Suitability of different precipitation data sources for hydrological analysis: a study from Western Ghats, India

2022 ◽  
Vol 194 (2) ◽  
Author(s):  
Beeram Satya Narayana Reddy ◽  
Shahanas P. V. ◽  
S. K. Pramada
Keyword(s):  
Western Ghats ◽  
Data Sources ◽  
Precipitation Data

scholarly journals A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

2018 ◽  
Vol 56 (1) ◽  
pp. 79-107 ◽  
Author(s):  
Qiaohong Sun ◽  
Chiyuan Miao ◽  
Qingyun Duan ◽  
Hamed Ashouri ◽  
Soroosh Sorooshian ◽  
...  
Keyword(s):  
Data Sources ◽  
Data Sets ◽  
Precipitation Data ◽  
Global Precipitation

scholarly journals Assessing two precipitation data sources at basins of special interest to hydropower production in Brazil

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Alberto Assis dos Reis ◽  
Wilson dos Santos Fernandes ◽  
Maria-Helena Ramos
Keyword(s):  
Real Time ◽  
River Flow ◽  
Electricity Sector ◽  
Data Sources ◽  
Precipitation Data ◽  
Primary Input ◽  
Spatial Trend ◽  
River Flow Forecasting ◽  
Hydropower Production ◽  
Merge Dataset

ABSTRACT Accurate estimates of precipitation amounts are necessary to evaluate river flows, assess water-related risks (floods and drought) and quantify water availability for a broad range of water uses, such as water supply, agriculture, navigation and energy production. Especially in the context of operations in the Brazilian electricity sector, where the electrical system is essentially hydrothermal and more than 65% of its production comes from hydroelectric generation, real-time observed precipitation plays a key role as a primary input for hydrological models and river flow forecasting. It is thus crucial to build knowledge on and quantify river basin precipitation and its uncertainties. In this paper, we evaluate two sources of real-time (or near real-time) precipitation data, the TRMM-MERGE dataset from the CPETC and the CPC dataset, distributed by NOAA. Our assessment is based on 41 river basins in South America and covers the period 1997-2017. We investigated differences for different time resolutions (daily, monthly and annual precipitation) and their impact on the simulation of streamflows. Substantial differences were found between the two data sources, which seem to be amplified in the second decade. A spatial trend was found towards higher TRMM-MERGE precipitation values than CPC values when moving from north and west in the study area. We also found evidence that differences in precipitation propagate to simulated flows, with large percent differences in precipitation resulting in even larger percent differences in streamflow.

Impacts of different precipitation data sources on water budgets

2004 ◽  
Vol 298 (1-4) ◽  
pp. 311-334 ◽  
Author(s):  
Jianzhong Guo ◽  
Xu Liang ◽  
L. Ruby Leung
Keyword(s):  
Data Sources ◽  
Precipitation Data ◽  
Water Budgets

scholarly journals Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1665 ◽  
Author(s):  
Paweł Gilewski ◽  
Marek Nawalany
Keyword(s):  
Rain Gauge ◽  
Data Sources ◽  
Efficiency Coefficient ◽  
Precipitation Data ◽  
Rainfall Runoff ◽  
Satellite Precipitation ◽  
Mountainous Catchment ◽  
Runoff Modeling ◽  
Precipitation Estimates ◽  
Event Based

Precipitation is one of the essential variables in rainfall-runoff modeling. For hydrological purposes, the most commonly used data sources of precipitation are rain gauges and weather radars. Recently, multi-satellite precipitation estimates have gained importance thanks to the emergence of Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG GPM), a successor of a very successful Tropical Rainfall Measuring Mission (TRMM) mission which has been providing high-quality precipitation estimates for almost two decades. Hydrological modeling of mountainous catchment requires reliable precipitation inputs in both time and space as the hydrological response of such a catchment is very quick. This paper presents an inter-comparison of event-based rainfall-runoff simulations using precipitation data originating from three different sources. For semi-distributed modeling of discharge in the mountainous river, the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) is applied. The model was calibrated and validated for the period 2014–2016 using measurement data from the Upper Skawa catchment a small mountainous catchment in southern Poland. The performance of the model was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), Pearson’s correlation coefficient (r), Percent bias (PBias) and Relative peak flow difference (rPFD). The results show that for the event-based modeling adjusted radar rainfall estimates and IMERG GPM satellite precipitation estimates are the most reliable precipitation data sources. For each source of the precipitation data the model was calibrated separately as the spatial and temporal distributions of rainfall significantly impact the estimated values of model parameters. It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution. The analysis of the simulation time-steps indicates that time aggregation of precipitation data from 1 to 2 h (not exceeding the response time of the catchment) provide a significant improvement of flow simulation results for all the models while further aggregation, up to 4 h, seems to be valuable only for model based on rain gauge precipitation data.

scholarly journals Comparing Sentinel-1, Sentinel-2, and Landsat-8 data in the early recognition of irrigated areas in central Italy

2021 ◽  
Vol 52 (4) ◽  
Author(s):  
Lorenzo Vergni ◽  
Alessandra Vinci ◽  
Francesca Todisco ◽  
Francesco Saverio Santaga ◽  
Marco Vizzari
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Early Recognition ◽  
Central Italy ◽  
Climatic Conditions ◽  
Data Sources ◽  
Landsat 8 ◽  
Irrigation Season ◽  
Precipitation Data ◽  
Sentinel 2

This study evaluated the effectiveness of various remote sensing (RS) data (Sentinel-1, Sentinel-2, and Landsat 8) in the early recognition of irrigated areas in a densely cultivated area of central Italy. The study was based on crop data collected on more than 2000 plots in 2016 and 2017, characterized by quite different climatic conditions. The different RS data sources were used both alone and combined and with precipitation to define corresponding random forest (RF) classifiers whose overall accuracy (OA) was assessed by gradually increasing the number of available features from the beginning of the irrigation season. All tested RF classifiers reach stable OAs (OA 0.9) after 7-8 weeks from the start of the irrigation season. The performance of the radar indexes slightly improves when used in combination with precipitation data, but three weeks of features are required to obtain OA above 80%. The optical indices alone (Sentinel-2 and Landsat 8) reach OA ≈85% in the first week of observation. However, they are ineffective in cloudy conditions or when rainfed and irrigated fields have similar vigour. The most effective and robust indices are those based on combined sources (radar, optical, and meteorological), allowing OAs of about 92% and 96% at the beginning and in the middle of the irrigation season, respectively.

The potential of using satellite-related precipitation data sources in arid regions

2022 ◽  
pp. 201-237
Author(s):  
Mona Morsy ◽  
Peter Dietrich ◽  
Thomas Scholten ◽  
Silas Michaelides ◽  
Erik Borg ◽  
...  
Keyword(s):  
Arid Regions ◽  
Data Sources ◽  
Precipitation Data

Catching Up With and Integrating With Secondary Data Sources

PsycCRITIQUES ◽  
2011 ◽  
Vol 56 (5) ◽  
Author(s):  
Francis C. Staskon
Keyword(s):  
Secondary Data ◽  
Data Sources ◽  
Catching Up ◽  
Secondary Data Sources
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