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This study examines the atmospheric water cycle dynamics in the China-Mongolia Arid Region (CMAR), a region significantly affected by aridity. By employing a combination of Empirical Orthogonal Function (EOF) analysis, ERA5 reanalysis data, and the Dynamic Recycling Model (DRM), we investigate the spatial and temporal variations in the Precipitation Recycling Ratio (PRR) and Precipitable Water Conversion Rate (PWCR) over a forty-year period (1979-2021). Our findings reveal that both PRR and PWCR are generally higher but decreasing in most subregions of CMAR, suggesting a notable contribution of local moisture to precipitation. We also identify an increasing trend in PRR across the northwestern subregions and a decreasing trend in other areas. Similarly, PWCR exhibits an increasing trend in the northwestern and southern subregions, while decreasing elsewhere, implying a decline in water vapor conversion and recycling efficiency. Furthermore, our EOF analysis uncovers distinct spatial patterns, with dominant modes accounting for significant variances in PRR and PWCR, correlating with local variations in atmospheric moisture and advective changes. These results underscore the complex interplay between regional topography, atmospheric dynamics, and the hydrological cycle in CMAR. The insights from this study are vital for formulating effective water management strategies and adapting to climate change impacts in arid regions, holding broad implications for environmental science, climate studies, and sustainable resource management. Our findings reveal distinct spatial patterns and contrasting trends in precipitation recycling and water vapor conversion across the subregions of CMAR. This heterogeneity underscores the importance of conducting analyses at finer spatial scales to avoid contradictory conclusions that can arise from topographic influences when treating CMAR as a single unit. Future studies should focus on smaller subregions to accurately capture the intricacies of the water cycle in this topographically complex arid region.

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Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales non-linearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design. Computationally efficient surrogate models have been developed to address this issue. The recently developed Low-fidelity, Spatial analysis, and Gaussian Process Learning (LSG) model has shown strong performance in both computational efficiency and simulation accuracy. The LSG model is a physics-guided surrogate model that simulates flood inundation by first using an extremely coarse and simplified (i.e. low-fidelity) hydrodynamic model to provide an initial estimate of flood inundation. Then, the low-fidelity estimate is upskilled via Empirical Orthogonal Functions (EOF) analysis and Sparse Gaussian Process models to provide accurate high-resolution predictions. Despite the promising results achieved thus far, the LSG model has not been benchmarked against other surrogate models. Such a comparison is needed to fully understand the value of the LSG model and to provide guidance for future research efforts in flood inundation simulation. This study compares the LSG model to four state-of-the-art surrogate flood inundation models. The surrogate models are assessed for their ability to simulate the temporal and spatial evolution of flood inundation for events both within and beyond the range used for model training. The models are evaluated for three distinct case studies in Australia and the United Kingdom. The LSG model is found to be superior in accuracy for both flood extent and water depth, including when applied to flood events outside the range of training data used, while achieving high computational efficiency. In addition, the low-fidelity model is found to play a crucial role in achieving the overall superior performance of the LSG model.

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Water conservation function is a critical terrestrial ecosystem service in providing water supply and achieving water security, which has raised concerns under the pressure of climate change. However, the knowledge of variance on multi-time scale, spatiotemporal dynamic, and ecosystem variance of water conservation is insufficient. In this paper, the annual, monthly, and daily scales of water conservation and the spatiotemporal pattern of monthly water conservation were estimated based on the SWAT model from 2010 to 2020 in the Heihe River Basin (HRB). Additionally, EOF (Empirical orthogonal function) analysis was conducted to decompose the time series of water conservation function distribution into temporal coefficients and spatial patterns. The HRB was categorized into six representative ecosystems with three slope grades to illustrate the variance of water conservation function. The annual water conservation depth (WC) slightly decreased (-10.36 mm/10a) from 2010 to 2020, the monthly WC was dominated by the effects of seasonal variation, and the daily WC was highly nonlinear. The high variability and importance region is mainly located in the upstream and the central area of midstream, which deserves more attention for ecological management and priority protection. Moreover, the forest ecosystem is of the highest resilience and great ecological significance, which increased risk of reduced water conservation under the lack of precipitation. Even in a forest-dominated basin, water conservation can be impacted by other ecosystems with the strong influence of human activities. Our results provide scientific evidence for the improvement of water conservation capacity and making the adapted land use policy in Yellow River basins.

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Long-term measurements at the Mauna Loa Observatory (MLO) show that the CO2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22  ±  0.034 ppm decade-1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO2 fluxes from the Jena CarboScope CO2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of -0.10  ±  0.022 ppm decade-1 from 1959 to 2019, partly offsetting the observed long-term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC-ACTM simulations, the shorter-term variability of MLO SCA is nearly equally driven by varying ecological CO2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U-PDO). Since 1980, 44% of the wind-driven SCA decrease has been tied to a secular trend in the U-PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low-latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO2 fluxes using observations from low latitudes due to the complexity of circulation changes.

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Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3-60-day periods) in both GPH and SST and El Niño-Southern Oscillation (ENSO) at low frequencies (2-7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics.

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To understand changes in ecosystems, the appropriate scale at which to study them must be determined. Large marine ecosystems (LMEs) cover thousands of square kilometres and are a useful classification scheme for ecosystem monitoring and assessment. However, averaging across LMEs may obscure intricate dynamics within. The purpose of this study is to mathematically determine local and regional patterns of ecological change within an LME using empirical orthogonal functions (EOFs). After using EOFs to define regions with distinct patterns of change, a statistical model originating from control theory is applied (Nonlinear AutoRegressive Moving Average with eXogenous input - NARMAX) to assess potential drivers of change within these regions. We have selected spatial data sets (0.5° latitude × 1°longitude) of fish abundance from North Sea fisheries research surveys (spanning 1980-2008) as well as of temperature, oxygen, net primary production and a fishing pressure proxy, to which we apply the EOF and NARMAX methods. Two regions showed significant changes since 1980: the central North Sea displayed a decrease in community size structure which the NARMAX model suggested was linked to changes in fishing; and the Norwegian trench region displayed an increase in community size structure which, as indicated by NARMAX results, was primarily linked to changes in sea-bottom temperature. These regions were compared to an area of no change along the eastern Scottish coast where the model determined the community size structure was most strongly associated to net primary production. This study highlights the multifaceted effects of environmental change and fishing pressures in different regions of the North Sea. Furthermore, by highlighting this spatial heterogeneity in community size structure change, important local spatial dynamics are often overlooked when the North Sea is considered as a broad-scale, homogeneous ecosystem (as normally is the case within the political Marine Strategy Framework Directive).

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