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Conceptual diagram illustrating the underlying structure of the synthesis process. Modified from Thompson et al. (2011).
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This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, p...
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Context 1
... process was inspired by that of Thompson et al. (2011), which recognised that two complementary classes of activities are required in synthesis: (a) generative activities in which new questions are generated, and (b) consolidation activities in which the questions are prioritised, revised, merged and put into the context of the literature (Fig. 4). Steps 2 and 3 involved the generative activities, while Step 4 consisted of consolidation activities. During the VCSS a small working group, involving representatives and members of IAHS, IAH, EGU and AGU, was appointed to consolidate, interpret and synthesise the questions, as well as address potential biases in their selection. ...
Context 2
... process was inspired by that of Thompson et al. (2011), which recognised that two complementary classes of activities are required in synthesis: (a) generative activities in which new questions are generated, and (b) consolidation activities in which the questions are prioritised, revised, merged and put into the context of the literature (Fig. 4). Steps 2 and 3 involved the generative activities, while Step 4 consisted of consolidation activities. During the VCSS a small working group, involving representatives and members of IAHS, IAH, EGU and AGU, was appointed to consolidate, interpret and synthesise the questions, as well as address potential biases in their selection. ...
Citations
... The event emphasized that the influence of land surfaces on eco-hydrological processes has been, and will continue to be, the focal point of research in the hydrology field (Montanari et al., 2015). Among the 23 scientific challenges in hydrology identified by the International Association of Hydrological Sciences (IAHS), the effect of land surface change on hydrological extremes remains a subject that requires extensive investigation (Blöschl et al., 2019). In April 2022, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) approved the ninth phase of the intergovernmental hydrological program (IHP-IX, 2022-2029, emphasizing water safety science in a changing environment (UNESCO, 2022). ...
... There is no wonder that the number of dike failures in the Carpathian Basin has sharp maxima at the peak discharges during spring [74,75]. Notably, rain on top of snow often triggers extreme river runoffs [76,77]. Half of the extreme floods with extensive inundations along the river Danube was recorded since the beginning of the 20th century during the past three to four decades [78]. ...
In this mini-review, we present evidence from the vast literature that one essential part of the coupled atmosphere–ocean system that makes life on Earth possible, the water cycle, is exhibiting changes along with many attributes of the global climate. Our starting point is the 6th Assessment Report of the IPCC, which appeared in 2021, where the almost monograph-size Chapter 8, with over 1800 references, is devoted entirely to the water cycle. In addition to listing the main observations on the Earth globally, we focus on Europe, particularly on the Carpathian (Pannonian) Basin. We collect plausible explanations of the possible causes behind an observably accelerating and intensifying water cycle. Some authors still suggest that changes in the natural boundary conditions, such as solar irradiance or Earth’s orbital parameters, explain the observations. In contrast, most authors attribute such changes to the increasing greenhouse gas concentrations since the industrial revolution. The hypothesis being tested, and which has already yielded convincing affirmative answers, is that the hydrological cycle intensifies due to anthropogenic impacts. The Carpathian Basin, a part of the Danube watershed, including the sub-basin of the Tisza River, is no exception to these changes. The region is experiencing multiple drivers contributing to alterations in the water cycle, including increasing temperatures, shifting precipitation regimes, and various human impacts.
... In addition to the choice of model, the quantity, quality, and structure of input data are the most critical aspects for the accuracy and reliability of the simulation [72,73]. ...
This study aims to couple the support vector machine (SVM) model with a hydromete-orological wireless sensor network to simulate different types of flood events in a montane basin. The model was tested in the mid-latitude montane basin of Vydra in the Šumava Mountains, Central Europe, featuring complex physiography, high dynamics of hydrometeorological processes, and the occurrence of different types of floods. The basin is equipped with a sensor network operating in headwaters along with the conventional long-term monitoring in the outlet. The model was trained and validated using hydrological observations from 2011 to 2021, and performance was assessed using metrics such as R 2 , NSE, KGE, and RMSE. The model was run using both hourly and daily timesteps to evaluate the effect of timestep aggregation. Model setup and deployment utilized the KNIME software platform, LibSVM library, and Python packages. Sensitivity analysis was performed to determine the optimal configuration of the SVR model parameters (C, N, and E). Among 125 simulation variants, an optimal parameter configuration was identified that resulted in improved model performance and better fit for peak flows. The sensitivity analysis demonstrated the robustness of the SVR model, as different parameter variations yielded reasonable performances, with NSE values ranging from 0.791 to 0.873 for a complex hydrological year. Simulation results for different flood scenarios showed the reliability of the model in reconstructing different types of floods. The model accurately captured trend fitting, event timing, peaks, and flood volumes without significant errors. Performance was generally higher using a daily timestep, with mean metric values R 2 = 0.963 and NSE = 0.880, compared to mean R 2 = 0.913 and NSE = 0.820 using an hourly timestep, for all 12 flood scenarios. The very good performance even for complex flood events such as rain-on-snow floods combined with the fast computation makes this a promising approach for applications.
... Although trends have been the focus of a majority of papers studying the historical variability of floods and heavy precipitation, other forms of temporal variability have also been studied. For instance, the tendency of events to cluster into flood-rich and flood-poor periods has attracted attention (Blöschl, Bierkens, et al., 2019;Hall et al., 2014) and has been highlighted in some regions of Australia (Franks & Kuczera, 2002;Liu & Zhang, 2017) or Europe Merz et al., 2016). Such a low frequency variability, also referred to as persistence, may result from the influence of oceanic modes of climate variability such as the Pacific Decadal Oscillation (Wei et al., 2021). ...
Floods and heavy precipitation have disruptive impacts worldwide, but their historical variability remains only partially understood at the global scale. This article aims at reducing this knowledge gap by jointly analyzing seasonal maxima of streamflow and precipitation at more than 3,000 stations over a 100‐year period. The analysis is based on Hidden Climate Indices (HCIs). Like standard climate indices (e.g., Nino 3.4, NAO), HCIs are used as covariates explaining the temporal variability of data, but unlike them, HCIs are estimated from the data. In this work, a distinction is made between common HCIs, that affect both heavy precipitation and floods, and specific HCIs, that exclusively affect one or the other. Overall, HCIs do not show noticeable autocorrelation, but some are affected by noticeable trends. In particular, strong and wide‐ranging trends are identified in precipitation‐specific HCIs, while trends affecting flood‐specific HCIs are weaker and have more localized effects. A probabilistic model is then derived to link HCIs and large‐scale atmospheric variables (pressure, wind, temperature) and to reconstruct HCIs since 1836 using the 20CRv3 reanalysis. In turn this allows estimating the probability of occurrence of floods and heavy precipitation at the global scale. This 180‐year reconstruction highlights flood hot‐spots and hot‐moments in the distant past, well before the establishment of perennial monitoring networks. The approach presented in this study is generic and paves the way for an improved characterization of historical variability by making a better use of long but highly irregular station data sets.
... Their character and influence on the way the discipline is practiced may vary through time, but their intrinsic role in understanding and managing water resources and hazards, as well as in developing sound water policies dictates their continuing importance 3 . These data issues as captured by 6 includes how technology informs surface and subsurface data properties at different spatial and temporal scales, the relative value of traditional hydrological observation vs soft data (a qualitative observation from lay persons, data mining etc.) and extracting information from available data on human and water systems in order to inform the building process of socio-hydrological models and conceptualization. The foundation block on ultimate policy decision on Water Resources Management (WRM) lies predominantly in the understanding of the trend of hydrologic variables and how they interact and relate to each other or the opposite of it 7 www.nature.com/scientificreports/ ...
Inadequate knowledge on actual water availability, have raised social-economic conflicts that necessitate proper water management. This requires a better understanding of spatial–temporal trends of hydro-climatic variables as the main contributor to available water for use by sectors of economy. The study has analysed the trend of hydro-climatic variables viz. precipitation, temperature, evapotranspiration and river discharge. One downstream river gauge station was used for discharge data whereas a total of 9 daily observed and 29 grided satellite stations were used for climate data. Climate Hazards Group InfraRed Precipitation was used for precipitation data and Observational-Reanalysis Hybrid was used for Temperature data. Mann–Kendall Statistical test, Sen’s slope estimator and ArcMap Inverse Distance Weighted Interpolation functionality were employed for temporal, magnitude and spatial trend analysis respectively. Results confirmed that, spatially, there are three main climatic zones in the study area viz. Udzungwa escarpment, Kilombero valley and Mahenge escarpment. On temporal analysis, with exception of the declining potential evapotranspiration trend, all other variables are on increase. This is with catchment rates of 2.08 mm/year, 0.05 °C/year, 0.02 °C/year, 498.6 m³/s/year and − 2.27 mm/year for precipitation, Tmax, Tmin, river discharge and PET respectively. Furthermore, rainfalls start late by a month (November) while temperatures picks earlier by September and October for Tmax and Tmin respectively. Water availability matches farming season. However, it is recommended to improve water resources management practices to limit flow impairment as expansions in sectors of economy are expected. Furthermore, landuse change analysis is recommended to ascertain actual trend and hence future water uptake.
... Hence, large-scale water parcel tracking is needed to understand these regional to continental groundwater flow systems and thus to reasonably configure them in ESMs. With intensified climate change and human activities, this demand has become more pressing to understand the terrestrial water cycle in the changing world, which is one of the 23 unsolved hydrologic questions (Bloschl et al., 2019). However, we face an incredible computational burden to expand the above listed models to the scale of ESMs (Kollet et al., 2010). ...
Unprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, motivating the development of large‐scale hydrologic modeling for solutions. Water age/quality is as important as water quantity for understanding the terrestrial water cycle. However, scientific progress in tracking water parcels at large‐scale with high spatiotemporal resolutions is far behind that in simulating water balance/quantity owing to the lack of powerful modeling tools. EcoSLIM is a particle tracking model working with ParFlow‐CLM that couples integrated surface‐subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework on distributed, multi‐Graphics Processing Unit platforms with Compute Unified Device Architecture‐Aware Message Passing Interface for accelerating EcoSLIM to continental‐scale. In tests from catchment‐, to regional‐, and then to continental‐scale using 25‐million to 1.6‐billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where the parallelization is inadequate, and a standard parallel framework is also absent. The parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth System Models.
... Obtained results indicate decreasing trends in occurrence rate as well as their frequency of minor and strong events during the observed period. These results are in good agreement with results presented by Blöschl et al. (2019) who demonstrated that that changing climate causes decreasing floods in southern and eastern Europe. On the other hand, we argue that there is the increase of occurrence rate and frequency of extreme events at the beginning of the new millennia ( Figure 1 and table 1). ...
This paper investigates and improves understanding of the hydrological behavior of design parameters of bioretention cell (BC) towards different model responses (e.g., surface infiltration, surface outflow, and storage volume) by a global sensitivity analysis (GSA) based approach named the Sobol method. Results demonstrate that conductivity, vegetation volume, and berm height are the most sensitive parameters for surface infiltration and surface outflow, while, in addition to these parameters, soil thickness showed a higher sensitivity for storage volume. Other parameters represented limited sensitivities. Improved understanding of hydrological behaviours of design parameters of BC leads to efficient design configuration at the unit scale, which ultimately helps to improve the overall performance of BC to achieve target design goals at the catchment-scale.
... However, the transferability of CRR models under contrasting climate conditions still lacks strong interpretations and generalizations. Therefore, faced with numerous unknowns regarding this topic, there is an apparent need for more robust rainfall-runoff modelling to ensure that operational model applications (forecasting, design, etc.) deliver reliable results where nonstationary conditions are explicitly taken into account (the Unsolved Problems in Hydrology no.19 in the paper by Blöschl et al., 2019). In this paper, we present a literature review (1) to provide an overview of the development of most generally used strategy to allocate data for calibration and evaluation (Sect. ...
... Soil moisture is a crucial variable of the climate system, which affects plant transpiration and photosynthesis, and has implications for the sustainability of water resources and biogeochemical cycles. Therefore, it is necessary to have timely and accurate soil moisture information [1][2][3]. Traditional soil moisture measurement methods, such as the in situ soil hygrometer, are accurate but consume much labor and material resources and have long measurement cycles [4]. Although remote sensing technologies can overcome the disadvantage of small coverage area of traditional soil moisture monitoring methods, the temporal resolution is not ideal [5,6]. ...
... 1 Compared with the traditional methods, the MCD method has a stronger detection effect and can effectively enhance the accuracy of GNSS-IR soil moisture inversion. 2 The accuracy of soil moisture inversion can be improved by considering the frequency, amplitude, and phase of the multipath signals simultaneously. 3 Compared with the robust multiple linear regression model, machine learning algorithms can further enhance the modeling accuracy. ...
Soil moisture monitoring is widely used in agriculture, water resource management, and disaster prevention, which is of great significance for sustainability. The global navigation satellite system interferometric reflectometry (GNSS-IR) technology provides a supplementary method for soil moisture monitoring. However, due to the quality of the signal-to-noise ratio (SNR) measurements and the complex surface environment, inevitable outliers in multipath interference signal metrics (amplitude, frequency, and phase) were used as modeling variables to inverse GNSS-IR soil moisture. Besides, it is hard to use the univariate model to comprehensively analyze the relationship between the various factors, due to the poor fitting effect and weak generalization ability of the model. In this paper, the minimum covariance determinant (MCD) robust estimation and machine learning algorithms are adopted. The MCD robust estimation can eliminate outliers of the multipath signal metrics and machine learning algorithms, including the back propagation neural network (BPNN), Gaussian process regression (GPR), and random forest (RF), and can comprehensively establish nonlinear GNSS-IR soil moisture inversion models using multipath interference signal metrics. Moreover, the study of the modeling parameter selection for the three machine learning algorithms and the inversion results for single satellite and all satellites are also carried out to make the algorithms more generalizable. The results show that the correlation coefficients (R) and the root mean square error (RMSE) of the machine learning models for all satellite tracks are increased by 4.3~86.6% and reduced by 2.8~30%, respectively, compared with the MCD multiple regression model. The RF model with 80 decision trees and 1 node shows the clearest improvement. The total model using all satellite data has more generalization ability than the single satellite model but causes some loss of accuracy.
... Many rainfall-runoff and climatic models applying different spatial scales require soil moisture (SM) as an input parameter [1,2]. The study of temporal variations in SM in the unsaturated zone of forest soils is very complex [3,4]. The factors affecting water transport into the deeper layers have been well documented by many researchers in previous studies. ...
Soil moisture (SM) and temperature (ST) are critical factors in forest eco-hydrological research. In this study, we investigated the inter- and intra-annual changes in SM and ST profiles in a mixed Mediterranean maquis forest stand together with soil and meteorological parameters. Hourly data from three field measurements points at four depths (−5, −20, −40 and −70 cm) for 6 years were interpolated using the kriging method to produce annual SM and ST profiles. The results indicate that air temperature highly affects the upper 5 cm of the mineral soil. In general, it increases with depth in winter at an average rate of 0.036 °C/cm and decreases in summer (0.035 °C/cm), presenting higher values compared to air temperature from April to August and lower ones during the rest of the period. Precipitation is the main factor driving SM variations up to a superficial soil depth of 40 cm. The upper soil layer (0–40 cm) infiltrates water faster and presents high SM variability, especially in monthly and seasonal (year to year) time steps. The maquis forest stands are likely to be strongly affected by climate change, therefore the results of this study could be useful in hydrological and climate change studies focused on maquis vegetation water management.
