The spatio-temporal dynamics of suspended sediment sources based on a novel indexing approach combining Bayesian geochemical fingerprinting with physically-based modelling.

Applications of sediment source fingerprinting continue to increase globally as the need for information to support improved management of the sediment problem persists. In our novel research, a Bayesian fingerprinting approach using MixSIAR was used with geochemical signatures, both without and with informative priors based on particle size and slope. The source estimates were compared with a newly proposed Source Sensitivity Index (SSI) and outputs from the INVEST-SDR model. MixSIAR results with informative priors indicated that agricultural and barren lands are the principal sediment sources (contributing ∼5-85% and ∼5-80% respectively during two sampling periods i.e. 2018-2019 and 2021-2022) with forests being less important. The SSI spatial maps (using % clay and slope as informative priors) showed >78% agreement with the spatial map derived using the INVEST-SDR model in terms of sub-catchment prioritization for spatial sediment source contributions. This study demonstrates the benefits of combining geochemical sediment source fingerprinting with SSI indices in larger catchments where the spatial prioritization of soil and water conservation is both challenging but warranted.

Multifractal characterization of meteorological to agricultural drought propagation over India.

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

Exploring the last 50 years of Indian research on the impact of coal mining using bibliometric analysis with an overview of water-related impacts.

This paper throws light on the bibliometric review of the impact of coal mining in India over the past 50 years, emphasizing environmental, especially water-related impacts. The data were refined from the Web of Science database and analyzed in a bibliometric map visualization software tool, VOSviewer, to grasp the research focus, status quo and analyze the trend and direction of the work being carried out in this area. The methodology was covered in three phases: search and document selection, software and data extraction, and analysis of results and trends. The study results indicated that (i) the publication has increased in the past two decades (2001-2021) with a steep increase in the period from 2010 to 2021 with 74.68% article types documents and a mere 7.74% review documents. (ii) In India, the significant contribution is made by the Indian Institute of Technology (Indian School of Mines), Dhanbad with Department of Science and Technology as a primary funding agency. (iii) The bibliometric map of co-occurrence of author keywords showed that keywords relating to the "pollution" (connected to air, water) from "Jharia coalfield" have highest occurrences in the relevant published works of literature and topics like "reclamation," "mine spoil," and application of approaches like "remote sensing and GIS" have lower linkage strengths in general. (iv) The result of the co-citation network study has marked the most significant authors and the highly cited sources of the database revealing Ghose M.K. and Singh A.K. as among the most cited authors with citations more than 150 in the field of our interest. (v) The trend of publication in the research area of Water Resources showed a significant increase after 2015. The keyword occurrence map reveals that water quality studies have been extensively studied, but quantifications of the coal mining-induced changes in water regimes at river basin scales are absent.

Metaldehyde prediction by integrating existing water industry datasets with the soil and water assessment tool.

Metaldehyde (a synthetic aldehyde pesticide used globally in agriculture) has been internationally identified as an emerging contaminant of concern. This study aimed to integrate existing water industry, publicly available and purchased licensed datasets with the open-access Soil and Water Assessment Tool (SWAT), to establish if these datasets could be used to effectively model metaldehyde in river catchments. To achieve the study aim, a SWAT model was developed and calibrated for the River Medway catchment (UK). The results of calibration (1994-2004) and validation (2005-2016) of average daily streamflow (m3/s) showed that the SWAT model could simulate water balance well (P-factor 0.68-0.85 and R-factor 0.54-0.82, NSE 0.42-0.60). Calibration (P-factor 0.72 and R-factor 1.35, NSE 0.31) and validation (P-factor 0.49 and R-factor 1.37, NSE 0.16) for daily soluble metaldehyde (mg active ingredient) load was also satisfactory. The most sensitive pesticide parameters for metaldehyde simulation included the timing and amount of pesticide (kg/ha) applied to the hydrological response units, the pesticide percolation coefficient and pesticide application efficiency. Outputs from this research demonstrate the potential application of SWAT in large complex catchments where routine monitoring is in place, but isn't designed explicitly for the purpose of predictive modelling. The implications of this, are significant, because they suggest that SWAT could be applied universally to catchments using existing water industry datasets. This would allow more efficient use of historical datasets and would be applicable in situations where resources are not available for additional targeted monitoring programmes.