Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.     

The June 2013 Alberta catastrophic flooding event – part 2: fine‐scale precipitation and associated features

Data obtained from a variety of sources including the Canadian Lightning Detection Network, weather radars, weather stations and operational numerical weather model analyses were used to address the evolution of precipitation during the June 2013 southern Alberta flood. The event was linked to a mid‐level closed low pressure system to the west of the region and a surface low pressure region initially to its south. This configuration brought warm, moist unstable air into the region that led to dramatic, organized convection with an abundance of lightning and some hail. Such conditions occurred in the southern parts of the region whereas the northern parts were devoid of lightning. Initially, precipitation rates were high (extreme 15‐min rainfall rates up to 102 mm h^?1 were measured) but decreased to lower values as the precipitation shifted to long‐lived stratiform conditions. Both the convective and stratiform precipitation components were affected by the topography. Similar flooding events, such as June 2002, have occurred over this region although the 2002 event was colder and precipitation was not associated with substantial convection over southwest Alberta. Copyright © 2016 Her Majesty the Queen in Right of Canada. Hydrological Processes. © John Wiley & Sons, Ltd.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Trend and extreme occurrence of precipitation in a mid‐latitude Eurasian steppe watershed at various time scales

The confounding effects of step change invalidate the stationarity assumption of commonly used trend analysis methods such as the Mann–Kendall test technique, so previous studies have failed to explain inconsistencies between detected trends and observed large precipitation anomalies. The objectives of this study were to (1) formulate a trend analysis approach that considers nonstationarity due to step changes, (2) use this approach to detect trends and extreme occurrences of precipitation in a mid‐latitude Eurasian steppe watershed in North China, and (3) examine how runoff responds to precipitation trends in the study watershed. Our results indicate that annual precipitation underwent a marginal step jump around 1995. The significant annual downward trend after 1994 was primarily due to a decrease in summer rainfall; other seasons exhibited no significant precipitation trends. At a monthly scale, July rainfall after 1994 exhibited a significant downward trend, whereas precipitation in other months had no trend. The percentage of wet days also underwent a step jump around 1994 following a significant decreasing trend, although the precipitation intensity exhibited neither a step change nor any significant trend. However, both low‐frequency and high‐frequency precipitation events in the study watershed occurred more often after than before 1994; probably as either a result or an indicator of climate change. In response to these precipitation changes, the study watershed had distinctly different precipitation‐runoff relationships for observed annual precipitations of less than 300 mm, between 300 and 400 mm, and greater than 400 mm. Copyright © 2013 John Wiley & Sons, Ltd.     

Rainfall partitioning through a mixed cedar swamp and associated C and N fluxes in Southern Ontario,Canada

Partitioning of rainfall through a forest canopy into throughfall, stemflow, and canopy interception is a critical process in the water cycle, and the contact of precipitation with vegetated surfaces leads to increased delivery of solutes to the forest floor. This study investigates the rainfall partitioning over a growing season through a temperate, riparian, mixed coniferous‐deciduous cedar swamp, an ecosystem not well studied with respect to this process. Seasonal throughfall, stemflow, and interception were 69.2%, 1.5%, and 29.3% of recorded above‐canopy precipitation, respectively. Event throughfall ranged from a low of 31.5 ± 6.8% for a small 0.8‐mm event to a high of 82.9 ± 2.4% for a large 42.7‐mm event. Rain fluxes of at least 8 mm were needed to generate stemflow from all instrumented trees. Most trees had funnelling ratios <1.0, with an exponential decrease in funnelling ratio with increasing tree size. Despite this, stand‐scale funnelling ratios averaged 2.81 ± 1.73, indicating equivalent depth of water delivered across the swamp floor by stemflow was greater than incident precipitation. Throughfall dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) averaged 26.60 ± 2.96 and 2.02 ± 0.16 mg L^?1, respectively, which were ~11 and three times above‐canopy rain levels. Stemflow DOC averaged 73.33 ± 7.43 mg L^?1, 35 times higher than precipitation, and TDN was 4.45 ± 0.56 mg L^?1, 7.5 times higher than rain. Stemflow DOC concentration was highest from Populus balsamifera and TDN greatest from Thuja occidentalis trees. Although total below‐canopy flux of TDN increased with increasing event size, DOC flux was greatest for events 20–30 mm, suggesting a canopy storage threshold of DOC was readily diluted. In addition to documenting rainfall partitioning in a novel ecosystem, this study demonstrates the excess carbon and nitrogen delivered to riparian swamps, suggesting the assimilative capacity of these zones may be underestimated.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.     

A novel approach for estimating karst groundwater recharge in mountainous regions and its application in Switzerland

A pragmatic and simple approach for estimating the groundwater recharge of karst aquifers in mountainous regions by extrapolation of the hydrological regimes of gauged and well‐documented systems is presented. Specific discharge rates are derived using annual precipitation and spring measurements by taking into account catchment size and elevation, which are assumed to be the dominant factors. Reference sites with high data reliability are used for calibration and regional extrapolation. This is performed with normalized values employing spatial precipitation deviations and correlation with the elevation of the catchment areas. A tiered step procedure provides minimum and maximum normalized gradients for the relationship between recharge quantity and elevation for karst regions. The normalized recharge can therefore be obtained and extrapolated for any location using the spatial precipitation variability to provide an estimate of annual groundwater recharge. The approach was applied to Switzerland (approximately 7500 km² of karst terrain situated between 200 and over 4000 m a.s.l.) using annual precipitation data from meteorological stations for the years 2000 to 2011. Results show that the average recharge rates of different Swiss karst domains range from 20 to 46 L/km²s, which corresponds to an infiltration ratio between 0.6 and 0.9 of total precipitation. Despite uncertainties inherent in the approach, these results provide a benchmark for renewable karst groundwater resources in Switzerland of about 8.4 km³/year. The approach can be applied to any other mountainous karst region, that is, where a clear relationship between elevation, precipitation and recharge can be assumed. Copyright © 2015 John Wiley & Sons, Ltd.     

Using stable isotopes to understand seasonal and interannual dynamics in moisture sources and atmospheric circulation in precipitation

Stable isotopes in precipitation are useful tracers to strengthen understanding of climate change and hydrological processes. In this study, the moisture sources of 190 precipitation events in Beijing were analysed using the Hybrid Single‐particle Lagrangian Integrated Trajectory model, based on which we studied the relation between variations in precipitation δ¹⁸O and dynamics in moisture sources and atmospheric circulation in seasonal and interannual timescales. Categorization of 7 groups of moisture sources was performed, among which oceanic moisture sources presented lower δ¹⁸O in precipitation than continental moisture sources. The results show that seasonal variations of precipitation δ¹⁸O were caused by changes of moisture sources. In summer, moisture from proximal oceans dominated vapour transport to Beijing due to increasing monsoon strength and resulted in a relatively small variation of precipitation δ¹⁸O. At the interannual timescale, the variations of δ¹⁸O in summer precipitation were related to dynamics in oceanic moistures, showing depleted values when the contribution of oceanic moistures, especially the proportion of long‐distance oceanic moisture, was high. Further analysis indicated that changes of oceanic moisture sources were controlled by the strength of summer monsoons. These findings address the complexity of moisture sources in midlatitude monsoon areas and suggest that isotopic signals in precipitation have the potential to deduce changes in moisture sources and atmospheric circulation and can therefore serve for palaeoclimate reconstruction.     

Temporal stability of soil moisture in an oasis of northwestern China

Temporal stability of soil moisture has been widely used in hydrological monitoring since it emerged. However, the spatial analysis of temporal stability at the landscape scale is often limited because of insufficient sampling numbers. This work made an effort to investigate the spatial variations of temporal stability of soil moisture in an oasis landscape. The specific objectives of the study were to explore the spatial patterns of temporal stability and to determine the controlling factors of temporal stability in the desert oasis. A time series of soil moisture measurements were gathered on 23 occasions at 118 locations over 3 years in a rectangular transect of approximately 100 km². The nonparametric Spearman's rank correlation coefficient, standard deviation of relative difference (SDRD), and mean absolute bias error (MABE) were used to quantify the temporal stability of soil moisture. Results showed that the temporal stability of soil moisture was depth dependent and season dependent. The spatial pattern of soil moisture in a deep soil layer and between two same seasons generally had a high temporal stability. SDRD and MABE were spatially autocorrelated and exhibited strong spatial structures in the geographic space. The concept of temporal stability can be extended to describe the time‐stable areas of soil moisture with geostatistics. There were great differences between SDRD and MABE in describing the temporal stability of soil moisture and in identifying the controlling factors of temporal stability. In this case, MABE was a better alternative to estimate the areal mean soil moisture using representative locations than SDRD. Land use type, soil moisture condition, and soil particle composition were the dominant controls of temporal stability in the oasis. These insights could help to better understand the essence of temporal stability of soil moisture in arid regions.