Journal of Hydrology最新文献

The mutual benefits of real-time control and green and grey infrastructure for urban drainage system (UDS) renovations have drawn great attention. However, risks emerged due to equipment and structural failures, and no studies considered such failures while optimizing the renovation scheme. To address this issue, this study proposed a multi-criteria optimization method for the integrated design of green and grey infrastructure with real-time control rules, where the economic cost and system performance under normal and exceptional conditions were optimized simultaneously. Failure probabilities of equipment and structure were quantified using homogeneous Poisson process models, and failure losses were estimated with the aid of a machine learning-based surrogate model. This approach was tested with a combined UDS in China. Results indicate that: (1) More investment does not necessarily increase system resilience to failures. Equipment and structural failures can significantly lower the effectiveness of grey infrastructure and real-time control. Therefore, solutions with more investments in grey infrastructure, which indicate higher costs, experience greater failure losses. (2) System resilience to failures can be significantly improved while maintaining other objectives when compared with the traditional design scheme. The proposed method allows for a new perspective in addition to the cost-and-resilience two-objectives design of UDS renovations, especially for systems threatened by various failures.

Under the backdrop of global climate change, frequent drought events pose a severe and persistent threat to the normal functioning of ecosystems. Drought recovery time, the time it takes for an ecosystem to return to its pre-drought state, is a crucial metric for drought impact and ecosystem stability. However, most previous studies have focused on the drought recovery time of the region as a whole or a specific type of ecosystem. The differences in drought recovery time among different ecosystems and their driving factors are largely unknown. Therefore, this study utilizes multi-source fused Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) data and Gross primary productivity (GPP) data to construct drought and ecosystem indicators. Subsequently, the drought recovery time under different climates and ecosystem types in China from 2002 to 2017 is analyzed. Finally, the factors influencing the differences in drought recovery time among ecosystems are discussed in detail. The results indicate that there is significant spatial heterogeneity in drought recovery time among ecosystems in China, with the forest ecosystems in the Northeast and Southwest regions having the longest recovery time. The recovery time of forest ecosystems (4.32 months) is longer than that of cropland (4.07 months) and grassland (3.79 months) ecosystems, but there are also significant differences in the recovery time for the same ecosystems under different climate types. The differences in drought recovery time among different ecosystem types are primarily influenced by temperature and precipitation during drought recovery, and the response of ecosystems to drought. These results provide scientific support for adopting differentiated management strategies for different ecosystem types to cope with future climate change.

Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m³/m³ saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization’s impact on landslide risks in developing nations.

Internal erosion is one of the leading causes of failures and accidents of embankment dams, dikes, and slopes. The hydraulic loading acts as the driving force to detach the soil particles, while the initial soil microstructure determines the susceptibility of particle loss. In engineering practices, the soil may be subjected to cyclic hydraulic loadings due to water level fluctuations by extreme weather of intensive rainfall and drought. Under such conditions, the soil internal erosion process will be different from that under the steady unidirectional seepage, which has not been fully studied and needs urgent investigation. Thus, in this study, the internal erosion process and hydraulic characteristics of soils with different microstructures were investigated by both laboratory seepage tests and the discrete element method (DEM) simulation. The results indicate that the soil with a higher fine content was more structurally stable, and required a larger hydraulic gradient for erosion initiation. Once the internal erosion occurred, the particle loss and the soil hydraulic conductivity increased with increasing fine content. Additionally, when the applied hydraulic gradient was essentially the same, the soil experienced a server erosion under the cyclically than monotonically increased hydraulic gradients, and the amount of eroded soils increased with the increasing gradient amplitude. The results of this study will expand our understanding of the physical mechanism and hydraulic behaviors of soils subjected to cyclically increased hydraulic gradients and with different microstrures.

The end of the growing season (EOS), autumn phenology, is a significant indicator of vegetation health in terrestrial ecosystems. Higher frequency and intensity droughts are expected to have a greater impact on ecosystem homeostasis, and an urgent determination of the impact of temporal effects on autumn phenology is imperative to improve the understanding of ecosystem resilience and resistance and the stability of plant carbon sinks. This study aims to quantitatively assess the response of global autumn phenology to observed drought cumulative and lagged effects based on EOS and multi time-scales drought index (the Standardized Precipitation and Evapotranspiration Index, SPEI), and analyze the potential impact path of climate variables on EOS-SPEI relationship. Our results suggested that the cumulative and lagged effects of drought had a significant impact on the autumn phenology of approximately 27.00% and 48.73% of the vegetated area, respectively. The accumulated months were mostly concentrated on shorter time scales (1- to 3-month), and the lagged effect was mostly concentrated on 1 to 3 lagged months. These two effects on EOS were similar in different biomes and water availability, demonstrating that diverse biomes have different adaptation strategies to drought and the importance of water available for ecosystem drought mitigation. Precipitation and solar radiation had a direct negative impact on evaporation, whereas evaporation had a substantial directly positive impact on the intensity of drought effect on autumn phenology. The interaction between the climatic variables results in the accumulated months being directly positively regulated by thermal radiation, while the lagged months were directly positively influenced by precipitation, which indicates that the hydrothermal conditions at the onset of EOS occurrence govern the autumn phenology response to drought more than the occurrence time of EOS.

Water temperature is a key factor influencing biota of stream ecosystems. Hence, it is important to comprehend the environmental drivers of stream temperature for robust prediction of conditions and effective management of stream communities. Linear regression models are commonly used for predictive purposes, but their predictive capacity and interpretability can be significantly affected by their complexity and the structure of input data. In some cases, researchers may be obligated to favor prediction power or interpretability while compromising the other. Therefore, insight into relationships between model fit, correlation among predictor variables (i.e., multicollinearity), and level of temporal aggregation of data (i.e., data granularity) may be helpful to reduce such trade-offs. In this paper, we investigated these relationships within a hierarchical set of multiple linear regression (MLR) models examining environmental factors influencing stream temperature dynamics. Our findings showed that as the number of predictor variables (i.e., model complexity) increased, the magnitude of multicollinearity in MLR models increased, but model fit also increased. The results also revealed that using data averaged over longer time frames (i.e., coarser data granularity) yielded high multicollinearity, as indexed by variance inflation factor values (VIF) for all model predictors. This led to higher variance in parameter estimates (i.e., parameter instability) and potential challenges in model interpretation as the sign of parameter estimates changed in many streams examined. Multicollinearity was not the only reason for these changes in the sign of parameter estimates as they were also observed in simple linear regression models across varying levels of data granularity. Based on our findings, we conclude that the selection of data granularity is an important consideration in multiple regression modeling, with profound implications for model interpretability.

Although there have been many studies of groundwater inflow to small lakes, no systematic attention has been paid to the role of the time interval in the reliability of transient flow analysis. We addressed this issue in a two-year study of the isotope hydrology and water budget of a small lake in eastern Washington State (USA) that has been subject to limited management over several decades. The weighted local meteoric water line is δ²H = 7.14 δ¹⁸O – 5.22, reflecting the impact of convective recycling in this semi-arid region of inland northwestern North America. Groundwater inflow to the lake was quantified over two years using a short-interval isotopic transient mass balance approach. Calculated inflow was less than a fifth of the lake’s total water budget. Unrealistic temporal fluctuation of the calculated inflow was apparently correlated with fluctuation of the observed isotopic ratio of lake water (${\delta }_L$). We obtained realistic lake fluctuation and groundwater–lake exchange estimates by experimenting with the time interval of the isotope mass balance. It is crucial to acknowledge that each lake possesses unique characteristics that influence hydrologic and isotopic variations. Therefore, the optimal time interval for sampling and calculating mass balance may vary among different small lakes. Our findings have implications for long-term studies with frequent interval sampling.

The ease and efficiency with which conceptual rainfall runoff (CRR) models can be calibrated, as well as issues related to the uniqueness of their parameters, has received significant attention in literature. While several studies have tried to gain a better understanding of the underlying factors affecting these issues by examining the features of model error surfaces, this has generally been done in an ad-hoc fashion using lower-dimensional representations of higher-dimensional surfaces. In this paper, it is suggested that exploratory landscape analysis (ELA) metrics can be used to quantify key features of the error surfaces of CRR models, including their roughness and flatness, as well as their degree of optima dispersion throughout the surface. This enables key error surface features of CRR models to be compared in a consistent, efficient and easily communicable fashion for models with different combinations of attributes (e.g. model structure, catchment climate conditions, error metrics, and calibration data set lengths). Results from the application of ELA metrics to the error surfaces of 420 CRR models with different combinations of the above attributes indicate that increasing model complexity results in an increase in relative error surface roughness and relative optima dispersion and that, while increasing catchment wetness increases the relative roughness of error surfaces, it also decreases optima dispersion. This suggests that for the models considered in this study, optimisation efficiency is likely to decrease with increasing model complexity and catchment wetness, while optimisation difficulty is likely to increase and parameter uniqueness likely to decrease with model complexity and catchment dryness. While implications for choice of model complexity will need further work, this study highlights the potential value of the proposed approach to understanding the calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models.

In enhanced geothermal systems, fractured reservoir permeability significantly affects geothermal exploitation efficiency. However, the detailed effects are not fully understood while most previous literature ignored the spatial differences of reservoir permeability because of the complexity and heterogeneity of fracture distribution. This study aims to reveal the quantitative relationship between the geothermal system’s heat extraction performance and the distributed permeability, through investigating heat extraction ratio and flow impedance by building a 3D thermal-hydraulic coupled model with discrete fracture network. The current study also compared the effects of twenty-nine kinds of fracture network distributions at various depths. It is found that higher fracture permeability around production well ($800\times 10^{-12}$ m²) more significantly improves heat extraction rate by 30% rather than higher permeability in other areas in the first year. It is also found that fracture permeability changes on both sides of predominant flow regions cause a lower heat extraction rate. Lower fracture permeability at bottom and top layers ($25\times 10^{-12}$ m²) increases the heat extraction rate by 1.17 MW in the 30th year. The proppant distribution affects the pressure distribution in fractured reservoirs. Permeability distribution variation has small effects on the heat extraction ratio. These results provided theoretical basis for fractured reservoir construction, optimal proppant pumping scheme and reservoir thermal output prediction.