RAINFALL CHANGE DETECTION IN AFRICA USING REMOTE SENSING AND GIS BETWEEN 1999 – 2018

Many researchers used gauge data from weather stations for rainfall estimate across Africa. Since Africa lies within the tropics, there is possibility for variations in rain received from place to place. Therefore, there is need for excessive density of the gauges for accurate estimate of Africa’s rainfall. Due to numerous challenges, these cannot be achieved. This necessitates the application of remote sensing and GIS to detect changes in rainfall amount in Africa between 1999 and 2018. The data used was obtained from remote sensing satellite (TRMM) and analyzed using GIS application (IDRISI Taiga). The Simple Image Differencing was performed on the two annual mean images covering January to December, 1999 and January to December, 2018. This provides reliable information on rainfall estimate that can complement sparsely and unevenly distributed rain gauge network in Africa. The analysis shows that latitudinal locations, to some extent, determine spatial distribution of rainfall in Africa. It is also observed that significant changes in rainfall rate are mainly found around coastal regions. It was recommended that adequate ground data it needed to confirm these findings. African countries should provide adequate and justly distributed weather stations with on-net database for easy access to the data.

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Evaluation of INCA precipitation analysis using a very dense rain gauge network in southeast Austria

10.5194/hess-2021-34 ◽

2021 ◽

Author(s):

Esmail Ghaemi ◽

Ulrich Foelsche ◽

Alexander Kann ◽

Jürgen Fuchsberger

Keyword(s):

Precipitation Amount ◽

Rain Gauge ◽

Accurate Estimate ◽

Time Shift ◽

Grid Cells ◽

Wet Season ◽

Event Time ◽

Peak Intensity ◽

Precipitation Analysis ◽

Rain Gauge Network

Abstract. An accurate estimate of precipitation is essential to improve the reliability of hydrological models and helps for decision-making in agriculture and economy. Merged radar–rain-gauge products provide precipitation estimates at high spatial and temporal resolution. In this study, we assess the ability of the INCA (Integrated Nowcasting through Comprehensive Analysis) precipitation analysis product provided by ZAMG (the Austrian Central Institute for Meteorology and Geodynamics) in detecting and estimating precipitation for 12 years in southeast Austria. The blended radar–rain-gauge INCA precipitation analyses are evaluated using WegenerNet – a very dense rain gauge network with about 1 station per 2 km2 – as true precipitation. We analyze annual, seasonal, and extreme precipitation of the 1 km × 1 km INCA product and its development from 2007 to 2018. Based on the results, the performance of INCA can be divided into three different periods. From 2007 to 2011, the annual area-mean precipitation in INCA was slightly higher than WegenerNet, except in 2009. However, INCA underestimates precipitation in grid cells farther away from the two ZAMG meteorological stations in the study area (which are used as input for INCA), especially from May to September (wet season). From 2012 to 2014, INCA's overestimation of the annual-mean precipitation amount is even higher, with an average of 25 %, but INCA performs better close to the two ZAMG stations. From 2015 onwards, the overestimation is still dominant in most cells but less pronounced than during the second period, with an average of 12.5 %. Regarding precipitation detection, INCA performs better during the wet seasons. Generally, false events in INCA happen less frequently in the cells closer to the ZAMG stations than in other cells. The number of true events, however, is comparably low closer to the ZAMG stations. The difference between INCA and WegenerNet estimates is more noticeable for extremes. We separate individual events using a 1-hour minimum inter-event time (MIT) and demonstrate that INCA underestimates the events' peak intensity until 2012 and overestimates this value after mid-2012 in most cases. The overestimation of the peak-intensity is more pronounced during July. In general, the precipitation rate and the number of grid cells with precipitation are higher in INCA. Furthermore, 40 % of the individual events start earlier, and 50 % end later in INCA. Considering four extreme convective short-duration events, there is a time shift in peak intensity detection. The relative differences in the peak intensity in these events can change from approximately −40 % to 40 %. The results of this study can be used for further improvements of INCA products as well as for future hydrological studies in this area.

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A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

Remote Sensing ◽

10.3390/rs12010194 ◽

2020 ◽

Vol 12 (1) ◽

pp. 194

Author(s):

Yanyan Huang ◽

Hongli Zhao ◽

Yunzhong Jiang ◽

Xin Lu

Keyword(s):

Remote Sensing ◽

Network Design ◽

Spatial Information ◽

Measurement Data ◽

Remote Sensing Data ◽

Rain Gauge ◽

Precipitation Data ◽

Satellite Precipitation ◽

Sensing Data ◽

Rain Gauge Network

A well-designed rain gauge network can provide precise and detailed rainfall data for earth science research; meanwhile, satellite precipitation data has been developed to generate more real spatial features, which provides new data support for the improvement of ground station network design methods. In this paper, satellite precipitation data are introduced into the design of a rain gauge network and an optimized method for designing a rain gauge network that comprehensively considers the information content, spatiotemporality, and accuracy (ISA) of the data is proposed. After screening the potential stations, the average spatial information index of the rain gauge network, which is calculated from remote sensing data, is used to address the shortcomings of applying spatial information from single-use measurement data. Then, the greedy ranking algorithm is used to rank the order in which the rain gauges are added to the network. The results of the rain gauge network design in the upper reaches of the Chaobai river show that compared with two methods that do not consider spatiality or use only measured data to consider spatiality, the proposed method performs better in terms of the spatial layout and accuracy verification. This study provides new ideas and references for the design of hydrological station networks and explores the use of remote sensing data for the layout of ground-based station networks.

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