This study focuses on revisiting the old technique of testing the consistency of historic precipitation data from the Himalayas of Bhutan through a double mass curve (DMC) and residual curve (RC). The method of least square and regression analysis is employed to supplement by providing a statistical means to quantify and justify the inconsistency in the data. Inconsistency in the dataset may arise due to natural or anthropogenic activities and implies the change of precipitation regime over the region compared to the meteorologically homogeneous region or the change in method to collect data. As the precipitation data is pivotal for an assessment of water resources over the Himalayan catchments, testing the historical dataset for consistency is imperative when the data was collected manually. Precipitation data is limited in the country in both the time and space domain as the installation of dense rain gauge for precipitation measurement is challenging. Moreover, maintaining it would be extremely costly and difficult owing to the tough Himalayas terrain. It is vital to investigate the quality of available limited datasets and recommend the best method for rectifying the inconsistency thus capitalizing on the limited resources before progressing with the assessment. The test results are presented in four categories: Best, Good, Satisfactory and Poor category. It is observed that maximum stations fall in Good (32%) and least in Satisfactory (15%) with a sufficient number in Best (29%). It can be concluded that more of the Class C stations shows discontinuity when compared with class Astations AWS (Automatic Weather Station) stations. Further, it is proven that consistent data are also statistically homogeneous. Furthermore, this research puts forward an innovative approach of grouping stations for consistency tests based on the frequency of rainfall events. The result provides an avenue to apply robust algorithms to estimate the missing data since the stations are categorically tagged with their quality.

It is critical to quantify runoff that would be generated due to rainfall in a catchment which would in turn help in predicting the nature of response from a catchment that can be used to come up with a strategic planning and implementation to prevent from certain foreseen or unforeseen calamities. Most of the sub-catchments in Bhutan are un-gauged, which results in the insufficient hydrological data for validating the accuracy of any hydrological model. The current study is approached with identification of reliable hydrological model with the help of real data obtained through customized gauging stations developed using Arduino chip that works on batteries, using sonar sensors. To overcome the issue of insufficient data sets, Om chu river, situated at Phuentsholing is chosen to be gauged for the study. This practical approach towards the analysis of intensity and response of catchment, would help engineers to foresee the possible calamities if any and design the structures accordingly with the reasonable costing.

Unsteady flow measurement in the natural river systems have remained one of the most challenging hydraulic problems. Despite several attempts, there is not even a method that has proven satisfactory to quantify the flow during unsteady conditions. Hydromet station adopts the rating curve to convert the flow depth to discharge in most cases. With the recent advancement in the field of Internet of Things (IoT), sensors are widely available to record the flow depth at a small temporal resolution. In the present study, IoTs and hydrometry were integrated to automatize the discharge estimation through non-contact technique coupling with dynamic rating curve formulae (DRcF) in the Himalayan river. The presented idea is suitable to quantify flow, especially in the Himalayan river with rapid flow characterized by high turbulence and velocity, because only a non-contact method is feasible. A pilot gauging station was established in Om chhu using an ultrasonic sensor to obtain the stage data that serves as the input to the DRcF. A comparative evaluation of DRcF revealed that all the DRcF selected estimated discharge without any inconsistencies or stability issues, although a few models were implicit.