Bringing Statistical Learning Machines Together for Hydro-Climatological Predictions - Case Study for Sacramento San Joaquin River Basin, California

Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). New hydrological insights: The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the proposed approach showed better forecasting skills with preprocessed large scale climate variables rather than using the predefined indices. The proposed study is simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds

Seasonal predictability of the winter North Atlantic Oscillation from a jet stream perspective

The winter North Atlantic Oscillation (NAO) has varied on interannual and decadal timescales over the last century, associated with variations in the speed and latitude of the eddy-driven jet stream. This paper uses hindcasts from two operational seasonal forecast systems (the European Centre for Medium-range Weather Forecasts's seasonal forecast system, and the U.K. Met Office global seasonal forecast system) and a century-long atmosphere-only experiment (using the European Centre for Medium-range Weather Forecasts's Integrated Forecasting System model) to relate seasonal prediction skill in the NAO to these aspects of jet variability. This shows that the NAO skill realized so far arises from interannual variations in the jet, largely associated with its latitude rather than speed. There likely remains further potential for predictability on longer, decadal timescales. In the small sample of models analyzed here, improved representation of the structure of jet variability does not translate to enhanced seasonal forecast skill

Stationary waves and slowly moving features in the night upper clouds of Venus

At the cloud top level of Venus (65-70 km altitude) the atmosphere rotates 60 times faster than the underlying surface, a phenomenon known as superrotation. Whereas on Venus's dayside the cloud top motions are well determined and Venus general circulation models predict a mean zonal flow at the upper clouds similar on both day and nightside, the nightside circulation remains poorly studied except for the polar region. Here we report global measurements of the nightside circulation at the upper cloud level. We tracked individual features in thermal emission images at 3.8 and 5.0 $\mathrm{\mu m}$ obtained between 2006 and 2008 by the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS-M) onboard Venus Express and in 2015 by ground-based measurements with the Medium-Resolution 0.8-5.5 Micron Spectrograph and Imager (SpeX) at the National Aeronautics and Space Administration Infrared Telescope Facility (NASA/IRTF). The zonal motions range from -110 to -60 m s$^{-1}$, consistent with those found for the dayside but with larger dispersion. Slow motions (-50 to -20 m s$^{-1}$) were also found and remain unexplained. In addition, abundant stationary wave patterns with zonal speeds from -10 to +10 m s$^{-1}$ dominate the night upper clouds and concentrate over the regions of higher surface elevation.Comment: 15 pages, 4 figures, 6 supplementary figure

Quantifying Uncertainties in the Modelled Estimates of Extreme Precipitation Events at the Upper Thames River Basin

Assessment of climate change impact on hydrology at watershed scale incorporates downscaling of global scale climatic variables into local scale hydrologic variables and computations of risk of hydrologic extremes in future for water resources planning and management. Atmosphere-Ocean General Circulation (AOGCM) models are designed to simulate time series of future climate responses accounting for enthropogenically induced green house gas emissions. The climatological inputs obtained from several AOGCMs suffer the limitations due to incomplete knowledge arising from the inherent physical, chemical processes and the parameterization of the model structure. This study explores the methods available for quantifying uncertainties from the AOGCM outputs by considering fixed weights from different climate model means for the overall data lengths and provides an extensive investigation of the variable weight nonparametric kernel estimator based on the choice of bandwidths for investigating the severity of extreme precipitation events over the next century. The results of this study indicate that the variable width method is better equipped to provide more useful information of the uncertainties associated with different AOGCM scenarios. This study further indicates an increase of probabilities for higher intensities and frequencies of events. The applied methodology is flexible and can be adapted to any uncertainty estimation studies with unknown densities.https://ir.lib.uwo.ca/wrrr/1032/thumbnail.jp

Uncertainty Estimation of Extreme Precipitations Under Climate Change: A Non-Parametric Approach

Assessment of climate change impacts on hydrology at watershed scale incorporates (a) downscaling of global scale climatic variables into local scale hydrologic variables and (b) assessment of future hydrologic extremes. Atmosphere-Ocean Global Climate Models (AOGCM) are designed to simulate time series of future climate responses accounting for human induced green house gas emissions. The present study addresses the following limitations of climate change impact research: (i) limited availability of observed historical information; (ii) limited research on the detection of changes in hydrologic extremes; and (iii) coarse spatio-temporal resolution of AOGCMs for use at regional or local scale. Downscaled output from a single AOGCM with a single emission scenario represents only a single trajectory of all possible future climate realizations and cannot be representative of the full extent of climate change. Present research, therefore addresses the following questions: (i) how should the AOGCM outputs be selected to assess the severity of extreme climate events?; (ii) should climate research adopt equal weights from AOGCM outputs to generate future climate?; and (iii) what is the probability of the future extreme events to be more severe? Assessment of regional reanalysis hydro-climatic data has shown promising potential as an addition to the observed data in data scarce regions. A new approach using statistical downscaling based nonparametric data-driven kernel estimator is developed for quantifying uncertainties from multiple AOGCMs and emission scenarios. The results are compared with a Bayesian reliability ensemble average method. The generated future climate scenarios represent the nature and progression of uncertainties from several global climate models and their emission scenarios. Treating the extreme precipitation indices as independent realization at every time step, the kernel estimator provides variable weights to the multi-model quantification of uncertainties. The probabilities of the extreme indices have added useful insight into future climate conditions. Finally, the current method of developing future rainfall intensity-duration-frequency curves is extended by introducing a probabilistic weighted curve to include AOGCM and emission scenario uncertainties using the plug-in kernel. Present research has thus expanded the existing knowledge of dealing with the uncertainties of extreme events

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept to other positive variables of interest beyond the time domain. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Niño/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictor

Maize open-pollinated populations physiological improvement: validating tools for drought response participatory selection

Participatory selection—exploiting specific adaptation traits to target environments—helps to guarantees yield stability in a changing climate, in particular under low-input or organic production. The purpose of the present study was to identify reliable, low-cost, fast and easy-to-use tools to complement traditional selection for an e ective participatory improvement of maize populations for drought resistance/tolerance. The morphological and eco-physiological responses to progressive water deprivation of four maize open-pollinated populations were assessed in both controlled and field conditions. Thermography and Chl a fluorescence, validated by gas exchange indicated that the best performing populations under water-deficit conditions were ‘Fandango’ and to a less extent ‘Pigarro’ (both from participatory breeding). These populations showed high yield potential under optimal and reduced watering. Under moderate water stress, ‘Bilhó’, originating from an altitude of 800 m, is one of the most resilient populations. The experiments under chamber conditions confirmed the existence of genetic variability within ‘Pigarro’ and ‘Fandango’ for drought response relevant for future populations breeding. Based on the easiness to score and population discriminatory power, the performance index (PIABS) emerges as an integrative phenotyping tool to use as a refinement of the common participatory maize selection especially under moderate water deprivationinfo:eu-repo/semantics/publishedVersio