Representative Concentration Pathways Research Articles

Mapping and assessing the future provision of lake ecosystem services in Lithuania

Lakes supply multiple ecosystem services (ES), key to supporting socio-ecologic systems and human well-being. In the context of future land use and climate changes, it is imperative to anticipate potential impacts on lake ES supply. Hence, studies that deal with future lake ES, such as mapping, are lacking. In this work, we mapped and assessed the future supply of three ES: (1) maintenance of nursery conditions (nursery ES), (2) maintenance of chemical conditions of freshwaters (nutrient regulation ES), and (3) direct and indirect cultural outputs (recreation ES) in Lithuania. Four future scenarios were utilised, integrating land use and climate changes: A0 - business as usual; A1-urbanisation; A2: land abandonment and afforestation; and A3 - agricultural intensification. The projected year was 2050, following the intermediate Representative Concentration Pathway (RCP 4.5). The future scenarios were simulated using the open-source software Dinamica EGO based on a 6-step modelling framework. Statistical differences among the scenarios and ES were analysed by applying a Kruskal-Wallis ANOVA test. Spatial analysis was done by performing a Moran's I and Getis-Ord (Gi∗) hotspot analysis. The results showed significant differences in nursery and nutrient regulation ES. The highest supply in nursery ES was observed for the A0 scenario. For nutrient regulation ES, the lowest ES supply was identified for the A1 scenario and for recreation ES, the highest was found in the A2 scenario. The eastern and northeastern regions of Lithuania showed a high ES supply. Hot spots were only identified in the eastern region. These regions are associated with a high area covered by forests and protected areas. The central region shows a low ES supply, identified as a cold spot where the agricultural landscape dominates. The results of the PCA analysis revealed an association between nursery and recreation ES. Nutrient regulation was not associated with the other two ES. Mapping and assessing the impact of future scenarios is vital to anticipating the potential dynamics of lake ES, especially in the context of climate change. This information is essential in the context of environmental management, helping decision-makers to ensure a sustainable ES supply and contributing to human wellbeing.

REGIONÁLNE KLIMATICKÉ SCENÁRE FREKVENCIE VÝSKYTU DNÍ SO ZRÁŽKAMI NA SLOVENSKU

We present scenarios of how frequency of days with precipitation may change over the territory of Slovakia by 2050 and 2080 in the article. Climatic scenarios were created from three regional climate models EURO-CORDEX for the pessimistic emission scenario RCP8.5. We analysed the occurrence of the number of precipitation days for class intervals: 5 – 9.9 mm, 10 – 19.9 mm, 20 – 29.9 mm, 30 – 39.9 mm, as well as for open intervals ≥ 5 mm, ≥ 10 mm, ≥ 20 mm, ≥ 30 mm, and ≥ 40 mm. To correct systematic errors in climate models, we used data (daily precipitation totals) from the national network of rain gauges. The RCM models were corrected in two steps: first in terms of excessive occurrence of so-called “drizzle days”, and then in terms of systematic underestimation of daily sums across the entire distribution function (bias correction). The occurrence of days with daily rainfall exceeding 40 mm for the horizons of 2050 and 2080 is presented in the form of maps. As our analyses revealed, the annual frequency of days with daily rainfall exceeding 40 mm is expected to increase by approximately 20% by the middle of the 21st century and by up to 60% by the end of the century compared to the present. The paper also includes tables with relative changes and absolute frequencies of wet days for 75 selected locations in Slovakia.

Tracing human influence on rising surface air temperature in Venezuela.

The rise in surface air temperature (SAT) in Venezuela, leading to the loss of all its glaciers, underscores the urgency of understanding human contributions to this phenomenon. This study investigates the impact of anthropogenic climate forcings on SAT across Venezuela, employing observational data, multi-model simulations, and optimal fingerprinting method. Anthropogenic forcings have driven a 0.40-0.85 C SAT rise during the industrial era, with land use (LU) emerging as a significant driver (0.36-0.68 C), surpassing greenhouse gases (GHGs) (0.10-0.62 C). Conversely, anthropogenic aerosols (Aaer) exhibit a cooling effect (- 0.93 to - 0.25 C) on SAT. Projections under Representative Concentration Pathways 4.5 indicate substantial SAT increases by the 21st century's end, underscoring human-induced SAT rise. Effective management of regional Aaer and LU changes in Venezuela holds the potential for mitigating current and future warming and its subsequent impacts on the fragile ecosystem of this region.

Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling

AbstractThis study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning and urban flood risk management. The analysis employs two primary methodologies: remote sensing and hydrological modeling. Remote sensing was conducted using Google Earth Engine and Land Change Modeler in IDRISI Terrset software to analyze historical (1985–2022) and future (2050–2100) LULC. Hydrological modeling was performed using the Natural Resources Conservation Service curve number method to assess the overall impact of LULC changes on Gdańsk’s hydrology at the local scale. The Orunia basin, a critical area due to intensive LULC development, was selected for detailed hydrological analysis using the Hydrologic Modeling System (HEC-HMS). The analysis encompassed three scenarios: LULC changes, climate change, and combined LULC and climate change effects. The LULC analysis revealed a marked increase in urban area, a shift in forest and vegetation cover, and a reduction in agricultural land. HEC-HMS simulations showed an increase in the runoff coefficient across selected decades, which was attributed to the combined effect of LULC and climate change. The projected increases under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2050 and 2100 are projected to surpass those observed during the baseline period. The findings highlight that the synergistic effects of LULC and climate change have a more significant impact on Gdańsk’s hydrology at both local and basin scales than their separate effects. These insights into LULC shifts and urban hydrological responses hold implications for sustainable urban planning and effective flood risk management in Gdańsk and similar urban settings.

Assessing Drought Patterns in Al-Baha: Implications for Water Resources and Climate Adaptation

Due to growing water demands and changing hydro-meteorological variables brought on by climate change, drought is becoming an increasingly serious climate concern. The Al-Baha region of Saudi Arabia is the subject of this study because it is susceptible to both agricultural and meteorological droughts. This study investigates how climate change affects patterns of drought in Al-Baha by analyzing four drought indices (Agricultural Standardized Precipitation Index (aSPI), the Standardized Precipitation Index (SPI), the Rainfall Deficiency Index (RDI), and the Effective Reconnaissance Drought Index (eRDI)) for the years 1991–2022. Analysis of rainfall data was carried out to classify drought events according to their duration, frequency, and severity. Results showed that severe droughts occurred in 2009, 2010, 2012, 2016, and 2022, with 2010 being the worst year. Results also indicated a notable decrease in precipitation, which has resulted in extended dry spells. Several indices indicate that this tendency has significant ramifications for agriculture, particularly in areas where farming is a major economic activity. In addition, the possible occurrence of hydrological drought was also observed based on the negative values for the Reservoir Storage Index (RSI) in Al-Baha. Projections for the future under two Representative Concentration Pathways (RCPs) showed notable variations in temperature and precipitation. Both the RCP4.5 (low emission) and the RCP8.5 (high emission) projection scenarios indicate that drought conditions will likely worsen further. Depending on the emission scenario, it is projected to show a temperature increase of 1–2 °C, whereas the variability in precipitation projections indicates significant uncertainty, with a reduction change in the range of 1.2–27% between 2050 and 2100. The findings highlight the urgent need for proactive adaptation strategies, effective water resource management, and the development of sophisticated drought prediction tools. Addressing these challenges is crucial for sustaining agriculture and managing water scarcity in Saudi Arabia in the face of increasing drought risk.

Role of the warm Arctic cold Eurasian-like pattern on the near future warming rate of East Asian surface temperature

Abstract Internal climate variability (ICV) plays an important role in either accelerating or slowing down the rate of surface temperature warming in East Asia in the near future. To examine the influence of ICV on East Asian surface temperature in the near future, we mainly analyzed the data sets obtained from Max Planck Institute Grand Ensemble model simulations under the Representative Concentration Pathway 8.5 scenario. It is found that the ICV associated with the so-called Warm Arctic-Cold Eurasian (WACE)-like pattern contributes to the near-future warming rate of East Asian surface temperature. Similar results are also obtained from large ensemble model simulations participating in the Coupled Model Intercomparison Project Phase 6 under the Shared Socioeconomic Pathways 5-8.5 scenario. This implies that the near-term warming rate in East Asia could vary depending on how the climate model simulates the WACE-like pattern, indicating that the ability to accurately simulate ICV in climate models is crucial for future climate mitigation and adaptation policies.

Quantifying the Effect of Land Use and Land Cover Changes on Spatial-Temporal Dynamics of Water in Hanjiang River Basin

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by 96.275×108t, 81.210×108t, and 84.13×108t relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply.

Projected changes in forest fire season, the number of fires, and burnt area in Fennoscandia by 2100

Abstract. Forest fire dynamics are expected to alter due to climate change. Despite the projected increase in precipitation, rising temperatures will amplify forest fire risk from the present to the end of the century. Here, we analysed changes in fire season, the number of fires, and burnt area in Fennoscandia from 1951 to 2100. Regional simulations from the JSBACH–SPITFIRE ecosystem model (where SPITFIRE stands for SPread and InTensity of FIRE) were performed under two climate change forcing scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5) and three global climate driver models (CanESM2, CNRM-CM5, and MIROC5) with a 0.5° resolution. Simulations were forced by downscaled and bias-corrected EURO-CORDEX data. Generally, as a consequence of the projected longer fire season and drier fuel, the probability of fires is projected to increase. However, changes in fire season, the number of fires, and burnt area are highly dependent on climate projections and location. The fire season is estimated to increase on average from 20 ± 7 to 52 ± 12 d, starting from 10 ± 9 to 23 ± 11 d earlier and ending from 10 ± 10 to 30 ± 16 d later, compared to the reference period (1981–2010), by the end of the century (2071–2100). The results for Finland indicate a change in the number of fires, ranging from −7 ± 4 % to 98 ± 56 %, and a change in burnt area, ranging from −19 ± 24 % to 87 ± 42 %. These findings contribute to a better understanding of potential changes in the future fire seasons of northern Europe.

Impact scenarios on groundwater availability of southern Italy by joint application of regional climate models (RCMs) and meteorological time series.

Nowadays the phenomenon of global warming is unequivocal, as confirmed by the latest reports of the IPCC and studies of the climate-change impacts on ecosystems, global economy, and populations. The effect of climate change on groundwater is a very relevant task especially for regions dependent chiefly on groundwater availability, as for the southern Italy. In such a territorial framework, to achieve a detailed hydro-climatological characterization, an Ensemble of 15 RCMs (E15) derived from the EURO-CORDEX project was analyzed considering two IPCC Representative Concentration Pathways (RCP4.5 and RCP8.5). The E15 was calibrated over the period (1950-1996) by a statistical comparison with data observed by the regional meteorological network managed by the former National Hydrological Service (SIMN), Department of Naples. The effects of climate change on air temperature (T), precipitation (P) and, consequently, on actual evapotranspiration (ETR) and effective precipitation Pe (P - ETR) were analyzed until 2100. The latter was considered as a proxy of groundwater recharge of the principal aquifer systems, represented chiefly by the karst aquifers. As a principal result, it was found that the E15 is basically able to reproduce the observed annual precipitation (OBSP) and mean annual air temperature (OBST), being characterized by a very similar frequency distribution. Accordingly, an inferential statistical approach was performed for calibrating E15 precipitation (E15P) and air temperature (E15T) based on the compensation of the difference with OBSP (+ 7%) and OBST (-16%). The E15 projects a reduction in precipitation and an increase in air temperature under both RCPs, with a divergence point between the two scenarios occurring by about 2040. As a principal result, Pe shows declining trends for both RCP scenarios, reaching a decrease of the 11-yrs moving average down to -20%, for RCP4.5, and -50%, for RCP8.5, even if characterized by relevant inter-annual fluctuations.

A Study on the Growth Model of Natural Forests in Southern China Under Climate Change: Application of Transition Matrix Model

This study establishes a climate-sensitive transition matrix growth model and predicts forest growth under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, and RCP8.5) over the next 40 years. Data from the Eighth (2013) and Ninth (2019) National Forest Resource Inventories in Chongqing and climate data from Climate AP are utilized. The model is used to predict forest growth and compare the number of trees, basal area, and stock volume under different climate scenarios. The results show that the climate-sensitive transition matrix growth model has high accuracy. The relationships between the variables and forest growth, mortality, and recruitment correspond to natural succession and growth. Although the number of trees, basal area, and stock volume do not differ significantly for different climate scenarios, the forest has sufficient seedling regeneration and large-diameter trees. The growth process aligns with succession, with pioneer species being replaced by climax species. The proposed climate-sensitive transition matrix growth model fills the gap in growth models for natural secondary forests in Chongqing and is an accurate method for predicting forest growth. The model can be used for long-term prediction of forest stands to understand future forest growth trends and provide reliable references for forest management. Forest growth can be predicted for different harvesting intensities to determine the optimal intensity to guide natural forest management in Chongqing City. The results of this study can help formulate targeted forest management policies to deal more effectively with climate change and promote sustainable forest health.

Regionally Coupled Climate Model ROM Projects More Plausible Precipitation Change Over Central Equatorial Africa

AbstractUnraveling plausible future rainfall change (ΔPr) patterns is crucial for tailoring societies' responses to climate change‐induced hazards. This study compares rainfall projections from the regionally coupled ocean model (ROM) and its atmospheric component, the regional atmospheric model REMO, over Central Equatorial Africa (CEA). Both models are forced by the Earth system model MPI‐ESM‐LR following the Representative Concentration Pathway 8.5. Results reveal increased rainfall across most of CEA, with ROM projecting more widespread and intensified wetting than REMO, although REMO produces more precipitation under future conditions, underscoring the influence of historical biases on REMO's projection. Examining processes underpinning changes unveils strong controls of sea and land surface temperature changes in ΔPr differences between the two models. Specifically, ROM mitigates warming more over the Atlantic than over CEA landmass compared to REMO, inducing enhancement of the Congo Basin cell and increased precipitable water content through specific humidity, affecting deep convection. Both models project enhanced Sahel and Kalahari thermal lows, with ROM better depicting the Kalahari low's warmer nature than the Sahel low. The resulting temperature gradients strengthen the northern and southern shallow meridional Hadley overturning circulation. ROM simulates the wetter conditions than REMO, attributed to its weaker northern Hadley Cell, which restricts the likelihood of northward moisture divergence toward the Sahel. Additionally, differences in mid‐tropospheric moisture convergence differentiate between ROM and REMO's wetness relative to the historical period and under future conditions. ROM projections are more plausible, in association with the reliability of its added value under the historical climate and mechanisms underlying Δpr.

Shifts in the habitat suitability for large hammerhead sharks under climate change

One of the chief consequences of climate change is the worldwide redistribution of species in the pursuit of physiologically and ecologically favourable conditions. As part of one of the most threatened groups of vertebrates, assessing how climate change may affect the distribution patterns of key shark species is essential for their long-term conservation and management. The present study aims to assess how climate change projections (based on the representative concentration pathways 2.6, 4.5, 6.0, and 8.5) for the middle and end of the century may affect the global patterns of habitat suitability (HS) for large hammerhead sharks – a unique and particularly threatened group of elasmobranchs. Here, a decline in the global average HS is projected for both Sphyrna lewini and Sphyrna zygaena, with the scale of HS decline generally increasing with time and RCP severity, while a global increase in HS is generally projected for Sphyrna mokarran, albeit reduced over the long-term and overturned under long-term RCP8.5. Meanwhile, poleward shifts are projected for all three species, with considerable tropical declines for both S. lewini and S. zygaena. Additionally, regional shifts in suitability have the potential to affect population connectivity, the access to key habitats, and levels of exposure to other anthropogenic pressures, along with the effectiveness of conservation and management efforts. Taken together, these results showcase the importance of climate change for species’ extinction risk assessments and the urgency of the consideration of climate change within the design and implementation of conservation measures.

Anthropogenic Climate Change Will Intensify European Explosive Storms Analogous to Alex, Eunice, and Xynthia

Abstract Extratropical storms, particularly explosive storms or “weather bombs” with exceptionally high deepening rates, present substantial risks and are susceptible to climate change. Individual storms may exhibit a complex and hardly detectable response to human-driven climate change because of the atmosphere’s chaotic nature and variability at the regional level. It is thus essential to understand changes in specific storms for building local resilience and advancing our overall comprehension of storm trends. To address this challenge, this study compares analogs—storms with a similar backward track until making landfall—in two climates of three explosive storms impacting different European locations: Alex (October 2020), Eunice (January 2022), and Xynthia (February 2010). We use a large ensemble dataset of 105 members from the Community Earth System Model, version 1 (CESM1). These analogs are identified in two periods: the present-day climate (1991–2001) and a future climate scenario characterized by high anthropogenic greenhouse gas emissions [representative concentration pathway 8.5 (RCP8.5), 2091–2101]. We evaluate future changes in the frequency of occurrence of the storms and intensity, as well as in meteorological hazards and the underlying dynamics. For all storms, our analysis reveals an increase in precipitation and wind severity over land associated with the explosive analogs in the future climate. These findings underscore the potential consequences of explosive storms modified by climate change and their subsequent hazards in various regions of Europe, offering evidence that can be used to prepare and enhance adaptation processes. Significance Statement This study investigates the impact of climate change on explosive storms, or weather bombs, and their potential consequences for European regions. We project future scenarios of three specific storms, Alex, Eunice, and Xynthia, using a state-of-the-art climate model. Our findings reveal an increase in precipitation and wind over land associated with these storms, emphasizing the heightened risks associated with climate change. The significance lies in understanding the local implications of explosive storms, aiding in the development of resilient strategies and adaptation measures.

Agricultural innovation for climate change: limited but positive impacts of commercialized drought-tolerant corn

Abstract The extent to which new technologies can countervail the risks posed by climate change is a critical element for designing adaptation strategies. This study uses new experimental data spanning 17 US states from 2008 to 2023 to examine the potential impact of recently commercialized drought tolerant (DT) traits on both yield and yield resilience in US corn production. We find that there is no yield advantage for DT hybrids under average weather conditions, but they improve yield resilience, particularly with respect to precipitation. These effects are spatially heterogeneous, such that DT has a positive yield impact in the droughty, western US, but a small, or even negative, impact in the central and eastern US. In addition, the presence of DT reduces yield variance and kurtosis, and increases skewness, all of which imply a reduction in yield risk. Using the statistical model estimates, we project the impact of DT on corn yields under future climate conditions obtained from 20 general circulation models with two representative concentration pathways. The projected ensemble means of yield gains are 6.34 bu/acre and 5.39 bu/acre under moderate and extreme warming scenarios, respectively, by the mid-twenty-first century. These gains compensate for 23% and 13.5% of total yield loss due to climate change. Our results indicate that current commercial DT hybrids reduce yield risk, improve resilience with respect to precipitation, and have the potential to offer moderate benefits under climate change warming scenarios.

Projecting future minimum mortality temperature in China

Minimum mortality temperature (MMT) increases with global warming due to climate adaptation, which is crucial for the precise assessment of mortality burden attributed to climate change. Nevertheless, forecasting future MMT poses a challenge given the unavailability of future mortality data. Here, we attempted to develop a novel approach to project future MMT. First, we estimated the MMT of 334 locations in China using a distributed lag nonlinear model. Then, meta regression models were applied to investigate the associations between MMT and several temperature variables(Most Frequent Temperature(MFT), average daily mean temperature, average daily minimum temperature, average daily maximum temperature and percentiles of temperature from 1st to 100th). A generalized linear regression model was employed to investigate whether significant differences existed in the relationships between MMT and temperature from the 1st to the 100th percentile. Finally, an optional indicator of MMT for projecting future values was identified. Our results indicated that temperatures in the 85th to 89th percentiles were closely associated with MMT, with the 88th percentile temperature serving as the most effective indicator, as confirmed by meta-regression models. Using the 88th percentile of temperature as alternative indicator of MMT, compared with the period of 2006-2015, the projected MMT in most districts and counties in China tended to rise under three representative concentration pathways (RCPs) in the 2030s (2030-2039), 2060s (2060-2069), and 2090s (2090-2099). Our findings provide some insight to project future MMT for assessing mortality burden related to temperature change driven by global warming.

Acceleration of the Hydrological Cycle under Global Warming for the Poyang Lake Basin in Southeast China: An Age-Weighted Regional Water Tagging Approach

Abstract Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study, we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation, we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting (WRF) Model, namely, WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the Max Planck Institute Earth System Model (MPI-ESM) representative concentration pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980–89) and future (2040–49) times. In the warmer (+1.9°C for temperature and +2% for evaporation) and drier (−21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 h (from 14.3 h) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 h (from 12.9 h) partly due to slower fallout of precipitating moisture components. Significance Statement Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. This study presents the newly developed WRF-age model that allows us to follow the atmospheric water pathways, to derive atmospheric water residence times and atmospheric water transit times. We exemplarily show for an East Asian monsoon region that global warming leads to regionally decreased residence time for the tagged water vapor in the atmosphere due to enhanced local contributions, but increased transit time for the tagged precipitation over the land surface that is partly attributed to slower fallout of precipitating moisture components in the atmosphere. These findings reveal the physical mechanisms behind dry-getting-drier at regional scales.

Identifying and assessing pond best management practice under future climate scenarios

Climate change can play important roles in the hydrological processes within watershed with ponds as the Best Management Practice (BMP). Unlike several other studies, this study integrated remote sensing technique with hydrological model to identify and simulate pond BMP. Limited studies have been carried out to evaluate pond BMP in relation to the climate change impacts on hydrology and water quality particularly in Mississippi watersheds. The objective of this study was to classify ponds on satellite imagery within the Big Sunflower River Watershed (BSRW) using Google Earth Engine (GEE) and incorporate this data with Soil and Water Assessment Tool (SWAT) model to evaluate future hydrological and water quality outputs. The SWAT model was calibrated and validated against streamflow (R2 and NSE values from 0.81 to 0.56) and sediment (R2 and NSE values from 0.91 to 0.40). Future climate data for the mid (2040–2060) and late (2079–2099) centuries were utilized to create climate change scenarios (e.g., RCP 4.5 and 8.5). Results of this study projected that the average annual flow and sediment load will increase by 26–46%, and 107-150% respectively by the late century compared to the baseline period (2002–2021). However, the projected sediment load with modified pond BMP data used in the SWAT model could decrease average annual sediment load by 44–46% under both RCP scenarios. Seasonal data analysis determined that spring, summer, and fall sediment loads were projected to decrease up to 42%, 52%, and 46% respectively under both RCP scenarios due to pond BMP. This study can be useful for the development of climate-smart management strategies in agricultural watersheds.

Climate change impact assessment of variations in temperature trends across India: a case study

ABSTRACT Extreme weather events are becoming more severe and frequent worldwide. This study employs the Mann–Kendall test and Thein-Sen estimator for analysing the mean temperature trend in India’s four densely populated metropolitan cities of New Delhi, Kolkata, Mumbai, and Chennai under representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The trend reveals rising temperatures across four Indian regions with the change being more dominant under RCP 8.5 scenario. By the end of the twenty-first century, RCP 4.5 and 8.5 anticipate a significant rise in mean temperature of 6.0°C and 9.0°C in the Western Himalayas, 5.0°C and 7.5°C in East India and 4.0°C and 7.0°C in the Thar Desert’s south-eastern region. Spatio-temporal variation also indicated the influence of heat waves across different regions of India in the late twenty-first century. There should be relevant long-term and short-term policies to mitigate the effect of climate change and optimise the mortality rate caused by heat stress.

Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods

This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.

A comprehensive study of floodplain analysis utilising HEC-HMS, HEC-RAS, and GIS on the Kosasthalaiyar River sub-basin

ABSTRACT The present study focused on the Kosasthalaiyar River basin in Chennai, Tamil Nadu, India. We analysed the 2015 peak flood and forecasted the feature data in the representative concentration pathways (RCP) 4.5 scenario for various years. We used the scientific data management system (SDSM) software to downscale the Geophysical Fluid Dynamics Laboratory (GFDL) 2.0 general circulation models (GCMs). According to hydrograph analysis, 142.7, 75.31, 461.73, and 248.22 mm runoff can occur in 2030, 2050, 2080, and 2100, respectively. The current study estimates probable peak flows by performing floodplain analysis on the Kosasthalaiyar River sub-basin using the Hydrologic Engineering Centre's Hydrologic Modelling System (HEC-HMS), the Hydrologic Engineering Centre's River Analysis System (HEC-RAS), and geographic information system (GIS) tools. It is possible to observe that the two major peak floods, measuring 581.6 and 110.7 m3/s, respectively, will occur on 28 November 2030 at 10:20 a.m. and 12 December 2050 at 9:20 a.m. Additionally, high floods of 997 and 1,438.4 m3/s can be recorded on 20 December 2080 at 9:50 a.m. and 29 November 2100 at 9:40 a.m., respectively.