Abstract
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.
Highlights
Knowledge of surface precipitation is important in hydrological research and operations
High differences are more frequently observed towards positive differences. This means that when differences are high, it is more frequently due to higher values of rainfall given by the Tropical Rainfall Measuring Mission (TRMM)-MERGE dataset
We investigated if the differences between precipitation data from TRMM-MERGE and CPC vary when considering two time periods: Figure 5 shows a map with the average percent differences of annual precipitation for the period 1998-2007 and Figure 6 shows the same but for the period 2008-2017
Summary
Knowledge of surface precipitation is important in hydrological research and operations. At the global scale, gridded precipitation products have emerged since the late 1990s (Huffman et al, 1997; Adler et al, 2003) These products usually provide monthly estimates of surface precipitation from merged analyses that blend precipitation estimates from satellite data and in-situ rain gauge observations. While they can be useful for global climate change impact studies, finer space and temporal resolutions are often needed for hydrological applications that involve daily decision-making at continental or national scales, such as flood forecasting or hydropower operations (Alfieri et al, 2013; Fan et al, 2016; Emerton et al, 2016; Siqueira et al, 2018). Computer models that optimize the system’s operation, solving the hydrothermal dispatch problem, run once a week, every Thursday, providing forecasts of inflows on a daily and weekly basis for the first five weeks and on a monthly basis for the months (Operador Nacional do Sistema Elétrico, 2016)
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