Hydrological Processes最新文献

Seawater intrusion (SWI) is threatening coastal aquifers and farmland productivity worldwide. Although this phenomenon naturally occurs in coastal areas, it is intensified by anthropogenic activities such as groundwater pumping and land reclamation that cause a lowering of the hydraulic head and land subsidence. Moreover, the consequences of climate change such as sea level rise, increase of the mean temperature and the shifting of rainfall events to tropical regimes, have strong negative effects on groundwater quality and agriculture. Countermeasures against SWI are needed to maintain agricultural productivity and protect the freshwater resources in coastal areas. In the low-lying farmlands surrounding the southern Venice Lagoon, in northern Italy, SWI is exacerbated by land subsidence, the presence of sandy paleochannels connected to the lagoon subsurface, seawater encroachment into the river estuaries, the presence of fossil brine waters and peat deposits. This study provides a detailed hydrogeological and geochemical characterisation of an experimental agricultural field affected by SWI located in this area using a large dataset collected over the 4 years between 2019 and 2022. Furthermore, it presents the results of novel intervention established across the farmland in 2021 to mitigate saltwater contamination. This intervention involved a controlled discharge of freshwater supplied by a reclamation channel through a 200 m-long drainpipe buried 1.5 m below the field surface along a well-preserved sandy paleochannel. The interpretation of the collected data demonstrates that the freshwater recharge carried out in 2021 and 2022 effectively reduced the groundwater salinity along the paleochannel. Moreover, statistical analyses highlighted that a certain lateral spread of freshwater occurred too, although the variability of the monitored parameters in the sites located outside the sandy body was only partially explained by the drain activity.

Understanding plant-water relations is essential for effective regional water management and promoting ecologically sustainable development on the Loess Plateau, especially in the context of the Grain for Green project initiated in 1999. This study evaluated the variations in vegetation variables (leaf area index, enhanced vegetation index, solar-induced chlorophyll fluorescence and gross primary production) and hydroclimatic variables (precipitation, total water storage, aridity index and standardized precipitation evapotranspiration index) from 2003 to 2020, along with their interactions across the Loess Plateau. Our analysis revealed a general increase in vegetation variables, with the largest increase observed in the forest expansion areas. Precipitation and the aridity index exhibited significant upwards trends, while total water storage showed a significant decline, particularly in the forest expansion areas. Vegetation variables were more sensitive to changes in total water storage across the Loess Plateau. In the northwest region, where large-scale croplands and grasslands expansion occurred, vegetation variables also showed sensitivity to precipitation. Lag effect analysis revealed short time lags (1–3 months) between vegetation and hydroclimatic variables, expect for total water storage (6 months). Overall, human activities and climate factors contributed 58.4% and 41.6% to the increase in leaf area index, and 52.2% and 47.8% to the increase in gross primary production, respectively. In relatively arid environments, precipitation contributed over 50% to the observed vegetation greening. This study underscores the increasingly significant role of human activities in driving vegetation greening on the Loess Plateau, particularly in large-scale afforestation areas.

Distributed, coupled surface-groundwater hydrologic models are high-dimensional, given the necessity to reflect the spatially diverse nature of complex hydrologic processes. Furthermore, inverse/inference problems involving these high-dimensional models are naturally ill-posed, given the limited information content of state observations that are typically assimilated. Many inversion/inference algorithms do not cope well with high dimensionality, leaving the practitioner to make subjective choices related to uncertain model inputs. The objective of this study is to evaluate the impact of these subjective calibration choices within a formal sensitivity analysis, uncertainty analysis, and parameters estimation (SA-UA-PE) framework on model testing for a surface-subsurface hydrologic model. In doing so, we address the concepts of ‘over-parameterisation’ and ‘under-parameterisation’. We completed a series of numerical experiments, testing several otherwise subjective aspects of the calibration process: (1) the number (5, 10, 15, 20) and type (soil, aquifer, land surface, channel) of calibration parameters selected); (2) the type of state observations assimilated (streamflow, groundwater head); and (3) the length of testing period (1 to 14 years), using monthly streamflow and groundwater head as testing data. The experiments were completed for models of the Winnebago River watershed (Minnesota, Iowa), (significant tile drainage) and the Nanticoke River watershed (Delaware, Maryland (significant groundwater-channel interactions). The selected hydrologic model is SWAT+, using the gwflow module for physically based groundwater storage and flow modelling, and simulations are run for the 2000–2015 period. Through this process, we found that increasing the number of parameters from 5 to 15 improves the representation of streamflow, principally through an improvement of groundwater storage representation and baseflow generation, but minimal improvement when increasing to 20 parameters. Therefore, the SA-UA-PE process can be optimised based on an ideal number of parameters that yield adequate results while maintaining a lower computational burden. The method presented here can be used for any watershed, using integrated surface-subsurface hydrologic models.

In 2020, Colorado experienced the most severe wildfire season in recorded history, with wildfires burning 625 357 acres across the state. Two of the largest fires burned parts of Rocky Mountain National Park (RMNP), and a study was initiated to address concerns about potential effects on drinking water quality from mobilization of ash and sediment. The study took advantage of a wealth of pre-fire data from adjacent burned and unburned basins in western RMNP. Pre- and post-fire data collection included discrete sample collection and high-frequency water-quality measurements using in-stream sensors. Kruskal–Wallis tests on discrete data indicated that specific conductance, base cations, sulphate, chloride, nitrate, and total dissolved nitrogen concentrations increased post-fire, whereas silica and dissolved organic carbon (DOC) did not (p ≤ 0.05). In-stream sensors captured large spikes in concentrations of nutrients, turbidity, and DOC in the burned basin that were missed by discrete sampling. Sensor data indicated nitrate and turbidity increased by up to one and two orders of magnitude, respectively, from pre-event concentrations during storms, and DOC increased up to 3.5×. Empirical regression equations were developed using pre-fire data and applied to the post-fire period to estimate expected stream chemistry in the absence of fire (a ‘no-fire’ scenario). Overlays of actual post-fire chemistry showed the timing and magnitude of differences between observed and ‘estimated’ chemistry. For most solutes, observed post-fire concentrations were notably greater than expected under the ‘no-fire’ scenario, and differences were greatest during storm events. Comparison of data from the burned and unburned basins indicated DOC concentrations were affected by climate as well as fire. Results from this study demonstrate the importance of both pre-fire data and high-frequency data for characterizing dynamic hydrochemical responses in wildfire-affected areas.

Changes in the volume, rate, and timing of the snowmelt water pulse have profound implications for seasonal soil moisture, evapotranspiration (ET), groundwater recharge, and downstream water availability, especially in the context of climate change. Here, we present an empirical analysis of water available for runoff using five eddy covariance towers located in continental montane forests across a regional gradient of snow depth, precipitation seasonality, and aridity. We specifically investigated how energy-water asynchrony (i.e., snowmelt timing relative to atmospheric demand), surface water input intensity (rain and snowmelt), and observed winter ET (winter AET) impact multiple water balance metrics that determine water available for runoff (WAfR). Overall, we found that WAfR had the strongest relationship with energy-water asynchrony (adjusted r² = 0.52) and that winter AET was correlated to total water year evapotranspiration but not to other water balance metrics. Stepwise regression analysis demonstrated that none of the tested mechanisms were strongly related to the Budyko-type runoff anomaly (highest adjusted r² = 0.21). We, therefore, conclude that WAfR from continental montane forests is most sensitive to the degree of energy-water asynchrony that occurs. The results of this empirical study identify the physical mechanisms driving variability of WAfR in continental montane forests and are thus broadly relevant to the hydrologic management and modelling communities.