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The Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) is a flexible modelling framework reproducing the behaviour of 47 established hydrological models. MARRMoT can be used to calibrate and run models in a user-friendly and consistent way and is designed to facilitate the sharing of model code for reproducibility and to support intercomparison between hydrological models. Additionally, it allows users to create or modify models using components of existing ones. We present a new MARRMoT release (v2.1) designed for improved speed and ease of use. Whereas improved computational efficiency was the main driver for this redevelopment, MARRMoT v2.1 also succeeds in drastically reducing the verbosity and repetitiveness of the code, which improves readability and facilitates debugging. The process to create new models or modify existing ones within the toolbox is also simplified in this version, making MARRMoT v2.1 accessible for researchers and practitioners at all levels of expertise. These improvements were achieved by implementing an object-oriented structure and aggregating all common model operations into a single class definition that all models inherit from. The new modelling framework maintains and improves on several of the good practices built into the original MARRMoT and includes a number of new features such as the possibility of retrieving more output in different formats, which simplifies troubleshooting, and a new functionality that simplifies the calibration process. We compare outputs of 36 of the models in the framework to an earlier published analysis and demonstrate that MARRMoT v2.1 is highly consistent with the previous version of MARRMoT (v1.4), while achieving a 3.6-fold improvement in runtime on average. The new version of the toolbox and user manual, including several workflow examples for common application, are available from GitHub. [ABSTRACT FROM AUTHOR]

The performance of eight empirical equations for estimating ETo at 80 weather stations in Iran is evaluated. The equations assessed are Hargreaves (HGS), Trajkovic (TKC), Berti (BTI), Ravazzani (RZI), Irmak (IMK), Turc (TRC) and two Valiantzas methods (VTS1 and VTS2). The FAO56 reference crop Penman-Monteith (PM) equation is used as a baseline to evaluate their performance. Also, a Köppen climate classification map for Iran is developed and the best ETo method for each climate type identified. The updated Köppen climate map shows six climate sub-classes; BWh, BWk, BSh, BSk, Csa and Dsa in Iran with a percentage of land area covered by each sub-class of 43, 17, 7, 9, 11 and 13%, respectively. The best performing ETo equation for each climate class in Iran was HGS for BSh, VTS1 for BWk, and VTS2 for BSk, BWh, Csa and Dsa. [ABSTRACT FROM AUTHOR]

The assessment of direct radiative forcing due to atmospheric aerosols (ADRF) in the Indo Gangetic Plain (IGP), which is a food basket of south Asia, is important for measuring the effect of atmospheric aerosols on the terrestrial ecosystem and for assessing the effect of aerosols on crop production in the region. Existing comprehensive analytical models to estimate ADRF require a large number of input parameters and high processing time. In this context, here, we develop a simple model to estimate daily ADRF at any location on the surface of the IGP through multiple regressions of AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) and atmospheric water vapour using data from 2002 to 2015 at 10 stations in the IGP. The goodness of fit of the model is indicated by an adjusted R2 value of 0.834. The Jackknife method of deleting one group (station data) was employed to cross validate and study the stability of the regression model. It was found to be robust with an adjusted R2 fluctuating between 0.813 and 0.842. In order to use the year-round ADRF model for locations beyond the AERONET stations in the IGP, AOD, and atmospheric water vapour products from MODIS Aqua and Terra were compared against AERONET station data and they were found to be similar. Using MODIS Aqua and Terra products as input, the year-round ADRF regression was evaluated at the IGP AERONET stations and found to perform well with Pearson correlation coefficients of 0.66 and 0.65, respectively. Using ADRF regression model with MODIS inputs allows for the estimation of ADRF across the IGP for assessing the aerosol impact on ecosystem and crop production. [ABSTRACT FROM AUTHOR]

This article presents the author's views on several issues related to the importance of inter-annual rainfall variability in the patterning of agricultural systems as discussed by Robert Dewar of Storrs, Connecticut. The author holds that Dewar's argument in this regard is a significant contribution. The author supports the authenticity of Dewar's conclusion that inter-annual rainfall variability plays an important role as an environmental filter of crop migration in Southeast Asia.