Journal of Hydrology X最新文献

Groundwater, the second largest stock of freshwater on the planet, is an important water source used for municipal water supply, irrigation, or industrial needs. For instance, California’s arid Central Valley relies on groundwater resources to produce a quarter of the United States’ food demand as farmers rely on this precious resource when surface water is scarce. Despite its importance, the nexus between groundwater dynamics and climate drivers remains difficult to quantify, model, and predict because of the lack of a comprehensive observation network. In this study, machine learning techniques were used to predict groundwater levels with a 3-month forecasting horizon for the Sacramento River Basin. For this, publicly available meteorological and hydrological datasets and in-situ well-level measurements were used. Time series, ensemble-based, and deep-learning models including transformers were all tested, with an ensemble-based, XGBoost model, producing the best mean standard deviation percent error (MSPE) of 32.23% and a root mean squared error (RMSE) of 1.05 m (m) when using a 3- month forecasting horizon and when tested using a monthly rolling window over the years 2017–2020. The model proved to be better at predicting into wet months than the dry summer months and was found to be better at extracting seasonality than explaining well-level residuals, with well-specific features, as opposed to exogenous meteorological features specific to the hydrological unit of the well, ranking as the most important features to the model. Though other forecasting horizons were tested, a 3-month look-ahead window resulted in the best balance of precision and accuracy, where smaller forecasting horizons resulted in smaller RMSE but larger MSPE scores and vice-versa for larger forecasting horizons.

Headwater streams (HWS) are ecologically important components of montane ecosystems. However, they are difficult to map and may not be accurately represented in existing spatial datasets. We used topographically resolved climatic water balance data and satellite indices retrieved from Google Earth Engine to model the occurrence (presence or absence) of HWS across Northwest Montana. A multi-scale feature selection (MSFS) procedure and boosted regression tree models/machine learning algorithms were used to identify variables associated with HWS occurrence. In final model evaluation, models that included climatic water balance deficit were more accurate (83.5% ranging from 82.9% to 83.7%) than using only terrain indices (81.1% ranging from 80.7% to 81.4%) and improved upon estimates of stream extent represented by the National Hydrography Dataset Plus High Resolution (NHDPlus HR) (82.7% ranging from 82.5% to 83.1%). Including topoclimate captured the varying effect of upslope accumulated area across a strong moisture gradient. Multi-scale cross-validation, coupled with a MSFS algorithm allowed us to find a parsimonious model that was not immediately evident using standard cross-validation procedures. More accurate spatial model predictions of HWS have potential for immediate application in land and water resource management, where significant field time can be spent identifying potential stream impacts prior to contracting and planning.

Peridotite aquifers are ubiquitous on Earth, but most are in the deep-sea, and thus difficult to access. Ophiolites provide a unique opportunity to study peridotite aquifers, and the Oman Drilling Project established a Multi-Borehole Observatory in a peridotite terrain of the Samail ophiolite. We use the water level response of two 400-m deep boreholes (BA1B, BA1D) to solid Earth, ocean, and atmospheric tides to investigate the hydromechanical structure of the aquifer. The two boreholes are offset by ∼ 100 m but exhibit markedly different tidal responses, indicating a high degree of short-length-scale heterogeneity. Hole BA1B does not respond to tidal strain or barometric loading, consistent with the behavior of an unconfined aquifer. Hole BA1D responds to both tidal strain and barometric loading, indicating some degree of confinement. The response to applied strain, which includes a non-negligible ocean tidal loading component, is consistent with a partially confined, low conductivity aquifer. The response to barometric loading appears to be affected by the complex hydrological structure of the surficial zone and we were not able to fit the observations to within error. Aquifer conductivity estimates for Hole BA1D based on the response to tidal strain are within a factor of ∼ 3 of pumping test estimates.

In-stream habitat models at the meso-scale are increasingly used to quantify the effects of hydro-morphological pressures in rivers. The spatial distributions of water depth and velocity represent key attributes of physical habitat. Choosing between field surveys, hydraulic modeling or their integration is made depending on available tools, technical skills, budget and time. However, the sensitivity to such choices of estimated habitat conditions suitable for biological organisms, such as fish, is poorly known.

In this study, three commonly used approaches in hydraulic-habitat modeling were compared and tested on a mountain stream, the Mareta River (NE Italy). Two approaches were based on 2D hydraulic modeling, calculated on computational meshes with varying resolution and quality: (1) high-resolution meshes derived from topographical data obtained from Airborne Bathymetric LiDAR; (2) a mesh extrapolated from topographical cross-sectional profiles. The third approach (3) was based on in-stream surveys. From these, suitable channel-area for two fish species, the marble trout (juvenile and adult), and the European bullhead (adult), were estimated.

Results showed that decreasing mesh resolution and quality affects the simulated water depth and velocity distributions, both in terms of their average and their standard deviation. The largest differences were found for the in-stream survey-based results. Morphologically complex unit types, such as steps, rapids and pools were more sensitive than simpler mesohabitats, such as glides and riffles. The most sensitive hydro-morphological unit types to the chosen approach were backwaters, glides being the least sensitive, also in terms of their suitability as mesohabitats. Despite that, a key finding is that errors are minimized when deriving habitat - streamflow rating curves at the reach scale, for which all approaches were largely able to reproduce the main characteristics of the curve, i.e. maxima, minima and inflection points.

In soil hydraulics, it is crucial to establish an accurate representation of the relative hydraulic conductive curve (rHCC), K_r(h). This paper proposes a simple way to determine K_r(h), called the Modified Gardner Dual model (MGD), using a logarithmic extension of the classical Gardner exponential representation and including macropore flow effects. MGD has five parameters which are hydraulic constants clearly identified in the bilogarithmic representation of K_r(h). Two of them are related to the main inflection point coordinates of rHCC; from them, it is possible to determine the macroscopic capillary length of the infiltration theory. The model was tested in the suction interval 0 < h < 15,000 cm with a total of 249 soil samples from two databases, and employing a flexible representation of the Mualem-van Genuchten (MVG) equation as a reference. Using the RMSE statistics (with log base) to measure the fitting errors, we obtained a 31% reduction in errors (RMSE_MGD = 0.27, RMSE_MVG = 0.39). In 74% of the soils, including samples from the two databases, the reduction was 53% (RMSE_MGD = 0.19, RMSE_MVG = 0.40); the rHCC data fitting of this group was accurate over all the suction h intervals, with RMSE_MGD < 0.32 in each soil sample. In the remaining 26% of the samples, the quality of the MGD fitting degraded due mainly to the presence of multiple rHCC data inflection points. Therefore, in soils without this structural peculiarity, the proposed model revealed to be quite accurate in addition to being analytically simple. Another advantage of MGD is that its parameters depend mainly on the data with h around and lower than the main inflection suction value, which, in turn, never exceeded the 300-cm limit in this study. Hence, in soils that do not have multiple inflections, the extrapolations of the model in drier intervals (1000 cm < h < 15,000 cm) are reliable. The MGD parameter optimization software has been called KUNSAT. It is available in the Supplementary Material or from the corresponding author on request.

Many studies highlight the decrease in precipitation due to climate change in the Mediterranean region, making it a prominent hotspot. This study examines the combined impacts of climate change and three groundwater demand scenarios on the water resources of the Western Mountain Aquifer (WMA) in Israel and the West Bank. While commonly used methods for quantifying groundwater recharge and water resources rely on regression models, it is important to acknowledge their limitations when assessing climate change impacts. Regression models and other data-driven approaches are effective within observed variability but may lack predictive power when extrapolated to conditions beyond historical fluctuations. A comprehensive assessment requires distributed process-based numerical models incorporating a broader range of relevant physical flow processes and, ideally, ensemble model projections. In this study, we simulate the dynamics of dual-domain infiltration and precipitation partitioning using a HydroGeoSphere (HGS) model for variably saturated water flow coupled to a soil-epikarst water balance model in the WMA. The model input includes downscaled high-resolution climate projections until 2070 based on the IPCC RCP4.5 scenario. The results reveal a 5% to 10% decrease in long-term average groundwater recharge compared to a 30% reduction in average precipitation. The heterogeneity of karstic flow and increased intensity of individual rainfall events contribute to this mitigated impact on groundwater recharge, underscoring the importance of spatiotemporally resolved climate models with daily precipitation data. However, despite the moderate decrease in recharge, the study highlights the increasing length and severity of consecutive drought years with low recharge values. It emphasizes the need to adjust current management practices to climate change, as freshwater demand is expected to rise during these periods. Additionally, the study examines the emergence of hydrogeological droughts and their propagation from the surface to the groundwater. The results suggest that the 48-month standardized precipitation index (SPI-48) is a suitable indicator for hydrogeological drought emergence due to reduced groundwater recharge.

The water repellent behaviour of soils is a widely studied phenomenon given its implications for infiltration, runoff, erosion and preferential flow. However, the principles underlying the eventual penetration of water into affected soils remain poorly understood. Theoretical considerations of the energetics and kinetics involved as a water drop makes contact with a water repellent soil surface and eventually penetrates into the soil suggest three distinct stages in the overall process. These stages are 1) adhesional wetting as soil and water first make contact, followed by 2) a kinetic barrier transitional stage in which molecular reorganisation of organics on soil reduces the water-soil contact angle to allow the water drop to sit deeper over soil particles of initial contact such that there is contact with particles in directly underlying soil layers, and finally 3) branching interstitial wetting as water penetrates into the bulk soil. Studies presented here of optical microscopy, mass of soil initially wetted, penetration time through layers of soil of different thicknesses, and time-dependent measurements of contact angle, volume of water penetrated, and mass of soil wetted, all give results consistent with this model. However, only for highly water repellent soils can distinct stages in wetting be clearly resolved experimentally, presumably because only these soils have a high enough kinetic barrier in the transitional stage for good separation between stages. For less water repellent soils, while the general time dependent behaviour remains consistent with the model, the distinction between the three stages is not so easy to resolve experimentally. The roles of contact angle, particle size distribution and drop size in determining the rates of these stages is considered, and the implications of the model for understanding soil water repellency are discussed.

The growth of vegetation in ecosystems is influenced by hydro-climatic factors and biogeochemical cycles. Accurately modeling annual vegetation growth dynamics is essential for eco-hydrological modeling to estimate watershed hydrologic balance and nutrient cycling under changing environmental conditions. The Soil and Water Assessment Tool (SWAT) and its upgraded version SWAT+ are process-oriented river basin models widely used. However, the temperature-based approach to plant growth simulation in tropical regions has limitations due to the importance of soil moisture availability as a key driver of plant growth. This study proposes an innovative approach that incorporates a proxy soil moisture availability index based on monthly rainfall and potential evapotranspiration ratio. This approach identifies the start of the growing season within prescribed transition months and controls leaf drop rate throughout the year, a crucial process during leaf senescence. We evaluated the reliability of this approach by comparing SWAT+ simulated Leaf Area Index (LAI), evapotranspiration (ET), and net primary productivity (NPP) with benchmark remote sensing-based datasets for three landcover classes in the Mara River Basin (Kenya/Tanzania). Our results demonstrate that the improved plant growth module in SWAT+ developed in this study can simulate temporal vegetation growth dynamics of evergreen forest, savanna grassland, and shrubland land cover types consistently with good correlations (r > 0.5) and low average bias (<10%). Thus, the SWAT+ model with the enhanced plant growth module can be a robust tool for investigating the coupled carbon, nutrient, and water cycling in tropical and sub-tropical climates.