International Journal of Engineering Research and Advanced Technology (IJERAT) E-ISSN : 2454-6135 DOI: 10.31695/IJERAT.2019.3388 Volume.5, Issue 2 February -2019 www.ijerat.com Page 93 Licensed Under Creative Commons Attribution CC BY Spatio-temporal characterization of Agricultural Drought using Soil Moisture Deficit Index (SMDI) in the Upper Tana River basin, Kenya Raphael Muli Wambua 1* 1 Department of Agricultural Engineering Egerton University Kenya _______________________________________________________________________________________ ABSTRACT Occurrence of Agricultural drought in a river basin is associated with food insecurity. There is need to integrate agricultural drought characteristics in decision making within upper Tana River basin. In this research, characterization of spatio-temporal Agricultural Drought using Soil Moisture Deficit Index (SMDI), based on hydro-meteorological data was conducted. Eight meteorological stations with data for 41 years were used. The computation was interpolated using kriging interpolation technique within ArcGIS environment. The long-term soil-water was simulated using AquaCrop model. For station ID 9037112, the values of SMDI for dry season are lower than those for the wet seasons. It is deduced that the lower elevations of the basin in south- eastern parts exhibit more drought-prone areas than those in the higher elevations at north-western areas. From this study, Severity-area-frequency curves (SAF) curves for 2, 5, 10, 20, 50 and 100-year return periods were developed. The analysis of spatio-temporal drought characteristics can be adopted for prioritized mitigation of agricultural drought impacts. Key words: SMDI, AquaCrop model, Upper Tana River basin, SAF curves. ________________________________________________________________________________________________________ 1. INTRODUCTION Significant research has been conducted in water resources and hydrology but drought research is still inadequate in many river basins in the world. Agricultural drought is an event associated with low food production and or food insecurity in an area [1] and [36]. Agricultural drought refers to the deficit of soil moisture due to meteorological effects with different timing and effects. This drought depends upon soil moisture deficit [2], [33], water storage capacity of the soil and the hydro-meteorological variables. Since the drought is characterized by deficit of soil moisture content in the soil which is usually required by plants, it adversely affects any vegetation and or crop grown on land [3], [36], and [14]. Agricultural drought can be monitored by assessing soil moisture content levels. However, direct soil moisture data measurement is not available at regional and basin scales. To estimate soil moisture content, process-based models may be used. In such models, an integration of both the random variables of climate and physical properties of land are considered. One of the advantages of these models is that they can be used to provide information at different spatial and temporal resolutions. Some of these process-based models include the FAO developed AquaCrop model [5] and Soil and Water Assessment Tool (SWAT) [10]. The Aquacrop model has been used in modelling crop response to soil water availability in a number of studies. For instance, AquaCrop model was applied to evaluate wheat grain yield and crop biomass in China for irrigation systems [8]. It was also applied by [12] to assess crop grain yield and biomass response to soil water content and actual evapotranspiration under deficit irrigation conditions. There are two broad categories of drought indices; satellite based and the data driven drought indices [2]. The satellite or Remote Sensing (RS) indices involve the application of the science and art of obtaining information of points, objects, areas or phenomena through analysis of data acquired by a sensor, which is not in direct physical contact with the target of investigation [34]. On the other hand, the Data Driven Drought Indices use a single or a combination of hydro-meteorological variables as input parameters to assess drought intensity, duration, severity and magnitude. The two broad types of droughts may further be grouped into four main categories of droughts according to [43]. These include the Hydrological, Meteorological, Agricultural and Socio-economic droughts. The first three types are called the operational droughts and can be integrated into a drought management algorithm. Their relation can then be applied in development of water resource strategy in a river basin [16]. Propagation of hydrological and agricultural drought originates from meteorological droughts which develop from changing phenomena within the hydrological cycle. The three operational types of droughts are interconnected. For instance, Agricultural drought links meteorological and/or hydrological drought to agricultural impact. Agricultural droughts impact negatively on farming systems whenever they occur.