Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River basin

Abstract

[1] Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance closure, or with spatially distributed prognostic models that simultaneously balance both energy and water budgets over landscapes using predictive equations for land surface temperature and moisture states. Each modeling approach has complementary advantages and disadvantages, and in combination they can be used to obtain more accurate ET estimates over a variety of land and climate conditions, particularly for areas with limited ground truth data. In this study, energy and water flux estimates from diagnostic Atmosphere-Land Exchange (ALEXI) and prognostic Noah land surface models are compared over the Nile River basin between 2007 and 2011. A second remote sensing data set, generated with Penman-Monteith approach as implemented in the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16 ET product, is also included as a comparative technique. In general, spatial and temporal distributions of flux estimates from ALEXI and Noah are similar in regions where the climate is temperate and local rainfall is the primary source of water available for ET. However, the diagnostic ALEXI model is better able to retrieve ET signals not directly coupled with the local precipitation rates, for example, over irrigated agricultural areas or regions influenced by shallow water tables. These hydrologic features are not well represented by either Noah or MOD16. Evaluation of consistency between diagnostic and prognostic model estimates can provide useful information about relative product skill, particularly over regions where ground data are limited or nonexistent as in the Nile basin.

Upper Blue Nile basin water budget from a multi-model perspective

“…CLSMF2.5 agrees well with ALEXI during both high and low periods, whereas Noah3.3 has decreased ET values, close to GLEAM values during those low periods. Yilmaz et al (2014) also demonstrated that Noah3.2 captures ET well during the rainy season, but it underestimated ET during the winter months over agricultural areas or regions with shallow ground water tables in the Nile Basin. In this sense, the Noah-MP LSM (Niu et al, 2011) may better represent ET values in this region due to being coupled with an underlying water table scheme, which is not supported in Noah3.3.…”

“…Significant uncertainty in evapotranspiration analyses has been a known issue in the region (Yilmaz et al, 2014). In this study, we evaluated the 16 modeled ET estimates against ALEXI data, which have also been used to validate actual ET estimates in this region (Allam et al, 2016).…”

“…Monthly model-based evapotranspiration estimates were compared and evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) (Yilmaz et al, 2014) and the Global Land Evaporation Amsterdam Model (GLEAM) v3.0a (Martens et al, 2016).…”

“…Yilmaz et al (2014) demonstrated that ALEXI outperformed Noah LSM and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16 ET product to estimate ET over the Nile river basin. In this study, ALEXI ET rates are used as the reference for our evaluation.…”

Assessment of irrigation physics in a land surface modeling framework using non-traditional and human-practice datasets

“…These significant potential impacts of irrigation on temperature, clouds, precipitation, and related fluxes necessitate an appropriate representation of irrigation in coupled landatmosphere models. This need has been addressed via irrigation parameterizations in LSMs that largely fall into three types of schemes: (1) defined increases to soil moisture in one or more soil layers (Kueppers and Snyder, 2011;de Vrese et al, 2016), sometimes referred to as flood (Evans and Zaitchik, 2008); (2) the addition of water as pseudoprecipitation to mimic sprinkler systems (Ozdogan et al, 2010;Yilmaz et al, 2014); and (3) modifications to vapor fluxes as a proxy for increased evapotranspiration resulting from highly efficient (e.g., drip) irrigation (Douglas et al, 2006;Evans and Zaitchik, 2008). These schemes are generally dependent on parameter input datasets and user-defined thresholds, affording a degree of customization, but also introducing uncertainty and potential error.…”