Understanding Of Hydrologic Systems Research Articles

Pre-event base flow as a useful indicator for flood forecasting

The hydrological cycle comprises several components, including precipitation, infiltration, evaporation, transpiration, and runoff, with the latter being a vital part of the cycle that connects precipitation to bodies of water. modelling is necessary due to limitations in standard hydrological measurement techniques, providing a better understanding of hydrological systems that were previously inaccessible, and making it essential for water resource management. Event-based rainfall-runoff modelling is a prevalent method due to its simplicity, but conventional models are limited in accounting for antecedent conditions. In order to tackle this problem, pre-event baseflow is proposed as an indicator for assessing catchment conditions for flood forecasting. This study employs a novel event-based rainfall-runoff equation that incorporates rainfall characteristics, improving hydrological modelling accuracy, and outperforms conventional models in predicting direct flow in most case studies. Overall, the study demonstrates the potential for the new equation to improve hydrological modelling accuracy and aid in a wide range of applications such as flood prediction, water resources management, and planning.

Computational thinking for using models of water flow in environmental systems: Intertwining three dimensions in a learning progression

AbstractNearly a decade ago, the Framework for K‐12 Science Education argued for the need to intertwine science and engineering practices, disciplinary core ideas, and crosscutting concepts in performance expectations. However, there are few empirical examples for how intertwining three dimensions facilitates learning. In this study, we used a learning progressions approach to examine how student engagement in computational thinking (science and engineering practice) intertwines with learning about the flow of water through environmental systems (disciplinary core ideas) and understanding of systems and system models (crosscutting concept). We developed three secondary‐level curriculum units situated in current groundwater contamination and urban flooding contexts. Units included specially designed NetLogo computational models. Post‐assessments measured student performances in computational thinking processes and understanding of hydrologic systems. Using item response theory in our analysis, we identified distinct levels of performance on a learning progression. At the lower end, literal model users interacted with models and manipulated model interfaces to achieve a specified goal. In the middle, Model Technicians used computational models to solve real‐world problems. At the upper end, principle‐based model users used computational thinking processes and principles related to systems modeling and hydrology to explain how the models worked to predict water flow. Differences between performances of literal model users, model technicians, and principle‐based model users reflected shifts in how students made sense of the systems and system models crosscutting concept. These shifts in performances aligned with progress in computational thinking practices and finally with use of hydrology disciplinary core ideas. These findings contribute to understanding of how science and engineering practices, disciplinary core ideas, and crosscutting concepts intertwine during learning; how computational thinking practices develop; and how computational thinking about system models facilitates learning for environmental science literacy.

Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained directly on this information. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems.

Understanding and predicting large-scale hydrological variability in a changing environment

Abstract. In a context of climate, environmental, ecological and socio-economical changes, understanding and predicting the response of hydrological systems on regional to global spatial scales, and on infra-seasonal to multidecadal time-scales, are major topics that must be considered to tackle the challenge of water resource management sustainability. In this context, a number of strongly-linked key issues need to be addressed by the scientific community, including: (i) identifying climate drivers of hydrological variations, (ii) understanding the multi-frequency characteristics of hydroclimate variability, including evolution of extremes (meteorological/hydrological event scale to long-term natural/internal climate- or anthropogenic-driven variations and trends), (iii) assessing the influence of local- to regional-scale basin properties on hydrological system response to climate variability and change, (iv) identifying the evolving contribution of anthropogenic water use in observed hydrological variations. Based on pan-European collaborations, activities of the EURO-FRIEND “Large-scale variations in hydrological characteristics” group aim at generating new findings to improve our understanding of hydrological systems behavior sensu lato (i.e. surface and sub-surface) on large spatial and temporal scales (i.e continental – multidecadal). Through selected examples, this contribution emphasizes recent research developments in characterizing and modeling of climate-hydrology linkages at different temporal and spatial scales, as well as recent insights on climate-hydrology scaling characteristics (i.e. long-term persistence, dependance of processes, of hydrological behaviors, of large-scale climate/hydrology linkages on time-/spatial scales), long-term hydrometeorological reconstructions, and large-scale hydrological model refinement taking into account spatial heterogeneity of watershed physical characteristics.

Discharge Estimates for Ungauged Rivers Flowing over Complex High-Mountainous Regions based Solely on Remote Sensing-Derived Datasets

Reliable information about river discharge plays a key role in sustainably managing water resources and better understanding of hydrological systems. Therefore, river discharge estimation using remote sensing techniques is an ongoing research goal, especially in small, headwater catchments which are mostly ungauged due to environmental or financial limitations. Here, a novel method for river discharge estimation based entirely on remote sensing-derived parameters is presented. The model inputs include average river width, estimated from Landsat imagery by using the modified normalized difference water index (MNDWI) approach; average depth and velocity, based on empirical equations with inputs from remote sensing; channel slope from a high resolution shuttle radar topography mission digital elevation model (SRTM DEM); and channel roughness coefficient via further analysis and classification of Landsat images with support of previously published values. The discharge of the Lhasa River was then estimated based on these derived parameters and by using either the Manning equation (Model 1) or Bjerklie equation (Model 2). In general, both of the two models tend to overestimate discharge at moderate and high flows, and underestimate discharge at low flows. The overall performances of both models at the Lhasa gauge were satisfactory: comparisons with the observations yielded Nash–Sutcliffe efficiency coefficient (NSE) and R2 values ≥ 0.886. Both models also performed well at the upper gauge (Tanggya) of the Lhasa River (NSE ≥ 0.950) indicating the transferability of the methodology to river cross-sections with different morphologies, thus demonstrating the potential to quantify streamflow entirely from remote sensing data in poorly-gauged or ungauged rivers on the Tibetan Plateau.

Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam

In the last few decades tremendous progress has been made in the use of catchment models for the analysis and understanding of hydrologic systems. A common application involves the use of these models to predict flows at catchment outputs. However, the outputs predicted by these models are often deterministic because they focused only on the most probable forecast without an explicit estimate of the associated uncertainty. This paper uses Bayesian and Generalized Likelihood Uncertainty Estimation (GLUE) approaches to estimate uncertainty in catchment modelling parameter values and uncertainty in design flow estimates. Testing of join probability of both these estimates has been conducted for a monsoon catchment in Vietnam. The paper focuses on computational efficiency and the differences in results, regardless of the philosophies and mathematical rigor of both methods. It was found that the application of GLUE and Bayesian techniques resulted in parameter values that were statistically different. The design flood quantiles estimated by the GLUE method were less scattered than those resulting from the Bayesian approach when using a closer threshold value (1 standard deviation departed from the mean). More studies are required to evaluate the impact of threshold in GLUE on design flood estimation.

Wet/Dry Mapping: Using Citizen Scientists to Monitor the Extent of Perennial Surface Flow in Dryland Regions

Wet/dry mapping provides a low-cost, comprehensive snapshot for monitoring flow conditions in rivers with interrupted perennial (spatially intermittent) surface flow. When used in conjunction with more traditional point-specific stream flow or groundwater measurements, it provides a better understanding of hydrologic systems at the broad landscape or watershed scale. Through use of trained volunteers, we mapped reaches with surface water during the driest time of year to track annual variation in length and location of perennial flow. Data from 12 years of wet/dry mapping on the San Pedro River in Arizona, USA, showed 62 reaches with surface flow in every year, totaling 32% of the river length through the San Pedro Riparian National Conservation Area. They also show areas with high year-to-year variation in flow length, which indicate changes in local groundwater conditions and may provide early warning of ecological changes. Data and maps from this project have been useful for a wide variety of conservation, management, and research efforts.

Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling

In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models that require significant computational time to run and produce the desired output. In this paper we improve the computational efficiency of GLUE by sampling the prior parameter space using an adaptive Markov Chain Monte Carlo scheme (the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm). Moreover, we propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic and real-world streamflow data from two catchments with different hydrologic regimes.

Understanding catchment behavior through stepwise model concept improvement

Lack of data is one of the main limitations for hydrological modeling. However, it is often used as a justification for over simplifying, poorly performing models. If we want to enhance our understanding of hydrological systems, it is important to fully exploit the information contained in the available data, and to learn from model deficiencies. In this paper, we propose a methodology where we systematically update the model structure, progressively incorporating new hypotheses of catchment behavior. We apply this methodology to the Alzette river basin in Luxembourg, showing how stepwise model improvement helps to identify the behavior of this catchment. We show that the most significant improvement of the evolving model structure is associated to the characterization of antecedent wetness. This is improved accounting for interception, which affects vertical storage distribution, and accounting for rainfall spatial heterogeneity, which influences storage variations in the horizontal dimension. Overall, our results suggested that, due to the damping effect of the basin, the description of fast catchment response benefits more from spatially distributed information than that of slow catchment response.

Research Advances in Vadose Zone Hydrology through Simulations with the TOUGH Codes: Preface from the Guest Editors

Numerical simulators are playing an increasingly important role in advancing our fundamental understanding of hydrological systems. They are indispensable tools for managing groundwater resources, analyzing proposed and actual remediation activities at contaminated sites, optimizing recovery of oil, gas, and geothermal energy, evaluating subsurface structures and mining activities, designing monitoring systems, assessing the long-term impacts of chemical and nuclear waste disposal, and devising improved irrigation and drainage practices in agricultural areas, among many other applications. The complexity of subsurface hydrology in the vadose zone calls for sophisticated modeling codes capable of handling the strong nonlinearities involved, the interactions of coupled physical, chemical and biological processes, and the multiscale heterogeneities inherent in such systems. The papers in this special section of ''Vadose Zone Journal'' are illustrative of the enormous potential of such numerical simulators as applied to the vadose zone. The papers describe recent developments and applications of one particular set of codes, the TOUGH family of codes, as applied to nonisothermal flow and transport in heterogeneous porous and fractured media (http://www-esd.lbl.gov/TOUGH2). The contributions were selected from presentations given at the TOUGH Symposium 2003, which brought together developers and users of the TOUGH codes at the Lawrence Berkeley National Laboratory (LBNL) inmore » Berkeley, California, for three days of information exchange in May 2003 (http://www-esd.lbl.gov/TOUGHsymposium). The papers presented at the symposium covered a wide range of topics, including geothermal reservoir engineering, fracture flow and vadose zone hydrology, nuclear waste disposal, mining engineering, reactive chemical transport, environmental remediation, and gas transport. This Special Section of ''Vadose Zone Journal'' contains revised and expanded versions of selected papers from the symposium, with special attention to issues related to the vadose zone and unsaturated flow systems. The first paper, written by the original developer of TOUGH, Karsten Pruess, provides an overview of the history of the TOUGH codes, the main physical processes considered, their mathematical and numerical implementation, and case studies. That paper is followed by a review article summarizing inverse modeling applications performed by iTOUGH2. A subsequent group of papers deals with diverse unsaturated zone systems, highlighting the versatility of the code to handle a variety of processes in different geologic settings. Simulation capabilities of the TOUGH codes are increasingly used for geologic carbon sequestration studies as testified by the next group of papers. The final series of papers demonstrates the use of the TOUGH codes in support of remediation and engineering applications. These studies discuss biological and reactive chemical transport simulations, the design of clean-up operations and landfill management, and the analysis of engineered soil stabilization. As guest editors, we thank the authors for their interesting contributions, and the many reviewers for their careful and constructive review comments. Finally, on behalf of all of the authors and ourselves, we express our sincerest appreciation to Rien van Genuchten for providing the opportunity to publish these papers together in a Special Section of ''Vadose Zone Journal''.« less

Research Advances in Vadose Zone Hydrology through Simulations with the TOUGH Codes: Preface from the Guest Editors

Numerical simulators are playing an increasingly important role in advancing our fundamental understanding of hydrological systems. They are indispensable tools for managing groundwater resources; analyzing proposed and actual remediation activities at contaminated sites; optimizing recovery of oil