Distribution Of Hydrological Losses For Varying Rainfall And Antecedent Wetness Conditions

Hydrological loss is a vital component in many hydrological models, which are usedin forecasting floods and evaluating water resources for both surface and subsurface flows. Due to the complex and random nature of the rainfall runoff process, hydrological losses are not yet fully understood. Consequently, practitioners often use representative values of the losses for design applications such as rainfall-runoff modelling which has led to inaccurate quantification of water quantities in the resulting applications. The existing hydrological loss models must be revisited and modellers should be encouraged to utilise other available data sets. This study is based on three unregulated catchments situated in Mt. Lofty Ranges of South Australia (SA). The paper focuses on conceptual models for: initial loss (IL), continuing loss (CL) and proportional loss (PL) with rainfall characteristics (total rainfall (TR) and storm duration (D)), and antecedent wetness (AW) conditions. The paper introduces two methods that can be implemented to estimate IL as a function of TR, D and AW. The IL distribution patterns and parameters for the study catchments are determined using multivariate analysis and descriptive statistics. The possibility of generalising the methods and the limitations of this are also discussed. This study will yield improvements to existing loss models and will encourage practitioners to utilise multiple data sets to estimate losses, instead of using hypothetical or representative values to generalise real situations.

Uncertainty Characterization Of A Conceptual Rainfall-Runoff Model By Using Grey Numbers

A procedure for characterizing global uncertainty of a rainfall-runoff simulation model based on using grey numbers is presented. By using the grey numbers technique the uncertainty is characterized by an interval; once the parameters of the rainfall-runoff model have been properly defined as grey numbers, by using the grey mathematics and functions it is possible to obtain simulated discharges in the form of grey numbers whose envelope defines a band which represents the vagueness/uncertainty associated with the simulated variable. The grey numbers representing the model parameters are estimated in such a way that the band obtained from the envelope of simulated grey discharges includes an assigned percentage of observed discharge values and is at the same time as narrow as possible. The approach is applied to a real case study highlighting that a rigorous application of the procedure for direct simulation through the rainfall-runoff model with grey parameters involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the grey numbers representing the simulated discharges. Relying on this simplified procedure, the conceptual rainfall-runoff grey model is thus calibrated and the uncertainty bands obtained both downstream of the calibration process and downstream of the validation process are compared with those obtained by using a well-established approach, like the GLUE approach, for characterizing uncertainty. The results of the comparison show that the proposed approach may represent a valid tool for characterizing the global uncertainty associable with the output of a rainfall-runoff simulation model.

Uncertainties In Future Projections Of Extreme Rainfall: The Role Of Climate Model, Emission Scenario And Randomness

Climate change has resulted in substantial variations in annual extreme rainfall quantiles in different durations and return periods. Predicting the future changes in extreme rainfall quantiles is essential for various water resources design, assessment, and decision making purposes. Current Predictions of future rainfall extremes, however, exhibit large uncertainties. According to extreme value theory, rainfall extremes are rather random variables, with changing distributions around different return periods; therefore there are uncertainties even under current climate conditions. Regarding future condition, our large-scale knowledge is obtained using global climate models, forced with certain emission scenarios. There are widely known deficiencies with climate models, particularly with respect to precipitation projections. There is also recognition of the limitations of emission scenarios in representing the future global change. Apart from these large-scale uncertainties, the downscaling methods also add uncertainty into estimates of future extreme rainfall when they convert the larger-scale projections into local scale. The aim of this research is to address these uncertainties in future projections of extreme rainfall of different durations and return periods. We plugged 3 emission scenarios with 2 global climate models and used LARS-WG, a well-known weather generator, to stochastically downscale daily climate models’ projections for the city of Saskatoon, Canada, by 2100. The downscaled projections were further disaggregated into hourly resolution using our new stochastic and non-parametric rainfall disaggregator. The extreme rainfall quantiles can be consequently identified for different durations (1-hour, 2-hour, 4-hour, 6-hour, 12-hour, 18-hour and 24-hour) and return periods (2-year, 10-year, 25-year, 50-year, 100-year) using Generalized Extreme Value (GEV) distribution. By providing multiple realizations of future rainfall, we attempt to measure the extent of total predictive uncertainty, which is contributed by climate models, emission scenarios, and downscaling/disaggregation procedures. The results show different proportions of these contributors in different durations and return periods.

Extension And Testing Of A 2D Hydrodynamic Model For Direct Rainfall Runoff Simulation

While the simulation of flood risks originating from the overtopping of river banks is well covered within continuously evaluated programs to improve flood protection measures, flash flooding is not. Flash floods are triggered by short, local thunderstorm cells with high precipitation intensities. Small catchments have short response times and flow paths and convective thunder cells may result in potential flooding of endangered settlements. Assessing local flooding and pathways of flood requires a detailed hydraulic simulation of the surface runoff. Hydrological models usually do not incorporate surface runoff at this detailedness but rather empirical equations are applied for runoff detention. In return 2D hydrodynamic models usually do not allow distributed rainfall as input nor are any types of soil/surface interaction implemented as in hydrological models. Considering several cases of local flash flooding during the last years the issue emerged for practical reasons but as well as research topics to closing the model gap between distributed rainfall and distributed runoff formation. Therefore, a 2D hydrodynamic model, depth-averaged flow equations using the finite volume discretization, was extended to accept direct rainfall enabling to simulate the associated runoff formation. The model itself is used as numerical engine, rainfall is introduced via the modification of waterlevels at fixed time intervals. The paper not only deals with the general application of the software, but intends to test the numerical stability and reliability of simulation results. The performed tests are made using different artificial as well as measured rainfall series as input. Key parameters of the simulation such as losses, roughness or time intervals for water level manipulations are tested regarding their impact on the stability.

RWater – A Cyber Enabled Tool For Hydrologic Modeling And Analysis

Existing distributed hydrologic models are complex and computationally demanding for using as a rapid-forecasting policy-decision tool, or even as a class-room educational tool. In addition, platform dependence, specific input/output data structures and non-dynamic data-interaction with pluggable software components inside the existing proprietary frameworks make these models restrictive only to the specialized user groups. RWater is a web-based hydrologic analysis and modeling framework that utilizes the commonly used R software within the HUBzero cyber infrastructure of Purdue University. RWater is designed as an integrated framework for distributed hydrologic simulation, along with subsequent parameter optimization and visualization schemes. RWater provides platform independent web-based interface, flexible data integration capacity, grid-based simulations, and user-extensibility. RWater uses RStudio to simulate hydrologic processes on raster based data obtained through conventional GIS pre-processing. The program integrates Shuffled Complex Evolution (SCE) algorithm for parameter optimization. Moreover, RWater enables users to produce different descriptive statistics and visualization of the outputs at different temporal resolutions. The applicability of RWater will be demonstrated by application on two watersheds in Indiana for multiple rainfall events.

Statistical modelling of extreme rainfall in Madeira Island

Extreme rainfall events have triggered a significant number of flash floods in Madeira Island along its past and recent history. Madeira is a volcanic island where the spatial rainfall distribution is strongly affected by its rugged topography. In this thesis, annual maximum of daily rainfall data from 25 rain gauge stations located in Madeira Island were modelled by the generalised extreme value distribution. Also, the hypothesis of a Gumbel distribution was tested by two methods and the existence of a linear trend in both distributions parameters was analysed. Estimates for the 50– and 100–year return levels were also obtained. Still in an univariate context, the assumption that a distribution function belongs to the domain of attraction of an extreme value distribution for monthly maximum rainfall data was tested for the rainy season. The available data was then analysed in order to find the most suitable domain of attraction for the sampled distribution. In a different approach, a search for thresholds was also performed for daily rainfall values through a graphical analysis. In a multivariate context, a study was made on the dependence between extreme rainfall values from the considered stations based on Kendall’s τ measure. This study suggests the influence of factors such as altitude, slope orientation, distance between stations and their proximity of the sea on the spatial distribution of extreme rainfall. Groups of three pairwise associated stations were also obtained and an adjustment was made to a family of extreme value copulas involving the Marshall–Olkin family, whose parameters can be written as a function of Kendall’s τ association measures of the obtained pairs.

Influência de fatores ambientais na incidência do vírus da infecção hipodermal e necrose hematopoiética (IHHNV) no camarão Litopenaeus vannamei cultivado em fazendas do Estado do Rio Grande do Norte (RN)

The shrimp farming industry is the most profitable area of the aquaculture at Rio Grande do Norte (RN) state, which is one of the largest producers in Brazil. However the infections that affect the shrimp cause major economic losses. The infection is a result of the interaction between the shrimp, the environment and pathogen. The change of these factors may lead to a condition of stress and susceptibility to opportunistic infections. One of these infections caused by Infectious Hypodermal and Hematopoietic Necrosis Virus (IHHNV) is widely distributed in several countries and affects a wide range of hosts. To optimize conditions for production of Litopenaeus vannamei shrimp, the more species cultivated in Brazil, it is necessary to understand the effects of environmental factors in the susceptibility of this species to infections. The aim of this study was to determine the IHHNV prevalence and to investigate the influence of environmental factors as salinity, temperature, stocking density, dissolved oxygen and rainfall in the IHHNV incidence in L. vannamei grown in farms, in the RN state. To determine the IHHNV prevalence were used 1089 samples of L. vannamei collected in seven farms. To perform the study about the influence of environmental factors, 525 samples of L. vannamei shrimp were collected in eight farms located in regions of low (0-1 ), medium (21-30 ) and high (38-57 ) salinity, using extensive (≤15 shrimp/m2 ), semi-intensive (18-33 shrimp/m2) or intensive (>36 shrimp/m2) stocking density systems. The IHHNV infection was determined in pleopod and hemolymph using the polymerase chain reaction (PCR). The environmental factors were recorded during the collection of animals, using a refractometer to measure the salinity and a multi-parameter meter to measure the temperature and concentration of dissolved oxygen in the water. The IHHNV prevalence in RN was 43% (468 infected shrimp out of 1089), varying on different farms. On the seven farms studied, IHHNV prevalence ranged from 18.6% to 54.8%. The infection rates in the shrimp cultured in low, medium and high salinity were respectively 43.10% (125/290), 31.2% (15/48) and 24.6% (46/187) and was significantly higher in shrimp grown in low salinity (P<0.001). The infection rates in ponds of extensive, semi-intensive and intensive systems were respectively, 28.7%, 28.28% and 47.84%, and was significantly higher in high stocking densities (P<0.001). This study indicated a high IHHNV prevalence and a significant effect of salinity and stocking density, but not of the temperature, rainfall and dissolved oxygen on the IHHNV infection rate in the L. vannamei shrimp cultured in the northeastern Brazil

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Retrievals of a size parameter for phytoplankton and spectral light absorption by colored detrital matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil

Many efforts are currently oriented toward extracting more information from ocean color than the chlorophyll a concentration. Among biological parameters potentially accessible from space, estimates of phytoplankton cell size and light absorption by colored detrital matter (CDM) would lead to an indirect assessment of major components of the organic carbon pool in the ocean, which would benefit oceanic carbon budget models. We present here 2 procedures to retrieve simultaneously from ocean color measurements in a limited number of bands, magnitudes, and spectral shapes for both light absorption by CDM and phytoplankton, along with a size parameter for phytoplankton. The performance of the 2 procedures was evaluated using different data sets that correspond to increasing uncertainties: ( 1) measured absorption coefficients of phytoplankton, particulate detritus, and colored dissolved organic matter ( CDOM) and measured chlorophyll a concentrations and ( 2) SeaWiFS upwelling radiance measurements and chlorophyll a concentrations estimated from global algorithms. In situ data were acquired during 3 cruises, differing by their relative proportions in CDM and phytoplankton, over a continental shelf off Brazil. No local information was introduced in either procedure, to make them more generally applicable. Over the study area, the absorption coefficient of CDM at 443 nm was retrieved from SeaWiFS radiances with a relative root mean square error (RMSE) of 33%, and phytoplankton light absorption coefficients in SeaWiFS bands ( from 412 to 510 nm) were retrieved with RMSEs between 28% and 33%. These results are comparable to or better than those obtained by 3 published models. In addition, a size parameter of phytoplankton and the spectral slope of CDM absorption were retrieved with RMSEs of 17% and 22%, respectively. If these methods are applied at a regional scale, the performances could be substantially improved by locally tuning some empirical relationships.

Effects of rainfall on water quality in six sequentially disposed fishponds with continuous water flow

O estudo foi efetuado durante o período de chuva (dezembro-fevereiro) em seis viveiros de produção semi-intensiva de peixes, a fim de avaliar o efeito da chuva na qualidade da água de viveiros que apresentam fluxo contínuo de água, a qual é passada de um viveiro para outro sem tratamento prévio. Foram amostrados oito pontos de coleta nas saídas dos viveiros. O viveiro P1 (próximo à nascente) apresentou as menores concentrações físicas e químicas da água e as maiores no viveiro P4 (considerado um ponto crítico recebendo material alóctone proveniente de outros viveiros e do escoamento do setor de criação de rãs). A disposição seqüencial dos viveiros estudados promoveu aumento nas concentrações dos nutrientes, clorofila-a e condutividade. As chuvas características desta época do ano aumentaram o fluxo de água nos viveiros e conseqüentemente, carreando material particulado e dissolvido de um viveiro para outro e, promovendo um aumento das variáveis limnológicas em direção do P3 ao P6. Os resultados sugerem que a chuva no período de estudo afetou positivamente a qualidade da água dos viveiros estudados, porém, como os sistemas analisados estão dispostos em distribuição seqüencial e escoamento constante da água de viveiros e tanques paralelos sem tratamento prévio, cuidados devem ser averiguados para que o aumento do fluxo de água provocado pelas chuvas não tenha efeito adverso nos viveiros estudados.

Analysis of the Priestley-Taylor alpha parameter for a bean crop

This work deals with the Priestley-Taylor model for evapotranspiration in different grown stages of a bean crop. Priestley and Taylor derived a practical Formulation for energy partitioning between the sensible and latent heat fluxes through the a parameter. Bowen ratio energy balance (BREB) was carried out for daily sensible and latent heat flux estimations in three different crop stages. Mean daily values of Priestley-Taylor a parameter were determined for eleven days during the crop cycle. Diurnal variation patterns of a are presented for the growing, flowering and graining periods. The mean values of 1.13 +/- 0.33, 1.26 +/- 0.74, 1.22 +/- 0.55 were obtained for a day in the growing, in the flowering and for graining periods, respectively. Eleven days values of a are shown and gave a mean value of 1.23 +/- 0.10 which agree on the reported literature.

Effect of temperature, leaf wetness, and rainfall on the production of Guignardia citricarpa ascospores and on back spot severity on sweet orange

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic parameter estimates for buffalo milk yield, milk quality and mozzarella production and Bayesian inference analysis of their relationships

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bayesian inference in genetic parameter estimation of visual scores in Nellore beef-cattle

The aim of this study was to estimate the components of variance and genetic parameters for the visual scores which constitute the Morphological Evaluation System (MES), such as body structure (S), precocity (P) and musculature (M) in Nellore beef-cattle at the weaning and yearling stages, by using threshold Bayesian models. The information used for this was gleaned from visual scores of 5,407 animals evaluated at the weaning and 2,649 at the yearling stages. The genetic parameters for visual score traits were estimated through two-trait analysis, using the threshold animal model, with Bayesian statistics methodology and MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) threshold software. Heritability estimates for S, P and M were 0.68, 0.65 and 0.62 (at weaning) and 0.44, 0.38 and 0.32 (at the yearling stage), respectively. Heritability estimates for S, P and M were found to be high, and so it is expected that these traits should respond favorably to direct selection. The visual scores evaluated at the weaning and yearling stages might be used in the composition of new selection indexes, as they presented sufficient genetic variability to promote genetic progress in such morphological traits.