Pradeep Adhikari

Urban area expansion is happening at much faster rates in Asian and African cities than elsewhere in the world. This study uses multi-temporal Landsat images to map the urban extent of six small to large cities in West Africa at four different time steps from the early 1970s–2010. The selected cities are Kumasi of Ghana, Daloa of Cote d’Ivoire, Abuja and Kano in Nigeria, Kindia of Guinea, and Ouagadougou of Burkina Faso. All cities revealed significant urban growth in both urban area and population; however, it was apparent that there was a lot of variability in urban area development. Exponential urban growth rates in the cities were measured as ranging between 0.026 and 0.077, with allometric scaling factors matching those of other countries.

West Africa is one of the fastest growing regions of the world and depends heavily on rain-fed agriculture for its food production. This study evaluates 12 freely available land-cover and land-use (LCLU) data sets at the ecoregion, country, and pixel levels in West Africa to estimate croplands. The selected data sets are primarily derived using remote-sensing data, representing different time periods and using various classification schemes. The result shows a very high variability of the estimated cropland at all levels. Despite this variability, data sets having a finer spatial resolution and representing a similar time period – specifically data from the International Institute for Applied Systems Analysis-International Food Policy Research Institute (IIASA−IFPRI), Global Land Cover−Share (GLC−Share), Moderate Resolution Imaging Spectroradiometer–University of Maryland (MODIS−UMD), Global Cropland Extent, Moderate Resolution Imaging Spectroradiometer−International Geosphere Biosphere Programme (MODIS−IGBP), and GlobCover version 2.3 (GlobCover V23) – estimated comparable cropland areas at the ecoregion and country levels. The countrywide cropland area, obtained from the selected data sets, when compared with the sum of arable land and permanent crop area obtained from the Food and Agriculture Organization (FAO), showed a high coefficient of determination (R² > 0.95) for IIASA–IFPRI and GLC−Share. At the pixel level, at the original resolution, the newer data sets have a comparable user’s accuracy (UA>53%) and producer’s accuracy (PA>46%), except for the Global Cropland Extent data. Overall, two data sets – IIASA−IFPRI and GLC−Share – performed better in the region to estimate the cropland area at all levels.

Study of hydro-climatology at a range of temporal scales is important in understanding and ultimately mitigating the potential severe impacts of hydrological extreme events such as floods and droughts. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10- year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia basin. The spatiotemporal variability of the water cycle components were quantified using a hydrologic model, with in-situ and multi-satellite remote sensing datasets. The model is calibrated using daily observed discharge data for the period between 1985 and 1999, for which model performance is estimated with a Nash Sutcliffe Efficiency (NSCE) of 0.87 and 0.23% bias. The model validation showed an error metrics with NSCE of 0.65 and 1.04% bias. Moreover, the hydrologic capability of satellite precipitation (TRMM-3B42 V6) is evaluated. In terms of reconstruction of the water cycle components the spatial distribution and time series of modeling results for precipitation and runoff showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. The monthly peak runoff is observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. Satellite precipitation forcing data showed the potential to be used not only for the investigation of water balance but also for addressing issues pertaining to sustainability of the resources at the catchment scale.

In this study we developed an impact factor formula (IFF) to quantitatively attribute separately the impacts of climate change and local human activities on hydrological response (i.e. run‐off) in a sub‐basin of Yellow River for the period 1950–2000. Using the daily climatic data, we first calibrated and verified the variable infiltration capacity (VIC) hydrological model to the baseline period 1955–1970. Then we developed the basin's natural run‐off for the following three decades (1971–2000) using the VIC model without considering local human impacts, as the VIC model is benchmarked by the 1960's hydrological regime.On the basis of observed precipitation, run‐off and reconstructed natural run‐off data from 1971 to 2000, we quantified their long‐term trend, decadal and annual variations. Using daily climatic observations, we showed that the precipitation and run‐off have decreased from the baseline decade, the 1960s, indicating a drier hydrological regime for recent decades. We further applied the IFF to quantitatively attribute separately the impacts of reduced precipitation and increased temperatures from climate change and then of local human activities on hydrological run‐off response. It was found that climate change has a greater impact than human activities on the basin's run‐off for the three consecutive decades. The pCC (percentage change of run‐off due to climate change impact) is found to be 89% followed by 66% and 56% in 1970s, 1980s and 1990s, respectively. Over the decades, pHA (percentage change of run‐off due to human activities) has continuously increased from 11% to 44%. If the trend continues, in future, the pHA is going to outweigh pCC in this basin. This study provides a quantitative assessment methodology for water resources managers to understand the changing process of the hydrological cycle and attribute its causative factors in a sub‐basin of the Yellow River. Copyright © 2009 Royal Meteorological Society