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This study provides an assessment of changes in mean and extreme climate in northeast Thailand, focusing on the near‐future period (2021–2050). Spatiotemporal changes in climate extremes and return values are investigated compared to 1981–2010. Climate model‐related uncertainties are quantified using 14 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 8 models from phase 6 (CMIP6). CMIP6 models have a higher sensitivity to external forcings as the CMIP6 ensemble suggests an increase in maximum and minimum temperatures by 1.45°C (0.8–1.9°C) and 1.54°C (1.1–1.9°C) under the high emission scenario, which is greater than by CMIP5 ensemble: 1.10°C (0.5–1.7°C) and 1.13°C (0.7–1.6°C), respectively. No significant changes in annual rainfall are projected, although it will be temporally more uneven with decreases (6–11%) during the pre‐rainy season (March–May) and increases (2–8%) during the rainy season (June–October). The bootstrap analysis technique shows the inter‐model uncertainties for rainfall projections in CMIP6 have reduced by 40% compared to CMIP5. The annual number of hot days will increase more than twofold and warm nights, more than threefold. Near‐future will experience an increase in the rainfall intensity, a decrease in the number of rainy days, and an increase in the 20‐year return values of annual maximum 1‐day rainfall and consecutive 5‐days rainfall (>30%). In addition, the rainy season will be shortened in the future as onset and retreat are delayed, which may have implications in agricultural activities in the basin since cultivation is primarily rainfed. These findings suggest that anthropogenic activities will significantly amplify the climate extremes. The study results will be useful for managing climate‐related risks and developing adaptation measures to improve resilience towards potential climate hazards. [ABSTRACT FROM AUTHOR]

Half of Asian tropical forests were disturbed in the last century resulting in the dominance of secondary forests in Southeast Asia. However, the rate at which biomass accumulates during the recovery process in these forests is poorly understood. We studied a forest landscape located in Khao Yai National Park (Thailand) that experienced strong disturbances in the last century due to clearance by swidden farmers. Combining recent field and airborne laser scanning (ALS) data, we first built a high-resolution aboveground biomass (AGB) map over 60 km² of the forest landscape. We then used the random forest algorithm and Landsat time-series (LTS) data to classify landscape patches as non-forested versus forested on an almost annual basis from 1972 to 2017. The resulting chronosequence was then used in combination with the AGB map to estimate forest carbon recovery rates in secondary forest patches during the first 42 years of succession. The ALS-AGB model predicted AGB with an error of 14 % at 0.5-ha resolution (RMSE = 45 Mg ha-1) using the mean top-of-canopy height as a single predictor. The mean AGB over the landscape was of 291 Mg ha-1 showing a high level of carbon storage despite past disturbance history. We found that AGB recovery varies non-linearly in the first 42 years of the succession, with an increasing rate of accumulation through time. We predicted a mean AGB recovery rate of 6.9 Mg ha-1 yr-1, with a mean AGB gain of 143 and 273 Mg ha-1 after 20 and 40 years, respectively. These estimates are within the range of those reported for the well-studied Latin American secondary forests under similar climatic conditions. This study illustrates the potential of ALS data not only for scaling up field AGB measurements but also for predicting AGB recovery dynamics when combined with long-term satellite data. It also illustrates that tropical forest landscapes that were disturbed in the past are of utmost importance for the regional carbon budget and thus for implementing international programs such as REDD+. [ABSTRACT FROM AUTHOR]

Many Mediterranean countries have experienced water shortages during the last 20 years and future climate change projections foresee further pressure on water resources. This will have significant implications for irrigation water management in agricultural systems in the future. Through qualitative and quantitative empirical research methods carried out on a case study on four Mediterranean farming systems located in Oristano, Italy, we sought to understand the relationship between farmers' perceptions of climate change (i.e., increased temperature and decreased precipitation) and of present and future water availability for agriculture as forecasted by climatic and crop models. We also explored asymmetries between farmers' perceptions and present and future climate change and water scenarios as well as factors influencing perceptions. Our hypotheses were that farmers' perceptions are the main drivers of actual water management practices and that sustainable practices can emerge from learning spaces designed from the understanding of the gaps between perceptions and scientific evidences. Results showed that most farmers perceived that climate change is occurring or will occur in their area. They also perceived that there has been an increased temperature trend, but also increased precipitation. Therefore, they are convinced that they have and will have enough irrigation water for agriculture in the near future, while climate change projections foresee an increasing pressure on water resources in the Mediterranean region. Such results suggest the need for (i) irrigation management policies that take into account farmers' perceptions in order to promote virtuous behaviors and improve irrigation water use efficiency; (ii) new, well-designed learning spaces to improve the understanding on climate change expectations in the near future in order to support effective adaptive responses at the farm and catchment scales. [ABSTRACT FROM AUTHOR]

This study examines the shoreline change evolution at two spatiotemporal scales over ca. 18 km of Platamona-Maritza beach (northwest Sardinia, Italy). A multitemporal dataset spanning the period 1955-2010 was used and made up of archival aerial photographs, orthophotos, satellite imagery, light detection and ranging data, terrestrial laser scanner, Global Positioning System, and recent and historical topographic maps. We integrated this dataset by implementing a repeatable processing procedure, for which the global sensitivity for shoreline change estimation was evaluated (ca. 10 m as a product of time interval and change rate). The applied methodology indicates that the wet/dry boundary can be used as a good shoreline proxy. It is also demonstrated how reliable shoreline positional uncertainty (from 1.9 to 8.6 m) can be estimated whenever a large set of multitemporal data is available and geomatic tools are properly integrated. Results showed how shoreline evolutionary trends affected the entire coastal zone and roughly migrated from east to west, with the largest rates between 1970 and 2000. Where long-term analysis provided higher erosion rates, a sediment volumetric change was estimated, although only for a 2-year window and for a 300-m-wide stretch of beach. An analysis of geomorphic features along the coast also indicates variable longshore drift direction as a consequence of changes in a combination of wind, wave, and current regimes. A direct relationship between the evolutionary trend of hydrometric and pluviométrie data recorded at the study site highlight that, although rainfall regime is nearly constant, average discharge of main rivers underwent a dramatic decrease since 1965 in conjunction with land use change and upstream dam construction for agriculture and urban development. Therefore, it has been argued that river sediment supply also was reduced; hence, besides other natural and anthropogenic causes, it likely influenced erosion and accretion events in the southern sector of the Gulf of Asinara. [ABSTRACT FROM AUTHOR]

Groundwater is an important source of water supply in the Tak Special Economic Zone of Thailand. However, groundwater is under stress from climate change, land use change, and an increase in abstraction, affecting the groundwater level and its sustainability. Therefore, this study analyses the impact of these combined stresses on groundwater resources in the near, mid, and far future. Three Global Climate Models are used to project the future climate under SSP2-4.5 and SSP5-8.5 scenarios. According to the results, both maximum and minimum temperatures are likely to show similar increasing trends for both scenarios, with a rise of approximately 1 (1.5), 2 (3), and 3 (5) °C expected for SSP2-4.5 (SSP5-8.5) in each consecutive period. Annual rainfall is expected to continually increase in the future, with around 1500-1600 mm in rainfall (11ꟷ5.43% higher). Land use change is predicted for two scenarios: business as usual (BU) and rapid urbanisation (RU). The forest area is expected to increase to 30% (35%) coverage in 2090 for BU (RU) while agriculture is likely to reduce to 60% (50%) with the urban area increasing to 2.4% (7%). Water demand is predicted to increase in all future scenarios. The SWAT model is used to project recharge, which is likely to increase by 10-20% over time. The highest increase is predicted in the far future under SSP2 and RU scenarios. MODFLOW was used to project future groundwater resources, but due to the lack of consistent data, the time scale is reduced to yearly simulation. The results reveal that the groundwater level is expected to increase in the central part (urban area) of the study area and decrease along the boundary (agricultural area) of the aquifer. This research can aid policymakers and decision-makers in understanding the impact of multiple stressors and formulating adaptation strategies to manage groundwater resources in special economic zones., (Copyright © 2022. Published by Elsevier Inc.)

The rapid pace of urbanization blended with climate change has significantly altered surface and groundwater flows. In the context of tourism-driven economic potential areas, these drivers have greater effects, including threatening groundwater availability. This study assessed the combined impacts of climate and land use changes on the groundwater recharge (GWR) in Siem Reap, Cambodia utilizing Phase Six of the Coupled Model Intercomparison Project (CMIP6) global climate models (GCMs), DynaCLUE land-use model, and Soil Water Assessment Tool (SWAT). Three climate models CanESM5, EC_Earth3, and MIROC6, out of seven, best captured the observed data after performance evaluation through the entropy method, were bias-corrected linearly for two shared socioeconomic pathways (SSPs) - SSP2-4.5 and SSP5-8.5. The results indicate a general increase in precipitation under both SSPs, while the average annual maximum temperature is likely to increase by 0.024 °C/year and 0.049 °C/year under SSP2-4.5 and SSP5-8.5, respectively. A similar trend but relatively higher increase is expected for the minimum temperature. Furthermore, the historical land use change showed the expansion of urban settlement by 373% between 2004 and 2019 at the expense of forest and shrubland. Future land use projections from the DynaCLUE model show that the urban settlements in the study area are likely to expand, from their 2019 condition, by 55% in 2030, 209% in 2060, and 369% in 2090 under SSP2 and at double of these rates under SSP5 scenario. The GWR is expected to rise by 39-53% during the wet season and decrease by 13-29% during the dry season under both scenarios. Meanwhile, under constant land use, the GWR is likely to increase more compared to other scenarios, highlighting the importance of land use planning to policymakers and planners. Additionally, the study shall also be important to practitioners and researchers in understanding, planning, and evaluating the performance of multiple climate models in groundwater assessment., (Copyright © 2022 Elsevier Inc. All rights reserved.)

This study proposes a methodological framework to evaluate and rank climate models based on extreme climate indices of precipitation and temperature for impact studies in seven sectors: Cryosphere, Energy, Forestry/GHGs, Health, Agriculture & Food Security, Disaster Risk Reduction (flood and drought), and Water Resources & Hydrology. The ranking of the climate models is based on their performance in sector-relevant extreme climate indices. Extreme climate indices for observed and climate models' datasets for a historical period and overall performance statistics were used to create a payoff matrix. The payoff matrix then served as an input to a multi-criteria decision-making process to rank the climate models for each of the climate indices. The final sector-specific ranking was achieved by averaging the ranks obtained in the sector-relevant indices. The developed methodology is demonstrated with an application to the Songkhram River Basin (Thailand), a sub-basin of the Mekong. Eighteen CMIP6 GCMs are used for the proposed evaluation and ranking processes and four performance statistics were used. Weights to each of the four performance statistics were determined using the entropy method. Compromise programming was applied to rank the GCMs based on the distance technique. The results indicate that the six best performing models are different for different sectors, with the GFDL_CM4 model common in all the seven sectors considered in the study. KACE1_0_G, GFDL_ESM4, GFDL_CM4, MRI_ESM2_0, and ACCESS_ESM1_5 models are the five top (ranked 1 to 5 respectively) performing models for the Water Resources & Hydrology sector. The developed framework is generic and can be applied to any region or basin; at the same time, it can also provide researchers and policymakers with specific information on best-performing models for particular sectors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Recently, the availability of global landuse and land cover data has made hydrological simulation studies possible in data scarce regions. However, the suitability of these products as inputs in the hydrological modeling has not been investigated in detail. Therefore, this study aims to evaluate the use of global landuse and land cover products to simulate the hydrology of the Upper Yom River Basin located in the Northern part of Thailand. Three types of global landuse and landcover products i.e. ESACCI, MCD12Q1, GLC2000 were compared with the landuse product from Land Development Department (LDD) of Thailand. All of these products were used as input in hydrological model, Soil and Water Assessment Tool (SWAT), and performance of the model was compared to simulate hydrological regime including high flow, low flow and seasonal discharge at the outlet and upstream of the basin. The results show that the performance of the hydrological model in simulating the discharge at the basin outlet is better than in the upstream areas while using all types of landuse and land cover data. The model well simulated the annual discharge, wet-season discharge and base flow while using landuse and land cover products of ESACCI and MCD12Q1. Similarly, the high flow and dry-season discharge is well simulated while using MCD12Q1 landuse and landcover products compared to other three products. The results of this study is useful in selecting landuse and land cover products in simulating hydrology for water resources planning and management in data scarce regions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000-2018 across all the investigated lakes. Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement. All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of -0.038 °C/y during the period of 2000-2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000-2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Deforestation, climate change and invasive species constitute three global threats to biodiversity that act synergistically. However, drivers and rates of loss of freshwater biodiversity now and in the future are poorly understood. Here we focus on the potential impacts of global change on freshwater mussels (Order Unionida) in Sundaland (SE Asia), a vulnerable group facing global declines and recognized indicators of overall freshwater biodiversity. We used an ensemble of distribution models to identify habitats potentially suitable for freshwater mussels and their change under a range of climate, deforestation and invasion scenarios. Our data and models revealed that, at present, Sundaland features 47 and 32 Mha of habitat that can be considered environmentally suitable for native and invasive freshwater mussels, respectively. We anticipate that by 2050, the area suitable for palm oil cultivation may expand between 8 and 44 Mha, representing an annual increase of 2-11%. This is expected to result in a 20% decrease in suitable habitat for native mussels, a drop that reaches 30% by 2050 when considering concomitant climate change. In contrast, the habitat potentially suitable for invasive mussels may increase by 44-56% under 2050 future scenarios. Consequently, native mussels may compete for habitat, food resources and fish hosts with invasive mussels across approximately 60% of their suitable range. Our projections can be used to guide future expeditions to monitor the conservation status of freshwater biodiversity, and potentially reveal populations of endemic species on the brink of extinction. Future conservation measures-most importantly the designation of nature reserves-should take into account trends in freshwater biodiversity generally, and particularly species such as freshwater mussels, vital to safeguard fundamental ecosystem services., (Copyright © 2018 Elsevier B.V. All rights reserved.)