A new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application

Performance evaluation of CMIP6 models for application to hydrological modelling studies - A case study of Australia

Accurate estimation of climate change impacts on catchment hydrology is essential for effective future water management. The efficacy of such estimations is dependent on proper climate model selection. In this study, an attempt was made to formulate a methodology for climate model selection, evaluating eight climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models were assessed for their ability to simulate variables used in hydrological studies and large-scale atmospheric circulation influencing rainfall in Australia. Five statistical indicators Root Mean Square Error (RMSE), Spatial Correlation (SC), Percentage Bias (Pbias), Normalized Root Mean Square Error (NRMSE), and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the performance, and the models were ranked through Compromise Programming (CP), a multiple criteria decision making technique. Results show that HadGEM3-GC31-LL performed well in most of the categories considered and was top top-ranked model overall followed by GFDL-ESM4, CESM2-CAM6-RT, and CanESM5 for Australia. Conversely, MIROC6 consistently ranked lower in most of the categories. In the context of simulating hydrological variables, CESM2-CAM6-RT, HadGEM3-GC31-LL, and GFDL-ESM4 emerged as the top three models. The robustness of the proposed methodology suggests its applicability for model selection, making it a replicable approach for climate change impact assessment studies in diverse regions.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

Downscaling of environmental indicators: A review

Environmental indicators at different scales are important for environmental management, daily life, and scientific research. Because of the lack of statistics below a national scale for many environmental indicators, scholars have developed various downscaling methods to obtain finer-scale and diverse forms of data for different environmental indicators. However, the existing downscaling methods for environmental indicators are diverse and fragmented. Here, we reviewed the downscaling methods by reclassifying the environmental indicators from a life cycle perspective into five categories: natural resources use and related attributes; material and energy consumption; environmental discharge; climate change; and environmental footprints. We first provide a general introduction to downscaling theory in the environmental field, including definitions, techniques, and evolution. We then elaborate on downscaling methods and make an inventory of the five categories of environmental indicators. We summarize the downscaling methods commonly applied to specific indicators, scale transformation, the strengths and limitations of corresponding methods, and provide specific examples. Next, we discuss ways to select or construct downscaling methods based on four principles: objective orientation, data accessibility, model feasibility, and model adjustment. Finally, we explore the future direction of downscaling and provide insights for improving downscaling for environmental indicators. In this review, we generalize and clarify the downscaling techniques for environmental indicators, which will help facilitate the appropriate selection of downscaling methods by researchers.

Advanced time-lagged effects of drought on global vegetation growth and its social risk in the 21st century

Extensive studies have demonstrated the restricting effect of past and present drought conditions on vegetation growth over the past three decades. However, the underlying mechanism of the impact of prior drought on vegetation growth - along with the magnitude of its impact over the rest of the 21st century - remains uncertain. Herein, we examined the evolution and characteristics of global vegetation growth and drought for both baseline (1982-2014) and future (2015-2100) periods under four representative pathways using the gross primary productivity (GPP) and the Standardized Precipitation Evapotranspiration Index from the CMIP6. Further, we investigated the time-lagged effects of drought on vegetation growth and the intensity of population and economy exposure to drought by identifying drought-threatened areas under four emission scenarios. The results show that, at the end of the 21st century, the global terrestrial GPP will experience an increasing trend under four scenarios, especially in SSP5-8.5, with a growth rate of 0.032 kg C m-2/decade, which is 10 times higher than that in SSP1-2.6. From the SSP1-2.6 to the SSP5-8.5 scenario, the SPEI change rates are -0.03, -0.01, -0.017, and -0.018/decade, respectively, indicating that the intensity of global drought events will rise with increases in CO2 emissions. 28.3%, 24.7%, 30.4%, and 35% of global land exhibit downward mean time-lagged months in four scenarios, especially in the middle-high latitudes of the northern hemisphere (>45°N), indicating an advanced response of vegetation to drought. Nearly 8, 9.1, 12.9, and 11.5 billion people - valued at 94,138 (SSP1-2.6), 976,020 (SSP2-4.5), 526,595 (SSP3-7.0), and 204,728 (SSP5-8.5) billion US$, respectively - will be threatened by continuous drought. Globally, the population and economy exposure to moderate and extreme drought zones is larger, and the economic risk from extreme droughts is 8 times greater under the high emissions scenario than the low emissions scenario.

Runoff and soil erosion in the integrated farming systems based on micro-watersheds under projected climate change scenarios and adaptation strategies in the eastern Himalayan mountain ecosystem (India)

Land degradation caused by soil erosion (SE) in forests converted into cropland under climate change, particularly with increased rainfall intensity, is of great concern to the agricultural sustainability of the tropical mountain ecosystem. We evaluated the response of six hilly micro-watersheds (HMW) under different Integrated Farming Systems (IFSs) to SE in multi-model climate change scenarios using the Water Erosion Prediction Project (WEPP) model. The IFSs were forestry (HMW₁), abandoned shifting cultivation (HMW₂), livestock with fodder crops (HMW₃), agroforestry (HMW₄), agri-horti-silvi-pastoral (HMW₅), and horticulture (HMW₆) established on a hilly slope (32.0-53.2%) of the eastern Himalayas (Meghalaya, India). The WEPP model was calibrated and validated with measured runoff and soil loss data of 24 years for each of the six IFSs. The projected annual SE (average) for all HMWs increased in all RCPs. The IFS based on shifting cultivation (HMW₂) was the most vulnerable, with the highest percentage increase in SE (46-235%) compared to the baseline years (1976-2005) under RCP 8.5. The cultivated IFSs (HMW₃ to HMW₆) had 47.8-57.0% less runoff and 39.2-74.6% less soil loss than HMW₂ under RCP 8.5. Of these, HMW₆ followed by HMW₄ and HMW₅ were the most effective at minimizing soil loss. Simulation results showed a reduction in soil loss through adaptive strategies such as mulching with broom grasses, stones, field beans, and the introduction of subsurface drainage. Adoption of IFS based on horticulture and agroforestry with bio-mulching on steep slopes is an effective measure to control soil erosion in the eastern Himalaya (India).

Response of Precipitation in Tianshan to Global Climate Change Based on the Berkeley Earth and ERA5 Reanalysis Products

Global climate change has readjusted a global-scale precipitation distribution in magnitude and timing. In mountainous areas, meteorological stations and observation data are very limited, making it difficult to accurately understand the response of precipitation to global climate change. Based on ECMWF Reanalysis v5 precipitation products, Berkeley Earth global temperature, and typical atmospheric circulation indexes, we integrated a gradient descent-nonlinear regression downscaling model, cross wavelet transform, and wavelet correlation method to analyze the precipitation response in Tianshan to global climate change. This study provides a high-resolution (90 m × 90 m) precipitation dataset in Tianshan and confirms that global warming, the North Pacific Pattern (NP), the Western Hemisphere Warm Pool (WHWP), and the Atlantic Multidecadal Oscillation (AMO) are related to the humidification of Tianshan over the past 40 years. The precipitation in Tianshan and global temperature have a resonance period of 8–15 months, and the correlation coefficient is above 0.9. In Tianshan, spring precipitation is determined mainly by AMO, North Tropical Atlantic Sea Level Temperature, Pacific Interdecadal Oscillation (PDO), Tropical North Atlantic Index, WHWP, NP, summer by NP, North Atlantic Oscillation, and PDO, autumn by AMO, and winter by Arctic Oscillation. This research can serve the precipitation forecast of Tianshan and help in the understanding of the regional response to global climate change.

Adaptation of urban drainage networks to climate change: A review

The present work reviews the main challenges regarding adaptation of urban drainage networks to climate change by comparing 32 case studies from 29 articles, published between 2003 and 2020. The aim is to: (i) identify the state-of-the-art scientific approaches of adaptation of urban drainage networks to climate change; (ii) assess whether or not these approaches incorporated monetization of the adaptation practices and the associated costs/benefits; and (iii) define a novel approach (Blueprint) for the future development and assessment of urban drainage network adaptation to climate change and other drivers. First, the motivation is provided that makes urban drainage adaptation a globally relevant issue. Second, the main impacts of climate change on precipitation, flooding and urban drainage systems are discussed. Then, current practices are described. Finally, a blueprint for an integrated urban adaptation framework to climate change and other drivers is proposed. Our research indicated that future quantity and quality of urban runoff is not widely addressed in the scientific literature. The Storm Water Management Model is the most widely used software in modeling adaptation options. Solutions such as plans of maintenance and rehabilitation, public awareness, flood forecasting and warning, mobility measures and insurance measures are not widely reflected in the literature. Uncertainties of climate projections and bias correction methods are still significant, and uncertainties of socio-economic scenarios, hydrologic and hydrodynamic models, and adaptation options are not fully addressed. Finally, environmental cost and benefits associated with the ecosystem services provided by the adaptation options are not fully addressed.