An appraisal of flood events using IMD, CRU, and CCSM4-derived meteorological data sets over the Vaigai river basin, Tamil Nadu (India)

2019 ◽  
Vol 5 (4) ◽  
pp. 1731-1744 ◽  
Author(s):  
Satish Nagalapalli ◽  
Arnab Kundu ◽  
R. K. Mall ◽  
D. Thattai ◽  
S. Rangarajan
Keyword(s):  
River Basin ◽  
Tamil Nadu ◽  
Meteorological Data ◽  
Data Sets ◽  
Flood Events

scholarly journals The potential of global reanalysis datasets in identifying flood events in Southern Africa

2018 ◽  
Vol 22 (9) ◽  
pp. 4667-4683 ◽  
Author(s):  
Gaby J. Gründemann ◽  
Micha Werner ◽  
Ted I. E. Veldkamp
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Southern Africa ◽  
Large Scale ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Added Value ◽  
Flood Events ◽  
Global Models ◽  
Global Reanalysis

Abstract. Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global reanalysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global reanalysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme (EU-FP7) eartH2Observe (E2O) project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that, while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25° resolution global model, when compared to the lower-resolution 0.5° models, thus underlining the added value of increased-resolution global models. The skill of the global hydrological models (GHMs) in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though this could be improved if further details on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local-scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global reanalysis data for identifying impacting flood events in data-sparse regions.

scholarly journals The potential of global re-analysis datasets in identifying flood events in Southern Africa

2018 ◽  
Author(s):  
Gaby J. Gründemann ◽  
Micha Werner ◽  
Ted I. E. Veldkamp
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Southern Africa ◽  
Large Scale ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Analysis Data ◽  
Added Value ◽  
Flood Events ◽  
Global Models

Abstract. Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global re-analysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global re-analysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resource Re-analysis (WRR) dataset developed in the EU FP7 eartH2Observe project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25 degrees resolution global model, when compared to the lower resolution 0.5 degrees models, thus underlining the added value of increased resolution global models. The skill of the global hydrological models in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though could be improved if further detail on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global re-analysis data for identifying impacting flood events in data sparse regions.

Estimation of snow/glacier melt contribution in the upper part of the Beas River basin, Himachal Pradesh, using conventional and SNOWMOD modeling approach

2015 ◽  
Vol 6 (4) ◽  
pp. 880-890 ◽  
Author(s):  
Rajeev Saran Ahluwalia ◽  
S. P. Rai ◽  
S. K. Jain ◽  
D. P. Dobhal ◽  
Amit Kumar
Keyword(s):  
River Basin ◽  
Meteorological Data ◽  
Himachal Pradesh ◽  
Conventional Approach ◽  
Annual Average ◽  
Modeling Approach ◽  
Glacier Melt ◽  
Water Region

In the present study, an attempt has been made to estimate the snow/glacier melt contribution in the head water region of the Beas Basin using a conventional hydrograph approach and a modeling (SNOWMOD) technique. The discharge and other meteorological data from 1996 to 2008 of the Manali site were used for the study. The results of SNOWMOD modeling reveal that snow/glacier melt contribution to the Beas River in the head water region varied between 52 (minimum) and 56% (maximum) with an annual average of 54% during the study period. The results obtained using the conventional approach showed the contribution of snow/glacier melt varied between 48 (minimum) and 52% (maximum) with an annual average of 50%. Results obtained using both techniques corroborate each other. This study reveals that the Beas River is mainly sustained by the snow/glacier melt contribution in the head water region.

scholarly journals Analysis of evapotranspiration and biomass in pastures with degradation indicatives in the Upper Tocantins River Basin, in Brazilian Savanna

Revista CERES ◽  
2016 ◽  
Vol 63 (6) ◽  
pp. 754-760 ◽  
Author(s):  
Ricardo Guimarães Andrade ◽  
Antônio Heriberto de Castro Teixeira ◽  
Janice Freitas Leivas ◽  
Sandra Furlan Nogueira
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Meteorological Data ◽  
Simple Algorithm ◽  
Production Potential ◽  
Pasture Degradation ◽  
Ndvi Time Series ◽  
Tocantins River ◽  
Remote Sensing Techniques ◽  
Degraded Pastures

ABSTRACT The objective of this study was to apply the Simple Algorithm For Evapotranspiration Retrieving (SAFER) with MODIS images together with meteorological data to analyze evapotranspiration (ET) and biomass production (BIO) according to indicative classes of pasture degradation in Upper Tocantins River Basin. Indicative classes of degraded pastures were obtained from the NDVI time-series (2002-2012). To estimate ET and BIO in each class, MODIS images and data from meteorological stations of the year 2012 were used. The results show that compared to not-degraded pastures, ET and BIO were different in pastures with moderate to strong degradation, mainly during water stress period. Therefore, changes in energy balance partition may occur according to the degradation levels, considering that those indicatives of degradation processes were identified in 24% of the planted pasture areas. In this context, ET and BIO estimates using remote sensing techniques can be a reliable indicator of forage availability, and large-scale aspects related to the degradation of pastures. It is expected that this knowledge may contribute to initiatives of public policies aimed at controlling the loss of production potential of pasture areas in the Upper Tocantins River Basin in the state of Goiás, Brazil.

scholarly journals Geochemical and statistical evaluation of groundwater in the Thamirabarani river basin, south India

2021 ◽  
Author(s):  
Gajendran Chellaiah ◽  
Basker ◽  
Hima Pravin ◽  
Suneel Kumar Joshi ◽  
Sneha Gautam
Keyword(s):  
River Basin ◽  
Tamil Nadu ◽  
Statistical Evaluation ◽  
Anthropogenic Activities ◽  
Monsoon Season ◽  
Principal Component ◽  
Chloride Ion ◽  
Temporal Analysis ◽  
Point Sources ◽  
International Standards

Abstract In the present study, an attempt has been made to develop the dictate metrics using a multi-proxy approach, i.e., spatial-temporal analysis, statistical evaluation, and hydrogeochemical analysis for 45 water samples located in the Thamirabarani river basin in Tamil Nadu, India. In order to evaluate the aptness of developed metrics for agriculture and domestic needs, eleven years dataset was analyzed and compared with national and international standards. Monitoring and analysis results revealed that the concentration of calcium and chloride ion was on the higher side in all the selected locations. These higher values may be attributed to the regional point sources such as untreated water disposal and off-peak sources such as agriculture practices. The principal component analysis resulted in 84.2% of the total variance in the post-monsoon season dataset. The major analyzed cations and anions were observed in the following order: Na+> Ca2+> Mg2+> K+ and Cl−> HCO3−> SO42−> NO3−, respectively. Overall, this study revealed that the studied area's groundwater quality was significantly affected by the high salinity in the region, probably due to anthropogenic activities and unprotected river sites.

scholarly journals Model – TCCON comparisons of column-averaged methane with a focus on the stratosphere

2016 ◽  
Author(s):  
Andreas Ostler ◽  
Ralf Sussmann ◽  
Prabir K. Patra ◽  
Sander Houweling ◽  
Marko De Bruine ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Meteorological Data ◽  
Michelson Interferometer ◽  
Total Carbon ◽  
Data Sets ◽  
Transport Models ◽  
Improved Model ◽  
The Impact ◽  
Chemistry Experiment

Abstract. The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model-TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. In comparison to simulated model fields we find an improved model-TCCON XCH4 agreement for all models with MIPAS-based stratospheric CH4 fields. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 ppb to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 ppb to 4.3 ppb), and LMDz (Laboratoire de Météorologie Dynamique model with Zooming capability; from 6.8 ppb to 4.3 ppb), respectively. MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduce the average XCH4 bias for ACTM (3.3 ppb), but increase the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that the range of satellite-based stratospheric CH4 is insufficient to resolve a possible stratospheric contribution to differences in total column CH4 between TCCON and TM5 or LMDz. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. This implies that there is a need to better understand the impact of individual model transport components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) on modeled stratospheric CH4.

scholarly journals Analysing flood history and simulating the nature of future floods using Gumbel method and Log-Pearson Type III: the case of the Mayurakshi River Basin, India

2021 ◽  
Vol 19 (1) ◽  
pp. 43-69
Author(s):  
Aznarul Islam ◽  
Biplab Sarkar
Keyword(s):  
River Basin ◽  
Low Frequency ◽  
Flood Frequency ◽  
Flood Events ◽  
Significance Level ◽  
Type Iii ◽  
Return Probability ◽  
Pearson Type ◽  
Anderson Darling ◽  
Normally Distributed

AbstractFloods of the Mayurakshi River Basin (MRB) have been historically documented since 1860. The high magnitude, low-frequency flood events have drastically changed to low magnitude, high-frequency flood events in the post-dam period, especially after the 1950s, when the major civil structures (Massanjore dam, Tilpara barrage, Brahmani barrage, Deucha barrage, and Bakreshwar weir) were constructed in the MRB. The present study intends to find out the nature of flood frequency using the extreme value method of Gumbel and Log-Pearson type III (LP-III). The results show that the highest flood magnitude (11,327 m3 s−1) was observed during 1957–2009 for the Tilpara barrage with a return probability of 1.85% and the lowest (708 m3 s−1) recorded by the Bakreshwar weir during 1956–77 with a return probability of 4.55%. In the present endeavour, we have computed the predicted discharge for the different return periods, like 2, 5, 10, 25, 50,100, and 200 years. The quantile-quantile plot shows that the expected discharge calculated using LP-III is more normally distributed than that of Gumbel. Moreover, Kolmogorov–Smirnov (KS) test, Anderson–Darling (AD), and x2 distribution show that LP-III distribution is more normally distributed than the Gumbel at 0.01 significance level, implying its greater reliability and acceptance in the flood simulation of the MRB.

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