Using Gravity Recovery and Climate Experiment data to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes

The amount of lying snow calculated by a land surface model depends in part on the amount of snowfall in the meteorological data that are used to drive the model. We show that commonly used data sets differ in the amount of snowfall, and more generally precipitation, over four large Arctic basins. An independent estimate of the cold-season precipitation is obtained by combining water balance information from the Gravity Recovery and Climate Experiment (GRACE) with estimates of evaporation and river discharge and is generally higher than that estimated by four commonly used meteorological data sets. We use the Joint UK Land Environment Simulator (JULES) land surface model to calculate the snow water equivalent (SWE) over the four basins. The modelled seasonal maximum SWE is 38 % less than observation-based estimates on average, and the modelled basin discharge is significantly underestimated, consistent with the lack of snowfall. We use the GRACE-derived estimate of precipitation to define per-basin scale factors that are applied to the driving data and increase the amount of cold-season precipitation by 28 % on average. In turn this increases the modelled seasonal maximum SWE by 30 %, although this is still underestimated compared to observations by 19 % on average. A correction for the undercatch of precipitation by gauges is compared with the the GRACE-derived correction. Undercatch correction increases the amount of cold-season precipitation by 23 % on average, which indicates that some, but not all, of the underestimation can be removed by implementing existing undercatch correction algorithms. However, even undercatch-corrected data sets contain less precipitation than the GRACE-derived estimate in some regions, and it is likely that there are other biases that are not currently accounted for in gridded meteorological data sets. This study shows that revised estimates of precipitation can lead to improved modelling of SWE, but much more modest improvements are found in modelled river discharge. By providing methods to better define the precipitation inputs to the system, the current study paves the way for subsequent work on key hydrological processes in high-latitude basins.

On the Uncertainty and Changeability of the Estimates of Seasonal Maximum Flows

A classical approach to flood frequency modeling is based on the choice of the probability distribution to best describe the analyzed series of annual or seasonal maximum flows. In the paper, we discuss the two main problems, the uncertainty and instability of the upper quantile estimates, which serve as the design values. Ways to mitigate the above-mentioned problems are proposed and illustrated by seasonal maximum flows at the Proszówki gauging station on the Raba River. The inverse Gaussian and generalized exponential distributions, which are not commonly used for flood frequency modeling, were found to be suitable for Polish data of seasonal peak flows. At the same time, the heavy tailed distributions, which are currently recommended for extreme hydrological phenomena modeling, were found to be inappropriate. Applying the classical approach of selecting the best fitted model to the peak flows data, significant shifts in the upper quantile estimates were often observed when a new observation was added to the data series. The method of aggregation, proposed by the authors, mitigates this problem. Elimination of distributions that are poorly fitted to the data series increases the stability of the upper quantile estimates over time.

The surface albedo of the Greenland Ice Sheet between 1982 and 2015, and its relationship to the ice sheet’s surface mass balance and ice discharge

The Greenland Ice Sheet is losing mass at a significant rate, primarily driven by increasing surface melt-induced runoff. Because the ice sheet’s surface melt is closely connected to changes in the surface albedo, studying multidecadal changes in the ice sheet’s albedo offers insight into surface melt and associated changes in its surface mass balance. Here, we first analyse the CLARA-A2 SAL satellite-based surface albedo dataset, covering 1982–2015, to obtain decadal albedo trends for each summer month. We also examine the rates of albedo change during the early summer, supported with atmospheric reanalysis data from MERRA-2, to discern changes in the intensity of early summer melt, and their likely drivers. We find that rates of albedo decrease during summer melt have accelerated during the 2000s relative to early 1980s, and that the surface albedos now often decrease to values typical of bare ice at elevations 50–100 m higher on the ice sheet. The southern margins exhibit the opposite behaviour, though, and we suggest this is due to increasing snowfall over the area. We then correct the mass balance estimates observed by the GRACE satellite mission with state-of-the-art ice discharge estimates to obtain observation-based estimates for the surface mass balance. The CLARA albedo changes are regressed with this data to obtain a proxy surface mass balance timeseries for the summer periods 1982–2015. This proxy timeseries is compared with latest regional climate model estimates from the MAR model. We show that the proxy timeseries agrees with MAR through the analyzed period within the associated uncertainties of the data and methods, demonstrating and confirming that surface runoff has dominated the rapid mass loss period between 1990s and 2010s. Finally, we extend the analysis to GrIS basin scale to examine discharge-albedo relationships in order to ascertain if the surface melt contributes to discharge acceleration via basal lubrication. While there is little evidence of surface melt-induced ice flow acceleration at annual timescales, we find time lags between seasonal maximum runoff production and seasonal maximum discharge rate to be in agreement with recent modelling results.

Using GRACE to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes

The amount of lying snow calculated by a land surface model depends in part on the amount of snowfall in the meteorological data that are used to drive the model. We show that commonly-used data sets differ in the amount of snowfall, and more generally precipitation, over four large Arctic basins. An independent estimate of the cold season precipitation is obtained by combining water balance information from the Gravity Recovery and Climate Experiment (GRACE) with estimates of evaporation and river discharge, and is generally higher than that estimated by four commonly-used meteorological data sets. We use the Joint UK Land Environment Simulator (JULES) land surface model to calculate the snow water equivalent (SWE) over the four basins. The modelled seasonal maximum SWE is 38 % less than observation-based estimates on average and the modelled basin discharge is significantly underestimated, consistent with the lack of snowfall. We use the GRACE-derived estimate of precipitation to define per-basin scale factors that are applied to the driving data and increase the amount of cold season precipitation by 28 % on average. In turn this increases the modelled seasonal maximum SWE by 30 %, although this is still underestimated compared to observations by 19 % on average. A correction for undercatch of precipitation by gauges is compared with the the GRACE-derived correction. Undercatch correction increases the amount of cold season precipitation by 23 % on average, which indicates that some, but not all, of the underestimation can be removed by implementing existing undercatch correction algorithms. However, even undercatch-corrected data sets contain less precipitation than the GRACE-derived estimate in some regions, and it is likely that there are other biases that that are not currently accounted for in gridded meteorological data sets. This study shows that revised estimates of precipitation can lead to improved modelling of SWE, but much more modest improvements are found in modelled river discharge. By providing methods to better define the precipitation inputs to the system, the current study paves the way for subsequent work on key hydrological processes in high-latitude basins.

Research on water interconnections within the Sar Mountains aquatorium by radioactive hydrogen isotope tritium

The waters of two lakes and two streams that dominate in the Sar Mountains aquatorium were analyzed. The tritium profile of soil was also recorded. All samples were taken at approximately the same time, together with samples of precipitation. During the period of taking samples, the ambient temperature conditions were taken into account for ten days around the time of sampling. Monitoring of the seasonal maximum with taking into account the obtained tritium soil profile and the temperature on and around the day of sampling unambiguously showed that all tested water in the Sar Mountains aquatorium is of atmospheric origin and as such unsuitable for any major transformation for commercial purposes.

Use of Non-Parametric Approaches on Normality of Hydrologic Variables

Parametric approaches in statistical analysis assume that any given data are normally distributed. Therefore, the test of whether this conventional assumption is valid should be made in this context of the available data’s normality before being passed to the application of statistical tests. The paper is focused on the normality methodologies commonly used in literature, named Kolmogorov-Smirnov, Jarque-Bera, D’agostino, Anderson Darling, Shapiro-Wilk and Ryan Joiner. In the study, the seasonal maximum data from eight streamflow gauging stations in Yesilirmak Basin was used as material. The normality in the 59% of the whole data sets were obtained as the highest result by the Kolmogorov –Smirnov approach, when compared to the other normality tests considered in the study.

A Comparison of Temperature Data from Automated and Manual Observing Networks in Georgia and Impacts of Siting Characteristics

The objectives of this study were to compare average monthly and seasonal maximum and minimum temperatures of the Georgia Automated Environmental Monitoring Network (AEMN) to those of geographically close (i.e., paired) manual observations from U.S. Historical Climatology Network (USHCN) stations and Cooperative Observer Program (COOP) stations for the period 2002–13, and to evaluate the extent to which differences in siting characteristics of paired AEMN–USHCN stations contribute to the temperature differences. Correlations for monthly and seasonal maximum and minimum temperatures of paired AEMN–USHCN and AEMN–COOP stations were high and almost always significant, although the correlations for seasonal minimum temperatures were slightly lower than those of maximum temperatures, especially for summer. Monthly maximum and minimum temperatures and seasonal maximum temperatures of paired AEMN–USHCN and AEMN–COOP stations were significantly different in only a few instances, while seasonal minimum temperatures were more often significantly different, particularly for summer. The stronger relationship between maximum temperatures than minimum temperatures for paired stations is logical given that minimum temperatures typically occur when a shallow, decoupled nocturnal boundary layer is more sensitive to local conditions [e.g., land use/land cover (LULC)]. Stepwise regressions confirmed that a portion of the variance of seasonal minimum temperatures of paired AEMN–USHCN stations was explained by differences in LULC, while the variance in seasonal maximum temperatures was explained better by differences in elevation. Despite the generally close relationships between temperatures of paired stations and a portion of the differences being explained, an abrupt change from manual networks to the AEMN without data adjustments would change the Georgia climate record on monthly and seasonal time scales.