Salts in the root zone have high spatial variability, changes rapidly and adversely affects soil quality and crop productivity. Rapid detection of electrical conductivity (EC) using visible-near infrared (Vis-NIR) and midinfrared (MIR) spectroscopy can alleviate the adverse effects on soil and plant, which through conventional method is time consuming. Soils were collected from the Indo-Gangetic plains and analyzed for EC using conventional, Vis-NIR, MIR spectroscopy and there was wide variation in EC measured by the conventional method. The spectral regions in 460-500 and 1890-1906 nm in the Vis-NIR region and 4200-4310, 5275-5280, 6660-6670, 7305-7310 and 8290-8300 nm in the MIR region were sensitive to detection of EC. Partial least square regression (PLSR) outperformed random forest regression (RF), support vector regression (SVR), and multivariate adaptive regression splines (MARS) both in Vis-NIR and MIR region during calibration. The ratio of performance deviation (RPD), coefficient of determination (R2) and root mean square error (RMSE) of the validation dataset were used to assess the prediction accuracy and the predictive performance of PLSR (2.44, 0.84, 0.21), RF (1.95, 0.81, 0.20), SVR (2.09, 0.78, 0.22) and MARS (1.81, 0.73, 0.27) models. PLSR model performed very well in the Vis-NIR range; however, in the MIR range, RF (1.43, 0.52, 0.20), followed by PLSR (1.40, 0.55, 0.35), performed better than SVR (1.39, 0.53, 0.35) and MARS (1.29, 0.44, 0.37). Vis-NIR spectroscopy with PLSR algorithm predicted EC better than MIR spectroscopy and would be the method of choice for rapid estimation and prediction of EC in the study region.

The analysis of drought onset and their potential relationship to drought severity (deficit volume) are critical for providing timely information for agricultural operations, such as cultivation planning and crop productivity monitoring. A coupling between drought timing and deficit volume can be used as a proxy for drought-related damage estimation and associated risks. Despite its high importance, so far little attention was paid to determine the timing of drought and its linkage with deficit volume for hydrological droughts. This study utilizes quality-controlled streamflow observations from 1965 to 2018 to unveil regional patterns of hydrological drought onset, trends in event-specific deficit volume, and nonlinear relationships between onset timing and deficit volume across 97 rain-dominated catchments in Peninsular India (8-24o N, 72-87o E). Our results show a shift towards earlier hydrological drought onset in conjunction with a decrease in deficit volume during the Indian monsoon (June-September) season, which is contrasted by a delayed onset in the pre-monsoon (March-May) and post-monsoon (October-February) seasons. Further, approximately one-third of the catchments show a significant nonlinear dependency between drought deficit volume and onset time. We find environmental controls, such as soil organic carbon, vertical distance to channel network, and soil wetness are dominant factors in influencing droughts. Our analysis provides new insights into the causal chain and physical processes linking climatic and physiographic controls on streamflow drought mechanisms, which can support drought forecasting and climate impact assessment studies.