Evaluation Of Rainfall Products Research Articles

Evaluation of Rainfall Products in Semi-Arid Areas: Application to the Southeast of the Republic of Djibouti and a Focus on the Ambouli Catchment

The Republic of Djibouti is a small country in the Horn of Africa and, as in most developing countries, rain gauges are sparse and data are scarce. This study aims to report on the reliability of gridded precipitation datasets (P datasets) across the Republic of Djibouti through direct comparisons with rain gauge measurements from the annual to the daily time scales. Our specific objective is to be able to use such products in the context of hydrological modeling at a daily time step. Given the scarcity of available data in the Republic of Djibouti, our study was carried out on two time windows (1980–1990 and 2008–2013) and two gauge networks with different spatial resolutions: the southeast of the Republic of Djibouti (5000 km2) and the Ambouli catchment (794 km2), which drains the city of Djibouti. The reliability of these products is analyzed with quantitative metrics and categorical metrics, exclusively at a daily time step for the latter. The performance of the P datasets degrades from the annual time scale to the daily time scale. Even though the same products exhibit the best performance at the various time scales, the performance of most of the products differs from one spatial scale to another. Our results demonstrate the importance of the temporal and spatial windows, as the same products can perform differently according to the scale. For all the spatiotemporal scales, the most reliable product is MSWEP v.2.2. This P dataset is derived from a combination of satellite products (multiple sensors such as infrared and passive microwave), reanalysis products, and rain gauge observations. A strong discrepancy between rain gauge observations and P datasets is revealed according to the categorical metric at a daily time step. The analysis of rainfall events triggering runoff, using a 10 mm rainfall threshold, showed that the most efficient products were unable to accurately detect such events at a daily time step, with a significant underestimation of rainfall events higher than 10 mm. None of these products, even the most reliable, can be used for a calibration/validation of a hydrological model at a daily time step.

Comparison of Simulated Spatial Patterns Using Rain Gauge and Polarimetric-Radar-Based Precipitation Data in Catchment Hydrological Modeling

Abstract With the advance of the weather radar technology, dual-polarization (dual-pol) radar data are now available for hydrological studies, which go beyond the traditional rainfall products relying purely on rain gauge data. Previous studies have focused on the evaluation of rainfall products and their hydrological responses using point-based observational data; however, spatial patterns of simulated hydrological variables are equally important to be considered in order to fully address the distributed effect of the precipitation estimates. In the present study, we compare three rainfall estimations based on rain gauge, single-polarization, and dual-pol radar data. Special attention is given to the use of the two radar products and their corresponding hydrological simulations of both surface water and groundwater. Performance of the hydrological simulations is evaluated based first on traditional point-based observations of stream discharge and groundwater head, and second on remotely sensed land surface temperature data. For the latter, the empirical orthogonal function analysis, which quantifies spatial pattern similarities, is employed. The Skjern River catchment in western Denmark is selected as the study site, and the results show that all three models perform equally well in terms of the traditional aggregated evaluation criteria, such as Nash–Sutcliffe efficiency (NSE) and RMSE on time series data. It is found that the differences of simulated hydrological spatial patterns are sensitive to rainfall signal intensity, as well as the simulation scale in space (<100 km2) and time (subdaily). Our study suggests that the currently available observational data have limited capabilities to clearly differentiate the performance of the three applied models due to the low resolution.

Evaluation of several rainfall products used for hydrological applications over West Africa using two high‐resolution gauge networks

AbstractThe evaluation of rainfall products over the West African region will be an important component of the Megha‐Tropiques (MT) Ground Validation (GV) plan. In this paper, two dense research gauge networks from Benin and Niger, integrated in the MT GV plan, are presented and are used to evaluate several currently available global or regional satellite‐based rainfall products. Eight products—the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction Center Morphing method (CMORPH), Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) 3B42 real‐time and gauge‐adjusted version, Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) African Rainfall Estimate (RFE), Estimation des Precipitation par SATellite (EPSAT), and Global Precipitation Climatology Project One Degree Daily estimate (GPCP‐1DD)—are compared to the ground reference. The comparisons are carried out at daily, 1° resolution, over the rainy season (June–September), between the years 2003 and 2010. The work focuses on the ability of the various products to reproduce salient features of the rainfall regime that impact the hydrological response. The products are analysed on a multi‐criteria basis, focusing in particular on the way they distribute the rainfall within the season and by rain rate class. Standard statistical diagnoses such as the correlation coefficient, bias, root mean square error and Nash skill score are computed and the inter‐annual variability is documented. Two simplified hydrological models are used to illustrate how the nature and structure of the product error impact the model output in terms of runoff (calculated using the Soil Conservation Service method, SCS, in Niger) or outflow (calculated with the ‘modèle du Génie Rural à 4 paramètres Journalier’, GR4J model, in Benin). Copyright © 2013 Royal Meteorological Society