Without a doubt the first step in any water resources management is the rainfall-runoff modeling over the watershed. However considering high stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall-runoff modeling as well as other fields of hydrology. In the current research, the wavelet analysis was linked to the ANN concept for modeling Ligvanchai watershed rainfall-runoff process at Tabriz, Iran. For this purpose the main time series of two variables, rainfall and runoff, were decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the runoff discharge 1 day ahead. The obtained results show the proposed model can predict both short and long term runoff discharges because of using multi-scale time series of rainfall and runoff data as the ANN input layer.Keywords Artificial neural network · Black box model · Rainfall-runoff modeling · Wavelet transform · Ligvanchai watershed
Stem cells are pivotal for development and tissue homeostasis of multicellular animals, and the quest for a gene toolkit associated with the emergence of stem cells in a common ancestor of all metazoans remains a major challenge for evolutionary biology. We reconstructed the conserved gene repertoire of animal stem cells by transcriptomic profiling of totipotent archeocytes in the demosponge Ephydatia fluviatilis and by tracing shared molecular signatures with flatworm and Hydra stem cells. Phylostratigraphy analyses indicated that most of these stem-cell genes predate animal origin, with only few metazoan innovations, notably including several partners of the Piwi machinery known to promote genome stability. The ancestral stem-cell transcriptome is strikingly poor in transcription factors. Instead, it is rich in RNA regulatory actors, including components of the "germ-line multipotency program" and many RNA-binding proteins known as critical regulators of mammalian embryonic stem cells.Porifera | stem cells | evolution | uPriSCs | RNA binding
had magnitude of 9.0 on the Richter Scale with the epicenter approximately 70 km east of the Oshika Peninsula in Miyagi Prefecture. This earthquake triggered terrible tsunami waves which hit the coast of Japan and propagated around the Pacific Ocean. The earthquake and tsunami caused extensive and severe infrastructural damage, such as damages of coastal protection structures and buildings, and significantly changed coastal and river morphology. This paper presents tsunami-induced coastal and estuarine morphology changes in Miyagi Prefecture, Japan, and subsequent recovery process in each study area. On sandy coasts, discontinuous coastal protection is likely to be severely damaged, resulting in serious erosion in the surrounding sandy coast. Furthermore, severe breaching was observed on sandy coasts where formerly river mouth was located, due to strong return flow from the catchment area. The restoration process of the coast and estuaries is highly dependent on sediment supply availability in the surrounding area. Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. H. Tanaka et al. 1250010-2 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. Coastal and Estuarine Morphology Changes 1250010-3 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. 1250010-5 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. 1250010-6 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. Coastal and Estuarine Morphology Changes 1250010-16 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. 1250010-19 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. 1250010-23 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/23/15. For personal use only. H. Tanaka et al.
At 14:46 JST on March 11, 2011 a magnitude 9.0 earthquake (2011 Tohoku Earthquake and Tsunami) occurred off the Pacific Coast of Miyagi Prefecture. This study investigated the extensive changes in beach morphology due to the earthquake and tsunami along the 15 km Northern Sendai Coast using remotely sensed data. The remote sensing analysis on the beach topography and coastal forest demonstrated the following notable characteristics of beach morphological change: erosion of the northern barrier at the mouths of the Nanakitagawa and Natorigawa Rivers; erosion at an old river channel; scour landward of the seawalls in the longshore direction; erosion and deposition in beach areas with detached breakwaters; and deposition in coastal forest areas. Linkage of the deposition in the forest areas with the damage type of coastal forests was observed. The impact of the 1250009-1Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by UNIVERSITY OF QUEENSLAND on 08/16/15. For personal use only. K. Udo et al.earthquake and tsunami on the beach morphology was serious; roughly 60% of the study area was degraded by 0.2-0.5 m in elevation mainly due to land subsidence, and a total of 0.4 km 2 of beach area was eroded mainly due to erosion of the northern barrier at the mouths of the Nanakitagawa and Natorigawa Rivers. This study explores the geographical changes brought on by a tremendous earthquake and tsunami, which will help to elucidate the mechanisms of coastal forest destruction, beach erosion, and their interaction during tsunami events.
Abstract:Rainfall-runoff modelling, as a surface hydrological process, on large-scale data-poor basins is currently a major topic of investigation that requires the model parameters be identified by using basin physical characteristics rather than calibration. This paper describes the application of the TOPMODEL framework accompanied by a kinematic wave model to the Karun River sub-basins in southwestern Iran with just one conceptual parameter for calibration. ISLSCP1, HYDRO1K and Reynolds data sets are presented in a geographical information system and used as data sources for meteorological information, hydrological features and soil characteristics of the study area respectively. The results show that although the model developed can adequately predict flood runoff in the catchment with only one calibrated parameter, it is suggested that the effect of surface reservoirs be considered in the proposed model.
This paper presents an assessment of the changes in future floods. The ranked area-average heavy daily rainfall amounts simulated by a super-high-resolution (20 km mesh) global climate model output are corrected with consideration of the effects of the topography on heavy rainfall patterns and used as a basis to model design storm hyetographs. The rainfall data are then used as the input for a nearly calibration-free parameter rainfall–runoff model to simulate floods in the future climate (2075–2099) at the Upper Thu Bon River basin in Central Vietnam. The results show that although the future mean annual rainfall will not be considerably different compared to the present-day climate (1979–2003), extreme rainfall is projected to increase vigorously, leading to a similar order of intensification of future floods. It is very likely that the flood peak with a 25-year recurrence will increase approximately 42% relative to the present-day climate. The occurrence of floods with a 10-year recurrence may exceed those with a 25-year recurrence in the present-day climate. The projection results also exhibit insignificant uncertainties caused by an artificial neural network-based bias correction model. Additionally, the presented bias correction model shows advantages over a simple climatology scaling method.
Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP) neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model) for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.
Abstract:A short-term flood inundation prediction model has been formulated based on the combination of the super-tank model, forced with downscaled rainfall from a global numerical weather prediction model, and a one-dimensional (1D) hydraulic model. Different statistical methods for downscaled rainfall have been explored, taking into account the availability of historical data. It has been found that the full implementation of a statistical downscaling model considering physically-based corrections to the numerical weather prediction model output for rainfall prediction performs better compared with an altitudinal correction method. The integration of the super-tank model into the 1D hydraulic model demonstrates a minimal requirement for the calibration of rainfall-runoff and flood propagation models. Updating the model with antecedent rainfall and regular forecast renewal has enhanced the model's capabilities as a result of the data assimilation processes of the runoff and numerical weather prediction models. The results show that the predicted water levels demonstrate acceptable agreement with those measured by stream gauges and comparable to those reproduced using the actual rainfall. Moreover, the predicted flood inundation depth and extent exhibit reasonably similar tendencies to those observed in the field. However, large uncertainties are observed in the prediction results in lower, flat portions of the river basin where the hydraulic conditions are not properly analysed by the 1D flood propagation model.
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