Hydrological Measurements Research Articles

Retention time of lakes in the Larsemann Hills oasis, East Antarctica

Abstract. This study provides first estimates of the water transport timescale for five lakes located in the Larsemann Hills oasis (69∘23′ S, 76∘20′ E) in East Antarctica. We estimated lake retention time (LRT) as a ratio of lake volume to the inflow and outflow terms of a lake water balance equation. The LRT was evaluated for lakes of epiglacial and landlocked types, and it was assumed that these lakes are monomictic, with water exchange occurring during the warm season only. We used hydrological observations collected in four seasonal field campaigns to evaluate the LRT. For the epiglacial lakes Progress and Nella/Scandrett, the LRT was estimated at 12–13 and 4–5 years, respectively. For the landlocked lakes Stepped, Sarah Tarn and Reid, our results show a great difference in the LRT calculated from the outflow and inflow terms of the water balance equation. The LRTs for these lakes vary depending on the methods and errors inherent to them. We relied on the estimations from the outflow terms, since they are based on hydrological measurements with better quality. Lake Stepped exchanged water within 1.5 years. Sarah Tarn and Lake Reid are endorheic ponds, with water loss mainly through evaporation. Their LRTs were estimated as 21–22 and 8–9 years, respectively. To improve the LRT estimates, special hydrological observations are needed to monitor the lakes and streams during the warm season with a uniform observational programme.

Долгосрочное прогнозирование речного стока по спутниковым данным и сезонной информации

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different AI methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area (SCA) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.

Enhanced Long-term and Snow-based Streamflow Forecasting by Artificial Intelligent Methods Using Satellite Imagery and Seasonal Information

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.

Do ARMA Models Provide Better Gap Filling in Time Series of Soil Temperature and Soil Moisture? The Case of Arable Land in the Kulunda Steppe, Russia

The adoption of climate-smart agriculture requires the comprehensive development of environmental monitoring tools, including online observation of climate and soil settings. They are often designed to measure soil properties automatically at different depths at hour or minute intervals. It is essential to have a complete dataset to use statistical models for the prediction of soil properties and to make short-term decisions regarding soil tillage operations and irrigation during a vegetation period. This is also important in applied hydrological studies. Nevertheless, the time series of soil hydrological measurements often have data gaps for different reasons. The study focused on solving a problem of gap-filling in hourly time series of soil temperature and moisture, measured at the 30 cm depth using a weighted gravitation lysimeter station while meteorological data were recorded simultaneously by a weather station. The equipment was installed in the Kulunda Steppe in the Altai Krai, Russia. Considering that climate conditions affect soil temperature and moisture content directly, we did a comparative analysis of the gap-filling performance using the three imputation methods—linear interpolation, multiple linear regression, and extended ARMA (p,q) models with exogenous climatic variables. The results showed that, according to the minimum of the mean absolute error, ARMA (p,q) models with optimally selected order parameters, and an adaptive window, had some advantages compared to other single-imputation methods. The ARMA (p,q) model produced a good quality of gap-filling in time series with the mean absolute error of 0.19 °C and 0.08 Vol. % for soil temperature and moisture content, respectively. The findings supplemented the methodology of hydrological data processing and the development of digital tools for the online monitoring of climate and soil properties in agriculture.

Hydrologic evaluation of a poplar phytoextraction system

This paper reports the results of a seven-year study of a phytoremediation system consisting of 349 hybrid poplars (Populous deltoids x Populus nigra ‘DN-21’) that were emplaced to reduce the migration of impacted groundwater toward a nearby stream. For the relatively small tree stand, the achievement of hydraulic containment was challenged by several factors, including topographic constraints on tree placement, spatial heterogeneity in depth-to-groundwater, and lack of controls on surface runoff. However, the interpretation of measured diurnal water table fluctuations indicated substantial and increasing levels of groundwater extraction, consistent with poplar sap flow measurements collected during two growing seasons. The project also provided unique educational experiences for undergraduate and graduate students from multiple academic disciplines who participated in monitoring, site maintenance, and data interpretation. Novelty statement The paper adds to the limited literature on poplar-based phytoextraction of contaminated groundwater by describing a seven-year case study with detailed hydrologic measurements. Novel elements include (1) a very large data set of poplar sap flow measurements that provides insight into potential spatial and temporal variability, and (2) a detailed application of an under-utilized analytical tool (water table fluctuations) to evaluate hydraulic capture.

Subsurface Moisture Regulates Himalayan Groundwater Storage and Discharge

AbstractThrough the release of groundwater, most mountain rivers run year‐round despite their small catchments and sporadic precipitation. This makes mountain ranges important sources of reliable freshwater for downstream populations in many parts of the world. However, due to a lack of ground instrumentation, little is known about groundwater dynamics in mountainous landscapes. Recent research has shown that the amount of moisture trapped in the soil and weathered rocks in the vadose zone can significantly buffer groundwater recharge and runoff but the wider recognition of this effect on major mountain systems has not been yet established. In this study, we test whether the moisture reservoir has an impact on hydrological fluxes in a steep Himalayan catchment during three monsoon seasons. We measured an array of parameters including relative seismic velocity changes from ambient noise correlations. This noninvasive technique allows us to monitor groundwater dynamics in conjunction with classical hydrological measurements. We found that the moisture saturation in the vadose zone controls the onset of groundwater recharge and runoff and therefore determines the annual water availability supplied by monsoon precipitation. We model this dynamic using a surface layer that has a finite storage capacity that controls the connectivity of surface flux to groundwater. The extension of this concept, which is thought to apply widely in flat and undulating landscapes, to steep mountain topography with thin and discontinuous soils underlain by regolith and bedrock has important implications for mountain hydrology.

Spatial and temporal dynamics of nitrogen exchange in an upwelling reach of a groundwater‐fed river and potential response to perturbations changing rainfall patterns under UK climate change scenarios

AbstractWe report the complex spatial and temporal dynamics of hyporheic exchange flows (HEFs) and nitrogen exchange in an upwelling reach of a 200 m groundwater‐fed river. We show how research combining hydrological measurement, geophysics and isotopes, together with nutrient speciation techniques provides insight on nitrogen pathways and transformations that could not have been captured otherwise, including a zone of vertical preferential discharge of nitrate from deeper groundwater, and a zone of rapid denitrification linking the floodplain with the riverbed. Nitrate attenuation in the reach is dominated by denitrification but is spatially highly variable. This variability is driven by groundwater flow pathways and landscape setting, which influences hyporheic flow, residence time and nitrate removal. We observed the spatial connectivity of the river to the riparian zone is important because zones of horizontal preferential discharge supply organic matter from the floodplain and create anoxic riverbed conditions with overlapping zones of nitrification potential and denitrification activity that peaked 10–20 cm below the riverbed. Our data also show that temporal variability in water pathways in the reach is driven by changes in stage of the order of tens of centimetres and by strength of water flux, which may influence the depth of delivery of dissolved organic carbon. The temporal variability is sensitive to changes to river flows under UK climate projections that anticipate a 14%–15% increase in regional median winter rainfall and a 14%–19% reduction in summer rainfall. Superimposed on seasonal projections is more intensive storm activity that will likely lead to a more dynamic and inherently complex (hydrologically and biogeochemically) hyporheic zone. We recorded direct evidence of suppression of upwelling groundwater (flow reversal) during rainfall events. Such flow reversal may fuel riverbed sediments whereby delivery of organic carbon to depth, and higher denitrification rates in HEFs might act in concert to make nitrate removal in the riverbed more efficient.

Long-Term Monitoring Reveals Differential Responses of Mussel and Host Fish Communities in a Biodiversity Hotspot

Biodiversity hotspots can serve as protected areas that aid in species conservation. Long-term monitoring of multiple taxonomic groups within biodiversity hotspots can offer insight into factors influencing their dynamics. Mussels (Bivalvia: Unionidae) and fish are highly diverse and imperiled groups of organisms with contrasting life histories that should influence their response to ecological factors associated with local and global change. Here we use historical and contemporary fish and mussel survey data to assess fish and mussel community changes over a 33 year period (1986–2019) and relationships between mussel abundance and their host fish abundance in Bogue Chitto Creek, a tributary of the Alabama River and a biodiversity hotspot. Mussel abundance declined by ~80% and community composition shifted, with eight species previously recorded not found in 2019, and a single individual of the endangered Pleurobema decisum. Fish abundances increased and life history strategies in the community appeared stable and there was no apparent relationship between mussel declines and abundance of host fish. Temporal variation in the proportion of life history traits composing mussel assemblages was also indicative of the disturbances specifically affecting the mussel community. However, changes and declines in mussel assemblages in Bogue Chitto Creek cannot be firmly attributed to any specific factor or events because of gaps in historical environmental and biological data. We believe that mobility differences contributed to differential responses of fish and mussel communities to stressors including habitat degradation, recent droughts and invasive species. Overall, our work indicates that monitoring biodiversity hotspots using hydrological measurements, standardized survey methods and monitoring invasive species abundance would better identify the effects of multiple and interactive stressors that impact disparate taxonomic groups in freshwater ecosystems.

The Presence and Widespread Distribution of Dark Sediment in Greenland Ice Sheet Supraglacial Streams Implies Substantial Impact of Microbial Communities on Sediment Deposition and Albedo

AbstractMelting on the Greenland Ice Sheet leads to extensive supraglacial stream networks. These streams accumulate low‐albedo sediments disproportionately contribute to melting. Studies analyzing supraglacial sediment distribution and hydrodynamic properties are rare. Here, we examine a 130‐m supraglacial stream reach in southwest Greenland using drone imagery, bathymetry, and hydrology measurements from 2017. Sediment covered 24% of the channel and had a mass‐median diameter of 0.027 mm. We applied calculations of critical Shields stress to determine the minimum water depth needed to initiate sediment movement. In order for theoretical critical water depths to match observed depths, sediment grains would need to be 2.48 mm (near the grain size for cryoconite granules) indicating that microbial growth within sediment caused extensive flocculation. Without flocculation, sediment would flush out of floodplains and supraglacial streams would have significantly higher albedos. Supraglacial stream albedo might therefore be sensitive to changing stream chemistry, temperature, and meltwater supply.

A Comparative Study Between Artificial Neural Networks and Fuzzy Inference System for Estimation and Filling of Missing Runoff Data at Al-Jawadiyah Station

Runoff is one of the most important components of the hydrological cycle, and having complete series of runoff data is essential for any hydrological modelling process. This study compares artificial neural networks and fuzzy inference systems for estimation of runoff data at the Al-Jawadiyah hydrometric station. This study used only the runoff data at Al-Jawadiyah station in addition to the runoff values measured at Al-Amiri station on the Syrian-Lebanese border. Many experiments were conducted, and a very large number of artificial neural networks were trained with changing the number of hidden layers, the number of neurons, and the training algorithms until the best network was reached according to the regression criteria and the root mean of the error squares between the measured values and the predicted values. Also, many fuzzy inference models have been prepared, changing the number and type of membership functions until the most accurate model has been reached. The results showed the high reliability of both the artificial neural network models and the fuzzy inference models in estimating runoff in the study area, and the comparison between the results showed the great convergence of the two models with a slight preference for the fuzzy models. This study recommends using the rest of the artificial intelligence models and comparing them to arrive at the most accurate model. This helps prepare a complete series of hydrological and climatic measurements that form a basis for preparing an accurate hydrological model for the study area.

Estimation and filling of missing runoff data at Al-Jawadiyah station using artificial neural networks

Runoff is one of the most important components of the hydrological cycle, and having complete series of runoff data is essential for any hydrological modelling process. This study aims to estimate the runoff at Al-Jawadiyah hydrometric station using artificial neural networks. This study used only the runoff data at Al-Jawadiyah station in addition to the runoff values measured at Al-Amiri station on the Syrian-Lebanese border. Many experiments were conducted and a very large number of artificial neural networks were trained with changing the number of hidden layers, the number of neurons and the training algorithms until the best network was reached according to the regression criteria and the root mean of the error squares between the measured values and the predicted values, and the network (2:12:1) was adopted in the process of filling the gaps in the runoff time series at Al-Jawadiyah station during the study period. This study recommends working on preparing complete series of hydrological and climatic measurements that form a basis for preparing an accurate hydrological model for the study area.

Project and Control Allocation of a 3 DoF Autonomous Surface Vessel With Aerial Azimuth Propulsion System

To gather hydrological measurements is a difficult task for Autonomous Surface Vessels. It is necessary for precise navigation considering underwater obstacles, shallow and fast water flows, and also mitigate misreadings due to disturbs caused by their propulsion system. To deal with those problems, this paper presents a new topology of an Autonomous Surface Vessel (ASV) based on a catamaran boat with an aerial propulsion system with azimuth control. This set generates an over-actuated 3 Degree of Freedom (DoF) ASV, highly maneuverable and able of operating over the above-mentioned situations. To deal with the high computational cost of the over-actuated control allocation (CA) problem, this paper also proposes a Fast CA (FCA) approach. The FCA breaks the initial nonlinear system into partially-dependent linear subsystems. This approach generates smaller connected systems with overlapping solution spaces, generating fast and robust convergence, especially attractive for embedded control devices. Both proposals, i.e., ASV and FCA, are assessed through mathematical simulations and real scenarios.

Study of high-frequency acoustic current tomography

<p indent="0mm">The sea-land interface is the most concentrated region of human marine development activities, and online monitoring of river current is very important for material flux and ecosystem evolution. In this study, the high-frequency acoustic current tomography of sea-land interface was studied. Based on the principle of acoustic reciprocity, the acoustic inversion algorithm of current was investigated. The experiment was carried out on the Jiulongjiang sea-land interface in Zhangzhou. The high-frequency transducer, with a center frequency of <sc>60 kHz</sc> and bandwidth of <sc>40–80 kHz,</sc> was used. According to the sound speed profile and underwater topographic survey, ray acoustic simulation was performed to reveal high-frequency sound propagation. The experimental results showed that the acoustic signal has high-signal amplitude and peak value of cross-correlation, which can be used to reconstruct the current of the river. Real-time monitoring of the river current during the typhoon “haibeisi” was achieved. The results showed that high-frequency acoustic tomography can be applied to the study of hydrodynamics of sea-land interface, hydrological measurement, and monitoring of extreme events, such as flood peak and heavy rainfall.

Case Study: Reconstruction of Runoff Series of Hydrological Stations in the Nakdong River, Korea

Reliable runoff series is sine qua non for flood or drought analysis as well as for water resources management and planning. Since observed hydrological measurement such as runoff can sometimes show abnormalities, data quality control is necessary. Generally, the data of adjacent hydrological stations are used. However, difficulties are frequently encountered when runoff series of the adjacent stations have different flow characteristics. For instance, when the correlation between the up- and downstream locations in which the stations are located is used as the main criterion for quality control, difficulties can occur. Therefore, this study aims to suggest a method to reconstruct an abnormal daily runoff series in the Nakdong River, Korea. The variational mode decomposition (VMD) technique is applied to the runoff series of the three target stations: Goryeong County (Goryeong bridge) and Hapcheon County (Yulji bridge and Jeogpo bridge). These runoff series are also divided into several intrinsic mode functions (IMFs) that are governed by basin runoff and disturbed flow caused by the hydraulic structure. The decomposition results based on VMD show that the runoff components in a particular station that is influenced by hydraulic structures could be reconstructed using adjacent stations, but the residual mode could not. The runoff reconstruction model using an artificial neural network (ANN), the two “divided” modes, and the residual component is established and applied to the runoff series for the target station (Yulji bridge in Hapcheon County). The reconstructed series from the model show relatively good results, with R2 = 0.92 and RMSE = 99.3 in the validation year (2019). Abnormal runoff series for 2012 to 2013 at the Yulji bridge station in Hapcheon County are also reconstructed. Using the suggested method, a well-matched result with the observations for the period from 2014 onwards is produced and a reconstructed abnormal series is obtained.

Impact of an urban area on the dynamics and features of suspended solids transport in a small catchment during floods

The paper concerns the impact of the diverse land use in the Silnica river catchment, within the city of Kielce, on the size and dynamics of concentration and load of transported suspended solids, and the variability of the mineral particles mean grain size, during various flood types. This small river flows through the centre of Kielce, the city of 200,000 population in Poland. In its course, the river flows through areas of various land use. In the upper reaches, the catchment is dominated by forests, in the middle the river flows through a reservoir and the lower – through an urban area. Hydrological measurements and transport of the suspended solids were carried out simultaneously in three hydrometric cross-sections of the Silnica, which close the sub-catchments of the land use as mentioned above. The investigations were carried out during floods of various origins: induced by snowmelt, torrential rainfall and frontal rainfall. The analysis indicates that the two rainfall-induced flood types, characteristic for the summer season, show high runoff dynamics. In contrast, snowmelt-induced floods show high concentration and load of the transported suspended solids. In the section of the watercourse flowing through the city, there is an abrupt increase in runoff and suspended solids concentration. Thus, there may be periodic threats to fish and invertebrates, leading to a reduction in the population of living organisms.

From Patch to Catchment: A Statistical Framework to Identify and Map Soil Moisture Patterns Across Complex Alpine Terrain

Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (102-&amp;gt;103 m) and microtopography (&amp;lt;100-102 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems.

Changes in hillslope hydrology in a perched, shallow soil system due to clearcutting and residual biomass removal

AbstractSustainable fuels legislation and volatility in energy prices have put additional pressures on the forestry sector to intensify the harvesting of biomass for “advanced biofuel” production. To better understand how residual biomass removal after harvest affects forest hydrology in relatively low slope terrain, a Before‐After‐Control‐Impact (BACI) study was conducted in the USDA Forest Service's Marcell Experimental Forest, Minnesota, USA. Hydrological measurements were made from 2010–2013 on a forested hillslope that was divided into three treatment blocks, where one block was harvested and residual biomass removed (Biomass Removed), the second was harvested and residual biomass left (Biomass Left), and the last block was left as an Unharvested Control. The pre‐harvest period (2 years) was 2010–11 and post‐harvest (2 years) was 2012–13. Water table elevation at the upslope and downslope position, subsurface runoff, and soil moisture were measured between May–November. Mixed effect statistical models were used to compare both the before‐after and “control” treatment ratios (ratios between harvested hillslopes and the Unharvested Control hillslope). Subsurface runoff significantly increased (p &lt; .05) at both harvested hillslopes but to a greater degree on the Biomass Left hillslope. Greater subsurface runoff volumes at both harvested hillslopes were driven by substantial increases during fall, with additional significant increases during summer on the Biomass Left hillslope. The hydrological connectivity, inferred from event runoff ratios, increased due to harvesting at both hillslopes but only significantly on the Biomass Left hillslope. The winter harvest minimized soil disturbance, resulting in no change to the effective hydraulic conductivity distribution with depth. Thus, the observed hydrological changes were driven by increased effective precipitation and decreased evapotranspiration, increasing the duration that both harvested hillslopes were hydrologically active. The harvesting of residual biomass appears to lessen hydrological connectivity relative to leaving residual biomass on the hillslope, potentially decreasing downstream hydrological impacts of similar forestry operations.

Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data

AbstractTo fill the observations gap on ungauged streams, crowdsourced distributed hydrologic measurements were considered as a potential supplement for observational data networks. However, citizen science data come with uncertainty as they are provided by the general public. In order to investigate this uncertainty, a decision tree methodology was applied to evaluate existing citizen science data of stream stage based on the CrowdHydrology (CH) network. Quality control (QC) flags were developed and applied to CH sites, dividing Level 1 dataset (raw dataset) into Level 2 (flagged dataset) and Level 3 (processed dataset). Error estimates were calculated to determine uncertainty in the citizen science data. The results indicate that the decision tree could provide reliable QC for citizen science data and demonstrate how uncertainty can be quantified in the QC datasets.

Twelve‐Year Dynamics and Rainfall Thresholds for Alternating Creep and Rapid Movement of the Hooskanaden Landslide From Integrating InSAR, Pixel Offset Tracking, and Borehole and Hydrological Measurements

AbstractThe Hooskanaden landslide is a large (~600 m wide × 1,300 m long), deep (~30 – 45 m) slide located in southwestern Oregon. Since 1958, it has had five moderate/major movements that catastrophically damaged the intersecting U.S. Highway 101, along with persistent slow wet‐season movements and a long‐term accelerating trend due to coastal erosion. Multiple remote sensing approaches, borehole measurements, and hydrological observations have been integrated to interpret the motion behaviors of the slide. Pixel offset tracking of both Sentinel‐1 and Sentinel‐2 images was carried out to reconstruct the 3‐D displacement field of the 2019 major event, and the results agree well with field measurements. A 12‐year displacement history of the landslide from 2007 to 2019 has been retrieved by incorporating offsets from Light Detection and Ranging (LiDAR) digital elevation model (DEM) gradients and Interferometric Synthetic Aperture Radar (InSAR) processing of ALOS and Sentinel‐1 images. Comparisons with daily/hourly ground precipitation reveal that the motion dynamics are predominantly controlled by intensity and temporal pattern of rainfall. A new empirical threefold rainfall threshold was therefore proposed to forecast the dates for the moderate/major movements. This threshold relies upon antecedent water‐year and previous 3‐day and daily precipitation and was able to represent observed movement periods well. Adaptation of our threshold methodology could prove useful for other large, deep landslides for which temporal forecasting has long been generally intractable. The averaged characteristic hydraulic conductivity and diffusivity were estimated as 6.6 × 10−6 m/s and 6.6 × 10−4 m2/s, respectively, based on the time lags between rainfall pulses and slide accelerations. Hydrologic modeling using these parameters helps to explain the ability of the new rainfall threshold.

Snowmelt causes different limitations on transpiration in a Sierra Nevada conifer forest

Snowmelt is a major source for summer soil water storage that supports tree growth in the Mediterranean climate of the Sierra Nevada, California. The timing and magnitude of snow-water contributions to soil moisture are controlled by energy and water availability that varies based on climate (and thus weather) and topography (i.e. elevation, slope, and aspect). Few studies, thus far, have explored how transpiration rate is affected by differences in the timing and magnitude of snowmelt across years and locations. Here we used meteorological and hydrological measurements on north and south aspects in the Sagehen Creek catchment (Sierra Nevada, California) to investigate the controls on the timing of conifer sap flow, a proxy for transpiration rate, in response to the timing and magnitude of snowmelt. We found that sap flow prior to its seasonal peak in July was largely controlled by air temperature and that spring sap flow was reduced by cloudy conditions. Years with earlier snow disappearance caused earlier peak sap flow, which was nearly coincident with the beginning of soil moisture limitations than years with later snow disappearance. Because Sagehen Creek receives little summer rainfall, years with earlier snowmelt and peak sap flow had more total days with soil moisture limitation on sap flow. Overall, our results suggest that earlier snow disappearance and more spring rain (both expected to result from climate change) will potentially shift the peak of sap flow earlier in the growing season and increase the duration of water limitations during summer. These insights about the timing and duration of water limitations could be used as benchmarks to test physically based models and better constrain predictions of transpiration rates in snow-dominated landscapes similar to the study site.