Journal of Hydrology X最新文献

River water temperature measurement networks suffer from an inadequate spatial coverage and a lack of data. No methods exist for the regional estimation of river water temperature at ungauged sites based on data series from gauged sites. The development of such methods is hence of significant importance. It is proposed in this study to develop a Temperature-Duration-Curve (TDC) based method to estimate river water temperature at ungauged sites on a real-time basis. A Generalised Additive Model (GAM) based method is used to estimate TDCs at ungauged sites. The estimated TDCs are then used in combination with a spatial interpolation method to obtain daily temperature estimates at ungauged sites. Results are compared with a simple method based on the geographical distance weighted average of neighboring stations. The approaches are applied to 126 river thermal stations located on Atlantic salmon rivers in eastern Canada. Leave-one-out cross validation results indicate that the TDC based methods are robust and outperform the geographical distance weighted method.

Water temperature plays a primary role in driving ecological processes in streams due to its direct impact on biogeochemical cycles and the physiological processes of stream fauna, such as growth, development, and the timing of life history events. Streams influenced by snowpack melt are generally cooler in the summer and demonstrate less sensitivity to climate variability in what is commonly referred to as “climate buffering”. Despite the substantial influence of snowpack on stream temperature and expected changes in snowpack accumulation and melt timing with climate change, methods for representing snowpack in statistical models for stream temperature have not been well explored. In this investigation, we quantified the extent of stream temperature buffering in free-flowing streams across a geographically diverse region in the Pacific Northwest USA. We demonstrated that statistical models of daily mean stream temperature can be improved by explicitly accounting for temporal variability in a small number of climate covariates believed to be mechanistically related to stream temperature. Our novel statistical approach included as predictors combinations and interactions between the following variables: (1) air temperature, (2) lagged air temperature (where the lag duration varied according to its relationship with flow on a given day at that site), (3) flow, (4) snowpack in the upstream catchment, and (5) day of year. We found that sites with substantial snow influence were associated with increased air temperature buffering during the warm season and longer air temperature lags (>30 days during spring high flows and ∼ 10 days during late summer low flows) compared to sites where precipitation predominantly fell as rain (<6 days year-round). By accounting for snowpack and temporal variation in lagged heat transfer processes, our models were able to accurately predict seasonal patterns and interannual variability in stream temperature in validation data from years not used in model fits using publicly available data sources (average RMPSE ∼ 0.80).

Data from a 389 km² watershed and one of its 14.5 km² subbasins were used to assess the effects of sampling frequency on the estimation accuracy of the exceedance frequency (EF) for suspended solids and nitrate-nitrogen in streams. Values of EF estimated from 17 subsampling schemes were compared with the actual EF (EF_a) at different threshold concentrations. The coefficient of variation and relative bias were used to measure the estimation accuracy. Results indicated that the EF_a of the larger watershed was much lower than that of the smaller watershed for both suspended solids and nitrate-nitrogen. We also found that EF_a can be modeled as an exponential function of the threshold concentration. For the EF estimations, the coefficient of variation decreased with increasing sampling frequency and increasing EF_a. The relative bias tended to be negative when EF_a was low or the threshold concentration was high, reaching -75% in some cases. This result implies that reported EF values based on low-frequency data could be severely underestimated due to the high possibility of missing large events. However, there were also cases with positive relative bias, implying overestimation of EF due to over representation of large events. These findings can be used to determine adequate sampling frequencies for water-quality parameters, avoiding common observed biases (mostly negative) in the estimation of EF for extreme pollution events.

Laura K. Lautz is a premier mentor, collaborator, and researcher at the intersection of natural hydrologic systems and humans. Her research has shifted the paradigm around measuring and understanding the impacts of surface water and groundwater interactions across spatial and temporal scales. She has done this by testing and refining new methods and by collaborating with, training, supporting, and mentoring diverse scientists. Here, we review her research across five themes, summarizing the prior status of the field, what Lautz contributed, as well as new directions in the field inspired by her work. Lautz’s research expanded our understanding of the impacts of stream restoration on surface water-groundwater interactions, where she tested new field methods and showed that restoration structures increase hyporheic exchange, locally altering biogeochemical function of the streambed. She refined novel methods for measuring surface water-groundwater exchanges and worked to make these methods easily accessible through freely available software. Her research group greatly expanded the use of heat as a quantitative tracer of hydrologic processes via the well-used VFLUX and HFLUX programs. Her research evaluated the impacts of surface water-groundwater interactions in urban streams, showing the substantial fluxes of nutrients and chloride that can move through those exchanges and the potential for groundwater to help buffer contamination. To assess groundwater impacts on streamflow below tropical glaciers, she used a wide range of field methods to reveal the sensitivity of these systems to climate change. Finally, she built tools to quantify natural brine contamination of drinking water wells in areas that may later be subject to high-volume hydraulic fracturing, creating a needed ‘pre-fracking’ dataset. Through this process, she identified multiple sources of salinity that are already reaching wells in these systems. Overall, this research has been done with a focus on mentoring and training the next generation of hydrologists, including work to specifically train for careers beyond academia, and facilitating early career scientists to realize their innate potentials. With former trainees in careers across industry, government, and academia, Dr. Laura K. Lautz is now working to build cross-disciplinary research at even larger scales, across federal research units, guaranteeing that an even larger impact on hydrology is still to come.

As glaciers across the world continue to recede, there is a concern that their loss as a fresh water reservoir within mountainous basins will have a negative impact on stream temperatures and downstream water resources. Currently, there are relatively few glacio-hydrological models (GHMs) appropriate to study such phenomena and studies that have used GHMs generally acknowledge the high uncertainty associated with their simulations. Calibration techniques present a particular issue in GHMs as available glacier observations are limited and errors in the glacierized portion of a basin can be compensated by errors in the non-glacierized portion. Using as a study site the Cheakamus Basin in British Columbia, Canada, we 1) present a new, fully-coupled GHM, 2) analyze the effects different calibration techniques have on the model’s summer streamflow projections, and 3) compare the fully-coupled GHM results to projections using a one-way GHM. The calibration techniques studied vary in terms of glacier representation (dynamic/static), and glacier constraint (mass balance/thinning rates/thinning rates and area change). We find projected future climate forcings are sufficiently strong in the Cheakamus Basin so as to generally make the sign and significance of changes to the basin’s hydrology insensitive to the calibration and projection procedures studied. However, the variation among these procedures produces significant changes in the projected magnitude of future hydrological changes and therefore should be carefully considered in studies where precision beyond the sign and significance of change is required. Based on analysis of the variation within each procedure’s set of model outputs, we conclude 1) the two-way GHM has benefits over the one-way model, 2) calibration using dynamic glaciers and a thinning rate constraint is preferable for the new GHM, and 3) there is a need for additional studies on the uncertainties associated with the calibration of glacio-hydrological models.

The Publisher regrets that this article is an accidental duplication of an article that has already been published in Journal of Hydrology, Volume 610, July 2022, 127955, https://doi.org/10.1016/j.jhydrol.2022.127955. The duplicate article has therefore been withdrawn.

The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

Extreme precipitation heavily affects society and economy in Africa because it triggers natural hazards and contributes large amounts of freshwater. Understanding past changes in extreme precipitation could help us improve our projections of extremes, thus reducing the vulnerability of the region to climate change. Here, we combine high-resolution satellite data (1981–2019) with a novel non-asymptotic statistical approach, which explicitly separates intensity and occurrence of the process. We investigate past changes in extreme daily precipitation amounts relevant to engineering and risk management. Significant ($\alpha =0.05$) positive and negative trends in annual maximum daily precipitation are reported in ∼20 % of Africa both at the local scales (0.05°) and mesoscales (1°). Our statistical model is able to explain ∼90% of their variance, and performs well (72% explained variance) even when annual maxima are explicitly censored from the parameter estimation. This suggests possible applications in situations in which the observed extremes are not quantitatively trusted. We present results at the continental scale, as well as for six areas characterized by different climatic characteristics and forcing mechanisms underlying the ongoing changes. In general, we can attribute most of the observed trends to changes in the tail heaviness of the intensity distribution (25% of explained variance, 38% at the mesoscale), while changes in the average number of wet days only explain 4% (12%) of the variance. Low-probability extremes always exhibit faster trend rates than annual maxima (∼44% faster, in median, for the case of 100-year events), implying that changes in infrastructure design values are likely underestimated by approaches based on trend analyses of annual maxima: flexible change-permitting models are needed. No systematic difference between local and mesoscales is reported, with locally-varying impacts on the areal reduction factors used to transform return levels across scales.