Visionary Reflections from a Crystal Clear Pool of Water Scientists

Our goal is to keep the planet blue For that we need some green To justify our requests we need not dream A surfeit of quandaries around us gleam Pondering growing bread can make some of us see red. The phosphorescence of sheets white and bright may impede our amity with creatures of the bight. A lot of what we put in the air accumulates in receptors beyond repair. Inscribing the chain of cause and effect in blood could lead to a flood Keeping our clients mellow with trustworthy numbers can turn us yellow Even as on issues wet and profligate we readily pontificate Integrating the disciples of many creeds is the cry to keep the well from running dry

A Kernel Quantile Function Estimator For Flood Frequency Analysis

A kernel estimator (KQ) of the quantile function is presented here. Boundary kernels are used for extrapolation of tail quantiles. The bandwidth of the estimator is chosen using an automatic, plus-in method. Confidence intervals for the estimated quantile are estimated by bootstrapping. Comparisons of the estimator with selected tail probability estimators are offered. The KQ estimator presented here is shown to be competitive with other estimators

The Great Salt Lake: A Barometer of Low Frequency Climate Variability

Low frequency (interannual or longer period) climatic variability is of interest bacause of its sugnificance for the understanding and prediction of protracted climatic anomalies. Closed basin lakes are sensitive to long term climatic fluctuations and integrate out high frequency variability. It is thus natural to examine the records of such lakes to better understand long term climate dynamics. Here we use Singular Spectral Analysis (SSA) and Multi-Taper Spectral Analysis (MTM) to analyze the time series of Great Slat Lake (GSL) monthly volume changes from 1848-1992, and monthly precipitation, temperature and streamflow for nearby stations with 74 or more years of data. This analysis reveals high fractional variance in 15-18, 10-12, 3-7 and 2 year frequency bands, which seem to be consistent across time series. The putative decadal and interdecadal signals appear to be related to large scale climate signals discussed recently. The interannual signals are consistent with El Nino Souther Oscillation (ENSO) and quasi-biennial variability identified by others. Propects for improved prediction of the GSL volume and of protreacted wet/dry periods in the Western United States are discussed

Nonparametric Stratigraphic Interpretation from Drill Log Data

Near surface groundwater contamination is a widespread problem. The potential for contamination of deep aquifers in such areas depends on the vertical hydraulic gradient as well as the extent and location of interconnection between the upper and lower aquifers. In alluvial, sedimentary environment, the geologic units are typically weakly connected lenses or layers of high or low conductivity with variable size, geometry and orientation. Drill logs provide qualitative, local information on such aquifer heterogeneities. A binary (high or low conductivity) indicator function is used to describe the aquifer system. A nonparametric statistical methodology for assessing the probability that a particular location in the aquifer has high or low conductivity using drill log information is developed. The stochastic behavior of the sedimentary process in the vertical is of particular interest. Example applications using data from Lake Bonneville deposits in Salt Lake County, Utah are presented

Novel stacking models for improved extreme rainfall predictions under climate change scenarios.

Future projections under global warming scenarios of local extreme precipitations by downscaling models is still open challenge. A number of downscaling statistical models have been proposed to link large scale atmospheric circulation features, as simulated by Global Circulation Models (GCMs) and/or Regional Circulation Models (RCMs), to the temporal and spatial distribution of local rainfalls. Despite the efforts, comparisons between simulations and observations show that statistical downscaling methods, although able to realistically reproduce most of the mean rainfall attributes as seasonal or monthly rainfall amount, fail to simulate extreme precipitation with acceptable accuracy. This is due to the difficulties to: (i) select the optimal set of atmospheric variables used as predictors; (ii) solve the non-linear dependencies that link the rains to the atmospheric variables; (iii) assess the temporal dependencies between wet and dry states. To overcome such criticalities, in order to improve extreme precipitation forecasting, in this study we introduce in rainfall downscaling a paradigm already known in other disciplines of data science: the "stacking models". Stacking models combine different simulations from multiple predictive models. According to this approach we used Random Forest, extreme gradient boosting and Non-homogeneous Hidden Markov Model (NHMM). The validation was performed first on the individual models, calibrating the parameters individually and evaluating them globally with a cross validation approach. The performance of the proposed stacking model is assessed by comparing the daily rainfall amount simulations with those obtained by a state-of-the-art NHMM model, in which the probability of the rainfall occurrence is just modeled using a logistic regression with parameters depending upon climatology variables. We show that the stacking model outperforms the latter model, especially in simulating the extreme precipitations. Furthermore, such performance improvement is obtained by using a minor number of atmospheric predictors. Once the downscaling model has been calibrated and validated, we evaluated changes of precipitation extremes under climate change scenarios. The simulations were performed using the variables obtained from a GCM, Community Climate System Model v4 - NCAR, whose scenario is defined by CMIP5 - RCP 8.5. To evaluate the confidence bands of the simulated rainfall it was used an ensemble of simulations obtained by running the latter GCM with different initial conditions. The Lazio region was chosen as a study case. The Lazio Region is located in Central Italy, whose hydrogeological features make it particularly vulnerable to eventual future changes of hydrological cycle such as those induced by climate change. The Mediterranean is made up of many of these vulnerable areas, which makes the application of the method to this case study of general interest

21st Century Projections of High Streamflow Events in the UK and Germany

Radiative effects of anthropogenic changes in atmospheric composition are expected to enhance the hydrological cycle leading to more frequent and intense floods. To explore if there will be an increased risk of river flooding in the future, 21st century projections under global warming scenarios of High Streamflow Events (HSEs) for UK and German rivers are carried out, using a model that statistically relates large-scale atmospheric predictors - 850 hPa Geopotential Height (GPH850) and Integrated Water Vapor Transport (IVT) - to the occurrence of HSEs in one or simultaneously in several streamflow gauges. Here, HSE is defined as the streamflow exceeding the 99th percentile of daily flowrate time series measured at streamflow gauges. For the common period 1960-2012, historical data from 57 streamflow gauges in UK and 61 streamflow gauges in Germany, as well as, reanalysis data of GPH850 and IVT fields, bounded from 90W to 70E and from 20N to 80N are used. The link between GPH850 configurations and HSEs, and more precisely, identification of the GPH850 states potentially able to generate HSEs, is performed by a combined Kohonen Networks (Self Organized Map, SOM) and Event Syncronization approach. Complex network and modularity methods are used to cluster streamflow gauges that share common GPH850 configurations. Then a model based on a conditional Poisson distribution, in which the parameter of the Poisson distribution is assumed to be a nonlinear function of GPH850 and IVT, allows for the identification of GPH850 state and threshold of IVT beyond which there is the HSE highest probability. Using that model, projections of 21st century changes in frequency of HSEs occurrence in UK and Germany are estimated using the simulated fields of GPH850 and IVT from selected GCMs belonging to the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Among the different GCMs, those are selected whose retrospective predictor fields have consistent statistics with the corresponding reanalysis data

A Combined Atmospheric Rivers and Geopotential Height Analysis for the Detection of High Streamflow Event Probability Occurrence in UK and Germany

The role of atmospheric rivers (ARs) in inducing High Streamflow Events (HSEs) in Europe has been confirmed by numerous studies. Here, we assume as HSEs the streamflows exceeding the 99th percentile of daily flowrate time series measured at streamflow gauges. Among the indicators of ARs are: the Integrated Water Vapor (IWV) and Integrated Water Vapor Transport (IVT). For both indicators the literature suggests thresholds in order to identify ARs. Furthermore, local thresholds of such indices are used to assess the occurrence of HSEs in a given region. Recent research on ARs still leaves room for open issues: 1) The literature is not unanimous in defining which of the two indicators is better. 2) The selection of the thresholds is based on subjective assessments. 3) The predictability of HSEs at the local scale associated with these indices seems to be weak and to exist only in the winter months. In order to address these issues, we propose an original methodology: (i) to choose between the two indicators which one is the most suitable for HSEs predictions; (ii) to select IWT and/or IVT (IVT/IWV) local thresholds in a more objective way; (iii) to implement an algorithm able to determine whether a IVT/IWV configuration is inducing HSEs, regardless of the season. In pursuing this goal, besides IWV and IVT fields, we introduce as further predictor the geopotential height at 850 hPa (GPH850) field, that implicitly contains information about the pattern of temperature, direction and intensity of the winds. In fact, the introduction of the GPH850 would help to improve the assessment of the occurrence of HSEs throughout the year. It is also plausible to hypothesize, that IVT/IWV local thresholds could vary in dependence of the GPH850 configuration. In this study, we propose a model to statistically relate these predictors, IVT/IWV and GPH850, to the simultaneous occurrence of HSEs in one or more streamflow gauges in UK and Germany. Historical data from 57 streamflow gauges in UK and 61 streamflow gauges in Germany, as well as reanalysis data of the 850 hPa geopotential fields bounded from 90W to 70E and from 20N to 80N are used. The common period is 1960 to 2012. The link between GPH850 and HSEs, and more precisely, the identification of the GPH850 states potentially able to generate HSEs is performed by a combined Kohonen Networks (Self Organized Map, SOM) and Event Syncronization approach. Complex network and modularity methods are used to cluster streamflow gauges that share common GPH850 configurations. Then a model based on a conditional Poisson distribution is carried out, in which the parameter of the Poisson distribution is assumed to be a nonlinear function of GPH850 state and IVT/ IWV. This model allows for the identification of the threshold of IVT/IWV beyond which there is the HSE highest probabilit

Interpretation of Drill Log Data: DLOG3D - A Probabilistic Tool for Analyzing Subsurface Soil Variability

Groundwater contamination potential is directly related to the location of zones of high hydraulic conductivity. Sedimentary depositional environments can be quite complex. Pockets of sand of varying thickness and orientation may be scattered over the aquifer. A contaminant pathway can esist where these pockets or lenses are interconnected. Aquifer pump tests do not provide useful information on the occurrence of such lenses. Drill log data directly sample the soil, but may represent very local sections of the aquifer, and are of relatively poor quality. Here we present a methodology (DLOG3D) that allows a probabilistic interpretation of drill log data that accounts for these uncertainties. DLOG3D output can be used to assess the likelihood that a zone of high conductivity exists in a location, and/or to map such zones in the vertical and the horizontal

Simulation of Daily Precipitation from a Nonparametric Renewal Model

Wet/dry spell characteristics of daily precipitation are of interest for a number of hydrologic applications (e.g., flood forecasting or assessment of erosion potential). Here, we examine issues related to designing an appropriate nonparametric scheme that focuses on spell characteristics for resampling historically daily precipitation data. A subset of the nonparametric wet/dry spell model presented in Lall et al (1993) is tested with synthetic data to justify the strategy proposed for applications. An application of the nonparametric wet/dry spell model to a Utah data set follows. Performance is judged on a set of statistical measures. Numerical comparisons of model performance with a parametric alternating renewal model are also offered for this data set. our presentation stresses data exploratory aspects rather than formal hypothesis testing