Hydrological Processes最新文献

Soil erosion is a key concern with regard to ecosystem functionality and food, fibre and bioenergy productions worldwide. Therefore, understanding the mechanisms and controls of soil erosion, particularly the link between soil aggregate stability and soil erodibility, is of utmost importance. The use of disturbed samples and sieved soil to determine the involved erodibility and aggregate stability is standard in soil erosion studies. However, soil erodibility estimation based on disturbed-soil samples can be inaccurate as it involves changes in the architecture of the considered soil, possibly leading to overestimations. Moreover, a necessity for evaluating soil erodibility beyond intrinsic soil characteristics (e.g. texture) exists. The objective of this research was to assess the erodibility impact of soil disturbance. Undisturbed-soil cores with dimensions of 45 cm (length) × 30 cm (width) × 10 cm (depth) were extracted while preserving their architecture. An A horizon corresponding to brown clayey subtropical oxisol soil from Southern Brazil was used for performing an experiment that involved simulation of 58–mm h⁻¹ rain for 30 min. A total of seven replicate experiments were performed for each soil condition (i.e. undisturbed and disturbed soils). Results show that soil architecture deterioration had a larger impact on the involved soil loss than runoff. Further, soil structure failure did not affect the aggregate stability per se. Notably, the soil erodibility and loss were approximately 10 times larger under the disturbed-soil condition than under the undisturbed-soil condition (interrill erodibility: 4.30 × 10⁷ and 4.39 × 10⁶ kg s m⁻⁴, respectively; soil loss: 0.925 and 0.094 kg m⁻², respectively). Overall, the intrinsic soil characteristics did not change; however the soil architecture deterioration considerably increased the erodibility.

Rainfall redistribution plays a crucial role in the water cycle. However, the main factors affecting the redistribution of rainfall remain uncertain. We chose three different plantations—cork oak (Quercus variabilis Bl.), oriental arborvitae (Platycladus orientalis L.) and black locust (Robinia pseudoacacia L.)—to investigate the role of plantations in rainfall redistribution and to determine the main factors influencing rainfall redistribution. The results indicated that cork oak exhibited the highest stemflow (0.34%) and the lowest canopy interception (12.58%), whereas black locust had the lowest stemflow (0.21%), and oriental arborvitae displayed the greatest canopy interception (32.8%). Under different density conditions for cork oaks, the stemflow was highest (0.39%) in low-density forests with 750 trees ha⁻² and lowest (0.34%) in medium-density forests with 1100 trees ha⁻². Meanwhile, the highest canopy interception (17.68%) was observed in high-density forests (1300 trees ha⁻²), while the lowest interception rate (9.22%) was found in low-density forests. The main factors affecting rainfall redistribution and their contribution rates were as follows: bark roughness index (35%), wind speed (18.6%), tree species (14.2%), diameter at breast height (11.2%), stand density (9.6%) and rainfall amount (5.4%). Our findings suggested that structural characteristics of trees are the primary factors affecting rainfall redistribution. Planting cork oak in the rocky mountain regions of North China is recommended because of its substantial stemflow production, particularly under low-density growth conditions. Therefore, this study has significant guiding implications for the selection of afforestation tree species in similar rocky mountain areas globally.

Climate change and anthropogenic influences amplify drought complexity, entangle non-stationarity (NS) and further challenge drought comprehension. This study aims to understand the dynamic evolution of drought propagation patterns due to climatic and anthropogenic pressures by assessing the non-stationary linkages between hydrological variables and drought characteristics. It employs four standardized drought indicators to comprehensively examine the spatio-temporal evolution of meteorological (MD) and hydrological (HD) drought characteristics. Data from 29 semi-arid catchments from six river basins in Peninsular India, are analyzed to uncover distinct drought propagation patterns. This study utilizes a novel Non-overlapping Block-stratified Random Sampling (NBRS) approach to detect NS in drought characteristics and hydrological variables, shedding light on the underlying drivers of this dynamic behavior. The results indicate similarities in drought behavior for the Sabarmati, Mahi and Tapi (SMT) basins compared with the Godavari, Krishna and Pennar (GKP) basins, with shorter (longer) propagation times noted for SMT (GKP) basins. While HD severity decreases over time in SMT basins, it intensifies in GKP basins, which are linked to intensive anthropogenic interventions such as river regulation and reservoir operations, thus resulting in prolonged and intensified droughts. Rainfall primarily exhibits time-invariance, while significant NS is observed in potential evapotranspiration (particularly in the Krishna and Pennar basins), streamflow and baseflow across all basins. The study also identified three distinct drought propagation patterns in these basins, highlighting cases where MD did not transition to HD, instances of HD occurring without preceding MD and synchronous propagation of MD to HD. The study outcomes provide profound insights into the evolution of drought dynamics under climatic and anthropogenic pressures, which will aid policymakers and stakeholders in formulating strategies for drought preparedness and response.

The length and frequency of extreme fire weather has increased across the globe in recent decades, with potential deleterious consequences to streamflow quantity, timing and quality. Changes in the hydrologic regime following wildfire can have substantial downstream consequences, affecting communities and ecosystems through flooding, erosion, loss of habitat and degraded water quality. While there are many studies that address post-wildfire hydrology across the globe, there are few studies in the snow-dominated regions. The 2017 Elephant Hill wildfire in south-central BC burned across or adjacent to four watersheds with long-term streamflow gauges providing a rare opportunity to evaluate hydrologic change. Several approaches were used to identify patterns of change following the wildfire, all of which suggest increased post-fire flows. The before-after-control-impact design showed significant increases in annual, spring and summer water yield from the small (49 km²) Arrowstone Creek watershed (30%, 21% and 86%, respectively). Significant increases in spring water yield were observed in the larger (5318 km²) Bonaparte River watershed (48%). Annual and summer water yield increased in the Bonaparte River (31% and 58%, respectively) but these changes were not statistically significant. In both the Bonaparte River and Arrowstone Creek, the onset of spring freshet (26 days earlier in both) was significantly advanced, however, the timing of maximum snowmelt discharge was significantly advanced (27 days earlier) only in Arrowstone Creek. Smaller changes were also observed in the reference watersheds; however, these were not statistically significant. The difference in results between the small and large watershed, as well as the effects of weather and watershed attributes, highlight the need for continued research into the relationships between wildfire and hydrologic regime across diverse landscapes.

Ecological drought has emerged as a critical research topic within eco-hydrology, driven by the increased drought risk associated with global warming. This study aims to unravel the hydrological processes driving ecological drought in Northwest China by (1) constructing standardized indices for precipitation, groundwater storage anomaly, and ecological water deficit to detect variation across different hydrological components; (2) developing a framework to assess the joint and relative impacts of precipitation and groundwater variations on ecological drought; and (3) identifying the primary hydrological drivers of ecological drought across different regions. The results indicate that the joint impact of precipitation and groundwater on ecological drought variation dominates approximately 60% of the area, primarily in arid and semi-arid regions. The average contribution of this joint impact to the alleviation of ecological drought ranges between 0.26 and 0.43 across all seasons. Notably, groundwater scarcity, rather than precipitation variation, is the primary driver of ecological drought in regions such as southern Shaanxi, southeastern Gansu, and southern Qinghai, accounting for 12.7% to 21.8% of the total area. These insights into the complex hydrological processes underlying ecological drought have significant implications for water resource management and ecosystem conservation in drought-prone regions. This research provides valuable information for mitigating drought impacts and protecting vulnerable ecosystems in Northwest China and similar regions worldwide.

The formation and concentration of liquid water (LW) in the snowpack constitute key processes linking snow and runoff. Hence, the LW content of the snowpack represents a crucial target variable to investigate for snowmelt-induced runoff predictions. In this study, we capture the wet snow dynamics at higher than hectometre resolution in the alpine headwater catchment Rofental, Tyrol, Austria (98.1 km²) by means of distributed model simulations and remote sensing data for the 5 year period 10/2017–09/2022. The model simulations are conducted using the intermediate complexity open-source snow-hydrological model openAMUNDSEN. Simulation results are compared to wet snow maps (WSM) derived from Sentinel-1 data. Our investigations indicate that distributed snow models of intermediate complexity, such as openAMUNDSEN and satellite-based wet snow data are well capable of capturing the wet snow dynamics in high spatial and temporal resolutions. The areal extents of wet snow as well as the upward movement of the wet snow line to higher elevation with progressing snowmelt are captured well by both approaches. In order to evaluate the snow simulations, we use fractional snow cover (FSC) data based on Sentinel-2, which proved to provide valuable small-scale snow and snow redistribution patterns in alpine catchments. The comparison of model simulations with FSC maps with more than 50% of the non-glaciated area being cloud-free (i.e. 364 images) results in an accuracy of 0.91. This study represents a further step towards a serviceable operational snow-hydrological monitoring and modelling framework for mountain regions including wet snow dynamics in high spatial and temporal resolutions.

Mountains have a critical role in freshwater supply for downstream populations. As the climate changes, groundwater stored in mountains may help buffer the impacts to declining water resources caused by decreased snowpack and glacier recession. However, given the scarcity of groundwater observation wells in mountain regions, it remains unclear how mountain groundwater is being impacted by climate change across ecoregions. This study quantifies temporal trends in mountain groundwater levels and explores how various climatic, physiographic and anthropogenic factors affect these trends. We compiled data from 171 public groundwater observation wells within mountain regions across Canada and the United States, for which at least 20 years of monthly data is available. The Mann-Kendall test for monotonic trend revealed that 54% of these wells have statistically significant temporal trends (p < 0.05) over the period of record, of which 69% were negative and therefore indicating overall declining groundwater storage. Wells in the western mountain ranges showed stronger trends (both positive and negative) than the eastern mountain ranges, and higher elevation wells showed fewer negative trends than the low elevation (<400 m asl) wells (p < 0.05). Correlation, Kruskal-Wallis tests, stepwise multiple linear regression and random forest regression were used to identify factors controlling groundwater trends. Statistical analysis revealed that lower-elevation mountain regions with higher average annual temperatures and lower average annual precipitation have the greatest declines in groundwater storage under climate change. Trends in temperature and precipitation, and ecoregion were also important predictors on groundwater level trends, highlighting geographic differences in how mountain wells are responding to climate change. Furthermore, sedimentary bedrock aquifers showed markedly more negative trends than crystalline bedrock aquifers. The findings demonstrate that the impact of climate change on mountain water resources extends to the subsurface, with important implications for global water resources.

Isotope-enabled models provide a means to generate robust hydrological simulations. However, daily isotope-enabled rainfall-runoff models applied to larger spatial scales (>100 km²) require more input data than conventional non-isotope models in the form of precipitation isotope time series, which are difficult to generate even with point station measurements. Spatially distributed isotope data can be circumvented by isotope-enabled climate models. Here, we evaluate the hydrological simulations of the J2000-isotope enabled hydrological model driven with data from corrected and un-corrected isotope-enabled global and regional climate models (isotope-enabled global spectral model [IsoGSM] and isotope-enabled regional spectral model [IsoRSM], respectively) compared with 1 year of measured reference station and a yearly average precipitation isotope input for a pilot site, the data-scarce sub-humid Eerste River catchment in South Africa. The models driven by all input products performed well for upstream and downstream discharge gauges with Nash Sutcliffe efficiency (NSE) from 0.58 to 0.85 and LogNSE of 0.66 to 0.93. The simulated δ²H stream isotopes using the reference J2000-iso and J2000-isoRSM were good for the main river with a stream Kling Gupta efficiency (KGE) of between 0.4–0.9 and the top 100 Monte Carlo simulations varying by around 5‰ for δ²H. For smaller tributaries the model was unable to capture the measured stream isotopes due to biased precipitation isotope inputs. Adjusting the J2000-iso with a bias corrected IsoRSM improved the stream and groundwater isotope simulation and outperformed the model driven by an average yearly precipitation isotope input. Differences in simulated hydrological processes were only evident between the models when evaluating percolation with unrealistic simulations for the standard J2000 model. While the regional climate model is computationally more intensive than its global counterpart, it provided better stream isotope simulations and improvements to simulated percolation. Our results indicate that isotope-enabled climate models can provide useful input data in data scarce regions for hydrological models, where improved water management to address climate change impacts is needed.

Evaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability.