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Global warming has accelerated surface water loss around the world. This study investigates in detail the change and attribution of potential evapotranspiration (PET) across China from 1901 to 2100 by the Hargreaves model, based on a 1-km temperature dataset downscaled from the low-spatial-resolution datasets using a Delta downscaling framework. Results showed that (1) relative to 1961–1990, PET increased by 0.62% in the historic period (1901–2017) and 6.43–12.89% for the future period (2018–2100), suggesting considerable future drying for China. Moreover, these increments had strong spatial variations and the largest increases were detected in high-elevation regions; (2) PET over entire China demonstrated a nonsignificant upward trend during the historic period and significant upward trends for the future period. For each period and GCM, significant upward PET trends occupied a much larger percent area than significant downward trends; (3) PET variations during the historic period were most sensitive to mean temperature (TMP), while in the future period it was more sensitive to maximum temperature (TMX), suggesting a change in the primary sensitivity factor due to global warming; (4) minimum temperature (TMN) made the largest contribution (45%) to PET variations during the historic period, while TMX had the largest contribution (36–40%) in the future period. Therefore, the primary contributing factor might transform from TMN to TMX under climate change; and (5) PET variations exhibited strong spatial heterogeneity, detected on fine geographic scales, due to the use of downscaled dataset. Overall, the results present a deep insight for planning coping strategies of global warming in China.

Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating spatiotemporal hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short‐Term Memory Recurrent Neural Network (LSTM‐RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada‐ a large and unique basin. Importantly, discharge performance is assessed out‐of‐sample with k‐fold replication across gauges. Training LSTMs with disaggregated data significantly improved model accuracy. Specifically, there was a 9.6% increase in the mean Nash‐Sutcliffe Efficiency and a 4.6% increase in the mean Kling‐Gupta Efficiency, indicating a better agreement between predicted and actual observations in terms of mean, variability, and correlation. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data‐driven discharge predictions. Plain Language Summary Accurate river discharge prediction is critical for sustainable water resource management and effective flood mitigation. Traditional methods often treat the entire river basin as a homogenous unit, neglecting crucial hydrologic and hydrometeorological variations that significantly impact water flow across different locations. This “lumped” approach can lead to inaccurate predictions. We propose a “distributed” modeling approach incorporating detailed information about the river basin's spatial heterogeneity. Applying this method to the Mackenzie River, a vast and complex river system in Canada, resulted in significantly more accurate discharge predictions compared to traditional lumped models. This confirms the critical importance of considering the river basin's spatial variability for better understanding and predicting water flow dynamics. Our work paves the way for enhanced water management strategies and improved flood preparedness by providing more precise and reliable discharge predictions. Key Points Current Machine Learning models rely on aggregated hydrometeorological data, ignoring the spatial heterogeneity inherent in river systems Incorporating topological‐guided spatiotemporal hydrologic data can improve understanding of discharge dynamics within the river basin Topologically guided river hierarchies help aggregate hydrological data at various scales, enhancing the accuracy of discharge predictions

Geochemically driven alterations of fractures in multi‐mineral media can create altered layers (ALs) at the fracture‐matrix interface. Spatial variations in the AL significantly influence mass transfer across the interface, and the hydraulic and mechanical properties of the fractured medium. A real‐rock based microfluidic experiment reported spatial variations in AL thickness despite the initially smooth fracture surface, suggesting potential effects of matrix heterogeneity on AL development. However, the respective contribution of structural and mineralogical characteristics is still poorly understood. Using the microfluidic experimental data and a micro‐continuum reactive transport model, we systematically evaluated how micro‐porosity and initial mineral texture impact AL development and thus the overall reactive transport behaviors. Our simulation results confirmed that the extent of AL spatial variations, mainly controlled by mineralogical texture, influences the evolution of reaction and permeability in different ways. Accounting for spatial heterogeneity in mineral distribution produces “channeling” structures in ALs and lower overall reaction (by up to 35.6%), but larger permeability increase (by up to 9.8%). The characteristic length of the reactive mineral cluster was observed to dominate the internal texture of ALs. Whereas the presence of micro‐porosity can enhance mineral accessibility via improving connectivity for flow and transport, and lead to both higher bulk reaction, that is, thicker ALs, and permeability enhancement. Considerations of surface roughness with characteristic length on the same order of magnitude as mineral texture did not change the overall development of AL, which further highlights the importance of accounting for rock matrix properties in predicting long‐term evolution of fractured media. The resulting spatial variations of ALs and their impacts on bulk properties, however, are expected to be further complicated by the coupling of chemical and mechanical processes, and may trigger matrix disaggregation, erosion and other mechanisms of fractured media alteration. Key Points Spatial variations in altered layers affect the evolution of bulk reaction and permeability in fractured media The characteristic length of reactive mineral clusters dominates the internal texture of the altered layer and permeability enhancement The presence of micro‐porosity in the matrix promotes mineral accessibility, reactive transport, and thus thickening of altered layers

The present study used the recently developed Remote Sensing-Based Ecological Index (RSEI) to assess the temporal-spatial variation of ecological quality in the metropolitan city of Isfahan (Iran) as a member of the UNESCO Creative Cities Network. This study was conducted from the Landsat TM/OLI satellite images of 2004, 2009, 2014 and 2019. The RSEI was synthesized by principal component analysis for four indices of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Land Surface Moisture (LSM) and Normalized Differential Build-up, and Bare Soil Index (NDBSI) based on the framework of the Pressure-State-Response (PSR) in the aforementioned years. The ecological quality of the city was assessed by RSEI over a 15-year period. The index has a value range of 0 (completely poor ecological quality) to 1 (completely desirable). In addition, the spatial heterogeneity of RSEIs at different intervals was assessed by the Moran index. The results showed that the RSEI value was always less than 0.4, which indicated the unfavourable ecological quality of the city. This index was 0.34, 0.37, 0.26 and 0.30 in 2004, 2009, 2014 and 2019, respectively. Therefore, the ecological quality of the city did not have a constant trend during the studied period and had several fluctuations, which could be attributed to the natural and anthropogenic changes in the studied period. Additionally, the results of the Moran index showed a steady decline, which indicated a declining homogeneity during this period. Matching the calculated RSEIs with the realities of the region at each time interval suggested that the index could be a useful tool for assessing urban ecological quality.

Wetlands are the largest natural source of methane (CH4), but spatial variability in fluxes complicates prediction, budgeting, and mitigation efforts. Despite the many environmental factors identified as CH4 drivers, the overall influence of wetland spatial heterogeneity on CH4 fluxes remains unclear. We identified five dominant patch types—submersed aquatic vegetation (SAV), emergent forbs, sedges/rushes, grasses, and open water—within a freshwater wetland in Maryland, USA, and measured CH4 fluxes using a combined chamber and eddy covariance approach from June to September 2021. Because patch types integrate co-occurring environmental factors, we hypothesized that CH4 flux is best characterized at the patch scale. Chamber measurements from representative patches showed distinct CH4 signals; fluxes from grasses and sedges/rushes were highest, while fluxes from SAV and forbs were lower but skewed, suggesting episodic emission pulses. Open water had the lowest fluxes. Differences between patches were consistent over time, and spatial variability was greater between patches than within them, highlighting patches as key drivers of flux variability. By combining chamber fluxes with eddy covariance data in a Bayesian framework, we provide evidence that patch-type fluxes scale over space and time. Understanding spatial heterogeneity is essential for quantifying wetland contributions to global biogeochemical cycles and predicting the impacts of environmental change on wetland ecosystem processes. Our study demonstrates the importance of vegetation patch types in structuring spatial variability and supports a patch-explicit representation to reduce uncertainty in wetland CH4 fluxes.

This work aims to study the spatio-temporal variability of turbidity in Lake Chascomús using 34 years (1987–2020) of Landsat (TM, ETM + , and OLI) and Sentinel-2-MSI optical data and to understand this variability in terms of environmental variables. A semi-analytical algorithm, using reflectance in the red and near-infrared bands, was calibrated for Landsat and Sentinel-2 bands and tested using in situ turbidity measurements. The best performance was found using only the near-infrared band with 12.84% median accuracy and -12.84% bias when comparing in situ radiometric measurements and field data. When satellite-derived turbidity was compared to in situ values, the median accuracy was 31.8% and the bias 13.22%. Monthly climatological turbidity maps revealed spatial heterogeneity in Lake Chascomús, with differences observed between the north-west and south-east regions, particularly in summer and winter. Turbidity showed marked seasonal dynamics, with a minimum in autumn and a maximum in spring. Annual climatological turbidity maps showed significant inter-annual variability. Generalized linear models showed turbidity was positively associated with wind speed and photosynthetic active radiation (26.2% of the variability explained). Remote sensing was found to be a fundamental complement to traditional field-based methods for monitoring water quality parameters and allowing a better description of their spatio-temporal variability.

Decades of research have revealed relationships between the abundance of coral reef taxa and local conditions, especially at small scales. However, a rigorous test of covariation requires a robust dataset collected across wide environmental or experimental gradients. Here, we surveyed spatial variability in the densities of major coral reef functional groups at 122 sites along a 70 km expanse of the leeward, forereef habitat of Curaçao in the southern Caribbean. These data were used to test the degree to which spatial variability in community composition could be predicted based on assumed functional relationships and site-specific anthropogenic, physical, and ecological conditions. In general, models revealed less power to describe the spatial variability of fish biomass than cover of reef builders (R2 of best-fit models: 0.25 [fish] and 0.64 [reef builders]). The variability in total benthic cover of reef builders was best described by physical (wave exposure and reef relief) and ecological (turf algal height and coral recruit density) predictors. No metric of anthropogenic pressure was related to spatial variation in reef builder cover. In contrast, total fish biomass showed a consistent (albeit weak) association with anthropogenic predictors (fishing and diving pressure). As is typical of most environmental gradients, the spatial patterns of both fish biomass density and reef builder cover were spatially autocorrelated. Residuals from the best-fit model for fish biomass retained a signature of spatial autocorrelation while the best-fit model for reef builder cover removed spatial autocorrelation, thus reinforcing our finding that environmental predictors were better able to describe the spatial variability of reef builders than that of fish biomass. As we seek to understand spatial variability of coral reef communities at the scale of most management units (i.e., at kilometer- to island-scales), distinct and scale-dependent perspectives will be needed when considering different functional groups.

Denitrification is a major source of the greenhouse gas N 2 O. As a result of spatial heterogeneity of organic carbon, oxygen and nitrate, denitrification is observed even under relatively dry conditions. However, it is unclear whether denitrification potentials of microbial communities exhibit spatial patterns relative to variations in distance to soil pores facilitating oxygen exchange and nutrient transfer. Thus, we determined genetic and process-level denitrification potentials in two contrasting soils, a cropland and a grassland, with respect to the distance to air-filled pores. An X-ray computed tomography aided sampling strategy was applied for precise sampling of soil material. Process-level and genetic denitrification potentials in both soils were spatially variable, and similar with respect to distance to macropores. In the cropland soil, a minor increase of process-level potentials with distance to pores was observed and related to changes in NO 3 − rather than oxygen availability. Genetic denitrification potentials after the short-term incubations revealed a certain robustness of the local community. Thus, distance to macropores has a minor impact on denitrification potentials relative to the observed spatial variability. Our findings support the notion that the impact of macropore induced changes of the environmental conditions in soil does not overrule the high spatial variability due to other controlling factors, so that the rather minor proportion of spatial heterogeneity of functional genes and activity potentials related to macropore distances in soil need not be considered explicitly in modelling denitrification.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan’an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan’an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan’an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Significant differences in urban greening have occurred in Chinese cities, accompanied by China’s rapid urbanization. However, there are relatively few studies on the spatial differentiation of urban greening in China at the city level. In addition, there is no unanimous conclusion on the main factors influencing the spatial differentiation of urban greening. Based on 2014 emission inventory data from 289 cities, the spatial differentiation pattern and spatial correlation characteristics of the urban green space ratio, urban green coverage rate, and public green area per capita were calculated and analyzed using global and local Moran’s I . We then used ordinary least squares, spatial error model, spatial autoregression, and geographically weighted regression to quantify the impact and spatial variations of China’s economy on urban greening. The results showed (1) a significant spatial dependence and heterogeneity existed in urban greening values, and the patterns showed influences of both the stage of economic development and spatial agglomeration; (2) regression models revealed per capita GDP had a positive effect on the urban green space ratio and public green area per capita while the urbanization rate, secondary industry, urban land, and population density had opposite effects on these two greening indexes; and (3) geographically weighted regression revealed per capita GDP had a greater influence on urban greening in the northwestern region than in the southeastern region. The study could constitute a valuable reference for mid-to-long-term green space planning policy in diverse parts of China and could further assist in coordinating the development of urban greening and economic growth.