Outcomes

Publications

Authors: Prakhar Sharma, Swathi S Prashanth, Ashutosh Sharma and Sumit Sen

Abstract: The Mountain Ecosystems, such as the Himalayan region, not only provide numerous Ecosystem Services (ES) to millions of people living in mountainous areas but also provide essential ES to those living downstream. However, there is often an uneven distribution of ES availability and its value across different regions. Therefore, a better understanding of the spatial heterogeneity of ES is required for efficient and sustainable management of ES. This study thoroughly reviews literary data to examine ES's spatial distribution and economic values across the Western, Central, and Eastern Himalayas. The literature was searched in the Scopus online database using the Boolean method from specific keywords such as "Ecosystem Services" AND "Himalayas". A systematic review of 76 of the most relevant literature sources yielded 31 unique ES divided into three categories: Provisioning (PES), Regulating (RES), and Cultural (CES). The distribution of reviewed literature is relatively balanced across the Himalayas (Western: 30.26%, Central: 32.89%, Eastern: 36.84%); however, the analysis identified a bias towards PES (43.54%), highlighting a need for increased research focus on RES (36.48%) and CES (19.98%). Notably, water-related services such as PES9 (Surface water used as a material (non-drinking purposes)) and PES8 (Surface water for drinking) have been ranked highest in all regions of the Himalayas yet have not been comprehensively studied in terms of their quantification and valuation. Furthermore, while most literature focused on the identification of ES (73.68%), there is a significant lack of attention to quantification (39.47%) and valuation (23.68%) of ES in the region. The average economic values for PES, RES, and CES were 446.75 USD/ha/year, 1128.81 USD/ha/year, and 457.51 USD/ha/year, respectively, indicating higher valuation for RES. This underlines the need for a more balanced research approach that includes identifying and thoroughly quantifying and valuing all types of ES in the Himalayas.

Read: https://iopscience.iop.org/article/10.1088/1748-9326/ad9abc 

Abstract: The accurate and reliable simulation and prediction of runoff in the Beas River Basin are becoming more and more important due to the increased uncertainty posed by climate change, which is making it difficult to manage water resources efficiently. In order to minimize the effect of flash floods, estimating the accurate peak flow is essential. It can be challenging to comprehend and anticipate peak flow due to natural streamflow variance as well as the streamflow management offered by dams and reservoirs. Which makes it difficult to mimic hydrologic behavior on a daily scale with reliable accuracy. This study evaluated the efficacy of physics-aided machine learning (ML) based regression models for modeling streamflow in combination with process-based hydrological SWAT (soil and water assessment tool). Performance of eight machine learning (ML) models including linear regression (LR), multi-layer perceptron (MLP), light gradient-boosting machine (LGBM), extreme gradient boosting (XGBoost), kernel ridge (KR), elastic net (EN), Bayesian ridge (BR), and gradient boosting (GB) have been analyzed and compared with the calibrated-SWAT (cSWAT) model. The Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and coefficient of determination (R2) were used to assess the effectiveness of both models. Results showed that the uncalibrated SWAT in combination with ML regression models (cSWAT-ML) performed well and found comparable to calibrated SWAT (cSWAT), though, few ML regression models in combination with uncalibrated SWAT (uSWAT-MLmodels models) performed superior. cSWAT model performed well with R2 values of 0.73, RMSE value of 276.92 m3/s and NSE value of 0.72. In uSWAT-ML, EN and BR have obtained better results with R2 values of 0.89 and 0.89, NSE values of 0.87 and 0.87, and RMSE values of 158.31 m3/s and 159.48 m3/s. The approached uSWAT-ML models have effectively predicted the peak stream flow rates with models BR and EN have predicted with better results of R2 value of 0.71 each. This study’s findings highlight the potential of all the eight ML models as promising techniques for predicting the peak flow discharge values when uncalibrated process-based models are combined.

Hydrometeorological analysis of July-2023 floods in Himachal Pradesh, India

Authors: Vivek Gupta, Bilal Syed, Ashish Pathania, Saran Raaj, Aliva Nanda, Shubham Awasthi & Dericks P. Shukla 

Read more: https://doi.org/10.1007/s11069-024-06520-5. 

Abstract: In recent decades, the Himalayas have seen increasing extreme precipitation events. Climate change has impacted the occurrence and distribution of extreme precipitation events across the Himalayas. Patterns of both western disturbances and the Indian summer monsoon are undergoing significant changes in nature due to climate change. However, the magnitude and intensity of flood in a stream are not always linearly dependent on the amount of precipitation. Other factors, such as topography, land use, soil characteristics, and antecedent hydrological conditions, play a pivotal role in modulating the response of a watershed to an extreme precipitation event. On July 07–11, 2023, several districts of Himachal Pradesh faced devastating floods resulting in loss of life, infrastructure, and environmental damage with significant economic consequences. Developing a resilient solution for managing such events and reducing damage requires an in-depth understanding of multiple causative factors of such extreme events. In this paper, we analyzed the meteorological and hydrological factors that caused the flooding situation in Himachal Pradesh during July 2023. Hydrometeorological data from several observation stations were analyzed along with reanalysis data from ERA5, SMAP-L4, and FLDAS-NOAH to understand the causative factors that lead to peak floods. The compounding of extremely heavy rainfall with near-saturation antecedent moisture content and snowmelt was found to be the leading factor in inflating and sustaining the flood peak.


Other publications


Conferences 

Ms. Achala Singh, working under Dr. Priyank Sharma, at IIT Indore, presented the outcomes of REFRESH project at HYDRO 2024 conference. 

The presentation focused on Coincidental compound extremes in Himalayan River basin.