Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data

Abstract

Abstract. 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 Correspondence to: Y. Hong (yanghong@ou.edu) 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.

Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains

“…Daniel (2011) gives a good comparison of different frequently used models but the number of different models is simply too large to be covered entirely. The integration of raster data sets has been implemented in different models such as the MIKE SHE (Cooper et al, 2006;Liu et al, 2012) or CREST (Khan et al, 2011) models, and several more examples of raster data input are available (Merritt et al, 2006;Stahl et al, 2008;Wood et al, 2004). While each of these models has specific qualities, none of them globally outperforms the others.…”

“…This is due to the high computational expense that is needed to create such data sets. The abundance and increasing accuracy in GCMs, RCMs, and remote sensing data has led to their greater use in hydrological modelling (Khan et al, 2011;Awange et al, 2011;Liu et al, 2012;Bookhagen and Burbank, 2010) and glacier evolution studies (Gardelle et al, 2012(Gardelle et al, , 2013Lutz et al, 2014;Sorg et al, 2012). In cold, arid, mountainous regions, GCMs are often favoured over remote sensing data.…”

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…Kalinga and Gan [80] developed an infrared-microwave rainfall algorithm (IMRA), which adjusts the QPE retrieved from the TRMM and GOES IR sensors with the TMI data, and applied it to the conceptual SACramento Soil Moisture Accounting (SAC-SMA) model. Since the launch of the TRMM, the TRMM rainfall products (e.g., TMPA) have been extensively implemented in streamflow/flood simulations [81][82][83][84][85][86][87][88]. Asante et al [84] developed a linear geospatial streamflow simulation system using remotely sensed land and hydrologic inputs, including the TRMM precipitation product.…”

Water Balance Modeling in a Semi-Arid Environment with Limited in situ Data Using Remote Sensing in Lake Manyara, East African Rift, Tanzania

“…The climate of East Africa is principally influenced by the seasonal shift of the Intertropical Convergence Zone (ITCZ) [11,22] and Indian Ocean Dipole (IOD). The IOD is an ocean-atmosphere interaction, over the Indian Ocean, creating alternating positive and negative Sea Surface Temperature (SST) anomalies.…”

“…Some ground measurement and satellite gravimetry derived equivalent water thickness has been employed to verify the trend of the Lake Manyara water balance [10]. Over recent years, several studies have both applied remote sensing observations and gravimetry recovery and climate experiment (GRACE) measurements to study the hydrological cycles of lakes, rivers and associated basins in the East African Rift and elsewhere [10][11][12][13][14][15]. Changes in a basin's water budget are directly linked to local gravity variations [16].…”