Journal of Hydrologic Engineering

A thorough understanding of the hydrosphere is crucial for the sustainable evolution of human society and the ecosystem in a rapidly changing world. This understanding can only come from well-trained professionals in the field of hydrology working in research and practice. In civil and environmental engineering, this knowledge is the basis for the design of infrastructure and its management. This paper briefly reviews the historical development of engineering hydrology education from the middle of the twentieth century. The twentieth century was characterized by the establishment in the 1950s and 1960s of a clear, modern, and durable vision for hydrology education as a distinct formal program of study, and the consolidation in the 1990s of the original vision. In recent years, a series of publications has expanded the traditional vision of hydrology education. This recent literature emphasizes formalized approaches to hydrology education, including community-developed curricular resources, data-based and modeling-based curricula, formally assessed pedagogies, and formalization of nontraditional pedagogies. Based on these findings, the authors present several challenges for hydrology education in the 21st century. Central themes of the challenges for hydrology education are the development of international hydrology education communities and networks, shared learning technologies—partially driven by the need for a more mechanistic approach to engineering hydrology, formalized and validated pedagogies, and adaptations of international best educational practices to regionally specific hydrology and socioeconomic context.

The past few decades have witnessed the development of numerous catchment models, often with increasing structural complexity and mathematical sophistication. While such models have certainly provided a better understanding of catchments and associated processes, they are also often catchment-specific, region-specific, or process-specific. Serious concerns on this modeling trend have been increasingly raised in recent times and, consequently, the need for a generic catchment classification framework in hydrology has been emphasized. There have indeed been some attempts to advance the idea of such a classification framework. Such studies have investigated different ways of developing a framework, including river morphology, river regimes, hydroclimatic factors, landscape and land use parameters, hydrologic similarity indexes, hydrologic signatures, ecohydrologic factors, geostatistical properties, entropy, nonlinear and chaotic properties, data mining, and other relevant characteristics and methods. Although useful in their own ways, these studies are largely inadequate for a generic classification framework. In addition to the limitations that exist in each of the different forms, a coherent effort to bring these disparate forms together for a workable classification is also missing. This study highlights the challenges that the existing approaches pose in the development of a generic classification framework. It argues for an appropriate basis, a suitable methodology, and key components for such a framework. In particular, it discusses the vital role of system complexity as an appropriate basis for the classification framework and the potential of nonlinear dynamics, networks, and other modern concepts of complex systems science for assessing system complexity. The study also offers a three-step procedure for formulation and verification of a catchment classification framework.

Sea-level rise (SLR) is one of the most damaging impacts of climate change. Rising sea levels lead to loss of coastal wetlands, coastal flooding, degradation of coastal ecosystems, and a general loss of quality of life. Due to its potential impacts on coastal management and on population health and safety, the impact of climate change on SLR has drawn significant attention in recent literature. SLR is associated with processes including glacial activity, ice-sheet melting, thermal expansion of sea water, hydrologic events such as increased or decreased land-based discharges, and local effects such as El Niño and La Niña, all of which are complexly linked to changes in global temperature. Unfortunately, many of these physical processes are not well understood in their relation to climate change, and the scientific knowledge required to represent them fully in predictive analysis is so complex that many current studies are shifting away from physical climate models to the application of empirical, semiempirical, and stochastic models. Another important aspect of these modeling studies is their characterization of the geographic pattern of SLR, which is used to evaluate the threat of SLR to specific geographic locations. In this application area, many of the current analysis techniques are also still in a fairly preliminary stage. The purpose of this study is to provide a review of this literature and to compare some of the successful attempts at modeling SLR. In closure, we also discuss a new dynamic system modeling approach that can successfully characterize the spatial variability of SLR.

The long-standing issue of hydrological predictions in ungauged basins has received increased attention due to the recent International Association of Hydrological Sciences (IAHS) decade on predictions in ungauged basins (PUB). Since the outset of PUB, many have noted that the best way to confront an ungauged basin is to first make some basic streamflow measurements. This study explores the value of a rudimentary gauging campaign for making predictions in an ungauged basin. The well-studied Maimai watershed in New Zealand was used as a hypothetical ungauged basin, and this study was designed to start with no runoff data and add iteratively different subsets of the available data to constrain the calibration of a simple three-reservoir conceptual catchment model. These subsets included single runoff events or a limited number of point values—in other words, what could be measured with limited, campaign-like field efforts in an ungauged basin. In addition, different types of soft data were explored to constrain the model calibration. Model simulations were validated using the available runoff data from different years. It was found that surprisingly little runoff data were necessary to derive model parameterizations that provided good results for the validation periods, especially when these runoff data were combined with soft data. The relative value of soft data increased with decreasing amount of streamflow data. The findings from the Maimai watershed suggest that, when starting with no flow information, one event or 10 observations during high flow provide almost as much information as three months of continuously measured streamflow for constraining the calibration of a simple catchment model.

Dams have become an integral part of basic infrastructure by offering indispensable benefits like irrigation, hydropower, domestic and industrial water supply, flood control, drought mitigation, navigation, fish farming, and recreation. As controversial as they have been during the last decades due to negative social and environmental impacts, the limited and uneven distribution of water at the global level has made the world realize that more dams, mostly large dams, are needed if development is to be promoted and if basic human needs are to be covered. Overall, it has been global dynamics in terms of water, energy (including trade aspects), food, and climate securities that has recasted the role of dams triggering massive investment on construction and modernization of multiple projects all over the world. It is thus fundamental to continue improving project planning and implementation to avoid unnecessary social and environmental costs. This paper discusses the role of dams on development, hydropower as the main source of renewable energy, the potential it holds to promote regional development, resettlement as the most critical factor still facing construction of large dams, and the role an entirely new group of actors are having in investment of dam projects at the national, regional, and global levels.

Downscaling is a fundamental procedure in the assessment of the future climate change impact at regional and watershed scales. Hence, it is important to investigate the spatial variability of the climate conditions that are constructed by various downscaling methods to assess whether each method can properly model the climate conditions at various spatial scales. To gain some insight into this issue with respect to statistical versus dynamical downscaling approaches, an assessment of the precipitation variability from a popular statistical downscaling method [bias correction with spatial disaggregation (BCSD)] and a dynamical downscaling method [by MM5: Fifth-Generation National Center for Atmospheric Research (NCAR)/Penn State Mesoscale Model] was performed. This assessment is based on the historical NCAR/National Center for Environmental Prediction (NCEP) reanalysis data, a community climate system model version 3 (CCSM3) global climate model (GCM) control run for the 1950–1999 period, and the CCSM3 GCM A1B emission scenario simulations for a projection period. Two spatial characteristics are investigated: (1) the normalized standard deviation (NSD), and (2) the precipitation change over the northern California region. The results of this investigation show that BCSD-based NSD and local precipitation change values do not show realistic spatial variation. Instead, they show interpolated spatial patterns from the coarse grid data set of two-degree resolution for both historic and projection periods. Meanwhile, MM5-based NSD and local precipitation change values show realistic spatial characteristics of precipitation variability for each month (December and July), for each season [December-January-February (DJF) and June-July-August (JJA)], and for the annual values, due to the heterogeneity in the northern California study region’s land characteristics. Both PRISM (Parameter-elevation Regressions on Independent Slopes Model) and MM5-simulated precipitation values have similar spatial structures, and they describe well how the NSD values and the precipitation field change locally in a spatially diverse pattern over the study region. Hence, BCSD procedure and MM5 simulations show significant differences for the 100-year average precipitation change. Opposite wet and dry trends between the precipitation estimated by the BCSD method and by MM5 are found in many places (especially in high-elevation areas). The BCSD method has limitations in projecting future precipitation values. As such, it is questionable whether the BCSD method is suitable for the assessment of the impact of future climate change at regional, watershed, and local scales as the future climate will evolve in time and space as a nonlinear system with land–atmosphere feedbacks.

Groundwater, one of the world’s most important natural resources, is under constant threat of exploitation with increasing population and economic development. Proper understanding and modeling of subsurface water movement has been an enduring challenge for hydrologists and practitioners. Current modeling efforts are plagued by the complex heterogeneity within the subsurface, reconciliation with spatial and temporal scales, and lack of supporting data. Long-term consequences of droughts in aquifers and efficient management of the available resources in arid and semiarid regions of the world deserve special attention. Assessing the potential impacts of climate change on groundwater is yet another long-term challenge that confounds both researchers and managers. With groundwater being likened to fossil fuels in some parts of the world, conservation and management of these resources have become imperative. Developing new models that account for uncertainties and provide more realistic assessment of predictive capabilities is needed for devising effective management practices. Current data acquisition techniques need to be improved for reliable modeling and impact studies. In this paper, some of these major challenges in groundwater hydrology are discussed, and their possible implications are presented.

Groundwater quality has been a long-standing concern to water engineers and managers. Numerous studies have examined the causes of contamination, and designs for remediation systems have been developed for contamination from domestic, agricultural, and industrial activities. There is renewed interest in groundwater quality of aquifers from recent activities such as induced saltwater intrusion, hydraulic fracturing, carbon dioxide (${\mathrm{CO}}_2$) sequestration, and deep geologic storage of nuclear waste. These topics are reviewed and discussed in this paper, focusing on the new challenges resulting from these activities. In this context, modeling flow and transport mechanisms in fractured rocks is relevant, and some recent advances in the field of fracture hydrology are highlighted. More advanced techniques of analysis, namely, fractional advection dispersion equations, are discussed in light of new interest in this topic. It is clear that advances in theory, numerical modeling, and experimentation are needed to address the complex challenges associated with groundwater quality.

Estimating the uncertainty of hydrological models remains a relevant challenge in applied hydrology, mostly because it is not easy to parameterize the complex structure of hydrological model errors. A nonparametric technique is proposed as an alternative to parametric error models to estimate the uncertainty of hydrological predictions. Within this approach, the above uncertainty is assumed to depend on input data uncertainty, parameter uncertainty and model error, where the latter aggregates all sources of uncertainty that are not considered explicitly. Errors of hydrological models are simulated by resampling from their past realizations using a nearest neighbor approach, therefore avoiding a formal description of their statistical properties. The approach is tested using synthetic data which refer to the case study located in Italy. The results are compared with those obtained using a formal statistical technique (meta-Gaussian approach) from the same case study. Our findings prove that the nearest neighbor approach provides simplicity in application and a significant improvement in regard to the meta-Gaussian approach. Resampling techniques appear therefore to be an interesting option for uncertainty assessment in hydrology, provided that historical data are available to provide a consistent description of the model error.

There remains a great deal of uncertainty about uncertainty estimation in hydrological modeling. Given that hydrology is still a subject limited by the available measurement techniques, it does not appear that the issue of epistemic error in hydrological data will go away for the foreseeable future, and it may be necessary to find a way to allow for robust model conditioning and more subjective treatments of potential epistemic errors in prediction. In this paper an attempt is made to analyze how this is the result of the epistemic uncertainties inherent in the hydrological modeling process and their impact on model conditioning and hypothesis testing. Some ideas are proposed about how to deal with assessing the information in hydrological data and how it might influence model conditioning based on hydrological reasoning, with an application to rainfall-runoff modeling of a catchment in northern England, where inconsistent data for some events can introduce disinformation into the model conditioning process. A methodology is presented to make an assessment of the relative information content of calibration data before running a model that can then inform the evaluation of model runs and resulting prediction uncertainties.

The present contribution reviews a suite of grand challenges related to assessment of climate change impacts on freshwater resources. Among them are challenges related to the detection and attribution of changes in observed records, projections for the future, changes in hydrologic extremes (floods and droughts), assessing and reducing uncertainty, and adaptation to change under uncertainty. The global water system is very complex, so that it is difficult to disentangle individual contributions of various factors to changes in freshwater variables at any scale. As for detection and attribution of changes in global river discharge in twentieth century, variations in precipitation were the main force. Other major factors were temperature effects on evapotranspiration, direct effects of rising atmospheric ${\mathrm{CO}}_2$ concentration on the physiology and abundance of vegetation, and anthropogenic changes in land cover and land use. A general finding regarding possible future trends in the water cycle is that wet regions will likely become wetter and dry regions to become drier in a warming world. Climate-driven hydrologic changes combine with other pressures on water resources, such as population growth, land use change, changes in life styles increasing water demand, and environmental pollution. A grand global challenge is to provide an adequate basis for adaptation decisions that must be made under strong uncertainty, without reliance on precise projections of changes in hydrologic variables.

In the context of global environmental and social change, with increasing pollution and decline of biodiversity of terrestrial and aquatic ecosystems having its deep roots in drastic modifications to hydrological mesocycles, there is an urgent need for a new approach for sustainability. The two often contradicting approaches to water resources management, i.e., (1) hydrotechnical, and (2) ecological, can be reconciled within the context of ecohydrology (EH). It seeks for the understanding of the underlying water-biota interactions as well as providing a new tool for management of water resources. While the majority of changes are nonreversible in the framework of ecohydrology it is possible to regulate (dual regulation) the processes, especially in novel ecosystems, as an alternative to conservation and restoration measures, in order to increase their carrying capacity in the four dimensions, as follows: (1) water resources, (2) biodiversity, (3) ecosystem services, and (4) resilience. The proposed approach aims to initiate a discussion and joint efforts of hydrological engineers, hydrologists, and ecologists towards formulation of the comprehensive strategy and scientific background for harmonization of society needs with the enhanced ecosystem potential. It stipulates for a change in paradigm not only in the environmental sciences but also in the global economy, engineering, and education systems.