ALBERT

Description: At present continental to global scale flood forecasting predicts at a point discharge, with little attention to detail and accuracy of local scale inundation predictions. Yet, inundation variables are of interest and all flood impacts are inherently local in nature. This paper proposes a large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas. The model was built for the Lower Zambezi River to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. ECMWF ensemble forecast (ENS) data were used to force the VIC (Variable Infiltration Capacity) hydrological model, which simulated and routed daily flows to the input boundary locations of a 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst representing the floodplain at an appropriate scale. The modeling system was calibrated using channel water levels from satellite laser altimetry and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of between one and two model resolutions compared to an observed flood edge and inundation area agreement was on average 86%. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km 2 and at model grid resolutions up to several km 2 .

Description: A class of capillary flows in unsaturated porous media is characterized by quasi-steady viscous flow confined behind curved air-water interfaces and within liquid bodies held by capillary forces along crevices and grain contacts. The geometry of the connected capillary liquid network within the pore space resembles channels that form between adjacent bubbles in foam (Plateau borders) with solid grains representing gas bubbles in foam. For simplified channel geometry we combine expressions for viscous flow with continuity considerations to describe the evolution of the channels cross-sectional area during gravity drainage. This formulation enables modeling of unsaturated flow without invoking the Richards equation and associated hydraulic functions. We adapt a formalism originally developed for foam “free drainage” (drainage under gravity) or “forced drainage” (infiltration front motion) to a class of unsaturated flows in porous media that require a few input parameters only (mean channel corner angle, air entry value and porosity) for certain initial and boundary conditions. We demonstrate that the reduction in capillary channel cross section yields a consistent description of self-regulating internal fluxes towards attainment of the so-called “field capacity” in soil and provides an alternative method for interpretation of outflow experiments for prescribed pressure boundary conditions. Additionally, the geometrically-explicit formulation provides a more intuitive picture of capillary flows across textural boundaries (changes in channel cross-section and number of channels). The foam drainage methodology expands the range of tools available for analyses of unsaturated flow processes and offers more realistic links between liquid configuration and flow dynamics in unsaturated porous media.

Description: Synthetic streamflows at different sites in a river basin are needed for planning, operation and management of water resources projects. Modeling the temporal and spatial dependence structure of monthly streamflow at different sites is generally required. In this study, the maximum entropy copula method is proposed for multisite monthly streamflow simulation, in which the temporal and spatial dependence structure is imposed as constraints to derive the maximum entropy copula. The monthly streamflows at different sites are then generated by sampling from the conditional distribution. A case study for the generation of monthly streamflow at three sites in the Colorado River basin illustrates the application of the proposed method. Simulated streamflow from the maximum entropy copula is in satisfactory agreement with observed streamflow.

Description: The Krycklan Catchment Study (KCS) provides a unique field infrastructure for hillslope to landscape-scale research on short and long-term ecosystem dynamics in boreal landscapes. The site is designed for process-based research assessing the role of external drivers including forest management, climate change, and long-range pollutant transport on forests, mires, soils, streams, lakes and groundwater. The over-arching objectives of KCS are to (1) provide a state-of-the-art infrastructure for experimental and hypothesis driven research, (2) maintain a collection of high quality, long-term climatic, biogeochemical, hydrological and environmental data, and (3) support the development of models and guidelines for research, policy and management.

Description: 3-D Hydraulic Tomography (3-D HT) is a method for aquifer characterization whereby the 3-D spatial distribution of aquifer flow parameters (primarily hydraulic conductivity, K) is estimated by joint inversion of head change data from multiple partially-penetrating pumping tests. While performance of 3-D HT has been studied extensively in numerical experiments, few field studies have demonstrated the real-world performance of 3-D HT. Here we report on a 3-D transient hydraulic tomography (3-D THT) field experiment at the Boise Hydrogeophysical Research Site which is different from prior approaches in that it represents a “baseline” analysis of 3-D THT performance using only a single arrangement of a central pumping well and 5 observation wells with nearly complete pumping and observation coverage at 1m intervals. We jointly analyze all pumping tests using a geostatistical approach based on the quasi-linear estimator of kitanidis [1995]. We reanalyze the system after progressively removing pumping and/or observation intervals; significant progressive loss of information about heterogeneity is quantified as reduced variance of the K field overall, reduced correlation with slug test K estimates at wells, and reduced ability to accurately predict independent pumping tests. We verify that imaging accuracy is strongly improved by pumping and observational densities comparable to the aquifer heterogeneity geostatistical correlation lengths. Discrepancies between K profiles at wells, as obtained from HT and slug tests, are greatest at the tops and bottoms of wells where HT observation coverage was lacking.

Description: Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical post-processing is required. In this work Bayesian model averaging (BMA) is applied to statistically post-process ensemble runoff raw forecasts for a catchment in Switzerland, at lead-times ranging from 1 to 240 hours. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead-time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead-times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead-times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the post-processed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

Description: We derive a series solution for the nonlinear Boussinesq equation in terms of the similarity variable of the Boltzmann transformation in a semi-infinite domain. The first few coefficients of the series have been known for a long time, having been obtained by a truncated inversion of the series solution of the Blasius equation, but no direct recurrence relation was known for the complete series representing the solution of the Boussinesq equation. The series turns out to have a finite radius of convergence, which we estimate with a numerical complex-plane integration method that identifies the singularities of the solution when the equation is extended to the complex plane. The homogeneous condition at the origin produces a singularity which complicates numerical solutions with Runge-Kutta methods. We present two variable transformations that circumvent the problem and that are best suited to the complex-variable and the real-variable versions of the equation, respectively. Using those tools, an approximate solution accurate to 1.75 × 10 -10 and valid for the entire positive real axis is then developed by matching a Padé approximant of the exact series and an asymptotic solution (to overcome the restriction imposed by the finite radius of convergence of the series), along the same lines of the expression proposed by Hogarth and Parlange [1999]. The accuracies of all of the existing and the newly proposed solutions are obtained.

Description: Field sampling in unwadeable and flashy flood events encounters the problem that lateral variability of flow hydraulics and sediment transport cannot be captured adequately, and there is also an accuracy problem because parameters change while being measured. Moreover, event based gravel-sand mixed transport data in rapidly changing conditions are largely missing, in particular for gravel-bed rivers in small catchments. In this study, field measurements of bed load, suspended load, flow velocities, water depths and cross section geometry were collected during flood events at a monitoring station near the mouth of the Versilia river, Italy. Since the observed hydrographs are characterized by short durations, to the order of a few hours, an analysis of the lateral and temporal flow variability was carried out to enable the design of a sampling strategy and to minimize the errors created by the time variations of discharge associated with unsteady flow conditions. The measurements were interpreted using a 1D hydro-morphodynamic numerical model simulating the dynamics of flow and sediment discharges during a flood event for a given return period. The flow and sediment rating curves were then developed through an integrated approach combining different methodologies: field measurements, laboratory analyses and mathematical modeling. The developed approach allows one to capture the main physical mechanisms associated to the transport of sand–gravel mixtures, such as selective transport, and the hysteretic behaviour of sediment transport produced by rapid and intense flood events.

Description: ABSTRACT A study was performed to characterize over land precipitation associated with tropical cyclones (TCs) for basins around the world based upon the International Best Track Archive for Climate Stewardship (IBTrACS). From 1998 to 2009, data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product 3B42, showed that TCs accounted for 5.5%, 7.5%, 6%, 9.5%, and 8.9% of the annual precipitation for impacted over land areas of the Americas, East Asia, South and West Asia, Oceania, and East Africa respectively, and that TC contribution decreased significantly within the first 150-km from the coast. Locally, TCs contributed on average to more than 25% and up to 61% of the annual precipitation budget over very different climatic areas with arid or tropical characteristics. East Asia represented the higher and most constant TC rain (118±19% mm y -1 ) normalized over the area impacted, while East Africa presented the highest variability (108±60% mm y -1 ), and the Americas displayed the lowest average TC rain (65±24% mm y -1 ) despite a higher TC activity. Furthermore, the maximum monthly TC contribution (8-11%) was found later in the TC season and depended on the peak of TC activity, TC rainfall, and the domain transition between dry and wet regimes if any. Finally, because of their importance in terms of rainfall amount, the contribution of TCs was provided for a selection of 50 urban areas experiencing cyclonic activity. Results showed that for particularly intense years, urban areas prone to cyclonic activity received more than half of their annual rainfall from TCs.

Description: A multivariate, multi-site daily weather generator is presented for use in decision-centric vulnerability assessments under climate change. The tool is envisioned useful for a wide range of socioeconomic and biophysical systems sensitive to different aspects of climate variability and change. The proposed stochastic model has several components, including 1) a wavelet decomposition coupled to an autoregressive model to account for structured, low-frequency climate oscillations, 2) a Markov Chain and k-nearest-neighbor (KNN) resampling scheme to simulate spatially-distributed, multivariate weather variables over a region, and 3) a quantile mapping procedure to enforce long-term distributional shifts in weather variables that result from prescribed climate changes. The Markov Chain is used to better represent wet and dry spell statistics while the KNN bootstrap resampler preserves the covariance structure between the weather variables and across space. The wavelet-based autoregressive model is applied to annual climate over the region and used to modulate the Markov Chain and KNN resampling, embedding appropriate low-frequency structure within the daily weather generation process. Parameters can be altered in any of the components of the proposed model to enable the generation of realistic time series of climate variables that exhibit changes to both lower-order and higher-order statistics at long-term (inter-annual), mid-term (seasonal), and short-term (daily) timescales. The tool can be coupled with impact models in a bottom-up risk assessment to efficiently and exhaustively explore the potential climate changes under which a system is most vulnerable. An application of the weather generator is presented for the Connecticut River basin to demonstrate the tool's ability to generate a wide range of possible climate sequences over an extensive spatial domain.