The integrated effects of climate and hydrologic uncertainty on future flood risk assessments

This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether hydrologic uncertainties are a significant source of uncertainty relative to other sources such as climate variability and change and (2) whether a statistical characterization of uncertainty from a lumped, conceptual hydrologic model is sufficient to account for hydrologic uncertainties in the modelling process. To investigate these questions, an ensemble of climate simulations are propagated through hydrologic models and then through a reservoir simulation model to delimit the range of flood protection under a wide array of climate conditions. Uncertainty in mean climate changes and internal climate variability are framed using a risk‐based methodology and are explored using a stochastic weather generator. To account for hydrologic uncertainty, two hydrologic models are considered, a conceptual, lumped parameter model and a distributed, physically based model. In the conceptual model, parameter and residual error uncertainties are quantified and propagated through the analysis using a Bayesian modelling framework. The approach is demonstrated in a case study for the Coralville Dam on the Iowa River, where recent, intense flooding has raised questions about potential impacts of climate change on flood protection adequacy. Results indicate that the uncertainty surrounding future flood risk from hydrologic modelling and internal climate variability can be of the same order of magnitude as climate change. Furthermore, statistical uncertainty in the conceptual hydrological model can capture the primary structural differences that emerge in flood damage estimates between the two hydrologic models. Copyright © 2014 John Wiley & Sons, Ltd.     

Typology of Municipal Wastewater Treatment Technologies in Latin America

This paper presents an analysis of the wastewater treatment plants in six Latin American and Caribbean countries. Based on a sample of 2734 municipal treatment facilities, the applied processes are classified by sizes (influent flow) and type of technologies. The distribution of the technologies is also presented for each of the six countries. In addition, a representative municipal wastewater characterization, based on influent data from 174 treatment plants, is proposed. Results show that stabilization ponds, activated sludge, and the upflow anaerobic sludge blanket reactors represent 80% of the treatment facilities of the sample, providing treatment to 81% of the total flow considered. Moreover, 67% of the plants in the sample are small (flow <25 L/s) and the very small facilities (influent flow <5 L/s) are extensively applied in the region (34% of the sample), especially in Mexico and Brazil. The use of very small treatment plants may result in low energy efficiency systems and on possible incompliance of the discharge standards. This common practice in several countries in Latin America should be revised in order to improve the environmental performance of such facilities.     

Seagrass importance for a small-scale fishery in the tropics: The need for seascape management

Small-scale fisheries (SSF) in tropical seascapes (mosaics of interconnected mangroves, seagrasses and corals) are crucial for food and income. However, management is directed mostly to corals and mangroves. This research analyzes the importance of seagrasses compared to adjacent ecosystems in Chwaka Bay, Zanzibar, Tanzania. Using fish landings; the study investigated: location of fishing effort, fish production (biomass and species), and monetary benefits (aggregated value and per capita income). Seagrasses were the most visited grounds providing highest community benefits. Per capita benefits were equivalent to those from corals and mangroves. All three habitats provided income just above extreme poverty levels; however catches from seagrass appeared more stable. Seagrass are key ecosystems supporting SSF and protection and management are urgently needed. Adoption of a seascape approach considering all ecosystems underpinning SSF and the social aspects of fishing and a shift in emphasis from pure conservation to sustainable resource management would be desirable.     

Weighing the importance of model uncertainty against parameter uncertainty in earthquake loss assessments

Epistemic uncertainties can be classified into two major categories: parameter and model. While the first one stems from the difficulties in estimating the values of input model parameters, the second comes from the difficulties in selecting the appropriate type of model. Investigating their combined effects and ranking each of them in terms of their influence on the predicted losses can be useful in guiding future investigations. In this context, we propose a strategy relying on variance-based global sensitivity analysis, which is demonstrated using an earthquake loss assessment for Pointe-à-Pitre (Guadeloupe, France). For the considered assumptions, we show: that uncertainty of losses would be greatly reduced if all the models could be unambiguously selected; and that the most influential source of uncertainty (whether of parameter or model type) corresponds to the seismic activity group. Finally, a sampling strategy was proposed to test the influence of the experts’ weights on models and on the assumed coefficients of variation of parameter uncertainty. The former influenced the sensitivity measures of the model uncertainties, whereas the latter could completely change the importance rank of the uncertainties associated to the vulnerability assessment step.     

Reference hydrologic networks I. The status and potential future directions of national reference hydrologic networks for detecting trends

Abstract

Identifying climate-driven trends in river flows on a global basis is hampered by a lack of long, quality time series data for rivers with relatively undisturbed regimes. This is a global problem compounded by the lack of support for essential long-term monitoring. Experience demonstrates that, with clear strategic objectives, and the support of sponsoring organizations, reference hydrologic networks can constitute an exceptionally valuable data source to effectively identify, quantify and interpret hydrological change—the speed and magnitude of which is expected to a be a primary driver of water management and flood alleviation strategies through the future—and for additional applications. Reference hydrologic networks have been developed in many countries in the past few decades. These collections of streamflow gauging stations, that are maintained and operated with the intention of observing how the hydrology of watersheds responds to variations in climate, are described. The status of networks under development is summarized. We suggest a plan of actions to make more effective use of this collection of networks.

Editor Z.W. Kundzewicz; Associate editor K. Hamed

Citation Whitfield, P.H., et al., 2012 Burn, D.H. 2012. Reference hydrologic networks, II. Using reference hydrologic networks to assess climate-driven changes in streamflow. Hydrological Sciences Journal, 57(8) (this issue)[Taylor & Francis Online] , [Google Scholar]. Reference hydrologic networks I. The status and potential future directions of national reference hydrologic networks for detecting trends. Hydrological Sciences Journal, 57 (8), 1562–1579.     

A new approach to rapid,desktop-level,environmental flow assessments for rivers in South Africa

Abstract

An approach is presented for desktop-level environmental flow requirement (EFR) determination that is aligned with the Habitat Flow–Stressor Response (HFSR) method which evolved in South Africa over recent years. The HFSR method integrates hydrological, hydraulic and ecological habitat data, involves ecological and hydraulic specialists and is data-intensive and time-consuming. The revised desktop method integrates hydrological information with estimates of channel hydraulic cross-sectional characteristics to generate habitat-type frequencies under changing flow conditions. This information is used with the expected natural habitat requirements to determine acceptable habitat availability under different levels of ecological protection, which is then used with the hydraulic data to define flow regime characteristics that meet the ecological objectives. The paper describes the model components, discusses the assumptions, data requirements and limitations and presents some example results. The revised desktop approach uses approaches that are aligned with the more complex methods and generates results that are similar.

Editor D. Koutsoyiannis; Guest editor M. Acreman

Citation Hughes, D.A., Desai, A.Y., Birkhead, A.L., and Louw, D., 2014. A new approach to rapid, desktop-level, environmental flow assessments for rivers in South Africa. Hydrological Sciences Journal, 59 (3–4), 673–687.     

The role of sensitivity analysis in hydrologic modeling

Sensitivity is a measure of the effect of change in one factor on another factor. Sensitivity analysis is potentially useful in all phases of the modeling process: model formulation, model calibration and model verification. The sensitivity of model parameters should be recognized as a special case of the above general definition. Parametric sensitivity is a vital part of most optimization techniques. However, other facets of sensitivity need to be recognized. The time-dependent nature of sensitivity should be considered in the formulation of hydrologic models. A variety of simplified hydrologic models are used to demonstrate the potential of sensitivity in all phases of the modeling process. The failure to recognize and exploit the potential of sensitivity analysis results primarily from the inadequacy of the mathematical foundations of sensitivity. A comprehensive mathematical framework of sensitivity is provided and additional research needs are identified.     

The soil-moisture zone in a physically-based hydrologic model

The present paper describes an approach to modelling the unsaturated soil-moisture zone in the framework of an integrated physically-based hydrologic response model. It is supposed that the subsurface flow regime may be viewed as two separate entities — a saturated flow system which may be modelled by standard two-dimensional regional techniques, and a single overlying unsaturated zone in which the flow is essentially vertical. Coupling takes place via the definition of saturation at the lower boundary of the unsaturated zone, and via a conservative water balance. Attention is focused on the computational procedure for the unsaturated zone as a self-contained module. The major difficulties are the definition of the interface between the saturated and unsaturated zones, the nonlinear character of the equation used to describe unsaturated flow, the inclusion of realistic atmospheric boundary conditions, and, the interaction between water uptake by plants and available soil-moisture. Each of these points is discussed, in turn, with the emphasis on mathematically formulating the problem in such a way that the most important physical features are reproduced with a minimal amount of computational effort. The text concludes with a few illustrative examples.     

The analysis of some monthly hydrologic time series

The paper presents the analysis of the following time series: monthly average temperatures at Urbana, Illinois (1915–1965); monthly precipitations at Morrisonville, Illinois (1896–1969); and monthly streamflows in the Sangamon River at Monticello, Illinois (1915–1969).The identification of a model for these series had been discussed in a previous paper (Torelli and Chow, 1972). In the latter it was shown that a simple transformation makes the series stationary.The analysis of the series is completed in the present paper. By a joint use of spectral and regression analysis one arrives at the formulation of models in which the deterministic process and the variances are described by means of trigonometric functions. This allows a considerable economy of parameters in comparison with the models based on the 24 sample monthly averages and variances.The practical advantages of such an economy of parameters are discussed.     

Uncertainty in hydrologic modelling for estimating hydrologic response due to climate change (Santiam River,Oregon)

This paper explores the predicted hydrologic responses associated with the compounded error of cascading global circulation model (GCM) uncertainty through hydrologic model uncertainty due to climate change. A coupled groundwater and surface water flow model (GSFLOW) was used within the differential evolution adaptive metropolis (DREAM) uncertainty approach and combined with eight GCMs to investigate uncertainties in hydrologic predictions for three subbasins of varying hydrogeology within the Santiam River basin in Oregon, USA. Predictions of future hydrology in the Santiam River include increases in runoff in the fall and winter months and decreases in runoff for the spring and summer months. One‐year peak flows were predicted to increase whereas 100‐year peak flows were predicted to slightly decrease. The predicted 10‐year 7‐day low flow decreased in two subbasins with little groundwater influences but increased in another subbasin with substantial groundwater influences. Uncertainty in GCMs represented the majority of uncertainty in the analysis, accounting for an average deviation from the median of 66%. The uncertainty associated with use of GSFLOW produced only an 8% increase in the overall uncertainty of predicted responses compared to GCM uncertainty. This analysis demonstrates the value and limitations of cascading uncertainty from GCM use through uncertainty in the hydrologic model, offers insight into the interpretation and use of uncertainty estimates in water resources analysis, and illustrates the need for a fully nonstationary approach with respect to calibrating hydrologic models and transferring parameters across basins and time for climate change analyses. Copyright © 2012 John Wiley & Sons, Ltd.     

Geologic and hydrologic settings for development of freshwater lenses in arid lands

Satellite observations were used to test the validity of previously identified favourable conditions for the formation of freshwater lenses, identify additional potential occurrences, and model modern potential recharge in the Raudhatain Watershed (3696) in northern Kuwait. Favourable conditions include infrequent yet intensive precipitation events, drainage depressions to collect the limited runoff, and presence of conditions (e.g. high infiltration capacity) that promote groundwater recharge and preservation (e.g. underlying saline aquifer) of infiltrating groundwater as freshwater lenses floating over saline aquifer water due to differences in density. Specifically, the following field and satellite‐based observations were noted for the Raudhatain Watershed: (1) Over ~30 precipitation events were identified from the Tropical Rainfall Measuring Mission precipitation data (1998–2009); (2) slope is gentle (2 m/km), and the surface is largely (80%) covered by alluvial deposits with high infiltration capacities (up to 9 m/day); (3) no flows and long‐term ponding were reported at the watershed outlet or detected from Landsat thematic mapper images; (4) infiltration is high based on increases in soil moisture content (from an advanced microwave scanning radiometer) and vegetation index following large precipitation events; and (5) freshwater lenses that overlie highly saline [total dissolved solids (TDS): >35 000] unconfined aquifers underlying the watershed are absent in the southern regions, where infiltrating fresh water mixes with the less saline groundwater (TDS: <10 000). Twenty potential locations (size: 1 to 75 km²) for freshwater lens development were identified in northern Kuwait, and continuous rainfall–runoff models (Soil Water and Assessment Tool) were applied to provide a first‐order estimation of the average annual recharge in the watershed (127 × 10⁶ m³) and freshwater lenses (8.17 × 10⁶ m³). Results demonstrate the settings for enhanced opportunities for groundwater recharge, outline the amounts of and preservation conditions for the groundwater feeding the freshwater lenses, and highlight potential applications and locations of freshwater lenses in similar settings elsewhere in the Arabian Peninsula and beyond. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimation of periodicities in hydrologic data

Periodicites in hydrologic data are frequently estimated and studied. In some cases the periodic components are subtracted from the data to obtain the stochastic components. In other cases the physical reasons for the occurrence of these periodicities are investigated. Apart from the annual cycle in the hydrologic data, periods corresponding to the 11 year sunspot cycle, the Hale cycle and others have been detected.The conclusions from most of these studies depend on the reliability and robustness of the methods used to detect these periodicities. Several spectral analysis methods have been proposed to investigate periodicities in time series data. Several of these have been compared to each other. The methods by Siddiqui and Wang and by Damsleth and Spjotvoll, which are stepwise procedures of spectrum estimation, have not been evaluated.     

Estimation of periodicities in hydrologic data

Periodicites in hydrologic data are frequently estimated and studied. In some cases the periodic components are subtracted from the data to obtain the stochastic components. In other cases the physical reasons for the occurrence of these periodicities are investigated. Apart from the annual cycle in the hydrologic data, periods corresponding to the 11 year sunspot cycle, the Hale cycle and others have been detected.The conclusions from most of these studies depend on the reliability and robustness of the methods used to detect these periodicities. Several spectral analysis methods have been proposed to investigate periodicities in time series data. Several of these have been compared to each other. The methods by Siddiqui and Wang and by Damsleth and Spjotvoll, which are stepwise procedures of spectrum estimation, have not been evaluated.Two of the methods of spectral analysis proposed by Siddiqui and Wang and by Damsleth and Spjotvoll are investigated in this study by using generated and observed data. Siddiqui and Wang's method is found to be superior to the Damsleth and Spjotvoll's method.     

The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.     

Improved estimators of correlation and R2 for skewed hydrologic data

ABSTRACT

The coefficient of determination R² and Pearson correlation coefficient ρ = R are standard metrics in hydrology for the evaluation of the goodness of fit between model simulations and observations, and as measures of the degree of dependence of one variable upon another. We show that the standard product moment estimator of ρ, termed r, while well-behaved for bivariate normal data, is upward biased and highly variable for bivariate non-normal data. We introduce three alternative estimators of ρ which are nearly unbiased and exhibit much less variability than r for non-normal data. We also document remarkable upward bias and tremendous increases in variability associated with r using both synthetic data and daily streamflow simulations from 905 calibrated rainfall–runoff models. We show that estimators of ρ = R accounting for skewness are needed for daily streamflow series because they exhibit high variability and skewness compared to, for example, monthly/annual series, where r should perform well.     

Control of hydrologic systems for multiple uses in a closed-loop framework

The role of linear control theory as an aid to the integral control of hydrologic systems is investigated for the case of a combined lake and aquifer storage system that supplies either a deterministic or stochastic water demand. Only lumped time-invariant systems are considered but both deterministic and stochastic inflows to storage are allowed. The computational example allows for recharge of lake water into the aquifer as well as for the subsequent diversion of pumped groundwater back to the lake. Stability criteria are presented for the closed-loop features of the overall control system. Under a quadratic loss criterion, a calculus of variations problem, subject to constraints imposed by the system equations can be solved for the optimal release policy from the lake and aquifer and optimal feedback policy from aquifer to lake.     

Use of the Beta-Dagum and Beta-Singh-Maddala distributions for modeling hydrologic data

Starting from a recent paper by Murshed (Stoch Environ Res Risk Assess 25:897–911, 2011) in which a good performance of the Beta-k distribution in analyzing extreme hydrologic events is shown, in this paper, we propose the use of two new four-parameters distribution functions strongly related to the Beta-k distribution, namely the Beta-Dagum and the Beta-Singh-Maddala distributions. More in detail, the new distributions are a generalization of a reparametrization of Beta-k and Beta-p distributions, respectively. For these distributions some particular interpretations in terms of maximum and minimum of sequences of random variables can be derived and the maximal and minimal domain of attraction can be obtained. Moreover, the method of maximum likelihood, the method of moments and the method of L-moments are examined to estimate the parameters. Finally, two different applications on real data regarding maxima and minima of river flows are reported, in order to show the potentiality of these two models in the extreme events analysis.     

Role of hypsometry and planform in basin hydrologic response

The effects of variations of drainage basin morphometry and relief characteristics on flood peak magnitude and time-to-peak are investigated using simulated stream networks. The networks are produced by three models: headward growth, systematic capture, and minimum power relaxation. Translational and kinematic wave flood routing were used to generate synthetic hydrographs. Peak discharge and time-to-peak are predictable to a high degree by five different sets of morphometric-relief parameters. In order of decreasing order of importance in predictive ability the parameters characterize basin size, relative relief, basin concavity, and basin shape. Both simulated and natural stream networks exhibit strong dependence of planimetric morphometry upon basin concavity. The effect of this dependency is to increase the effect of basin concavity upon flood hydrographs.     

Spatial interpolation schemes of daily precipitation for hydrologic modeling

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.