Hydrology and Earth System Sciences

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ISSN / EISSN : 1027-5606 / 1607-7938
Current Publisher: Copernicus GmbH (10.5194)
Total articles ≅ 4,645
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, , Antônio Alves Meira Neto, , Peter Troch
Hydrology and Earth System Sciences, Volume 25, pp 3105-3135; doi:10.5194/hess-25-3105-2021

Abstract:
In this paper, we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) and the coordinates of the streamflow stations provided by the Brazilian Water Agency, where only the stations with 30 years (1980–2010) of data and less than 10 % of missing records were included. Catchment areas range from 9 to 4 800 000 km2, and the mean daily streamflow varies from 0.02 to 9 mm d−1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage area for each catchment, aside from more than 100 attributes within the attribute classes. The collection and processing methods are discussed, along with the limitations for each of our multiple data sources. CABra intends to improve the hydrology-related data collection in Brazil and pave the way for a better understanding of different hydrologic drivers related to climate, landscape, and hydrology, which is particularly important in Brazil, having continental-scale river basins and widely heterogeneous landscape characteristics. In addition to benefitting catchment hydrology investigations, CABra will expand the exploration of novel hydrologic hypotheses and thereby advance our understanding of Brazilian catchments' behavior. The dataset is freely available at https://doi.org/10.5281/zenodo.4070146 and https://thecabradataset.shinyapps.io/CABra/ (last access: 7 June 2021).
, , , Leyang Wang, Yang Hong
Hydrology and Earth System Sciences, Volume 25, pp 3087-3104; doi:10.5194/hess-25-3087-2021

Abstract:
Revealing the error components of satellite-only precipitation products (SPPs) can help algorithm developers and end-users understand their error features and improve retrieval algorithms. Here, two error decomposition schemes are employed to explore the error components of the IMERG-Late, GSMaP-MVK, and PERSIANN-CCS SPPs over different seasons, rainfall intensities, and topography classes. Global maps of the total bias (total mean squared error) and its three (two) independent components are depicted for the first time. The evaluation results for similar regions are discussed, and it is found that the evaluation results for one region cannot be extended to another similar region. Hit and/or false biases are the major components of the total bias in most overland regions globally. The systematic error contributes less than 20 % of the total error in most areas. Large systematic errors are primarily due to missed precipitation. It is found that the SPPs show different topographic patterns in terms of systematic and random errors. Notably, among the SPPs, GSMaP-MVK shows the strongest topographic dependency of the four bias scores. A novel metric, namely the normalized error component (NEC), is proposed as a means to isolate the impact of topography on the systematic and random errors. Potential methods of improving satellite precipitation retrievals and error adjustment models are discussed.
, , Feihu Chen, Pierre-Antoine Versini, , Ioulia Tchiguirinskaia
Hydrology and Earth System Sciences, Volume 25, pp 3137-3162; doi:10.5194/hess-25-3137-2021

Abstract:
During the last few decades, the urban hydrological cycle has been strongly modified by the built environment, resulting in fast runoff and increasing the risk of waterlogging. Nature-based solutions (NBSs), which apply green infrastructures, have been more and more widely considered as a sustainable approach for urban storm water management. However, the assessment of NBS performance still requires further modelling development because of hydrological modelling results strongly depend on the representation of the multiscale space variability of both the rainfall and the NBS distributions. Indeed, we initially argue this issue with the help of the multifractal intersection theorem. To illustrate the importance of this question, the spatial heterogeneous distributions of two series of NBS scenarios (porous pavement, rain garden, green roof, and combined) are quantified with the help of their fractal dimension. We point out the consequences of their estimates. Then, a fully distributed and physically based hydrological model (Multi-Hydro) was applied to consider the studied catchment and these NBS scenarios with a spatial resolution of 10 m. A total of two approaches for processing the rainfall data were considered for three rainfall events, namely gridded and catchment averaged. These simulations show that the impact of the spatial variability in rainfall on the uncertainty of peak flow of NBS scenarios ranges from about 8 % to 18 %, which is more significant than those of the total runoff volume. In addition, the spatial variability in the rainfall intensity at the largest rainfall peak responds almost linearly to the uncertainty of the peak flow of NBS scenarios. However, the hydrological responses of NBS scenarios are less affected by the spatial distribution of NBSs. Finally, the intersection of the spatial variability in rainfall and the spatial arrangement of NBSs produces a somewhat significant effect on the peak flow of green roof scenarios and the total runoff volume of combined scenarios.
Hydrology and Earth System Sciences, Volume 25, pp 3041-3052; doi:10.5194/hess-25-3041-2021

Abstract:
Breakthrough curves (BTCs) are a valuable tool for qualitative and quantitative examination of transport patterns in porous media. Although breakthrough (BT) experiments are simple, they often require extensive sampling and multi-component chemical analysis. In this work, we examine spectral induced polarization (SIP) signals measured along a soil column during BT experiments in homogeneous and heterogeneous soil profiles. Soil profiles were equilibrated with an NaCl background solution, and then a constant flow of either CaCl2 or ZnCl2 solution was applied. The SIP signature was recorded, and complementary ion analysis was performed on the collected outflow samples. Our results confirm that changes to the pore-water composition, ion exchange processes and profile heterogeneity are detectable by SIP: the real part of the SIP-based BTCs clearly indicated the BT of the non-reactive ions as well as the retarded BT of cations. The imaginary part of the SIP-based curves changed in response to the alteration of ion mobility around the electrical double layer (EDL) and indicated the initiation and the termination of the cation exchange reaction. Finally, both the real and imaginary components of the complex conductivity changed in response to the presence of a coarser textured layer in the heterogeneous profile.
, , Svenja Schweikert, Cornelia Spengler, Hans Jürgen Hahn,
Hydrology and Earth System Sciences, Volume 25, pp 3053-3070; doi:10.5194/hess-25-3053-2021

Abstract:
In Germany, 70 % of the drinking water demand is met by groundwater, for which the quality is the product of multiple physical–chemical and biological processes. As healthy groundwater ecosystems help to provide clean drinking water, it is necessary to assess their ecological conditions. This is particularly true for densely populated urban areas, where faunistic groundwater investigations are still scarce. The aim of this study is, therefore, to provide a first assessment of the groundwater fauna in an urban area. Thus, we examine the ecological status of an anthropogenically influenced aquifer by analysing fauna in 39 groundwater monitoring wells in the city of Karlsruhe (Germany). For classification, we apply the groundwater ecosystem status index (GESI), in which a threshold of more than 70 % of crustaceans and less than 20 % of oligochaetes serves as an indication for very good and good ecological conditions. Our study reveals that only 35 % of the wells in the residential, commercial and industrial areas and 50 % of wells in the forested area fulfil these criteria. However, the study did not find clear spatial patterns with respect to land use and other anthropogenic impacts, in particular with respect to groundwater temperature. Nevertheless, there are noticeable differences in the spatial distribution of species in combination with abiotic groundwater characteristics in groundwater of the different areas of the city, which indicate that a more comprehensive assessment is required to evaluate the groundwater ecological status in more detail. In particular, more indicators, such as groundwater temperature, indicator species, delineation of site-specific characteristics and natural reference conditions should be considered.
, Ole Rössler, Jan Schwanbeck, Rolf Weingartner,
Hydrology and Earth System Sciences, Volume 25, pp 3071-3086; doi:10.5194/hess-25-3071-2021

Abstract:
Assessments of climate change impacts on runoff regimes are essential to climate change adaptation and mitigation planning. Changing runoff regimes and thus changing seasonal patterns of water availability strongly influence various economic sectors such as agriculture, energy production, and fishery and also affect river ecology. In this study, we use new transient hydrological scenarios driven by the most up-to-date local climate projections for Switzerland, the Swiss Climate Change Scenarios. These provide detailed information on changes in runoff regimes and their time of emergence for 93 rivers in Switzerland under three Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, and RCP8.5. These transient scenarios also allow changes to be framed as a function of global mean temperature. The new projections for seasonal runoff changes largely confirm the sign of changes in runoff from previous hydrological scenarios with increasing winter runoff and decreasing summer and autumn runoff. Spring runoff is projected to increase in high-elevation catchments and to decrease in lower-lying catchments. Despite the increases in winter and some increases in spring, the annual mean runoff is projected to decrease in most catchments. Compared to lower-lying catchments, runoff changes in high-elevation catchments (above 1500 m a.s.l.) are larger in winter, spring, and summer due to the large influence of reduced snow accumulation and earlier snowmelt and glacier melt. The changes in runoff and the agreement between climate models on the sign of change both increase with increasing global mean temperatures and higher-emission scenarios. This amplification highlights the importance of climate change mitigation. The time of emergence is the time when the climate signal emerges significantly from natural variability. Under RCP8.5, times of emergence were found early, before the period 2036–2065, in winter and summer for catchments with mean altitudes above 1500 m a.s.l. Significant changes in catchments below 1500 m a.s.l. emerge later in the century. Not all catchments show significant changes in the distribution of seasonal means; thus, no time of emergence could be determined in these catchments. Furthermore, the significant changes of seasonal mean runoff are not persistent over time in some catchments due to nonlinear changes in runoff.
, , Marie-Amélie Boucher, Camille Garnaud
Hydrology and Earth System Sciences, Volume 25, pp 3017-3040; doi:10.5194/hess-25-3017-2021

Abstract:
Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth by the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favorably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth–density–SWE records from 2878 nonuniformly distributed sites across Canada. These data cover almost 4 decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density, followed by the calculation of SWE. Second, testing several artificial neural network (ANN) structural characteristics improves estimates of SWE. Optimizing MLP parameters separately for each snow climate class gives a greater representation of the geophysical diversity of snow. Furthermore, the uncertainty of snow depth measurements is included for a more realistic estimation. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into ANN theory helps improve SWE estimation.
, Lifeng Luo, Andrew Finley
Hydrology and Earth System Sciences, Volume 25, pp 2997-3015; doi:10.5194/hess-25-2997-2021

Abstract:
In the past decades, data-driven machine-learning (ML) models have emerged as promising tools for short-term streamflow forecasting. Among other qualities, the popularity of ML models for such applications is due to their relative ease in implementation, less strict distributional assumption, and competitive computational and predictive performance. Despite the encouraging results, most applications of ML for streamflow forecasting have been limited to watersheds in which rainfall is the major source of runoff. In this study, we evaluate the potential of random forests (RFs), a popular ML method, to make streamflow forecasts at 1 d of lead time at 86 watersheds in the Pacific Northwest. These watersheds cover diverse climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes based on the timing of center-of-annual flow volume: rainfall-dominated, transient, and snowmelt-dominated. RF performance is benchmarked against naïve and multiple linear regression (MLR) models and evaluated using four criteria: coefficient of determination, root mean squared error, mean absolute error, and Kling–Gupta efficiency (KGE). Model evaluation scores suggest that the RF performs better in snowmelt-driven watersheds compared to rainfall-driven watersheds. The largest improvements in forecasts compared to benchmark models are found among rainfall-driven watersheds. RF performance deteriorates with increases in catchment slope and soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.
, , , Eric Gaume, Philippe Davy, Dimitri Lague, Lea Poinsignon, Frederic Pons
Hydrology and Earth System Sciences, Volume 25, pp 2979-2995; doi:10.5194/hess-25-2979-2021

Abstract:
Flash floods observed in headwater catchments often cause catastrophic material and human damage worldwide. Considering the large number of small watercourses possibly affected, the use of automated methods for flood inundation mapping at a regional scale can be of great help for the identification of threatened areas and the prediction of potential impacts of these floods. An application of three mapping methods of increasing level of complexity is presented herein, including a digital terrain model (DTM) filling approach (height above nearest drainage/Manning–Strickler or HAND/MS) and two hydrodynamic methods (caRtino 1D and Floodos 2D). These methods are used to estimate the flooded areas of three major flash floods observed during the last 10 years in southeastern France, i.e., the 15 June 2010 flooding of the Argens river and its tributaries (585 km of river reaches), the 3 October 2015 flooding of small coastal rivers of the French Riviera (131 km of river reaches) and the 15 October 2018 flooding of the Aude river and its tributaries (561 km of river reaches). The common features of the three mapping approaches are their high level of automation, their application based on a high-resolution (5 m) DTM, and their reasonable computation times. Hydraulic simulations are run in steady-state regime, based on peak discharges estimated using a rainfall–runoff model preliminarily adjusted for each event. The simulation results are compared with the reported flood extent maps and the high water level marks. A clear grading of the tested methods is revealed, illustrating some limits of the HAND/MS approach and an overall better performance of hydraulic models which solve the shallow water equations. With these methods, a good retrieval of the inundated areas is illustrated by critical success index (CSI) median values close to 80 %, and the errors on water levels remain mostly below 80 cm for the 2D Floodos approach. The most important remaining errors are related to limits of the DTM, such as the lack of bathymetric information, uncertainties on embankment elevation, and possible bridge blockages not accounted for in the models.
, Xiaoran Liu, Lei Shu, Shucheng Yin, Lingzhi Wang, Wei Fu, Yaoming Ma, Yaoxian Yang, Fanglin Sun
Hydrology and Earth System Sciences, Volume 25, pp 2915-2930; doi:10.5194/hess-25-2915-2021

Abstract:
Temporal and spatial variations of the surface aerodynamic roughness lengths (Z0 m) in the Nagqu area of the northern Tibetan Plateau were analysed in 2008, 2010 and 2012 using MODIS satellite data and in situ atmospheric turbulence observations. Surface aerodynamic roughness lengths were calculated from turbulent observations by a single-height ultrasonic anemometer and retrieved by the Massman model. The results showed that Z0 m has an apparent characteristic of seasonal variation. From February to August, Z0 m increased with snow ablation and vegetation growth, and the maximum value reached 4–5 cm at the BJ site. From September to February, Z0 m gradually decreased and reached its minimum values of about 1–2 cm. Snowfall in abnormal years was the main reason for the significantly lower Z0 m compared with that in normal conditions. The underlying surface can be divided into four categories according to the different values of Z0 m: snow and ice, sparse grassland, lush grassland and town. Among them, lush grassland and sparse grassland accounted for 62.49 % and 33.74 %, and they have an annual variation of Z0 m between 1–4 and 2–6 cm, respectively. The two methods were positively correlated, and the retrieved values were lower than the measured results due to the heterogeneity of the underlying surface. These results are substituted into the Noah-MP (multi-parameterisation) model to replace the original parameter design numerical simulation experiment. After replacing the model surface roughness, the sensible heat flux and latent heat flux were simulated with a better diurnal dynamics.
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