Extreme precipitation indices over India using CMIP6: a special emphasis on the SSP585 scenario

Evaluating the impact of ecological factors on the quality and habitat distribution of Lonicera japonica Flos using HPLC and the MaxEnt model

Introduction

The quality of traditional Chinese medicine is based on the content of their secondary metabolites, which vary with habitat adaptation and ecological factors. This study focuses on Lonicera japonica Flos (LJF), a key traditional herbal medicine, and aims to evaluate how ecological factors impact its quality.

Methods

We developed a new evaluation method combining high-performance liquid chromatography (HPLC) fingerprinting technology and MaxEnt models to assess the effects of ecological factors on LJF quality. The MaxEnt model was used to predict suitable habitats for current and future scenarios, while HPLC was employed to analyze the contents of key compounds. We also used ArcGIS for spatial analysis to create a quality zoning map.

Results

The analysis identified 21 common chromatographic peaks, with significant variations in the contents of Hyperoside, Rutin, Chlorogenic acid, Cynaroside, and Isochlorogenic acid A across different habitats. Key environmental variables influencing LJF distribution were identified, including temperature, precipitation, and elevation. The current suitable habitats primarily include regions south of the Yangtze River. Under future climate scenarios, suitable areas are expected to shift, with notable expansions in southern Gansu, southeastern Tibet, and southern Liaoning. The spatial distribution maps revealed that high-quality LJF is predominantly found in central and southern Hebei, northern Henan, central Shandong, central Sichuan, southern Guangdong, and Taiwan.

Discussion

The study indicates that suitable growth areas can promote the accumulation of certain secondary metabolites in plants, as the accumulation of these metabolites varies. The results underscore the necessity of optimizing quality based on cultivation practices. The integration of HPLC fingerprinting technology and the MaxEnt model provides valuable insights for the conservation and cultivation of herbal resources, offering a new perspective on evaluating the impact of ecological factors on the quality of traditional Chinese medicines.

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India

This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the Godavari River basin. The Sacramento model provided initial streamflow estimates for Kanhargaon, Nowrangpur, and Wairagarh. This approach melded traditional hydrological modeling with advanced multi-layer perceptron (MLP) capabilities. When combined with parameters like lagged rainfall, lagged streamflow, potential evapotranspiration (PET), and temperature variations, these initial outputs were further refined using the Sac-MLP model. A comparison with Sacramento revealed the superior performance of the Sac-MLP model. For instance, during training, the Nash Sutcliffe efficiency (NSE) values for the Sac-MLP witnessed an improvement from 0.610 to 0.810 in Kanhargaon, 0.580 to 0.692 in Nowrangpur, and 0.675 to 0.849 in Wairagarh. The results of the testing further corroborated these findings, as evidenced by the increase in the NSE for Kanhargaon from 0.890 to 0.910. Additionally, Nowrangpur and Wairagarh experienced notable improvements, with their NSE values rising from 0.629 to 0.785 and 0.725 to 0.902, respectively. Projections based on EC-Earth3 data across various scenarios highlighted significant shifts in rainfall and temperature patterns, especially in the far future (2071-2100). Regarding the relative change in annual streamflow, Kanhargaon projections under SSP370 and SSP585 for the far future indicate increases of 584.38% and 662.74%. Similarly, Nowrangpur and Wairagarh are projected to see increases of 98.27% and 114.98%, and 81.68% and 108.08%, respectively. This study uses EC-Earth3 estimates to demonstrate the Sac-MLP model's accuracy and importance in climate change water resource planning. The unique method for region-specific hydrological analysis provides vital insights for sustainable water resource management. This research provides a deeper understanding of climate-induced hydrological changes and a robust modeling approach for accurate predictions in changing environmental conditions.

Land suitability analysis for turmeric crop for humid tropical Kerala, India, under current and future climate scenarios using advanced geospatial techniques

BACKGROUND

Turmeric cultivation primarily thrives in India, followed by Bangladesh, Cambodia, Thailand, China, Malaysia, Indonesia and the Philippines. India leads globally in both area and production of turmeric. Despite this, there is a recognized gap in research regarding the impact of climate change on site suitability of turmeric. The primary objective of the present study was to evaluate both the present and future suitability of turmeric cultivation within the humid tropical region of Kerala, India, by employing advanced geospatial techniques. The research utilized meteorological data from the Indian Meteorological Department for the period of 1986-2020 as historical data and projected future data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Four climatic scenarios of shared socioeconomic pathway (SSP) from the Intergovernmental Panel on Climate Change AR6 model of MIROC6 for the year 2050 (SSP 1-2.6, SSP 2-4.5, SSP 3-7.0 and SSP 5-8.5) were used.

RESULTS

The results showed that suitable area for turmeric cultivation is declining in future scenario and this decline can be primarily attributed to fluctuations in temperature and an anticipated increase in rainfall in the year 2050. Notable changes in the spatial distribution of suitable areas over time were observed through the application of geographic information system (GIS) techniques. Importantly, as per the suitability criteria provided by ICAR-National Bureau of Soil Survey and Land Use Planning (ICAR-NBSS & LUP), all the districts in Kerala exhibited moderately suitable conditions for turmeric cultivation. With the GIS tools, the study identified highly suitable, moderately suitable, marginally suitable and not suitable areas of turmeric cultivation in Kerala. Presently 28% of area falls under highly suitable, 41% of area falls under moderately suitable and 11% falls under not suitable for turmeric cultivation. However, considering the projected scenarios for 2050 under the SSP framework, there will be a significant decrease in highly suitable area by 19% under SSP 5-8.5. This reduction in area will have an impact on the productivity of the crop as a result of changes in temperature and rainfall patterns.

CONCLUSION

The outcome of the present research suggests that the state of Kerala needs to implement suitable climate change adaptation and management strategies for sustaining the turmeric cultivation. Additionally, the present study includes a discussion on potential management strategies to address the challenges posed by changing climatic conditions for optimizing turmeric production in the region. © 2024 Society of Chemical Industry.

Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective

This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine learning (ML) models - artificial neural network (ANN), recurrent neural network (RNN), and adaptive fuzzy neural inference system (ANFIS) - for streamflow prediction under the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways (SSPs), specifically SSP245 and SSP585. Four key performance indicators, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), guide the evaluation. These models employ monthly precipitation, maximum and minimum temperatures as inputs, and discharge as the output, spanning 1985-2014. The ANN model with a 3-10-1 architecture outperforms RNN and ANFIS, displaying lower MSE, RMSE, MAE, and higher R2 values for both training (MSE = 20417, RMSE = 142, MAE = 71, R2 = 0.94) and testing (MSE = 9348, RMSE = 96, MAE = 108, R2 = 0.92) datasets. Subsequently, the superior ANN model predicts streamflow up to 2100 using SSP245 and SSP585 scenarios. These results underscore the potential of ANN models for robust futuristic streamflow estimation, offering valuable insights for water resource management and planning.

Projected climate extremes over agro-climatic zones of Ganga River Basin under 1.5, 2, and 3° global warming levels

Recurring floods, droughts, heatwaves, and other hydro-meteorological extreme events are likely to be increased under the climate change scenarios. The increased risk of these extreme events might have more exposure to the population; thus, it is important to discuss such extreme events and their projected behavior under a changing climate scenario. In the present study, we have computed the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under the Ganga River Basin (GRB)utilizing a high-resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25° × 0.25°) under global warming levels of 1.5 °C, 2 °C, and 3 °C. We found that the annual daily minimum temperature (TNN) showed a higher rise of about 67% than the maximum temperature (TXX) of 48% in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for the 3 °C global warming level. Along with extreme indices, the population exposed due to the impact of the extreme maximum temperature has also been analyzed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analyzed with 20-year return period using GEV distribution method. The study concludes that the exposed population to extreme temperature event experienced an increase from 46.99 to 52.16% for the whole Ganga Basin. Consecutive dry days (CDD) and consecutive wet days (CWD) both show a significant increasing trend, but CWD has a significant increase in the majority of the zones, while CDD shows a significant decreasing trend for some of the zones for three warming levels periods. Extreme climate indices help to understand the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and heatwaves to develop early warning systems and adaptation strategies to mitigate such events.

Coastal vulnerability assessment for the coast of Tamil Nadu, India-a geospatial approach

A coastal region is a section of land that borders a significant body of water, often the sea or ocean. Despite their productivity, they are sensitive to even little alterations in the outside environment. This study aims to develop a spatial coastal vulnerability index (CVI) map for the Tamil Nadu coast of India, which has diverse coastal and marine environments that are ecologically fragile zones. Climate change is expected to increase the intensity and frequency of severe coastal hazards, such as rising sea levels, cyclones, storm surges, tsunamis, erosion, and accretion, severely impacting local environmental and socio-economic conditions. This research employed expert knowledge, weights, and scores from the analytical hierarchy process (AHP) to create vulnerability maps. The process includes the integration of various parameters such as geomorphology, Land use and land cover (LULC), significant wave height (SWH), rate of sea level rise (SLR), shoreline change (SLC), bathymetry, elevation, and coastal inundation. Based on the results, the very low, low, and moderate vulnerability regions comprise 17.26%, 30.77%, and 23.46%, respectively, whereas the high and very high vulnerability regions comprise 18.20% and 10.28%, respectively. The several locations tend to be high and very high due to land-use patterns and coastal structures, but very few are contributed by geomorphological features. The results are validated by conducting a field survey in a few locations along the coast. Thus, this study establishes a framework for decision-makers to implement climate change adaptation and mitigation actions in coastal zones.