Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

Abstract

Understanding spatial drought characteristics is vital for planning adaptation and mitigation measures in river catchments. In many parts of the world, spatial drought information is not available due to lack of adequate evenly distributed data for spatial drought analyses. This study elucidates a spatial drought analysis in a data-scarce tropical catchment using remote sensing actual evapotranspiration (ET) and potential evapotranspiration (PET) data. Firstly, the time series of 690 images of remotely sensed ET and PET between the years 2000 and 2014 were spatially analyzed using the evapotranspiration deficit index (ETDI) approach to obtain ETDIs in the Kilombero River catchment (Tanzania). Then, spatio-temporal patterns of ETDIs were used to characterize drought frequency, total drought durations, total drought severity, and drought intensity. The frequency, durations, severity, and intensity of drought increased from the year 2000 towards 2014, causing substantial drought changes in the catchment. However, drought intensity revealed that those changes were mainly from no drought and mild drought to moderate drought. Between the years 2000 and 2014, no-drought areas and mild drought areas declined from 10% to 0% and from 42% to 19%, respectively, whereas moderate drought areas increased from 47% to 81% of the catchment size. Those changes of drought conditions were partly attributed to anthropogenic land cover change, especially in the southwest grasslands, and were partly attributed to meteorological factors in other parts of the catchment. This information is crucial for further land cover change and climate change investigations, as well as planning water and land resources in the Kilombero River catchment. Moreover, the study also demonstrates the potential of using publicly available remote sensing ET products and the ETDI approach for spatially characterizing drought in ungauged regions.

A Scheme to Estimate Diurnal Cycle of Evapotranspiration from Geostationary Meteorological Satellite Observations

“…H, LE, and G were simplified as functions of some unknown constants in one-day period, land surface temperature, and air temperature on the basis of the bulk heat transfer and heat conductivity equations, that is, the expressions in Equations ( 1)-(3). These unknown constants would be solved by the surface energy balance equation, which had been converted to solve the ill-posed problem of Equation (6). The accuracy of simplified parameterization affected the stability of the solution of the ill-posed problem.…”

“…ET and latent heat flux are different expressions of the same process and can be converted to each other. Accurate ET estimation is critical in many fields, such as atmospheric and hydrological processes, climate change, crop irrigation management, drought monitoring, and water resources management [4][5][6][7][8]. Remote sensing (RS) techniques provide an approach to obtain ET on the regional and global scales.…”

“…0 < n j=1 (d 3 ϕ 3, j + d 4 ϕ 4, j + d 5 )<= ET daily is the main improvement in this study, i.e., known daily ET was used to restrain LE estimation at the instantaneous scale. In theory, because there are seven unknown variables in Equations (1)-(3), at least seven sets of known values of T s , T a , and R n during the daytime are required as inputs of Equation(6). After obtaining the unknown constants of d 1 -7 , the instantaneous LE in one day is calculated by Equation (2) with known d 3 , d 4 , d 5 , T s, and T a .…”

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

“…Today, most communities have begun to develop drought monitoring and prediction systems so that they can adapt to its consequences in a more sustainable way. However, the task would be more challenging for the catchments suffering from scarce weather or hydrometric observatory stations [14]. To overcome this handicap in ungauged catchments, many studies adopted alternatives that use (i) nearby observatory records adjusted by spatial statistical techniques [15], (ii) remote sensing and satellite technology [16,17], and reanalysis products [18].…”

Sensitivity of the Evapotranspiration Deficit Index to Its Parameters and Different Temporal Scales

“…Bayissa, et al [2] used ETDI in comparisons of drought indices in the Upper Blue Nile Basin, Ethiopia. Wambura and Dietrich [22] used ETDI to analyze spatio-temporal drought in the Kilombero catchment, Tanzania. In all these studies, ETDI was computed using the specific ETDI formula.…”