Estimating areal rainfall over Lake Victoria and its basin using ground-based and satellite data

Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.

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“…Auer et al, 2007;Kizza et al, 2012;Vuille et al, 2000). There are many techniques for data estimation (e.g.…”

Section: Data Quality Issue Descriptionmentioning

confidence: 99%

Effects of undetected data quality issues on climatological analyses

et al. 2018

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“…For this purpose, either directly measured precipitation data from one land-based gauge station or interpolated precipitation data from several available nearby stations are used [34]. However, previous studies showed that for large lakes, the nocturnal lake-breeze effects can markedly enhance rainfall over the center of the lake, and hence the nearby land-based rainfall could considerably underestimate precipitation over the lake [49,50].…”

Section: Precipitation Over the Lake (P Lake )mentioning

confidence: 99%

Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data

Duan

Gao

2018

Remote Sensing

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Lake Tana is the largest lake in Ethiopia, and its lake outflow is the source of the Blue Nile River that provides vital water resources for many livelihoods and downstream/international stakeholders. Therefore, it is essential to quantify and monitor the water balance of Lake Tana. However, Lake Tana is poorly gauged, with more than 50% of Lake Tana Basin being ungauged from in-situ measurements, making it difficult to quantify the lake inflow from surrounding basins. The lack of in-situ measurements highlights the need for the innovative application of satellite remote sensing. This study explores how freely accessible satellite remote sensing can be used to complement routine weather data to quantify the water balance of Lake Tana and its surrounding catchments. This study particularly investigates whether the outflow from Lake Tana can be estimated with sufficient accuracy as the residual of the lake water balance. Monthly inflow into lake was computed as the total runoff from the surrounding catchments; the runoff was estimated as the residual of the land-based catchment water balance using satellite precipitation improved with an integrated downscaling-calibration procedure, satellite evapotranspiration, and a correction term for changes in land total storage (soil moisture storage and deep percolation). The outflow from Lake Tana was estimated as the residual of lake water balance by combining satellite-based lake precipitation, changes in water storage, and lake inflow with estimated lake evaporation. Evaluation using limited available measurements showed that estimated annual runoff for two gauged subbasins agreed well with measurements, with differences within 4%. The estimated annual outflow from Lake Tana was also close to measured outflow, with a difference of 12%. However, the estimated monthly runoff from catchments and monthly lake outflow were unsatisfactory, with large errors.

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“…1) is located in Tanzania, Uganda, and Kenya, where each accounts for 51, 43, and 6 % of the lake surface area respectively (Kizza et al, 2012). Lake Victoria is relatively shallow, with a mean depth of ∼ 40 m and a maximum depth of 84 m (UNEP, 2013) akin to many shallow, open surface-water bodies as well as permanent and seasonal wetlands occupying low-relief plateaus across the Great Lakes Region of Africa (Owor et al, 2011).…”

Section: Lakes Victoria and Kyogamentioning

confidence: 99%