Résultat de la recherche
Groundwater recharge dynamics by snowmelt
Comprendre les mécanismes de recharge des aquifères karstiques revêt une grande importance car ces aquifères sont utilisés par un quart de la population mondiale et que leurs dynamiques de recharge et de décharge sont souvent fortement couplées. Compte tenu de ce couplage, les ressources en eaux souterraines karstiques sont intrinsèquement sensibles aux processus de surface tels que le climat, où le moment et le volume de l'infiltration d'eau sont une conséquence directe des précipitations et de la température. Les changements climatiques devraient modifier le ratio précipitations de neige / liquide, en particulier dans les régions qui reçoivent actuellement une part importante des précipitations sous forme de neige.
Ainsi, nous avons consacré ce corpus de recherches à mieux comprendre 1. les mécanismes contrôlant la recharge des aquifères karstiques à partir de la fonte des neiges; 2. dans quelle mesure les modèles de traitement de la neige prédisent réellement l'infiltration de la fonte des neiges et quelles sont les incertitudes prévisionnelles entourant ces modèles; et 3. comment les schémas de recharge de l'aquifère karstique vont changer dans un climat qui se réchauffe. Nous avons collecté trois années de données sur un site unique où les taux de recharge peuvent être suivis dans une grotte peu profonde et qui peuvent être considérés comme un lysimètre surdimensionné et réel. Les données collectées reflétaient des comportements intégrés spatialement dans la zone de captage du lysimètre et nous donnaient une rare occasion de nous écarter de l’étude du système en utilisant des données ponctuelles.
À travers ces études, nous avons constaté qu’une quantité substantielle d’eau infiltrante était stockée dans la zone vadose (principalement dans les sols par rapport à l’epikarst), ce qui a conduit à une redistribution temporelle de l’eau des temps de fonte aux périodes froides sans infiltration de la fonte des neiges. Le stockage et le débit de la zone vadose exercent un contrôle puissant sur le débit de l'aquifère à l'échelle de la semaine, tandis que le stockage de la nappe phréatique devient dominant pendant des périodes prolongées sans apport. De plus, nous avons observé que l’incertitude prédictive du modèle de processus de neige est réduite avec une paramétrisation accrue des processus de fonte. Un calibrage rigoureux des modèles de processus sur neige, permettant l'optimisation du modèle, devrait devenir une pratique courante pour les gestionnaires de ressources en eau dans les régions froides. Enfin, nous avons constaté que l’augmentation de la température de l’air réduisait l’équivalent en eau de la neige d’un manteau neigeux à un moment donné, ainsi que sa durée de mise en place et que la répartition de la recharge tout au long de l’hiver pouvait avoir des effets importants sur la disponibilité des eaux souterraines, rendant les aquifères karstiques particulièrement vulnérables au changement climatique., Understanding the recharge mechanisms for karst aquifers is of great importance because these aquifers are relied upon by a quarter of Earth’s population, and because their recharge and discharge dynamics are often strongly coupled. Given this coupling, karst groundwater resources are inherently susceptible to surface processes such as climate, where the timing and volume of water infiltration is a direct consequence of precipitation and temperature. Climate change is anticipated to shift the ratio of snow to liquid precipitation, particularly in regions that currently receive a substantial portion of precipitation as snow.
Thus, we have dedicated this body of research to better understand 1. the mechanisms controlling karst aquifer recharge from snowmelt; 2. how well snow process models actually predict infiltration of snowmelt and what are the predictive uncertainties surrounding these models; and 3. how karst aquifer recharge patterns will shift in a warming climate. We collected three years of data from a unique field site where recharge rates can be tracked in a shallow cave, and which can be considered as an oversized, real-world lysimeter. The collected data embodied spatially integrated behaviors across the lysimeter’s catchment area, and allowed us a rare opportunity to depart from system study using point-data.
Through these studies we found that a substantial amount of infiltrating water was stored in the vadose zone (predominantly in soils versus the epikarst), which led to temporal redistribution of water from melt events to cold periods lacking snowmelt infiltration. Vadose zone storage and flow have a strong control on aquifer discharge at the scale of weeks, while phreatic storage becomes dominant during prolonged periods without input. Further, we observed that snow process model predictive uncertainty is reduced with increased parameterization of melt processes. Rigorous snow process model calibration, which allows for model optimization, should become standard practice for water resource managers in cold regions. Lastly, we found that increased air temperature reduces both a snowpack’s snow water equivalent at a given time and also its duration of emplacement and that recharge distribution throughout the winter can have significant impacts on groundwater availability, rendering karst aquifers particularly susceptible to climate change.
Snowmelt infiltration and storage within a karstic environment, Vers Chez le Brandt, Switzerland
Even though karstic aquifers are important freshwater resources and frequently occur in mountainous areas, recharge processes related to snowmelt have received little attention thus far. Given the context of climate change, where alterations to seasonal snow patterns are anticipated, and the often-strong coupling between recharge and discharge in karst aquifers, this research area is of great importance. Therefore, we investigated how snowmelt water transits through the vadose and phreatic zone of a karst aquifer. This was accomplished by evaluating the relationships between meteorological data, soil–water content, vadose zone flow in a cave 53 m below ground and aquifer discharge. Time series data indicate that the quantity and duration of meltwater input at the soil surface influences flow and storage within the soil and epikarst. Prolonged periods of snowmelt promote perched storage in surficial soils and encourage surficial, lateral flow to preferential flow paths. Thus, in karstic watersheds overlain by crystalline loess, a typical pedologic and lithologic pairing in central Europe and parts of North America, soils can serve as the dominant mechanism impeding infiltration and promoting shallow lateral flow. Further, hydrograph analysis of vadose zone flow and aquifer discharge, suggests that storage associated with shallow soils is the dominant source of discharge at time scales of up to several weeks after melt events, while phreatic storage becomes import during prolonged periods without input. Soils can moderate karst aquifer dynamics and play a more governing role on karst aquifer storage and discharge than previously credited. Overall, this signifies that a fundamental understanding of soil structure and distribution is critical when assessing recharge to karstic aquifers, particularly in cold regions.
Infiltration under snow cover: Modeling approaches and predictive uncertainty
Groundwater recharge from snowmelt represents a temporal redistribution of precipitation. This is extremely important because the rate and timing of snowpack drainage has substantial consequences to aquifer recharge patterns, which in turn affect groundwater availability throughout the rest of the year. The modeling methods developed to estimate drainage from a snowpack, which typically rely on temporallydense point-measurements or temporally-limited spatially-dispersed calibration data, range in complexity from the simple degree-day method to more complex and physically-based energy balance approaches. While the gamut of snowmelt models are routinely used to aid in water resource management, a comparison of snowmelt models’ predictive uncertainties had previously not been done. Therefore, we established a snowmelt model calibration dataset that is both temporally dense and represents the integrated snowmelt infiltration signal for the Vers Chez le Brandt research catchment, which functions as a rather unique natural lysimeter. We then evaluated the uncertainty associated with the degree-day, a modified degree-day and energy balance snowmelt model predictions using the nullspace Monte Carlo approach. All three melt models underestimate total snowpack drainage, underestimate the rate of early and midwinter drainage and overestimate spring snowmelt rates. The actual rate of snowpack water loss is more constant over the course of the entire winter season than the snowmelt models would imply, indicating that mid-winter melt can contribute as significantly as springtime snowmelt to groundwater recharge in low alpine settings. Further, actual groundwater recharge could be between 2 and 31% greater than snowmelt models suggest, over the total winter season. This study shows that snowmelt model predictions can have considerable uncertainty, which may be reduced by the inclusion of more data that allows for the use of more complex approaches such as the energy balance method. Further, our study demonstrated that an uncertainty analysis of model predictions is easily accomplished due to the low computational demand of the models and efficient calibration software and is absolutely worth the additional investment. Lastly, development of a systematic instrumentation that evaluates the distributed, temporal evolution of snowpack drainage is vital for optimal understanding and management of cold-climate hydrologic systems.
Infiltration under snow cover: Modeling approaches and predictive uncertainty
Groundwater recharge from snowmelt represents a temporal redistribution of precipitation. This is extremely important because the rate and timing of snowpack drainage has substantial consequences to aquifer recharge patterns, which in turn affect groundwater availability throughout the rest of the year. The modeling methods developed to estimate drainage from a snowpack, which typically rely on temporally-dense point-measurements or temporally-limited spatially-dispersed calibration data, range in complexity from the simple degree-day method to more complex and physically-based energy balance approaches. While the gamut of snowmelt models are routinely used to aid in water resource management, a comparison of snowmelt models’ predictive uncertainties had previously not been done. Therefore, we established a snowmelt model calibration dataset that is both temporally dense and represents the integrated snowmelt infiltration signal for the Vers Chez le Brandt research catchment, which functions as a rather unique natural lysimeter. We then evaluated the uncertainty associated with the degree-day, a modified degree-day and energy balance snowmelt model predictions using the null-space Monte Carlo approach. All three melt models underestimate total snowpack drainage, underestimate the rate of early and midwinter drainage and overestimate spring snowmelt rates. The actual rate of snowpack water loss is more constant over the course of the entire winter season than the snowmelt models would imply, indicating that mid-winter melt can contribute as significantly as springtime snowmelt to groundwater recharge in low alpine settings. Further, actual groundwater recharge could be between 2 and 31% greater than snowmelt models suggest, over the total winter season. This study shows that snowmelt model predictions can have considerable uncertainty, which may be reduced by the inclusion of more data that allows for the use of more complex approaches such as the energy balance method. Further, our study demonstrated that an uncertainty analysis of model predictions is easily accomplished due to the low computational demand of the models and efficient calibration software and is absolutely worth the additional investment. Lastly, development of a systematic instrumentation that evaluates the distributed, temporal evolution of snowpack drainage is vital for optimal understanding and management of cold-climate hydrologic systems.