Historical and projected response of Southeast Asian lakes surface water temperature to warming climate.

The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts. Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986-2020) and future (2020-2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT. Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century. This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region.

Lapse rate-adjusted bias correction for CMIP6 GCM precipitation data: An application to the Monsoon Asia Region.

Bias correction (BC) of General Circulation Models (GCMs) variables is a common practice when it is being used for climate impact assessment studies at regional scales. The present study proposes a bias correction method (LR-Reg) that first adjusts the original GCM precipitation for local lapse rate corrections and later bias corrects the lapse rate-adjusted GCMs precipitation data with linear regression coefficients. We evaluated LR-Reg BC method in comparison to Linear Scaling (LS) and Quantile Mapping (QMap) BC methods, and NASA's downscaled NEX data for Monsoon Asia region. This study used Coupled Model Intercomparison Project Phase 6 (CMIP6)-based MIROC6 GCM precipitation with historical and projected shared socio-economic pathways (SSP) scenarios (SSP245 and SSP585) datasets. The BC comparison results show that the relative percentage reduction in mean absolute error (MAE) values of LR-Reg over LS-BC was up to 10-30% while this relative reduction in MAE values of LR-Reg was 30-50% over QMap-BC and 75-100% over NASA's NEX-data. The future projected precipitation over Monsoon Asia during dry season shows more decreased precipitation by up to 100% mostly in the south Asia while during wet season shows more increased precipitation by up to 50% mostly in the northeastern China and in the Himalayan belts with respect to the baseline condition (1970-2005). The results on the average precipitation per 0.25 degree increase in latitude analysis shows that the maximums of average monsoon precipitation during baseline period occur at 0 and 25 degree latitudes while the projected monsoon precipitation during both SSP scenarios occurs at 10 and 20 degree latitudes which clearly shows an inward shift in the latitude axis for the projected precipitation in the Monsoon Asia.

Mapping the vulnerability of irrigation sand traps in a tropical volcanic basin, Indonesia.

Sand traps in irrigation networks are typically used in mitigating canal sedimentation. In irrigation networks located in basins of high sediment yield due to the presence of volcanoes, it is essential to assess the vulnerability of sand traps. Using sediment yield at irrigation scheme inlets, sand trap vulnerability can be evaluated. This study aims to understand the vulnerability of irrigation sand traps throughout the Progo-Opak-Serang (POS) Volcanic River Basin, Indonesia, via mapping the sediment yield distributions in the basin. We employed the Revised Universal Soil Loss Equation to estimate soil loss, where the results show that the average soil loss in the POS River Basin is 179.69 tons/ha/year that falls under the category of moderate erosion potential, while the average sediment yield for the whole basin is 51.04 tons/ha/year. Parts of the basin with high yields of more than 180 tons/ha/year were mostly found along the volcanic mountains such as Sindoro, Sumbing, Merapi, Merbabu, and Telomoyo, and the Menoreh Hills. The model demonstrated relatively high performance with R2, NSE, RMSE, and MAE of 0.89, 0.82, 0.14, and 0.11, respectively. Within the POS Basin, Badran, Kalibawang, and Blawong are the three most vulnerable irrigation sand traps, with sediment yield values of 252.83, 178.92, and 63.49 tons/ha/year, respectively; they are all located in sub-watershed outlets. The vulnerability assessment conducted in this study can be used for the decision support system to prioritize irrigation sand traps towards a more effective irrigation system development.

Analysis of potential nature-based solutions for the Mun River Basin, Thailand.

Despite the growth in research and applications of nature-based solutions (NBS) within the literature, there are limited applications in South East Asia, moreover studies which quantitatively assess the impacts of NBS could have on hazard reduction are scarce. This paper addresses this gap by developing and validating MCDA-GIS analysis to map how potential nature strategies could mitigate flood hazard if applied within the Mun River Basin, Thailand. Through a literature review, the top three solutions for flood and drought hazards were found: wetlands, re/afforestation, and changing crop types. These strategies were reviewed and validated with a MCDA-GIS methodology, through land use change (LUC) maps to depict different future scenarios. The results found that flood hazard did decrease when NBS were implemented in the catchment, especially for A/Reforestation, and to a greater extent when a combination of NBS were applied. This article provides specific insights into the current gaps of NBS publications, specifically considering the case of the Mun River Basin, Thailand.

Integrated assessment of climate change and reservoir operation on flow-regime and fisheries of the Sekong river basin in Lao PDR and Cambodia.

This study assesses the cumulative impact of climate change and reservoir operation on flow regime and fisheries in the Sekong River Basin. Ensemble of five selected Regional Climate Models (RCMs) were used to project the future climate under RCP4.5 and RCP8.5 scenarios. The projected future climate was used to simulate the future hydrology using the SWAT model while HEC-ResSim was utilized for reservoir simulation. Finally fish-flow relationship was developed to estimate the fish catch and productivity in future. Upon investigation we found that, Sekong River Basin is likely grow warmer and drier in future under climate change. The basin is expected to face 1.3-3.6 °C rise in mean annual temperature and receive 0-6% less annual rainfall in future. The wet season in the basin is anticipated to be drier (0% to -6%) while the dry season rainfall shows no particular trend (-3%-10%). Such a change in climate is likely to alter the mean annual flow in future between -3 and 5% at Attapeu, -6 to 2% at Ban Veunkhane, Lao PDR, and -7 to 1% at Siempang, Cambodia (basin outlet). Under climate change, we expect decrement in minimum flow but increment in the maximum flow while opposite is anticipated under reservoir operation. Operation of Xekaman 1 and Sekong 4A are likely to increase the minimum flow at river outlet by 32-59% and 13-18% respectively whereas maximum flow is expected to decrease by 28-5%. In addition, climate change is likely to have crucial impact on fisheries with up to 19% and 12% reduction in fish catches and fish productivity respectively. However, reservoirs tend to have negligible impact on fisheries.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98 °C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Evaluating aquifer stress and resilience with GRACE information at different spatial scales in Cambodia.

Groundwater exploitation for different sectors in Cambodia is expanding. Groundwater levels have already begun to decline in some parts of the country. Monitoring and assessing groundwater storage (GWS) change, aquifer stress and aquifer resilience will support the proper planning and management of the country's groundwater resources; however, information regarding groundwater in Cambodia is currently scarce. Thus, GWS change in Cambodia over the 15 years from April 2002 to March 2017 was assessed using remote-sensing-based Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets, with a comprehensive validation of the GRACE-derived groundwater storage anomaly (GWSA) with respect to in-situ field-based observations. The current study also investigated the impact of surface water storage (SWS) change in Tonle Sap Lake, South-East Asia's largest freshwater lake, on deriving the GWS change in Cambodia. The groundwater aquifer stresses (GAS), and aquifer resilience (AR) were also evaluated. The validation results were promising, with the correlation coefficient between satellite-based estimations and ground-based measurements ranging from 0.82 to 0.88 over four subbasins. The overall decreasing rate of GWS was found to be -0.63 mm/month, with two basins having the highest declining rate of more than 1.4 mm/month. Meanwhile, the aquifer experiencing stress during the dry season had a very low ability to quickly recover from these stresses. These findings emphasise that appropriate management is urgently needed to ensure the sustainability of the groundwater resource system in this country. Supplementary Information: The online version contains supplementary material available at 10.1007/s10040-022-02570-w.

L'exploitation des eaux souterraines s'étend au Cambodge dans différents secteurs. Les niveaux piézométriques ont déjà commencé à baisser sur quelques secteurs du pays. Surveiller et évaluer les changements de stock d'eau souterraine (GWS), la pression sur les aquifères et leur résilience supportera une planification et gestion correctes des ressources en eau dans le pays; toutefois, les informations en relation avec les eaux souterraines sont peu nombreuses aujourd'hui au Cambodge. Ainsi, les changements de GWS au Cambodge sur les 15 dernières années, d'avril 2002 à mars 2017 ont été évalués à l'aide des méthodes de télédétection basées sur le Gravity Recovery and Climate Experiment (GRACE) et les jeux de données du Global Land Data Assimilation System (GLDAS) avec une validation complète des anomalies de stockage d'eau souterraine (GWSA) de GRACE par l'utilisation d'observations de terrain. L'étude a également permis d'étudier l'impact des changements de stocks d'eau de surface (SWS) dans le lac Tonle Sap, le plus grand lac d'eau douce du sud-est asiatique, par dérivation des changements de GWS au Cambodge. Les pressions sur les eaux souterraines (GAS) et la résilience des aquifère (AR) ont également été évaluées. La validation des résultats est prometteuse, avec un coefficient de corrélation entre les estimations basées sur les données satellitaires et les mesures de terrain allant de 0.82 à 0.88 sur quatre sous-bassins. Un taux de baisse globale du GWS de ­0.63 mm/mois a été estimé, avec deux sous-bassins ayant des baisses plus fortes de plus de 1.4 mm/mois. Sur la même période, les aquifère qui subissent un stress durant les basses eaux montrent une faible capacité à récupérer de ce stress. Ces résultats montrent qu'une gestion adéquate est urgemment nécessaire pour assurer la durabilité de la ressource en eau souterraine dans ce pays.

La explotación de las aguas subterráneas para diferentes sectores en Camboya está en expansión. Los niveles de las aguas subterráneas ya han empezado a descender en algunas partes del país. El seguimiento y la evaluación de los cambios en el almacenamiento de las aguas subterráneas (GWS), el estrés del acuífero y la resiliencia del acuífero apoyarán la planificación y la gestión adecuadas de los recursos de aguas subterráneas del país; sin embargo, la información relativa a las aguas subterráneas en Camboya es actualmente escasa. Por lo tanto, se evaluó el cambio de GWS en Camboya durante los últimos 15 años, desde abril de 2002 hasta marzo de 2017, utilizando conjuntos de datos del Gravity Recovery and Climate Experiment (GRACE) y del Global Land Data Assimilation System (GLDAS) basados en la teledetección, con una validación exhaustiva de la anomalía de almacenamiento de aguas subterráneas (GWSA) derivada de GRACE con respecto a las observaciones in situ sobre el terreno. El presente estudio también investigó el impacto del cambio en el almacenamiento de agua superficial (SWS) en el lago Tonle Sap, el mayor lago de agua dulce del sudeste asiático, en la derivación del cambio del GWS en Camboya. También se evaluaron las tensiones de los acuíferos subterráneos (GAS) y la resistencia de los acuíferos (AR). Los resultados de la validación fueron promisorios, ya que el coeficiente de correlación entre las estimaciones basadas en satélites y las mediciones terrestres osciló entre 0.82 y 0.88 en cuatro subcuencas. La tasa global de disminución del GWS fue de ­0.63 mm/mes, con dos cuencas con la tasa de disminución más alta, de más de 1.4 mm/mes. Mientras tanto, el acuífero que experimentaba estrés durante la estación seca tenía una capacidad muy baja para recuperarse rápidamente de estas tensiones. Estos resultados ponen de relieve que se necesita urgentemente una gestión adecuada para garantizar la sostenibilidad del sistema de recursos hídricos subterráneos en este país.

A exploração de águas subterrâneas para diferentes setores no Camboja está se expandindo. Os níveis das águas subterrâneas já começaram a diminuir em algumas partes do país. O monitoramento e avaliação das mudanças no armazenamento de águas subterrâneas (AASub), estresse e resiliência do aquífero apoiarão o planejamento e a gestão adequados dos recursos hídricos subterrâneos do país; no entanto, as informações sobre as águas subterrâneas no Camboja são atualmente escassas. Assim, a mudança de AASub no Camboja nos últimos 15 anos, de abril de 2002 a março de 2017, foi avaliada usando conjuntos de dados Gravity Recovery and Climate Experiment (GRACE) e Global Land Data Assimilation System (GLDAS) baseados em sensoriamento remoto, com uma validação abrangente da anomalia de armazenamento de água subterrânea derivada do GRACE (AAASub) em relação a observações baseadas em campo. O estudo atual também investigou o impacto da mudança de armazenamento de água de superfície (AASup) no Lago Tonle Sap, o maior lago de água doce do Sudeste Asiático, na derivação da mudança de AASub no Camboja. Os estresses das águas subterrâneas no aquífero (SASub) e a resiliência do aquífero (RA) também foram avaliados. Os resultados da validação foram promissores, com o coeficiente de correlação entre as estimativas baseadas em satélite e as medições terrestres variando de 0.82 a 0.88 em quatro sub-bacias. A taxa global decrescente de AAS foi de ­0.63 mm/mês, com duas bacias tendo a maior taxa de declínio de mais de 1.4 mm/mês. Enquanto isso, o aquífero submetido a estresse durante a estação seca teve uma capacidade muito baixa de se recuperar rapidamente desses estresses. Esses achados enfatizam que uma gestão adequada é urgentemente necessária para garantir a sustentabilidade do sistema de recursos hídricos subterrâneos neste país.

Phytoplankton community variation and ecological health assessment for impounded lakes along the eastern route of China's South-to-North Water Diversion Project.

Interbasin water diversion projects have been proven to effectively alleviate water resource shortages in areas along water diversion lines, but few studies have focused on ecological health in impounded lakes compared with research on water quality and pollutants. Herein, monitoring data were collected during the nonwater diversion period (NWDP) and the water diversion period (WDP) from 2018 to 2019, and the index of biological integrity (IBI) method based on phytoplankton communities was used to evaluate the ecological health of the impounded lakes (Nansi Lake and Dongping Lake) along the eastern route of the South-to-North Water Diversion Project. The results demonstrated that water diversion improved the water quality of the impounded lakes during the WDP, especially total nitrogen and ammonia nitrogen. Meanwhile, the water diversion affected the phytoplankton community structure and diversity, and network analysis further revealed water diversion could be beneficial to the ecological health of impounded lakes. Furthermore, the P-IBI showed that the overall ecological health assessment was "good" during the WDP. Water diversion substantially improved the ecological health status and stability of the impounded lakes during the dry season. Finally, the direct correlations between the water quality parameters and the P-IBI were weak, and water quality parameters could indirectly affect the P-IBI by changing the phytoplankton community structure. These findings will enhance our understanding of the ecological health of the impounded lakes of the South-to-North Water Diversion Project. Furthermore, this study will provide a reference to support the ecosystem security of impounded lakes in other large water diversion projects.

Assessing alterations of water level due to environmental water allocation at multiple temporal scales and its impact on water quality in Baiyangdian Lake, China.

Lakes in arid/semiarid regions face problems of insufficient inflow and degradation of water quality, which threaten the health of the lake ecosystem. Baiyangdian Lake (BYDL), the largest lake in the North China Plain, is confronted with such challenges. The objective of this study was to improve understanding of how changes in water level influence water quality in the BYDL at different temporal scales, especially related to implementations of intermittent environmental water allocation activities in the past two decades, by using data on monthly lake water level, climate factors of precipitation and temperature, and lake water quality. The Mann-Kendall method and continuous wavelet analysis revealed that the lake water level shows a significant decreasing trend after 1967, and the period of 16-year was identified as the principal period for 1950-2018. Based on cross-wavelet transform and wavelet coherence analysis, the periodic agreement and coherence between water level and climatic factors decreased after 1997, when environmental water allocations started, indicating that the influences of climatic factors, i.e., precipitation and temperature, became weak. By utilizing the cross-wavelet transform and wavelet coherence analysis methods, the relationships between lake water level and water quality parameters of chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus were investigated. We found that the change in source and amount of environmental water allocation is one possible reason for the temporal evolution in joint variability between lake water level and water quality. Meanwhile, a dilution effect of freshwater allocated to BYDL was detected in the time-frequency domain. However, the result also indicates that the driving mechanism of water quality is complex due to the combined impacts of water allocation, nonpoint source pollution in the rainy season, and nutrient release from lake sediment. Our findings improve the general understanding of changes in water level in lakes located in arid and semiarid regions under climate change and intensive human activities, and also provide valuable knowledge for decision making in aquatic ecosystem restoration of BYDL and other similar lakes.

Spatiotemporal variations in evapotranspiration and its influencing factors in the semiarid Hailar river basin, Northern China.

Evapotranspiration (ET) is a critical variable in the world's water cycle, and plays a significant role in estimating the impact of environmental change on the regional hydrothermal cycle. Moreover, as an essential of eco-hydrological processes, changes in ET may exceptionally impact the local climate and provide indicative information on the eco-system's functioning. The Hailar River Basin (HRB), located in northern China, is one of the most sensitive areas to climate warming. Under the influence of climate change in recent years, the vegetation dynamics of the basin have been significant and have had profound effects on the regional water cycle conditions and hydrological processes. The HRB is located in a semiarid region and ET is the main mode of water consumption. The ET response to climate change and vegetation dynamics is the focus of research on ecohydrological processes in this basin. In this study, a distributed hydrological model, the BTOPMC model, is used to evaluate the actual ET in the HRB from 1981 to 2020, based on in situ meteorological data as well as LAI data obtained by satellite remote sensing. The seasonal, interannual and spatial dynamics of ET were characterized. The contribution of meteorological factors to ET was calculated by sensitivity analysis and multiple linear regression analysis, and the predominant elements influencing the difference in ET in the HRB were also discussed. The results show that: (1) estimated ET values can clarify over 85% of the seasonal variation in the observed values (R2= 0.79, P < 0.001; R2= 0.84, P < 0.001), which demonstrates that the model has a high precision. (2) Over the past 40 years, the annual ET has shown a clear increasing trend and a large spatial heterogeneity in its spatial distribution, which is consistent with the trend of vegetation. It mainly shows that the eastern forest area is larger than the central forest-grass transition area and the western meadow steppe area. (3) Sensitivity and influential factor contribution analyses show that the main factor driving interannual variability in ET is climate warming, followed by precipitation. At the same time, vegetation dynamics also play a crucial role in ET, especially in areas with different vegetation types and high coverage, while climatic factors also have a strong influence on ET indirectly through vegetation. Due to its special geographic location, the HRB is more sensitive to global climate change and is a typical ecologically fragile area. Therefore, this study has important scientific value and social significance for maintaining ecological security and the sustainable use of water resources.

Evaluating the influence of different environmental water allocation schemes on the water level of a typical shallow lake in semiarid regions: From the perspective of an integrated modeling approach.

Many lakes in semiarid regions around the world rely on environmental water allocation to maintain the health of the lake ecosystem. However, under changing environments, the competition for water resources between human society and natural ecosystems has intensified. How to manage environmental water allocation more reasonably and precisely has become an important issue. The largest lake on the North China Plain, Baiyangdian Lake (BYDL), is a typical lake facing such challenges. To provide feasible strategies for sustainable water allocation to BYDL, with the proper parameterization of hydrological processes, this study developed a 10-day temporal scale lake water level prediction model to quantify how environmental water allocation regulates the BYDL water level under different hydroclimatic conditions. Evaluation of model performance revealed that environmental water allocation rather than natural climatic periodicity dominates the variation in the BYDL water level. The model structure could be further improved with consideration of more detailed observations of both the surface runoff entering BYDL and the water area beneath the canopy of the reeds in BYDL. Analysis of 72 model simulation scenarios indicated that water allocations from multiple sources are indispensable and that the water resources that guarantee maintaining the BYDL water level within the ecologically suitable range vary substantially under different hydroclimatic conditions. More elaborate allocation plans are required both to improve the water quality and health of the aquatic ecosystem of BYDL and to reduce the risk of flooding. The findings from this study are valuable for guiding the implementation of environmental water allocations to lakes in semiarid regions worldwide.

Assessing the future climate change, land use change, and abstraction impacts on groundwater resources in the Tak Special Economic Zone, Thailand.

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.

A novel ecohydrological model by capturing variations in climate change and vegetation coverage in a semi-arid region of China.

Variations in vegetation are influenced by regional climate regimes and, in turn, control the water balance behavior in water-limited regions. Owing to its role in ecohydrological processes, vegetation is an essential link in modeling the relationships among climate conditions, vegetation patterns, and dynamic water balance behavior. However, previous ecohydrological models have been empirical and complex, without physically significant parameters. Here, we propose a novel ecohydrological model (a Budyko model-coupled vegetation model) that combines the impacts of climate change and vegetation variations, featuring simple and deterministic parameters. In addition to accounting for the fundamental water balance model and its factors, mean precipitation, potential evapotranspiration, runoff, and variations in water storage (δS), the model showed better performance when incorporating δS (RMSE = 2.72 mm yr-1) and its parameter ε -, which is mechanically and quantitively subject to the vegetation coverage (R2 = 0.95, p < 0.01). This was estimated as a function of vegetation potential canopy conductance, mean rainstorm depth, mean time between storms, and potential rate of evapotranspiration in a semi-arid watershed with impulsive precipitation in China (R2 = 0.80, p < 0.01). The model also found that vegetation growth was mainly controlled by soil water content and decoupled the impact of the total amount of precipitation on vegetation in the northeastern area of the watershed. Hence, our method presents a new tool for building an ecohydrological model that includes deterministic parameters of mechanical significance.

A generalized methodology for ranking climate models based on climate indices for sector-specific studies: An application to the Mekong sub-basin.

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.

Impacts of climate and land use change on groundwater recharge under shared socioeconomic pathways: A case of Siem Reap, Cambodia.

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.

Modification and upscaling of S-W model based on vertical distributions of soil moisture and vegetation root biomass.

Soil water is the dominant factor controlling evapotranspiration (ET) in arid and semi-arid regions. However, the widely used ET simulation models, such as the Shuttleworth-Wallace model (S-W model), are insufficient in simulating the direct influence of soil moisture (SM), especially in the root zone. Based on SM and ET field data, we found that the influence of SM on ET increased with soil depth in the grassland. Evaporation in the S-W model was optimized by SM at 0-5 cm as the root mean square error (RMSE) decreased from 1.4 to 0.17, while transpiration was optimized by SM at 10-20 cm as the RMSE decreased from 0.26 to 0.07. The modified S-W model incorporating SM was called the S-W-Ï´ model. To up-scale application and to verify the accuracy of the S-W-Ï´ model under watershed water balance, we replaced the ET simulation module based on the S-W model with our S-W-Ï´ model and the Block-wise TOPMODEL with Muskingum-Cunge routing method (BTOPMC) model that we used as the basis of our simulation. The influence of SM was determined by the proportion of root biomass of different vegetation types at different depths, and each depth interval was assigned a weighting reflecting its degree of influence. The results showed that the S-W-Ï´ model improved accuracy with all the modification schemes tested. The modification scheme determined by the vegetation root distribution pattern had the greatest effect, providing a 4% accuracy improvement. The modified ET and hydrological models have the potential to support water basin management to a greater extent.

Do Multiple Brain Lesions Always Connote Worse Outcomes? Appraisal Evidence from a Tertiary Care Center in Koshi/Purbanchal Province of Nepal.

Advances in medicine comprising diverse diagnostic and management modalities call for a bundle approach to improve patient care. This study aimed to present diagnostic patterns in patients with multiple intracranial lesions together with connoted survival implications. We retrospectively reviewed medical files of 85 patients with tumor and non-tumor intracranial lesions. Metastatic brain lesions were identified in 23.5% of patients. Neurological pathogenesis underlay 29.4%, infectious 21.2%, and vascular 14.1% of lesions, with the remaining portion comprising less frequent disorders. A favorable prognosis was predicted in 52/85 (61.2%) of the study population despite a variety of pathologies, which speaks for substantial improvements in outcomes of once hardly manageable or mortal brain disorders, comprising both common and rare conditions. The improvements are to the credit of advances in medical radio-imaging enhancing the diagnostic power which enables a precise stratification of brain pathologies. We emphasize the use of an algorithmic evaluation of patients presenting with multiple brain lesions for differential diagnosis and survival prognostication. There seems to be an ongoing transition from imperfect probabilistic prediction models to precision medicine, which determines advantages in disease management and outcome.

Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha.

This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak.

Multi-scale assessment of water security under climate change in North China in the past two decades.

Climate change is projected to affect the hydrological cycles in China, while the effects are expected to vary spatiotemporally. Understanding the variations in water security conditions and their sensitivity to climatic variables is crucial for assessing regional ecosystem responses to climate change. In the present study, we estimated the water yield capacity, an important indicator of water security in North China (NC), at a spatial resolution of 1 km during the last two decades based on the Budyko framework and quantified the sensitivity of water yield change to climate change among different vegetation types. The results showed that the performances of the Budyko framework were reliable both at the pixel scale and across large watersheds. The annual water yield in North China was estimated to be 7.61 ± 2.67 ∗ 1010 m3/yr, with an average mean water yield (MWY) of 49.51 ± 17.49 mm/yr. The spatial pattern of mean water yield change (MWYC) exhibited high heterogeneity; 46% of the study region was dominated by an increasing trend, while 9.84% was statistically significant (P < 0.05). Compared with temperature, the water yield capacity was more sensitive to precipitation variation. A consistent trend of variation was found in cropland between water yield and precipitation, while negative sensitivity coefficients were found in natural vegetation types. The variation in sensitivity coefficients (Swyp) in natural vegetation showed that in regions with a decrease in precipitation, the variation in water yield capacity also decreased, while in regions with an increase in precipitation from 0 to 8 mm/yr, the water yield capacity first decreased and then increased with precipitation. Our findings suggest that grass and shrubs would be more beneficial to regional water security in North China's revegetation, while afforestation would provide protection for the regional environment from extreme rainfall events.

Climate and land-use change impacts on spatiotemporal variations in groundwater recharge: A case study of the Bangkok Area, Thailand.

Groundwater contributes to the socioeconomic development of the Thai capital Bangkok and its vicinity. However, groundwater resources are under immense pressure due to population growth, rapid urbanisation, overexploitation, and climate change. Therefore, evaluating the combined impact of climate change and land-use change on groundwater recharge can be useful for developing sound groundwater management systems. In this research, the future climate is projected using three Regional Climate Models (RCMs), namely ACCESS-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM for three future periods: near future (2010-2039), mid future (2040-2069), and far future (2070-2099) under two Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 as suggested in the IPCC's Fifth Assessment Report. All RCMs project the temperature to rise incessantly, although future precipitation is predicted to fluctuate (increase and decrease) among the various RCMs and RCP scenarios. A Dyna-CLUE model is employed to analyse the future land-use change scenarios (low, medium, and high urbanisation), with the aim of expanding the built-up area and creating land-use maps covering the period to 2099. A hydrological model, WetSpass, is used to estimate groundwater recharge under future climate and land-use change. The findings reveal that groundwater recharge is expected to decrease in high and medium urbanisation areas, ranging from 5.84 to 20.91 mm/yr for the RCP 4.5 scenario and 4.07 to 18.72 mm/yr for RCP 8.5. In contrast, for the low urbanisation scenario, the model projects an increase in groundwater recharge ranging from 7.9 to 16.66 mm/yr for the RCP 4.5 scenario and 5.54 to 20.04 mm/yr for RCP 8.5.