Evaluation of High-Resolution Crop Model Meteorological Forcing Datasets at Regional Scale: Air Temperature and Precipitation over Major Land Areas of China

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

Regional Precipitation Model Based on Geographically and Temporally Weighted Regression Kriging

High-resolution precipitation field has been widely used in hydrological and meteorological modeling. This paper establishes the spatial and temporal distribution model of precipitation in Hubei Province from 2006 through 2014, based on the data of 75 meteorological stations. This paper applies a geographically and temporally weighted regression kriging (GTWRK) model to precipitation and assesses the effects of timescales and a time-weighted function on precipitation interpolation. This work’s results indicate that: (1) the optimal timescale of the geographically and temporally weighted regression (GTWR) precipitation model is daily. The fitting accuracy is improved when the timescale is converted from months and years to days. The average mean absolute error (MAE), mean relative error (MRE), and the root mean square error (RMSE) decrease with scaling from monthly to daily time steps by 36%, 56%, and 35%, respectively, and the same statistical indexes decrease by 13%, 15%, and 14%, respectively, when scaling from annual to daily steps; (2) the time weight function based on an exponential function improves the predictive skill of the GTWR model by 3% when compared to geographically weighted regression (GWR) using a monthly time step; and (3) the GTWRK has the highest accuracy, and improves the MAE, MRE and RMSE by 3%, 10% and 1% with respect to monthly precipitation predictions, respectively, and by 3%, 10% and 5% concerning annual precipitation predictions, respectively, compared with the GWR results.

Impact of Land Cover and Leaf Area Index on BVOC Emissions over the Korean Peninsula

Biogenic volatile organic compound (BVOCs) emissions are the largest VOC emission source globally, and are precursors to ozone and secondary organic aerosols, both of which are strong, short-lived climate pollutants. BVOC emissions are usually estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), which requires Plant Functional Types (PFTs) and Leaf Area Indexes (LAIs) as inputs. Herein, the effects of refined input data on regional BVOC emission estimates are analyzed. For LAIs, lower resolution MODerate-resolution Imaging Spectroradiometer (MODIS), and higher spatio-temporal resolution Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) LAI were generated. For PFTs, local land cover maps were developed, in addition to MODIS PFT. In South Korea, annual emissions of isoprene and monoterpenes in 2015 were estimated as 384 and 160 Gg/year, respectively, using STARFM LAI and Local PFT (Case 4). For North Korea, 340 Gg/year isoprene and 72 Gg/year monoterpenes emissions were estimated using STARFM LAI and MODIS PFT. These estimates were 14–110% higher than when using MODIS LAI and MODIS PFT (Case 1). Inter-comparison with satellite-based inverse isoprene emission estimates from GlobEmission shows 32% (North Korea) to 34% (South Korea) overestimation in bottom-up data. Our new vegetation inputs improve MEGAN performance and resulting BVOC emission estimations. Performance of Weather Research and Forecasting (WRF) meteorological modeling requires improvement, especially for solar radiation, to avoid overestimation of isoprene emissions.

Drought Risk Analysis, Forecasting and Assessment under Climate Change

Climate change is undoubtedly one of the world’s biggest challenges in the 21st century. Drought risk analysis, forecasting and assessment are facing rapid expansion, not only from theoretical but also practical points of view. Accurate monitoring, forecasting and comprehensive assessments are of the utmost importance for reliable drought-related decision-making. The framework of drought risk analysis provides a unified and coherent approach to solving inference and decision-making problems under uncertainty due to climate change, such as hydro-meteorological modeling, drought frequency estimation, hybrid models of forecasting and water resource management. This Special Issue will provide researchers with a summary of the latest drought research developments in order to identify and understand the profound impacts of climate change on drought risks and water resources. The ten peer-reviewed articles collected in this Special Issue present novel drought monitoring and forecasting approaches, unique methods for drought risk estimation and creative frameworks for environmental change assessment. These articles will serve as valuable references for future drought-related disaster mitigations, climate change interconnections and food productivity impacts.

AVALIAÇÃO DA PREVISIBILIDADE DO MODELO WRF-HYDRO EM MODELAGENS HIDROMETEOROLÓGICAS COM DIFERENTES RESOLUÇÕES NA BACIA DO TAQUARI-ANTAS

O presente estudo avalia o modelo WRF-Hydro uma ferramenta de previsão acoplada chuva/solo/vazão, buscando aperfeiçoar o grau de agilidade e confiabilidade das previsões na região considerada de grande vulnerabilidade, a bacia hidrográfica do Taquari-Antas/RS, localizada na região Sul do Brasil. A avaliação consistiu em analisar os resultados e a previsibilidade do modelo com diferentes resoluções espaciais na simulação de evento extremo ocorrido em janeiro de 2010. A primeira simulação foi realizada com duas grades do modelo meteorológico com 50 e 10 km e com a rede de canais com resolução de 1000 m. E a outra com três grades de 25, 5 e 1 km para meteorologia e rede de canais com 250 m. Avaliadas usando comparações da magnitude e da variabilidade dos fluxos da superfície da bacia, como precipitação e vazão do modelo com dados observados. Os resultados seguem as observações locais e apresentam bons resultados para servir como ferramenta em sistemas de alerta contra cheias nesta e em outras regiões. Predictability of the Wrf-Hydro Model in Hydrometeorological Modeling with Different Resolutions in the Taquari-Antas Basin A B S T R A C TThe present study evaluates the WRF-Hydro model, a rainfall/soil/flow coupled forecasting tool, aiming to improve the agility and reliability of the predictions in the region considered to be of great vulnerability, the Taquari-Antas/RS basin located in the region South of Brazil. The evaluation consisted of analyzing the results and predictability of the model with different spatial resolutions in the extreme event simulation that occurred in January 2010. The first simulation was performed with two grids of the 50 and 10 km meteorological model and with the channel network with resolution of 1000 m. And the other with three grids of 25, 5 and 1 km for meteorology and network of channels with 250 m. Evaluated using comparisons of magnitude and variability of basin surface fluxes, such as precipitation and flow of the model with observed data. The results follow local observations and present good results to serve as a tool in flood warning systems in this and other regions.Keywords: numerical forecasting, extreme events, WRF-Hydro, hydrological basin monitoring and hydro-meteorological modeling.

Evaluation of two hydrometeorogical ensemble strategies for flash flood forecasting over a catchment of the eastern Pyrenees

This study aims at evaluating the performances of flash flood forecasts issued from deterministic and ensemble meteorological prognostic systems. The hydro-meteorological modeling chain includes the Weather Research and Forecasting model (WRF) forcing the rainfall-runoff model MARINE dedicated to flash flood. Two distinct ensemble prediction systems accounting for (i) perturbed initial and lateral boundary conditions of the meteorological state and (ii) mesoscale model physical parameterizations, have been implemented on the Agly catchment of the Eastern Pyrenees with three sub-catchments exhibiting different rainfall regimes. Different evaluations of the performance of the hydrometeorological strategies have been performed: (i) verification of short-range ensemble prediction systems and corresponding stream flow forecasts, for a better understanding of how forecasts behave, (ii) usual measures derived from a contingency table approach, to test an alert threshold exceedance, and (iii) overall evaluation of the hydro-meteorological chain using the Continuous Rank Probability Score, for a general quantification of the ensemble performances. Results show that the overall discharge forecast is improved by both ensemble strategies with respect to the deterministic forecast. Threshold exceedance detections for flood warning also benefit from large hydro-meteorological ensemble spread. There are no substantial differences between both ensemble strategies on these test cases in terms both of the issuance of flood warnings and the overall performances, suggesting that both sources of external-scale uncertainty are important to take into account.

Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)

Ground surface characteristics (i.e., topography and landscape patterns) are important factors in geographic dynamics. Thus, the complexity of ground surface is a valuable indicator for designing multiscale modeling concerning the balance between computational costs and the accuracy of simulations regarding the resolution of modeling. This study proposes the concept of comprehensive surface complexity (CSC) to quantity the degree of complexity of ground by integrating the topographic complexity indices and landscape indices representing the land use and land cover (LULC) complexity. Focusing on the meteorological process modeling, this paper computes the CSC by constructing a multiple regression model between the accuracy of meteorological simulation and the surface complexity of topography and LULC. Regarding the five widely studied areas of China, this paper shows the distribution of CSC and analyzes the window size effect. The comparison among the study areas shows that the CSC is highest for the Chuanyu region and lowest for the Wuhan region. To investigate the application of CSC in meteorological modeling, taking the Jingjinji region for instance, we conducted Weather Research and Forecasting Model (WRF) modeling and analyzed the relationship between CSC and the mean absolute error (MAE) of the temperature at 2 meters. The results showed that the MAE is higher over the northern and southern areas and lower over the central part of the study area, which is generally positively related to the value of CSC. Thus, it is feasible to conclude that CSC is helpful to indicate meteorological modeling capacity and identify those areas where finer scale modeling is preferable.