Assessment of Various Empirical Soil Loss Estimation Equations in Arid Regions

One of the most commonly used equations to estimate soil erosion is the revised universal soil loss equation (RUSLE). Based on the early approach developed by the Soil Conservation Service of USA, the rainfall erosivity factor (R-factor) in the RUSLE equation requires sub-daily rainfall data, which is usually not available. Other empirical equations estimate R-factor based on available rainfall data like annual and monthly rainfall data. In arid regions such as the Arabian Peninsula, several studies estimated the R-factor based on these empirical equations without calibration. We propose in this paper to assess the applicability of some of these empirical equations against R-factor values calculated using as a reference the RUSLE approach. For this data, data from 104 stations with sub-daily rainfall was collected. The reference R-factor was calculated for the 104 stations. The results of seven empirical equations were tested against the reference R-factor. Most of the tested equations significantly underestimated the R-factor. Furthermore, the obtained RMSE and MAE values were almost as high as the average R-factor, with MAPE exceeding 100%. Therefore, it is recommended not to apply these equations in arid regions. A recalibration of the form of equation that gave the best results, gave an RMSE of 280 (Mj·mm/(ha·hr)) and the MAPE dropped to 47.6%.

A Novel Approach for the Joint Use of Rainfall Monthly and Daily Ground Station Data with TRMM Data to Generate IDF Estimates in a Poorly Gauged Arid Region

In poorly gauged regions, rainfall data are often short or even absent, hindering the possibility of estimating Intensity-Duration-Frequency (IDF) relations with operationally acceptable accuracy. In this research, a novel idea is presented for the use of three separate rainfall datasets: maximum annual daily data, monthly data and Tropical Rainfall Measuring Mission (TRMM) satellite data to develop robust IDF in Namibe, south ofAngola. TRMM data is used to derive relations between maximum monthly and maximum daily rainfall and between sub-daily and daily rainfall depths. Frequency analysis is undertaken on the mixed daily record using several distributions and the best fitting is selected based on discriminant plots of the distribution tails and the moment ratio diagram as well as Bayesian criteria. The IDF curves are derived based on the estimates of daily rainfall at various return periods, with the derived sub-daily rainfall duration ratios. Robust IDFs are thus developed for a scarce data region inAfrica.

Developing Intensity-Duration-Frequency Curves in Scarce Data Region: An Approach using Regional Analysis and Satellite Data

The availability of data is an important aspect in frequency analysis. This paper explores the joint use of lim- ited data from ground rainfall stations and TRMM data to develop Intensity Duration Frequency (IDF) curves, where very limited ground station rainfall records are available. Homogeneity of the means and variances are first checked for both types of data. The study zone is assumed to be belonging to the same region and checked using the Wiltshire test. An Index Flood procedure is adopted to generate the theoretical regional distribution equation. Rainfall depths at various return periods are calculated for all stations and plotted spatially. Regional patterns are identified and discussed. TRMM data are used to develop ratios between 24-hr rainfall depth and shorter duration depths. The regional patterns along with the developed ratios are used to develop regional IDF curves. The methodology is applied on a region in the North-West of Angola.