Role of stationary convective bands and antecedent conditions on the flood response to the Vaia storm (October 27-30, 2018) in the Eastern Italian Alps

2020 ◽  
Author(s):  
Mattia Zaramella ◽  
Lorenzo Marchi ◽  
Francesco Marra ◽  
Francesco Comiti ◽  
Stefano Crema ◽  
...  
Keyword(s):  
River Basin ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Second Phase ◽  
Italian Alps ◽  
Spatial Moments ◽  
Antecedent Conditions ◽  
Basin Scale ◽  
Flood Response ◽  
The Impact

<p>Between the morning of 27 October 2018 and the evening of 29 October 2018, heavy precipitation over the Eastern Italian Alps led to damaging flooding. The event, which occurred at the end of a climatic anomaly of prolonged drought, developed into two phases, with a first phase (October 27-28) dominated by more stratiform orographically-enhanced precipitation. After a short lull, a second and more intense phase of the event took place on the 29th, when a cold front from the Gulf of Lion entered the Mediterranean basin triggering explosive cyclogenesis. A characteristic of the second phase of the storm is the rainfall organization in well-defined convective bands, some of which persisted at the same location for up to 3 hours. The bands, aligned from southeast to northwest, were initially located downstream of the pre-alpine region.</p><p>The work aims to investigate the impact of the stationary convective bands and of the dry antecedent conditions on the flood response to the storm. The availability of high-resolution rainfall estimates from weather radar and of dense rain gauge network data, along with flood response observations from stream gauge data and post-event surveys, enables to study the hydrometeorological and hydrological mechanisms associated with this extreme storm and the consequent flood response.</p><p>Observational and model analyses of the hydrologic runoff in two areas heavily impacted by the storm (Noce river basin, in the Trentino Province, and upper Cordevole river basin, in the Veneto Region) illustrate how the structure and evolution of the stationary convective bands translate into scale dependent flood response. For the upper Cordevole river basin, the event envelope curve shows two peculiar behaviors: (a) basin scale ranging from 1 to 200 km<sup>2</sup>, with peak unit discharges decreasing from 10 to 4 m<sup>3</sup>s<sup>-1</sup>km<sup>-2</sup>; (b) basin scale ranging from 200 to 2000 km<sup>2</sup>, with smaller peak unit discharges. The spatial extent of the first region is controlled by the structure of the central convective band. Moreover, the spatial moments of catchment rainfall are exploited to identify the impact of the convective cells motion along the stationary band on the flood response.</p>

scholarly journals Evaluation and Hydrological Validation of GPM Precipitation Products over the Nanliu River Basin, Beibu Gulf

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1777 ◽  
Author(s):  
Kai Tong ◽  
Yinjun Zhao ◽  
Yongping Wei ◽  
Baoqing Hu ◽  
Yuan Lu
Keyword(s):  
River Basin ◽  
Grid Cell ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Beibu Gulf ◽  
Watershed Model ◽  
Basin Scale ◽  
Precipitation Estimates ◽  
Small River Basin ◽  
Global Precipitation

Adequate and high-quality precipitation estimates, from spaceborne precipitation radars, are necessary for a variety of applications in hydrology. In this study, we investigated the performance of two Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) products, against gauge observations over a small river basin, the Beibu Gulf—the Nanliu River basin, and evaluated their capability of streamflow simulation, based on a conceptual watershed model from April 2014 to December 2016. The results showed that both IMERG_Cal and IMERG_Uncal could roughly capture the spatial patterns of precipitation with slight over/underestimation (Relative Bias (RB) values of 6.5% and −5.5%, respectively) at a basin scale. At grid-cell scales, two IMERG products got an RB of −23.3% to 18.9%, Correlation Coefficient (CC) of 0.521 to 0.744, and Root Mean Square Error (RMSE) of 11.3 to 17.5 mm. There were some considerable errors in heavy precipitation events, and the IMERG significantly overestimated the amounts of these extreme events. The two IMERG products showed a higher accuracy and lower error rate, when detecting the light precipitation. IMERG-driven simulation had a better quality when the model was calibrated with satellite data rather than with rain gauge data. This analysis implied that IMERG products have potential in hydrological applications, in this region, and need further improvement in algorithms.

scholarly journals Cross-validating precipitation datasets in the Indus River basin

2020 ◽  
Vol 24 (1) ◽  
pp. 427-450 ◽  
Author(s):  
Jean-Philippe Baudouin ◽  
Michael Herzog ◽  
Cameron A. Petrie
Keyword(s):  
River Basin ◽  
Cross Validation ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Satellite Observations ◽  
Indus River ◽  
Mountain Areas ◽  
Temporal Representation ◽  
Basin Scale ◽  
Indus River Basin

Abstract. Large uncertainty remains about the amount of precipitation falling in the Indus River basin, particularly in the more mountainous northern part. While rain gauge measurements are often considered as a reference, they provide information for specific, often sparse, locations (point observations) and are subject to underestimation, particularly in mountain areas. Satellite observations and reanalysis data can improve our knowledge but validating their results is often difficult. In this study, we offer a cross-validation of 20 gridded datasets based on rain gauge, satellite, and reanalysis data, including the most recent and less studied APHRODITE-2, MERRA2, and ERA5. This original approach to cross-validation alternatively uses each dataset as a reference and interprets the result according to their dependency on the reference. Most interestingly, we found that reanalyses represent the daily variability of precipitation as well as any observational datasets, particularly in winter. Therefore, we suggest that reanalyses offer better estimates than non-corrected rain-gauge-based datasets where underestimation is problematic. Specifically, ERA5 is the reanalysis that offers estimates of precipitation closest to observations, in terms of amounts, seasonality, and variability, from daily to multi-annual scale. By contrast, satellite observations bring limited improvement at the basin scale. For the rain-gauge-based datasets, APHRODITE has the finest temporal representation of the precipitation variability, yet it importantly underestimates the actual amount. GPCC products are the only datasets that include a correction factor of the rain gauge measurements, but this factor likely remains too small. These findings highlight the need for a systematic characterisation of the underestimation of rain gauge measurements.

scholarly journals BUILDING THE MODEL OF MANAGEMENT AND INFORMATION SHARING OF ENVIRONMENTAL WATER QUALITY – DONG NAI BASIN AS A CASE STUDY

2011 ◽  
Vol 14 (1) ◽  
pp. 16-28
Author(s):  
Long Ta Bui ◽  
Truong Duy Cao ◽  
Huong Thi My Hoang
Keyword(s):  
Water Quality ◽  
Waste Management ◽  
Information Sharing ◽  
River Basin ◽  
Environmental Water ◽  
River Basin Scale ◽  
Legal Documents ◽  
Basin Scale ◽  
Basin Water ◽  
The Impact

Recently, due to the impact of natural factors and human activities, the water quality in several basins in Vietnam has been seriously degraded. Pressing issues happening in the entire river basin-scale is polluted by waste from urban and industrial areas, oil spills and waste management. So far the system of policies and legal documents relating to protection of water quality basin is still missing and not synchronized, ensure funding for activities to protect water quality basin not meeting actual requirements. In particularly, there is no information data system to cater for the management of basin water quality which is the core of the problem of environmental protection of river basins. The main reason that make pollution happened at the entire river basin scale is bad waste management. which partly due to the lack of a good system of technical data and legal documents related to protection of river basin water quality. In this paper, we present research results from the process of building model for management and information sharing of environmental water quality at Dong Nai river basin.

scholarly journals Estimating Probable Maximum Precipitation for Linau River Basin in Sarawak

2014 ◽  
Vol 5 (3) ◽  
pp. 1-5
Author(s):  
M. Hussain ◽  
S. Nadya ◽  
F.J. Chia
Keyword(s):  
Statistical Method ◽  
River Basin ◽  
Public Safety ◽  
Geographical Location ◽  
Hydraulic Modeling ◽  
Rain Gauge ◽  
Data Set ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
The Impact

 The probable maximum precipitation (PMP) is the greatest depth of precipitation for a given duration that is physically possible over a given size storm area at a particular geographical location at a certain time of the year. PMP is very important to be considered for the design of river regulating structures i.e Dams and Barrages to overcome any possible chance of overtopping failure as well as for public safety and hazards downstream of any of these structures. Especially if these structures located in the upstream of the of the populated town or city than the failure could damage severely such areas. As such the PMP convention is always a requirement as primary design dam/reservoir criteria when public safety is of concern. The PMP is used to derive Probable Maximum Flood (PMF), which further used in hydraulic modeling to check the impact assessments for such occasions. This paper focuses on estimation of PMP for Linau River Basin in Sarawak using statistical method proposed by World Meteorological Organization (WMO), which is described in its operational manual. Long Lidam and Long Laku are located in Linau River Basin but Long Laku has long discontinuity in the data set thus the rainfall series at Long Lidam is further used for PMP estimation. The missing data was in-filled using Belaga rain gauge station as Long Lidam rainfall has good correlation with Belaga rainfall data. Hershfield statistical method has been adopted to estimate the 24-hour duration PMP. The Probable Maximum Precipitation for 24-hour duration storm is estimated as 691 mm for the Linau River Basin.

Evaluation of Environmental Efficiency of Runoff Responsibility Distribution from the Perspective of Equity and Efficiency

2020 ◽  
Author(s):  
Chen Kuan Ling ◽  
Chang Hsueh Sheng ◽  
Cheng Hao Teng
Keyword(s):  
Land Use ◽  
River Basin ◽  
Land Use Planning ◽  
Catchment Area ◽  
Distribution Model ◽  
Spatial Unit ◽  
Basin Scale ◽  
Catchment Areas ◽  
The Government ◽  
The Impact

<p>In recent years, the risk of flooding disasters caused by climate change has increased, and a new concept of runoff sharing has been proposed in China. It is an operation method based on the area of ​​the catchment from the perspective of water conservancy. However, the basin area is also a spatial unit of human economic activity. Social and economic development and the distribution of runoff responsibilities clearly show a mutual measurement relationship, and the land has a certain social responsibility to handle its own runoff. How can it be distributed fairly and efficiently? The issue of responsibility for runoff sharing has become an important issue for joint initiatives in the field of soil and water. </p><p> </p><p>In the case of considering the watershed as a spatial scope, in addition to considering its own hydrological properties, there are also socioeconomic development issues that should be clarified and discussed step by step. Therefore, this study attempts to use the three-stage data envelopment analysis (DEA) method to consider hydrology The concept of interaction with the socio-economic environment takes into account the impact of exogenous factors on the allocation of runoff responsibility, and evaluates the efficiency of runoff responsibility. In view of this, from the standpoint of the government and residents sharing the runoff, this study effectively combines the different types of data of the social, economic, and ecological environments in the catchment areas to carry out a comprehensive assessment, and weighs out the optimal distribution efficiency of the overall river basin. </p><p> </p><p>This study is divided into three parts to clarify the distribution of runoff responsibilities, which are divided into: (1) Establishing an assessment framework for the distribution of river basin runoff responsibilities: Based on the analysis of the spatial unit of the catchment area, an attempt is made to integrate different regional development conditions, which can be summarized Appropriate and appropriate distribution methods; (2) Weighing the fairness and efficiency of the distribution of runoff responsibilities in the spatial unit of the watershed: Point out the current runoff responsibility distribution model and characteristics of the catchment area; (3) Attempt to develop the principles for the use of land use planning, Apply the concept of runoff responsibility to land use planning. </p><p> </p><p>Based on the results of this study, a more fair way to distribute runoff responsibilities is proposed, and a new perspective on social natural equality from the river basin scale is clarified. The key factors that affect the distribution of runoff responsibilities are clear. Efficiently undertake total runoff and provide policy planning advice. Try to discuss the issue of runoff responsibility allocation from the field of urban planning, provide river basin runoff responsibility with a planning vision, strengthen the spatial thinking of water and soil dialogue, and look forward to providing a new model of river basin governance in extreme climates. </p>

scholarly journals Response of Streamflow to Climate Changes in the Yellow River Basin, China

2011 ◽  
Vol 12 (5) ◽  
pp. 1113-1126 ◽  
Author(s):  
Zhifeng Yang ◽  
Qiang Liu
Keyword(s):  
River Basin ◽  
Climate Changes ◽  
Yellow River ◽  
Temporal Trends ◽  
Yellow River Basin ◽  
Baseline Period ◽  
The Yellow River ◽  
Basin Scale ◽  
The Yellow River Basin ◽  
The Impact

Abstract Climate changes impact hydrological processes and control streamflow at the basin scale. The present study was conducted to investigate the impact of climate change on streamflow in the Yellow River basin (YRB), China. The temporal trends of streamflow were explored by the Mann–Kendall method and a linear fit model, and the relationships between streamflow, precipitation, and potential evapotranspiration (ETp) were investigated. Furthermore, the contribution of climate changes to streamflow was revealed by Budyko’s method and a simple water balance model. The following results were obtained: (i) decreasing abruptness in streamflow occurred in 1990, and this date was used to divide the streamflow into two periods (baseline period and period of change); (ii) 67 of 80 stations showed decreasing trends with an average reduction of 10.37% of annual precipitation changes, while most of the stations displayed increasing trends with a 3.71% increase in annual ETp; (iii) the precipitation and ETp elasticity of streamflow, as expected, revealed that streamflow increases with increasing precipitation, whereas it decreases with increasing ETp; and (iv) the changes of precipitation and ETp reflected complementary effects on the reduction of streamflow from the baseline period to the period of change, the decreasing trend in precipitation being the main cause for the reduction of streamflow, but the declining rates of ETp causing a slight increase in streamflow.

scholarly journals Southern Rockies Watershed Project

2016 ◽  
Vol 92 (01) ◽  
pp. 39-42 ◽  
Author(s):  
Uldis Silins ◽  
Axel Anderson ◽  
Kevin D. Bladon ◽  
Monica B. Emelko ◽  
Micheal Stone ◽  
...  
Keyword(s):  
River Basin ◽  
Forest Disturbance ◽  
Rocky Mountain ◽  
Natural Disturbance ◽  
Forest Harvesting ◽  
Second Phase ◽  
Economic Implications ◽  
Mountain Watersheds ◽  
Comprehensive Information ◽  
Basin Scale

The Southern Rockies Watershed Project was initiated in 2003 to describe the impacts of severe natural disturbance by wildfire on a broad range of headwaters, larger river basin scale, and downstream water resources (Phase I). This watershed research is unique in that trans-disciplinary linkages between hydrology, biogeochemistry, aquatic ecology, downstream river basin processes, implications for human water use, and economic implications are providing broad insights into wildfire effects on water. A second phase of the research (Phase II) focuses on evaluating the effects of several alternative forest harvesting practices on these same water resource “values”. Collectively, this research is providing comprehensive information on watershed function after forest disturbance in Rocky Mountain watersheds.

scholarly journals Assessing Flood Risk of the Chao Phraya River Basin Based on Statistical Rainfall Analysis

2020 ◽  
Vol 15 (7) ◽  
pp. 1025-1039
Author(s):  
Shakti P. C. ◽  
Mamoru Miyamoto ◽  
Ryohei Misumi ◽  
Yousuke Nakamura ◽  
Anurak Sriariyawat ◽  
...  
Keyword(s):  
River Basin ◽  
Return Period ◽  
Flood Risk ◽  
Rain Gauge ◽  
Sustainable Growth ◽  
Type I ◽  
Type Iii ◽  
Pearson Type ◽  
Basin Scale ◽  
Chao Phraya River

The Chao Phraya River Basin is one of the largest in Asia and is highly vulnerable to water-related disasters. Based on rainfall gauge data over 36 years (1981–2016), a frequency analysis was performed for this basin to understand and evaluate its overall flood risk; daily rainfall measurements of 119 rain gauge stations within the basin were considered. Four common probability distributions, i.e., Log-Normal (LOG), Gumbel type-I (GUM), Pearson type-III (PE3), and Log-Pearson type-III (LP3) distributions, were used to calculate the return period of rainfall at each station and at the basin-scale level. Results of each distribution were compared with the graphical Gringorten method to analyze their performance; GUM was found to be the best-fitted distribution among the four. Thereafter, design hyetographs were developed by integrating the return period of rainfall based on three adopted methods at basin and subbasin scales; each method had its pros and cons for hydrological applications. Finally, utilizing a Rainfall-Runoff-Inundation (RRI) model, we estimated the possible flood inundation extent and depth, which was outlined over the Chao Phraya River Basin using the design hyetographs with different return periods. This study can help enhance disaster resilience at industrial complexes in Thailand for sustainable growth.

scholarly journals A Coupled Numerical Investigation of the Cape Fear River Basin during Hurricane Florence (2018)

2021 ◽  
Author(s):  
Dongxiao Yin ◽  
Z. George Xue ◽  
Daoyang Bao ◽  
Arezoo RafieeiNasab ◽  
Yongjie Huang ◽  
...  
Keyword(s):  
River Basin ◽  
Rainfall Simulation ◽  
Hydrological Response ◽  
Basin Scale ◽  
Rainfall Volume ◽  
Flood Response ◽  
Cape Fear River ◽  
Cape Fear ◽  
Flood Simulations ◽  
Peak Streamflow

In this study we adapted WRF-Hydro to the Cape Fear River basin (CFRB) to assess its performance during Hurricane Florence (2018). The model was first calibrated with a strategy of mixture of automatic and manual calibration during Florence and then evaluated with an independent hurricane event. With satisfactory NSE values (>0.4) achieved at all gages for hourly simulation, the model demonstrates its potential in simulating the flood response at both basin and sub-basin scale during hurricane events. The model’s capability in reproducing rainfall and properly translating it to hydrological response was further evaluated. The analysis suggests that the calibrated WRF-Hydro in combination with a series of WRF simulation using different microphysics schemes can provide reasonable flood simulations. The model reproduced peak streamflow observed at gage stations with acceptable errors in timing and amplitude. Meanwhile, positive(negative) bias in rainfall input is likely to be amplified (reduced) in streamflow forecast when simulated rainfall volume is larger than the “model true”. And the timing bias mostly inherited from rainfall simulation and calibration process.

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