Validation of satellite-based rainfall in Kalahari

Lekula, Moiteela; Lubczynski, M.; Shemang, Elisha; Verhoef, W.

doi:10.1016/j.pce.2018.02.010

Cited by 28 publications

(28 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Categorical validation statistics [48] is widely used for the evaluation of the skill of the satellite at a daily time-scale in detecting the occurrence of rainfall [8,11,14,17,20,28]. The validation statistics used are the probability of detection (POD), false alarm ratio (FAR), Frequency bias index (FBI) and the Heidke Skill Score (HSS) ( Table 2).…”

Section: Chirps ≥ 1 MMmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

et al. 2019

View full text Add to dashboard Cite

The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year−1), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.

show abstract

Section: Chirps ≥ 1 MMmentioning

confidence: 99%

“…The validations are done by comparing to gauge measurements. Each validation found different performance at different geographical regions, topographic conditions and seasons [2,7,9,11,[14][15][16][17][18][19][20]23,[26][27][28][29][30][31]. Comparisons between different satellite rainfall products have been done mainly at regional scale.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

et al. 2019

View full text Add to dashboard Cite

show abstract

“…mainly during summer rainy season. The mean annual rainfall in the CKB ranges from 380 mm yr −1 in the south-western, to 530 mm yr −1 in the north-eastern side of the study area (Lekula et al 2018b). Annual potential evapotranspiration in the CKB exceeds annual rainfall, ranging from 1,350 to 1,450 mm (Choudhury 1997).…”

Section: Study Area and Conceptual Modelmentioning

confidence: 93%

“…The rainfall in the CKB is highly spatially and temporally variable (Lekula et al 2018b;Obakeng et al 2007), with localized showers (Bhalotra 1987;Lekula et al 2018b). Almost all the rainfall, occurs from September to April, i.e.…”

Section: Study Area and Conceptual Modelmentioning

confidence: 99%

Use of remote sensing and long-term in-situ time-series data in an integrated hydrological model of the Central Kalahari Basin, Southern Africa

Lekula

Lubczynski

2019

Hydrogeol J

Self Cite

View full text Add to dashboard Cite

Distributed numerical models, considered as optimal tools for groundwater resources management, have always been constrained by availability of spatio-temporal input data. This problem is particularly distinct in arid and semi-arid developing countries, characterized by large spatio-temporal variability of water fluxes but scarce ground-based monitoring networks. That problem can be mitigated by remote sensing (RS) methods, which nowadays are applicable for modelling not only surface-water but also groundwater resources, through rapidly increasing applications of integrated hydrological models (IHMs). This study shows implementation of various RS products in the IHM of the Central Kalahari Basin (~200 Mm 2) multi-layered aquifer system, characterized by semi-arid climate and thick unsaturated zone, both enhancing evapotranspiration. The MODFLOW-NWT model with UZF1 package, accounting for variably saturated flow, was set up and calibrated in transient conditions throughout 13.5 years using borehole hydraulic heads as state variables and RS-based daily rainfall and potential evapotranspiration as driving forces. Other RS input data included: digital-elevation-model, land-use/land-cover and soils datasets. The model characterized spatio-temporal water flux dynamics, providing 13-year (2002-2014) daily and annual water balances, thereby evaluating groundwater-resource dynamics and replenishment. The balances showed the dominant role of evapotranspiration in restricting gross recharge to only a few mm yr −1 and typically negative net recharge (median, −1.5 mm yr −1), varying from −3.6 (2013) to +3.0 (2006) mm yr −1 (rainfall of 287 and 664 mm yr −1 respectively) and implying systematic water-table decline. The rainfall, surface morphology, unsaturated zone thickness and vegetation type/density were primary determinants of the spatiotemporal net recharge distribution.

show abstract

“…There are many achievements published about the evaluation of remote sensing precipitation products. [26][27][28][29][30]. The precision evaluation index of remote sensing precipitation products mainly includes a correlation coefficient, determination coefficient, scatter slope, fuzzy comprehensive score, etc.…”

Section: Introductionmentioning

confidence: 99%

Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region

Wang

Xia

2019

Applied Sciences

View full text Add to dashboard Cite

Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data.

show abstract

Validation of satellite-based rainfall in Kalahari

Cited by 28 publications

References 39 publications

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

Use of remote sensing and long-term in-situ time-series data in an integrated hydrological model of the Central Kalahari Basin, Southern Africa

Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region

Contact Info

Product

Resources

About