5) 396-402, issn: 0733-9496, ids: 068IF, doi: 10.1061/(ASCE)WR.1943-5452.0000150
  • May-Ostendorp, P, GP Henze, CD Corbin, B Rajagopalan and C Felsmann (2011), Model-predictive control of mixed-mode buildings with rule extraction. Build. Environ. Version: 1 46 (2) 428-437, issn: 0360-1323, ids: 681VH, doi: 10.1016/j.buildenv.2010.08.004
  • Kumar, KK, K Kamala, B Rajagopalan, MP Hoerling, JK Eischeid, SK Patwardhan, G Srinivasan, BN Goswami and R Nemani (2011), The once and future pulse of Indian monsoonal climate. Clim. Dynam. Version: 1 36 (12-Nov) 2159-2170, issn: 0930-7575, ids: 771NK, doi: 10.1007/s00382-010-0974-0
  • Salas, JD, CJ Fu and B Rajagopalan (2011), Long-Range Forecasting of Colorado Streamflows Based on Hydrologic, Atmospheric, and Oceanic Data. J. Hydrol. Eng. Version: 1 16 (6) 508-520, issn: 1084-0699, ids: 777SW, doi: 10.1061/(ASCE)HE.1943-5584.0000343
  • Weirich, SR, J Silverstein and B Rajagopalan (2011), Effect of average flow and capacity utilization on effluent water quality from US municipal wastewater treatment facilities. Water Res. Version: 1 45 (14) 4279-4286, issn: 0043-1354, ids: 797HZ, doi: 10.1016/j.watres.2011.06.002, PubMed ID: 21704355
  • Hwang, Y, MP Clark and B Rajagopalan (2011), Use of daily precipitation uncertainties in streamflow simulation and forecast. Stoch. Environ. Res. Risk Assess. Version: 1 25 (7) 957-972, issn: 1436-3240, ids: 808FQ, doi: 10.1007/s00477-011-0460-1
  • Regonda, S, E Zagona and B Rajagopalan (2011), Prototype Decision Support System for Operations on the Gunnison Basin with Improved Forecasts. J. Water Resour. Plan. Manage.-ASCE Version: 1 137 (5) 428-438, issn: 0733-9496, ids: 818WR, doi: 10.1061/(ASCE)WR.1943-5452.0000133
  • Nowak, KC, B Rajagopalan and E Zagona (2011), Wavelet Auto-Regressive Method (WARM) for multi-site streamflow simulation of data with non-stationary spectra. J. Hydrol. Version: 1 410 (2-Jan) 1-12, issn: 0022-1694, ids: 860SM, doi: 10.1016/j.jhydrol.2011.08.051
  • Stevenson, S, B Fox-Kemper, M Jochum, B Rajagopalan and SG Yeager (2010), ENSO Model Validation Using Wavelet Probability Analysis. J. Clim. Version: 1 23 (20) 5540-5547, issn: 0894-8755, ids: 681IW, doi: 10.1175/2010JCLI3609.1
  • Towler, E, B Rajagopalan, E Gilleland, RS Summers, D Yates and RW Katz (2010), Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory. Water Resour. Res. Version: 1 46 , Art. No. W11504, issn: 0043-1397, ids: 676XH, doi: 10.1029/2009WR008876
  • Apipattanavis, S, K Sabol, KR Molenaar, B Rajagopalan, YP Xi, B Blackard and S Patil (2010), Integrated Framework for Quantifying and Predicting Weather-Related Highway Construction Delays. J. Constr. Eng. Manage.-ASCE Version: 1 136 (11) 1160-1168, issn: 0733-9364, ids: 667JJ, doi: 10.1061/(ASCE)CO.1943-7862.0000199
  • Apipattanavis, S, F Bert, G Podesta and B Rajagopalan (2010), Linking weather generators and crop models for assessment of climate forecast outcomes. Agric. For. Meteorol. Version: 1 150 (2) 166-174, issn: 0168-1923, ids: 559ZQ, doi: 10.1016/j.agrformet.2009.09.012
  • Dutton, SJ, B Rajagopalan, S Vedal and MP Hannigan (2010), Temporal patterns in daily measurements of inorganic and organic speciated PM2.5 in Denver. Atmos. Environ. Version: 1 44 (7) 987-998, issn: 1352-2310, ids: 578SW, doi: 10.1016/j.atmosenv.2009.06.006
  • Bracken, C, B Rajagopalan and J Prairie (2010), A multisite seasonal ensemble streamflow forecasting technique. Water Resour. Res. Version: 1 46 , Art. No. W03532, issn: 0043-1397, ids: 578UP, doi: 10.1029/2009WR007965
  • Towler, E, B Rajagopalan, RS Summers and D Yates (2010), An approach for probabilistic forecasting of seasonal turbidity threshold exceedance. Water Resour. Res. Version: 1 46 , Art. No. W06511, issn: 0043-1397, ids: 613QF, doi: 10.1029/2009WR007834
  • Apipattanavis, S, B Rajagopalan and U Lall (2010), Local Polynomial-Based Flood Frequency Estimator for Mixed Population. J. Hydrol. Eng. Version: 1 15 (9) 680-691, issn: 1084-0699, ids: 639DN, doi: 10.1061/(ASCE)HE.1943-5584.0000242
  • Nowak, K, J Prairie, B Rajagopalan and U Lall (2010), A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. Water Resour. Res. Version: 1 46 , Art. No. W08529, issn: 0043-1397, ids: 639GK, doi: 10.1029/2009WR008530
  • Han, WQ, GA Meehl, B Rajagopalan, JT Fasullo, AX Hu, JL Lin, WG Large, JW Wang, XW Quan, LL Trenary, A Wallcraft, T Shinoda and S Yeager (2010), Patterns of Indian Ocean sea-level change in a warming climate. Nat. Geosci. Version: 1 3 (8) 546-550, issn: 1752-0894, ids: 645NK, doi: 10.1038/ngeo901
  • Block, P and B Rajagopalan (2009), Statistical-Dynamical Approach for Streamflow Modeling at Malakal, Sudan, on the White Nile River. J. Hydrol. Eng. Version: 1 14 (2) 185-196, issn: 1084-0699, ids: 395MW, doi: 10.1061/(ASCE)1084-0699(2009)14:2(185)
  • Towler, E, B Rajagopalan, C Seidel and RS Summers (2009), Simulating Ensembles of Source Water Quality Using a K-Nearest Neighbor Resampling Approach. Environ. Sci. Technol. Version: 1 43 (5) 1407-1411, issn: 0013-936X, ids: 412XX, doi: 10.1021/es8021182, PubMed ID: 19350911
  • Lee, E, TN Chase, B Rajagopalan, RG Barry, TW Biggs and PJ Lawrence (2009), Effects of irrigation and vegetation activity on early Indian summer monsoon variability. Int. J. Climatol. Version: 1 29 (4) 573-581, issn: 0899-8418, ids: 419LL, doi: 10.1002/joc.1721
  • Towler, E, B Rajagopalan and RS Summers (2009), Using Parametric and Nonparametric Methods to Model Total Organic Carbon, Alkalinity, and pH after Conventional Surface Water Treatment. Environ. Eng. Sci. Version: 1 26 (8) 1299-1308, issn: 1092-8758, ids: 474BN, doi: 10.1089/ees.2008.0341
  • Gangopadhyay, S, BL Harding, B Rajagopalan, JJ Lukas and TJ Fulp (2009), A nonparametric approach for paleohydrologic reconstruction of annual streamflow ensembles. Water Resour. Res. Version: 1 45 , Art. No. W06417, issn: 0043-1397, ids: 458HD, doi: 10.1029/2008WR007201
  • Rajagopalan, B, K Nowak, J Prairie, M Hoerling, B Harding, J Barsugli, A Ray and B Udall (2009), Water supply risk on the Colorado River: Can management mitigate?. Water Resour. Res. Version: 1 45 , Art. No. W08201, issn: 0043-1397, ids: 487CO, doi: 10.1029/2008WR007652
  • Barsugli, JJ, K Nowak, B Rajagopalan, JR Prairie and B Harding (2009), Comment on "When will Lake Mead go dry?" by T. P. Barnett and D. W. Pierce. Water Resour. Res. Version: 1 45 , Art. No. W09601, issn: 0043-1397, ids: 497LH, doi: 10.1029/2008WR007627
  • Apipattanavis, S, GJ McCabe, B Rajagopalan and S Gangopadhyay (2009), Joint Spatiotemporal Variability of Global Sea Surface Temperatures and Global Palmer Drought Severity Index Values. J. Clim. Version: 1 22 (23) 6251-6267, issn: 0894-8755, ids: 526GC, doi: 10.1175/2009JCLI2791.1
  • Podesta, G, F Bert, B Rajagopalan, S Apipattanavis, C Laciana, E Weber, W Easterling, R Katz, D Letson and A Menendez (2009), Decadal climate variability in the Argentine Pampas: regional impacts of plausible climate scenarios on agricultural systems. Clim. Res. Version: 1 40 (3-Feb) 199-210, issn: 0936-577X, ids: 543MM, doi: 10.3354/cr00807
  • Morin, J, P Block, B Rajagopalan and M Clark (2008), Identification of large scale climate patterns affecting snow variability in the eastern United States. Int. J. Climatol. Version: 1 28 (3) 315-328, issn: 0899-8418, ids: 286NJ, doi: 10.1002/joc.1534
  • Lee, E, TN Chase and B Rajagopalan (2008), Seasonal forecasting of East Asian summer monsoon based on oceanic heat sources. Int. J. Climatol. Version: 1 28 (5) 667-678, issn: 0899-8418, ids: 294UG, doi: 10.1002/joc.1551
  • Leavitt, SW, TN Chase, B Rajagopalan, E Lee and PJ Lawrence (2008), Southwestern US tree-ring carbon isotope indices as a possible proxy for reconstruction of greenness of vegetation. Geophys. Res. Lett. Version: 1 35 (12) , Art. No. L12704, issn: 0094-8276, ids: 317XC, doi: 10.1029/2008GL033894
  • Prairie, J, K Nowak, B Rajagopalan, U Lall and T Fulp (2008), A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data. Water Resour. Res. Version: 1 44 (6) , Art. No. W06423, issn: 0043-1397, ids: 321OU, doi: 10.1029/2007WR006684
  • Lee, E, TN Chase, PJ Lawrence and B Rajagopalan (2008), Model assessment of the observed relationship between El Nino and the northern East Asian summer monsoon using the Community Climate System Model Community Atmosphere Model-Community Land Model version 3 (CAM-CLM3). J. Geophys. Res.-Atmos. Version: 1 113 (D20) , Art. No. D20118, issn: 2169-897X, ids: 368CD, doi: 10.1029/2008JD009926
  • Lee, E, TN Chase and B Rajagopalan (2008), Highly improved predictive skill in the forecasting of the East Asian summer monsoon. Water Resour. Res. Version: 1 44 (10) , Art. No. W10422, issn: 0043-1397, ids: 368ET, doi: 10.1029/2007WR006514
  • Prairie, J, B Rajagopalan, U Lall and T Fulp (2007), A stochastic nonparametric technique for space-time disaggregation of streamflows. Water Resour. Res. Version: 1 43 (3) , Art. No. W03432, issn: 0043-1397, ids: 150GP, doi: 10.1029/2005WR004721
  • Grantz, K, B Rajagopalan, M Clark and E Zagona (2007), Seasonal shifts in the North American monsoon. J. Clim. Version: 1 20 (9) 1923-1935, issn: 0894-8755, ids: 167TE, doi: 10.1175/JCL14091.1
  • Grantz, K, B Rajagopalan, E Zagona and M Clark (2007), Water management applications of climate-based hydrologic forecasts: Case study of the Truckee-Carson River Basin. J. Water Resour. Plan. Manage.-ASCE Version: 1 133 (4) 339-350, issn: 0733-9496, ids: 180IZ, doi: 10.1061/(ASCE)0733-9496(2007)133:4(339)
  • Block, P and B Rajagopalan (2007), Interannual variability and ensemble forecast of upper Blue Nile basin Kiremt season precipitation. J. Hydrometeorol. Version: 1 8 (3) 327-343, issn: 1525-755X, ids: 184CU, doi: 10.1175/JHM580.1
  • Opitz-Stapleton, S, S Gangopadhyay and B Rajagopalan (2007), Generating streamflow forecasts for the Yakima River Basin using large-scale climate predictors. J. Hydrol. Version: 1 341 (4-Mar) 131-143, issn: 0022-1694, ids: 198RO, doi: 10.1016/j.jhydrol.2007.03.024
  • Biggs, TW, CA Scott, B Rajagopalan and HN Turral (2007), Trends in solar radiation due to clouds and aerosols, southern India, 1952-1997. Int. J. Clim. Version: 1 27 (11) 1505-1518, issn: 0899-8418, ids: 214HT, doi: 10.1002/joc.1487
  • Prairie, JR and B Rajagopalan (2007), A basin wide stochastic salinity model. J. Hydrol. Version: 1 344 (2-Jan) 43-54, issn: 0022-1694, ids: 218PE, doi: 10.1016/j.jhydrol.2007.06.029
  • Apipattanavis, S, G Podesta, B Rajagopalan and RW Katz (2007), A semiparametric multivariate and multisite weather generator. Water Resour. Res. Version: 1 43 (11) , Art. No. W11401, issn: 0043-1397, ids: 228FF, doi: 10.1029/2006WR005714
  • Zachman, BA, B Rajagopalan and RS Summers (2007), Modeling NOM breakthrough in GAC adsorbers using nonparametric regression techniques. Environ. Eng. Sci. Version: 1 24 (9) 1280-1296, issn: 1092-8758, ids: 231GS, doi: 10.1089/ees.2006.0223
  • Prairie, JR, B Rajagopalan, TJ Fulp and EA Zagona (2006), Modified K-NN model for stochastic streamflow simulation. J. Hydrol. Eng. Version: 1 11 (4) 371-378, issn: 1084-0699, ids: 054RQ, doi: 10.1061/(ASCE)1084-0699(2006)11:4(371)
  • Clark, MP, AG Slater, AP Barrett, LE Hay, GJ McCabe, B Rajagopalan and GH Leavesley (2006), Assimilation of snow covered area information into hydrologic and land-surface models. Adv. Water Resour. Version: 1 29 (8) 1209-1221, issn: 0309-1708, ids: 081LU, doi: 10.1016/j.advwatres.2005.10.001
  • Regonda, SK, B Rajagopalan, M Clark and E Zagona (2006), A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin. Water Resour. Res. Version: 1 42 (9) , Art. No. W09404, issn: 0043-1397, ids: 086BW, doi: 10.1029/2005WR004653
  • Regonda, SK, B Rajagopalan and M Clark (2006), A new method to produce categorical streamflow forecasts. Water Resour. Res. Version: 1 42 (9) , Art. No. W09501, issn: 0043-1397, ids: 086BW, doi: 10.1029/2006WR004984
  • Kumar, KK, B Rajagopalan, M Hoerling, G Bates and M Cane (2006), Unraveling the mystery of Indian monsoon failure during El Nino. Science Version: 1 314 (5796) 115-119, issn: 0036-8075, ids: 091LU, doi: 10.1126/science.1131152, PubMed ID: 16959975
  • Neumann, DW, EA Zagona and B Rajagopalan (2006), A decision support system to manage summer stream temperatures. J. Am. Water Resour. Assoc. Version: 1 42 (5) 1275-1284, issn: 1093-474X, ids: 103TJ, doi: 10.1111/j.1752-1688.2006.tb05300.x
  • Prairie, JR, B Rajagopalan, TJ Fulp and EA Zagona (2005), Statistical nonparametric model for natural salt estimation. J. Environ. Eng.-ASCE Version: 1 131 (1) 130-138, issn: 0733-9372, ids: 880VV, doi: 10.1061/(ASCE)0733-9372(2005)131:1(130)
  • Regonda, SK, B Rajagopalan, M Clark and J Pitlick (2005), Seasonal cycle shifts in hydroclimatology over the western United States. J. Clim. Version: 1 18 (2) 372-384, issn: 0894-8755, ids: 899JY, doi: 10.1175/JCLI-3272.1
  • Gangopadhyay, S, M Clark and B Rajagopalan (2005), Statistical downscaling using K-nearest neighbors. Water Resour. Res. Version: 1 41 (2) , Art. No. W02024, issn: 0043-1397, ids: 900LK, doi: 10.1029/2004WR003444
  • Regonda, SK, B Rajagopalan, U Lall, M Clark and YI Moon (2005), Local polynomial method for ensemble forecast of time series. Nonlinear Process Geophys. Version: 1 12 (3) 397-406, issn: 1023-5809, ids: 915DK
  • Singhrattna, N, B Rajagopalan, M Clark and KK Kumar (2005), Seasonal forecasting of thailand summer monsoon rainfall. Int. J. Climatol. Version: 1 25 (5) 649-664, issn: 0899-8418, ids: 920ZM, doi: 10.1002/joc.1144
  • Kumar, KK, M Hoerling and B Rajagopalan (2005), Advancing dynamical prediction of Indian monsoon rainfall. Geophys. Res. Lett. Version: 1 32 (8) , Art. No. L08704, issn: 0094-8276, ids: 922PX, doi: 10.1029/2004GL021979
  • Grantz, K, B Rajagopalan, M Clark and E Zagona (2005), A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts. Water Resour. Res. Version: 1 41 (10) , Art. No. W10410, issn: 0043-1397, ids: 978DK, doi: 10.1029/2004WR003467
  • Singhrattna, N, B Rajagopalan, KK Kumar and M Clark (2005), Interannual and interdecadal variability of Thailand summer monsoon season. J. Clim. Version: 1 18 (11) 1697-1708, issn: 0894-8755, ids: 937ZU, doi: 10.1175/JCLI3364.1
  • Gangopadhyay, S, M Clark, K Werner, D Brandon and B Rajagopalan (2004), Effects of spatial and temporal aggregation on the accuracy of statistically downscaled precipitation estimates in the upper Colorado River basin. J. Hydrometeorol. Version: 1 5 (6) 1192-1206, issn: 1525-755X, ids: 885VA, doi: 10.1175/JHM-391.1
  • Clark, M, S Gangopadhyay, L Hay, B Rajagopalan and R Wilby (2004), The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. J. Hydrometeorol. Version: 1 5 (1) 243-262, issn: 1525-755X, ids: 775TL, doi: 10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2
  • Clark, MP, S Gangopadhyay, D Brandon, K Werner, L Hay, B Rajagopalan and D Yates (2004), A resampling procedure for generating conditioned daily weather sequences. Water Resour. Res. Version: 1 40 (4) , Art. No. W04304, issn: 0043-1397, ids: 819PN, doi: 10.1029/2003WR002747
  • Yates, D, S Gangopadhyay, B Rajagopalan and K Strzepek (2003), A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resour. Res. Version: 1 39 (7) , Art. No. 1199, issn: 0043-1397, ids: 710ZG, doi: 10.1029/2002WR001769
  • Neumann, DW, B Rajagopalan and EA Zagona (2003), Regression model for daily maximum stream temperature. J. Environ. Eng.-ASCE Version: 1 129 (7) 667-674, issn: 0733-9372, ids: 692GG, doi: 10.1061/(ASCE)0733-9372(2003)129:7(667)
  • Rajagopalan, B, U Lall and SE Zebiak (2002), Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Mon. Weather Rev. Version: 1 130 (7) 1792-1811, issn: 0027-0644, ids: 556NL, doi: 10.1175/1520-0493(2002)130<1792%3ACCFTRA>2.0.CO%3B2
  • Rajagopalan, B, E Cook, U Lall and BK Ray (2000), Spatiotemporal variability of ENSO and SST teleconnections to summer drought over the United States during the twentieth century. J. Climate Version: 1 13 (24) 4244-4255, issn: 0894-8755, ids: 385EF, doi: 10.1175/1520-0442(2000)013<4244:SVOEAS>2.0.CO;2

Figure 1. Correlation maps of Indian rainfall with moist static energy (MSE) and sea surface temperatures (SST). Full caption in DOI: 10.1002/2014GL060429

Figure 2. Observed, modeled, and forecasted standardized rainfall anomalies. Full caption in DOI: 10.1002/2014GL060429