Journal of Hydrology X最新文献

Agriculture in regions with limited water availability is possible because of irrigation. Irrigated croplands are expanding, and irrigation water demand is increasing. Nevertheless, there is a limited understanding of how much water is consumed for irrigation and how effective irrigation increases crop productivity in various climates. In this study, we aim to understand how irrigation water affects crop productivity in different climates. To achieve this goal, we developed a simple approach to quantify irrigation quantities from SMAP satellite soil moisture observations based on a zero-dimensional bucket-type hydrology model. The central assumption is that irrigation quantities can be estimated from the gap between the modeled and observed soil moisture by iteratively providing irrigation as a model input until the soil moisture simulations agree well with the observations. We then used the estimated amount of irrigation to simulate water, energy, and carbon fluxes at two agricultural sites on the west coast of the US: one that was water-limited (Central Valley, CA) and one that was energy-limited (Eugene, OR). An agroecosystem model, AgroIBIS-VSF, was used to conduct simulations. To verify our simulations, we used data from two AmeriFlux Eddy covariance towers at each site. We found that incorporating estimated irrigation amounts into our simulations improved the accuracy of energy balance components and soil moisture predictions, reducing the root-mean-square error of soil moisture predictions by up to 22%. We also discovered that the irrigation value, in terms of increased productivity of actual irrigation water used, is more than five times more valuable at the energy-limited site than at the water-limited site. Soil hydraulic properties have a strong influence on irrigation water valuation. Our study highlights the potential of satellite soil moisture observations to improve our understanding of water productivity in different climates. By better understanding the efficiency of resources used for crop production, we can ensure the sustainability and resilience of agricultural systems, leading to better management practices.

The increase in extreme rainfall events due to climate change, combined with urbanisation, leads to increased risks to urban infrastructure and human life. Physically based urban flood models capable of producing water depth maps with sufficient spatial and temporal resolution are generally too slow for decision makers to react in time during an extreme event. We present a surrogate model with high temporal and spatial resolution for real-time prediction of water levels during a pluvial urban flood. We used machine learning techniques to achieve short computation times. The recursive approach used in this work combines convolutional and fully coupled multilayer architectures. The database for the machine learning was pre-simulated results from a physically based urban flood model. The forcing input of the prediction is precipitation and the output is water level maps with a temporal resolution of 5 min and a spatial resolution of 6 x 6 meters. The prediction performance can be considered promising for testing the model in real operational applications.

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.