Potential benefits of altering the cassava farming practices in the water shortage regions of Vietnam

The work was proceeded to define the suitable period for planting cassava crops in Han Thuan Bac district of Binh Thuan province, Vietnam to reduce the negative impacts of weather factors. The work was deployed applying the FAO-Aqua Crop model to calculate the tuber yield of cassava plants under the cassava farming practices (CFP) to seek the suitable planting period (SPP). The applied model was appraised through the calibration and validation procedures with the index of agreement (IA), correlation coefficient (CC) and the Root Mean Square Error (RMSE) varying from 0.82 to 0.88, 0.81 to 0.89 and 0.21 to 0.29, respectively. Based on the calibrated and validated procedures it can state that the proposed model is suitable for simulating the tuber yield of cassava across the study area. The simulated results indicated that the application of the CFP on Julian days from 110 to 150 for Vu Xuan crop and from 100 to 140for Vu He crop the tuber yield of cassava can improve up to 8.9 per cent and 6.0 per cent, respectively compared to the current farming practices.

Testing the accuracy of high-resolution satellite-based and numerical model output precipitation products over Ethiopia

The indirect rainfall estimates by satellites and numerical models are the alternative options for the regions lacking enough and accurate ground observations. However, these indirect estimates often lack homogeneity and need to be evaluated before application. This study used gauge observations to test the accuracy of recently produced high-resolution satellite-based and numerical model output rainfall products over Ethiopia. Tropical Applications of Meteorology Using Satellite data and Ground-Based Observations (TAMSAT v3.1), Climate Hazard group Infrared Precipitation with Stations (CHIRPS v2.0), and the ERA5 reanalysis products were evaluated at monthly, seasonal, and annual temporal scales for the years 1992–2009. The satellite products showed nearly similar characteristics with much better accuracy than the model reanalysis output, which underestimated the rainfall amounts. Both satellite and reanalysis products captured the shapes of the rainfall at a monthly scale but less accurately at a seasonal scale. In general, the satellite-based products outperformed the reanalysis data set with a high correlation coefficient and index of agreement values, as well as low Root Mean Square Error and BIAS values. On the other hand, the reanalysis (ERA5) product showed a considerable underestimation in all sites. Therefore, satellite-based products are more reliable for researches in the region. However, the algorithms in both satellites need further calibration for a better estimation of seasonal rainfall amounts.

Centuries of Heat Waves over India during 20th and 21st Century

An assessment of temperature extremes is made for the Indian subcontinent to identify the changes since 1951 to 2015, and for the future climate periods till 2100 for all the 21 CMIP5 (Coupled Model intercomparision Project phase 5) models and the representative concentration pathways RCP4.5 and RCP8.5 were examined for the period from 1 March to 31 May to characterize the heat waves in future climates and mean maximum and mean minimum bias were evaluated for the Indian subcontinent. Later two highest recorded temperature regions were chosen Northwest & Central India (NW&CIN) and only central India (CIN) box and the features of heat waves such as intensity and frequency were evaluated up to 2100. Corresponding temperature predictions from historical runs for the period 1951–2005 of 21 global CMIP model outputs and statistics were performed with the India Meteorological Department (IMD) gridded maximum temperature data for validation. Statistical metrics of BIAS, RMSE and MAE have indicated low BIAS, high correlation and high IOA (Index of Agreement) validating CMIP climate simulations. By analyzing the statistics of all the 21 models with respect to the observational gridded data from IMD came to conclusion that among all the 21 models 5 models were performing well for Indian region and having good index of agreement with IMD. The frequencies of the days having thresholds of 40 ºC, 42 ºC and 45 ºC for the maximum temperature over India during the pre-monsoon are evaluated up to 21st century. All models are showing that the intensity and frequency of heat waves were increasing significantly for both RCP4.5 and RCP8.5. Specifically, the characteristics of heat waves in terms of intensity, duration and area extent are calculated and compared to heat waves of the current climate.

Comparison of statistical indices for the evaluation of crop models performance

This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observationsaimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model.

Minimisation of Overestimation of River Flows in 1D- Hydrodynamic Modeling

One-dimensional hydrodynamic models overestimate river cross-section derived from freely available SRTM DEMs. The present study aims to minimize the overestimation of river flow. DEM-extracted cross-sections obtained from 30 m and 90 m resolutions show higher elevation values than the actual river cross sections of Krishna and Bhima rivers, India. To minimize the overestimation of the river flow, DEM-extracted cross-sections are modified using known cross-section of the river. The corrections for cross sections extracted from DEM, are obtained by subtracting the DEM-derived cross-sections from a known cross-section of the river. Monsoons flows that occurred in years 2006 and 2009 in Krishna and Bhimariver have been used for modeling. The MIKE HYDRO River model performance with modified DEM-extracted cross-sections of river improves as the correlation coefficient, root mean square error, index of agreement, Nash Sutcliffe efficiency & Percentage deviation in peak (%) values are improved.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

Hourly and Daily Reference Evapotranspiration with ASCE-PM Model for Paraná State, Brazil

The objective of this study was to verify the magnitude and trend of hourly reference evapotranspiration (EToh), as well as associate and analyze daily ETo (ETod) series and the sum of hourly ETo (ETo24h) in 24 h, estimated with the Penman-Monteith ASCE model for Paraná State (Cfa and Cfb climate type). Relative humidity (RH), temperature (T), solar radiation (Rs) and wind speed (u2) data were obtained from 25 meteorological stations from the National Meteorological Institute (INMET), between December 1, 2016 to November 8, 2018. The analyzes were performed by linear regression and associations considering the root mean square error, correlation coefficient and index of agreement. The EToh trend has a Gaussian distribution, with the highest values between 12:00 p.m. and 2:00 p.m., with the maximum average being 0.44 mm h−1 (Cfa climate type) and 0.35 mm h−1 (Cfb climate type). The average difference between the ETo24h and ETod values was small (5.1% for Cfa and 7.4% for Cfb), resulting in close linear associations. The results obtained indicate that EToh has good potential to be used in planning and management in the field of soil and water engineering, in Paraná State.

GYMEE: A Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine Based on Landsat, Weather, and Soil Data

In this study, we used Landsat Earth observations and gridded weather data along with global soil datasets available in Google Earth Engine (GEE) to estimate crop yield at 30 m resolution. We implemented a remote sensing and evapotranspiration-based light use efficiency model globally and integrated abiotic environmental stressors (temperature, soil moisture, and vapor deficit stressors). The operational model (Global Yield Mapper in Earth Engine (GYMEE)) was validated against actual yield data for three agricultural schemes with different climatic, soil, and management conditions located in Lebanon, Brazil, and Spain. Field-level crop yield data on wheat, potato, and corn for 2015–2020 were used for assessment. The performance of GYMEE was statistically evaluated through root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), relative error (RE), and index of agreement (d). The results showed that the absolute difference between the modeled and predicted field-level yield was within ±16% for the analyzed crops in both Brazil and Lebanon study sites and within ±15% in the Spain site (except for two fields). GYMEE performed best for wheat crop in Lebanon with a low RMSE (0.6 t/ha), MAE (0.5 t/ha), MBE (−0.06 t/ha), and RE (0.83%). A very good agreement was observed for all analyzed crop yields, with an index of agreement (d) averaging at 0.8 in all studied sites. GYMEE shows potential in providing yield estimates for potato, wheat, and corn yields at a relative error of ±6%. We also quantified and spatialized the soil moisture stress constraint and its impact on reducing biomass production. A showcasing of moisture stress impact on two emphasized fields from the Lebanon site revealed that a 12% difference in soil moisture stress can decrease yield by 17%. A comparison between the 2017 and 2018 seasons for the potato culture of Lebanon showed that the 2017 season with lower abiotic stresses had higher light use efficiency, above-ground biomass, and yield by 5%, 10%, and 9%, respectively. The results show that the model is of high value for assessing global food production.

Efficient Urban Inundation Model for Live Flood Forecasting with Cellular Automata and Motion Cost Fields

The mitigation of societal damage from urban floods requires fast hydraulic models for emergency and planning purposes. The simplified mathematical model Cellular Automata is combined with Motion Cost fields, which score the difficulty to traverse an area, to the urban inundation model CAMC. It is implemented with simple matrix and logic operations to achieve high computational efficiency. The development concentrated on an application in dense urban built-up areas with numerous buildings. CAMC is efficient and flexible enough to be used in a “live” urban flood warning system with current weather conditions. A case study is conducted in the German city of Wuppertal with about 12,000 buildings. The water depth estimation of every time step are visualized in a web-interface on the basis of the virtual globe NASA WorldWind. CAMC is compared with the shallow water equations-based model ANUGA. CAMC is approximatively 5 times faster than ANUGA at high spatial resolution and able to maintain numerical stability. The Nash-Sutcliffe coefficient (0.61), Root Mean Square Error (0.39 m) and Index of Agreement (0.65) indicate acceptable agreement for water depth estimation but identify different areas where important deviations occur. The estimation of velocity performs considerably less well (0.34 for Nash-Sutcliffe coefficient, 0.13 ms − 1 for Root Mean Square Error, and 0.39 for Index of Agreement) because CA ignores momentum conservation.