Journal of Hydrology X最新文献

Krypton-85 and other radioactive noble gases are widely used for groundwater dating purposes. ⁸⁵Kr analysis require large volumes of water to reach the analytical requirements. Conventionally, this water is pumped to the surface to be degassed with a gas extraction system. The large pumping rate may disturb the natural flow field and requires substantial field logistics. Hence, we propose a new in-situ degassing method, in which membrane contactors are used to degas the groundwater directly in the well and gas is collected at the surface. This way, field work is facilitated, groundwater system disturbance is minimized, and the gas sample is collected at a specific depth. We demonstrate the tightness of the system regarding atmospheric air contamination for a collection times of 24 h, which is sufficient for both low-level counting and laser-based counting methods for ⁸⁵Kr. The minimal borehole diameter is 7.5 cm for the prototype presented in this research but can easily be reduced to smaller diameters. In a case study, we compare the results obtained with the new passive method with those from a conventional packer setup sampling. Additionally, ³H/³He samples were collected for both sampling regimes and the dating results were compared with those from ⁸⁵Kr. A good agreement between tracer ages is demonstrated and the age stratigraphy is consistent with the expected age distribution for a porous unconfined aquifer. In addition, our study emphasizes the differences between the age information sampled with various methods. In conclusion, we demonstrate that the new in situ quasi-passive method provides a more representative age stratigraphy with depth in most cases.

Anthropogenic activities are altering flood frequency-magnitude distributions along many of the world’s large rivers. Yet isolating the impact of any single factor amongst the multitudes of competing anthropogenic drivers is a persistent challenge. The Usumacinta River in southeastern Mexico provides an opportunity to study the anthropogenic driver of tropical forest conversion in isolation, as the long meteorological and discharge records capture the river’s response to large-scale agricultural expansion without interference from development activities such as dams or channel modifications. We analyse continuous daily time series of precipitation, temperature, and discharge to identify long-term trends, and employ a novel approach to disentangle the signal of deforestation by normalising daily discharges by 90-day mean precipitation volumes from the contributing area in order to account for climatic variability. We also identify an anthropogenic signature of tropical forest conversion at the intra-annual scale, reproduce this signal using a distributed hydrological model (VMOD), and demonstrate that the continued conversion of tropical forest to agricultural land use will further exacerbate large-scale flooding. We find statistically significant increasing trends in annual minimum, mean, and maximum discharges that are not evident in either precipitation or temperature records, with mean monthly discharges increasing between 7% and 75% in the past decades. Model results demonstrate that forest cover loss is responsible for raising the 10-year return peak discharge by 25%, while the total conversion of forest to agricultural use would result in an additional 18% rise. These findings highlight the need for an integrated basin-wide approach to land management that considers the impacts of agricultural expansion on increased flood prevalence, and the economic and social costs involved.

Stable isotopes of hydrogen (δ²H) and oxygen (δ¹⁸O) provide important quantitative measures of lake hydrology and water balance, particularly in lakes where monitoring of fluxes is incomplete. However, little is known of the relative effects of seasonal variation in water isotopes on estimates of lake hydrology, particularly over decadal scales. To address this gap, we measured water isotopes bi-weekly May-September during 2003–2016 in seven riverine lakes within the 52,000 km² Qu’Appelle River drainage basin of the Canadian Prairies. Analyses revealed that within-year variation in δ¹⁸O values routinely exceeded that among years, reflecting rapid changes in water source, particularly in lakes with water residence times <1 year. Isotopic variation was greatest during spring following snowmelt, except in large deep lakes which exhibited limited differences among seasons or years. In contrast, large hydrological events (e.g., 1-in-140-year flood in 2011) homogenized isotopic values, even among riverine lakes separated by over 150 km, and exerted particularly strong legacy effects on large lakes. Overall, study lakes exhibited a strongly positive moisture balance (evaporation < inflow), despite regional precipitation deficits of 30 cm yr⁻¹, with greater reliance on rainfall (vs. snow) and possibly evaporation in downstream lakes within more humid regions. We conclude that seasonal samples of water isotopes are required to characterize the hydrology of shallow lakes, or those with unknown reliance on snowmelt waters, as well as to better quantify lake susceptibility to climate variability.

The classification of characteristics of flood events, like peak, volume, duration and baseflow components is essential for many hydrological applications such as multivariate flood statistics, the validation of rainfall-runoff models and comparative hydrology in general. The basis for estimations of these characteristics is formed by flood event separation. It requires an indicator for the time when a flood peak occurs as well as the definition of the beginning and end of a flood event and a subdivision of the total volume into direct and baseflow components. However, the variable nature of runoff and the multiple processes and impacts that determine rainfall-runoff relationships make a separation difficult, especially an automation of it. We propose a new statistics-based flood event separation that was developed to analyse long series of daily discharges automatically to obtain flood events for flood statistics. Moreover, the related flood-inducing precipitation is identified, allowing the estimation of the flood-inducing rainfall and the runoff coefficient. With an additional tool to manually check the separation results easily and quickly, expert knowledge can be included without much effort. The algorithm was applied to seven basins in Germany, covering alpine, mountainous and flatland catchments with different runoff processes. In a sensitivity analysis, the impact of chosen parameters was evaluated. The results show that the algorithm delivers reasonable results for all catchments and only needs manual adjustment for long timeslots with increasing or high baseflow. It reliably separates flood events only instead of all runoff events and the estimated beginning and end of an event was shifted in mean by less than one day compared to manual separation.

Many hydrologic and hydraulic (H&H) engineering applications require spatial rainfall distribution over a watershed, but point precipitation frequency estimates, such as those provided by NOAA Atlas 14, are only applicable for relatively small areas. For larger areas, areal reduction factors (ARFs) are commonly used to transform a point precipitation frequency estimate of a given duration and frequency to a corresponding areal estimate. The most common source of ARFs for the United States is Technical Paper 29 (TP-29), published in 1958, although there have been significant increases in record length and types of available data and several new methods for computing ARFs have been proposed over the last several decades. This study applied up-to-date precipitation data products and analysis methods with a watershed-based approach to investigate factors that affect ARF variabilities, and to compare ARFs across multiple US hydrologic regions. Our overall findings are in line with other recent studies showing that ARFs decrease with increasing area, increase with increasing duration, and decrease with increasing return period. In particular, we found a strong geographical variability across different US hydrologic regions, suggesting that ARF are specific to regional climate patterns and geographical characteristics and should not be applied arbitrarily to other locations. The results also reveal the importance of record length, especially for long return period ARFs. The study demonstrates the need to improve ARFs with new data and methods to support more reliable areal precipitation frequency estimates for H&H applications.

Soil moisture content (SMC) retrievals from synthetic aperture radar (SAR) observations do not exactly match with in situ references due to imperfect retrieval algorithms, and uncertainties in the model parameters, SAR observations and in situ references. Information on the uncertainty of SMC retrievals would contribute to their applicability. This paper presents a methodology for deriving the SMC retrieval uncertainty and decomposing this in its constituents. A Bayesian calibration framework was used for deriving the total uncertainty and the model parameter uncertainty. The methodology was demonstrated with the integral equation method (IEM) surface scattering model, which was employed for reproducing Sentinel-1 backscatter (${\sigma }^0$) observations and the retrieval of SMC over four sparsely vegetated fields in the Netherlands. For two meadows the calibrated surface roughness parameter distributions are remarkably similar between the ascending and the descending Sentinel-1 orbits as well as between the two meadows, and yield consistent SMC retrievals for the calibration and validation periods (RMSDs of 0.076 m³ m⁻³ to 0.11 m³ m⁻³). These results are promising for operational retrieval of SMC over meadows. In contrast, the surface roughness parameter distributions of two fallow maize fields differ significantly and the surface roughness conditions changing over time result in less consistent SMC retrievals (calibration RMSDs of 0.096 m³ m⁻³ and 0.13 m³ m⁻³ versus validation RMSDs of 0.26 m³ m⁻³). The SMC retrieval uncertainty derived with the Bayesian calibration successfully reproduces the uncertainty estimated empirically using in situ references. The main uncertainty originates from the in situ references and the Sentinel-1 observations, whereas the contribution from the surface roughness parameters is relatively small. The presented research yields further insights into the surface roughness of agricultural fields and SMC retrieval uncertainties, and these insights can be used to guide SAR-based SMC product developments.

Floods have caused severe destruction and affected communities in different ways throughout history. Flood events are being exacerbated by climate change and hence it is increasingly necessary to have a more accurate understanding of various aspects of flood hazard, particularly for pedestrians. The focus of this study is therefore to investigate different criteria to assess the flood hazard for pedestrians and to propose improvements in assessing such hazards. The revised mechanics-based approach reported herein gives results based on a full physical analysis of the forces acting on a body and can be universally applied as the method can be fine-tuned for different region of the world. The results from flood hazard assessments can be used to: design evacuation plans, improve resilience of sites prone to flooding and plan more resilient future developments. Extreme flood events in the UK and documented for Boscastle (2004) and Borth (2012) were used as case studies. Two approaches were considered, including: (i) a mechanics-based approach, and (ii) an experimental-based approach, with the criteria for the stability of pedestrians in floods being compared for the criteria used by regulatory authorities in Australia, Spain, UK and USA. The results obtained in this study demonstrate that the mechanics-based methods are preferable in determining flood hazard rating assessments.

This study evaluates the potential of assimilating phenology observations using a direct insertion (DI) method by constraining the modeled terrestrial carbon dynamics with synthetic observations of vegetation condition. Specifically, observations of leaf area index (LAI) are assimilated in the Noah-Multi Parameterization (Noah-MP) land surface model across the continental United States during a 5-year period. An observing system simulation experiment (OSSE) was developed to understand and quantify the model response to assimilating LAI information through DI when the input precipitation is strongly biased. This is particularly significant in data poor regions, like Africa and South Asia, where satellite and re-analysis products, known to be affected by significant biases, are the only available precipitation data to drive a land surface model. Results show a degradation in surface and rootzone soil moisture after assimilating LAI within Noah-MP, but an improvement in intercepted liquid water and evapotranspiration with respect to the open-loop simulation (a free run with no LAI assimilation). In terms of carbon and energy variables, net ecosystem exchange, amount of carbon in shallow soil, and surface soil temperature are improved by the LAI DI, although canopy sensible heat is degraded. Overall, the assimilation of LAI has larger impact in terms of reduced systematic and random errors over the Great Plains (cropland, shrubland, and grassland). Moreover, LAI DA shows a greater improvement when the input precipitation is affected by a positive (wet) bias than the opposite case, in which precipitation shows a dry bias.