Land Water-Storage Variability over West Africa: Inferences from Space-Borne Sensors

Ferreira, Vagner G.; Asiah, Zibrila; Xu, Junzeng; Gong, Zheng; Andam-Akorful, Samuel A.

doi:10.3390/w10040380

Cited by 12 publications

(10 citation statements)

References 58 publications

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“…Confirming the previous results, Figure 14 indicates that the application of GRACE data in computing SSI and M SDI reveal stronger drought patterns, which are distributed over larger areas. To complement our investigation, we investigate the extent of droughts in the Niger (Ferreira et al, 2018),…”

Section: Figure 12 Figure 13mentioning

confidence: 99%

Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections

Forootan

Khaki

Schumacher

et al. 2019

Science of The Total Environment

143

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Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydrometeorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are

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Section: Figure 12 Figure 13mentioning

confidence: 99%

Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections

Forootan

Khaki

Schumacher

et al. 2019

Science of The Total Environment

143

View full text Add to dashboard Cite

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“…The land use practices as the construction of large dams could have an influence on surrounding climate [67] and can lead to microclimatic changes [68]. It has been proved to have high impact on the rainfall [69] and on climate system [73,74] of a given region. The study of Mara river in East Africa region exhibits that the conversion of forests to agriculture and grassland in the basin headwaters is likely to reduce dry season flows and increase peak flows, leading to greater water scarcity at critical times of the year and exacerbating erosion on hillslopes [75].…”

Section: Basic Statistical Analysis Of Precipitation and Dischargementioning

confidence: 99%

Assessment of Spatio-Temporal Changes of Land Use and Land Cover over South-Western African Basins and Their Relations with Variations of Discharges

et al. 2018

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West African basins play a vital role in the socio-economic development of the region. They are mostly trans-boundary and sources of different land use practices. This work attempts to assess the spatio-temporal land use and land cover changes over three South Western African basins (Volta, Mono and Sassandra basins) and their influence on discharge. The land use and land cover maps of each basin were developed for 1988, 2002 and 2016. The results show that all the studied basins present an increase in water bodies, built-up, agricultural land and a decline in vegetative areas. These increases in water bodies and land use are as a result of an increase in small reservoirs, of dugouts and of dam constructions. However, the decline in some vegetative clusters could be attributed to the demographic and socio-economic growth as expressed by the expansion of agriculture and urbanization. The basic statistical analysis of precipitation and discharge data reveals that the mean annual discharge varies much more than the total annual precipitation at the three basins. For instance, in the entire Volta basin, the annual precipitation coefficient of variation (CV) is 10% while the annual discharge CV of Nawuni, Saboba and Bui are 43.6%, 36.51% and 47.43%, respectively. In Mono basin, the annual precipitation CV is 11.5% while the Nangbeto and Athieme annual discharge CV are 37.15% and 46.60%, respectively. The annual precipitation CV in Sassandra basin is 7.64% while the annual discharge CV of Soubre and Dakpadou are 29.41% and 37%, respectively. The discharge varies at least three times much more than the precipitation in the studied basins. The same conclusion was found for all months except the driest months (December and January). We showed that this great variation in discharge is mainly due to land use and land cover changes. Beside the hydrological modification of the land use and land cover changes, the climate of the region as well as the water quality and availability and the hydropower generation may be impacted by these changes in land surfaces conditions. Therefore, these impacts should be further assessed to implement appropriate climate services and measures for a sustainable land use and water management.

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“…To resist abnormal TWS anomalies that creates biases, this study uses the median of monthly TWS anomalies, in contrast to that of the mean of TWS anomalies proposed by Zhao et al [ 42 ]. In addition, the median was used due to its robustness for extreme values while computing the monthly normal value [ 16 ]. Therefore, the GRACE-DSI is modified as

where

.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Traditional indirect WL monitoring is generally based on an empirical rating curve between the WL and an inundated area using high-resolution satellite images (e.g., [ 14 , 15 ]). A linear relationship between the WL and the surface area changes of a surface reservoir has also been applied (e.g., [ 16 ]). However, the relationship varies across different segments of the river, and it also changes over time in the same place due to natural factors, such as erosion and sedimentation [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Water Level Reconstruction and Prediction Based on Space-Borne Sensors: A Case Study in the Mekong and Yangtze River Basins

Fok

Chen

et al. 2018

Sensors

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Water level (WL) measurements denote surface conditions that are useful for monitoring hydrological extremes, such as droughts and floods, which both affect agricultural productivity and regional development. Due to spatially sparse in situ hydrological stations, remote sensing measurements that capture localized instantaneous responses have recently been demonstrated to be a viable alternative to WL monitoring. Despite a relatively good correlation with WL, a traditional passive remote sensing derived WL is reconstructed from nearby remotely sensed surface conditions that do not consider the remotely sensed hydrological variables of a whole river basin. This method’s accuracy is also limited. Therefore, a method based on basin-averaged, remotely sensed precipitation from the Tropical Rainfall Measuring Mission (TRMM) and gravimetrically derived terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) is proposed for WL reconstruction in the Yangtze and Mekong River basins in this study. This study examines the WL reconstruction performance from these two remotely sensed hydrological variables and their corresponding drought indices (i.e., TRMM Standardized Precipitation Index (TRMM-SPI) and GRACE Drought Severity Index (GRACE-DSI)) on a monthly temporal scale. A weighting procedure is also developed to explore a further potential improvement in the WL reconstruction. We found that the reconstructed WL derived from the hydrological variables compares well to the observed WL. The derived drought indices perform even better than those of their corresponding hydrological variables. The indices’ performance rate is owed to their ability to bypass the influence of El Niño Southern Oscillation (ENSO) events in a standardized form and their basin-wide integrated information. In general, all performance indicators (i.e., the Pearson Correlation Coefficient (PCC), Root-mean-squares error (RMSE), and Nash–Sutcliffe model efficiency coefficient (NSE)) reveal that the remotely sensed hydrological variables (and their corresponding drought indices) are better alternatives compared with traditional remote sensing indices (e.g., Normalized Difference Vegetation Index (NDVI)), despite different geographical regions. In addition, almost all results are substantially improved by the weighted averaging procedure. The most accurate WL reconstruction is derived from a weighted TRMM-SPI for the Mekong (and Yangtze River basins) and displays a PCC of 0.98 (and 0.95), a RMSE of 0.19 m (and 0.85 m), and a NSE of 0.95 (and 0.89); by comparison, the remote sensing variables showed less accurate results (PCC of 0.88 (and 0.82), RMSE of 0.41 m (and 1.48 m), and NSE of 0.78 (and 0.67)) for its inferred WL. Additionally, regardless of weighting, GRACE-DSI displays a comparable performance. An external assessment also shows similar results. This finding indicates that the combined usage of remotely sensed hydrological variables in a standardized form and the weighted averaging procedure could lead to an improveme...

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Land Water-Storage Variability over West Africa: Inferences from Space-Borne Sensors

Cited by 12 publications

References 58 publications

Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections

Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections

Assessment of Spatio-Temporal Changes of Land Use and Land Cover over South-Western African Basins and Their Relations with Variations of Discharges

Water Level Reconstruction and Prediction Based on Space-Borne Sensors: A Case Study in the Mekong and Yangtze River Basins

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