Spatial controls of microbial pesticide degradation in soils – A model-based scenario analysis

Author(s):  
Erik Schwarz ◽  
Swamini Khurana ◽  
Luciana Chavez Rodriguez ◽  
Johannes Wirsching ◽  
Christian Poll ◽  
...  

<p>Despite all legislative efforts, pesticides persist in soils at low concentrations and are leached to groundwater. This environmental issue has previously been associated with control factors relevant in natural soils but elusive in lab experiments and standard modeling approaches. One such factor is the small-scale spatial distribution of pesticide-degrading microorganisms in soil. Microbes are distributed heterogeneously in natural soils. They are aggregated in biogeochemical “hotspots” at the centimeter scale. The aim of our study is to investigate the relevance of such aggregation for pesticide degradation. For this, we upscaled the effect of the heterogeneity-induced accessibility limitations to degradation to the soil-column scale and analyzed kinetic constraints and amplifying factors under contrasting unsaturated flow regimes.</p><p>We performed a 2D spatially explicit, site-specific model-based scenario analysis for bioreactive transport of the model pesticide 4-chloro-2-methylphenoxyacetic acid (MCPA) in an arable soil (Luvisol). Stochastic centimeter-scale spatial distributions of microbial degraders were simulated with a spatial statistical model (log Gaussian Cox process), parametrized to meet experimentally observed spatial distribution metrics. Three heterogeneity levels were considered, representing homogenized soil conditions, and the lower and upper limit of expected microbial spatial aggregation in natural soils. Additionally, two contrasting precipitation scenarios (continuous light rain vs. heavy rain events directly following MCPA application) were assessed. A reactive transport model was set up to simulate a 0.3 m x 0.9 m soil column based on hydraulic and bioreactive measurements from a soil monitoring station (Germany, SM#3/ DFG CRC 1253 CAMPOS).</p><p>Our simulations revealed that heavy precipitation events were the main driver of pesticide leaching. Leached amounts from the topsoil increased by two to five orders of magnitude compared to the light rain scenario and at max. ca. 20 ng was leached from 90 cm after one year. With the increasing spatial aggregation of microbial degraders, upscaled pesticide degradation rates decreased, and considerable differences emerged between homogeneous and highly aggregated scenarios. In the latter, leaching from the plow layer into the subsoil was more pronounced and MCPA was detectable (LOD = 4 µg/kg) 5-6 times longer. In heterogeneous scenarios, degradation in microbial hotspots was mainly diffusion-limited during “hot moments” (times of high substrate availability), with a fraction of MCPA simultaneously “locked in” in coldspots with low microbial abundance. During intense precipitation events MCPA was remobilised from these coldspots by advective-dispersive transport, thereby increasing pesticide accessibility.</p><p>Our results indicate that predicted environmental concentrations and detectability of pesticides might be underestimated if spatial heterogeneity of microbial degraders is neglected, and they highlight the importance of heavy rain events as drivers of leaching and substrate accessibility.</p>

2021 ◽  
Vol 22 (5) ◽  
pp. 1199-1219
Author(s):  
Zhangkang Shu ◽  
Jianyun Zhang ◽  
Junliang Jin ◽  
Lin Wang ◽  
Guoqing Wang ◽  
...  

AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.


2017 ◽  
Author(s):  
Xiangde Xu ◽  
Xueliang Guo ◽  
Tianliang Zhao ◽  
Xingqin An ◽  
Yang Zhao ◽  
...  

Abstract. In Eastern China (EC), strong anthropogenic emissions deteriorate the atmospheric environment harbored by the upstream Tibetan and Loess Plateaus, building a south-north zonal distribution of high anthropogenic aerosols. This research analyzed the interannual variability of precipitations with different intensities in the EC region from 1961 to 2010. We found that the frequency of light rain significantly decreased and the occurrence of rainstorm, especially the extraordinary rainstorm significantly increased over the recent decades. The extreme precipitation events presented the same interannual variability pattern with the frequent haze events. Moreover, the extreme rainfall events of various intensities showed a regular interannual variability trend. During the 1980s, the regional precipitation trends in EC showed an obvious "transform" from more light rain to more extreme rainstorms. The running correlation analysis of interdecadal variation further verified that the correlation between the increasing aerosol emissions and the frequency of abnormal precipitation events tended to be more significant in the EC. The correlation between atmospheric visibility and low cloud amounts, which are both closely related with aerosol concentrations, had a spatial distribution of "northern positive and southern negative" pattern, and the spatial distribution of the frequency variability of regional rainstorms was "southern positive and northern negative". After the 1990s, the visibility in summer season deteriorated more remarkably than other seasons, and the light rain frequency decreased obviously while the rainstorm and extraordinary heavy rainfall occurred more frequently. There were significant differences in the interdecadal variation trends in light rain and rainstorm events between the high aerosol concentration areas in the EC and the relatively "clean area" in western China. The aircraft measurements over the EC confirmed that the diameters of cloud droplets decreased under high aerosol concentration condition, thereby inhibiting weak precipitation process.


Author(s):  
Sergey V. Kostarev ◽  
◽  
Andrey L. Vetrov ◽  
Bogdan A. Sivkov ◽  
Anna A. Pomortseva ◽  
...  

The paper analyzes radar characteristics of mesoscale precipitation systems associated with heavy precipitation in Western Urals. The characteristics under study, obtained from the Doppler weather radar, include the maximum height of the radar echo of clouds and precipitation, meteorological phenomena, speed and direction of the radar echo movement. An attempt was made to classify mesoscale precipitation systems with respect to their horizontal scales and geometrical features as well as precipitation patterns. Statistical features of radar characteristics of cloud systems causing heavy rain were calculated taking into account their classification by horizontal scales. Radar meteorological characteristics were obtained from instruments in the Izhevsk city and Ufa city for the warm period of 2016–2017. The results can be used during nowcasting of heavy precipitation events.


2017 ◽  
Vol 34 (5) ◽  
pp. 1001-1019 ◽  
Author(s):  
Biyan Chen ◽  
Zhizhao Liu ◽  
Wai-Kin Wong ◽  
Wang-Chun Woo

AbstractWater vapor has a strong influence on the evolution of heavy precipitation events due to the huge latent heat associated with the phase change process of water. Accurate monitoring of atmospheric water vapor distribution is thus essential in predicting the severity and life cycle of heavy rain. This paper presents a systematic study on the application of tomographic solutions to investigate water vapor variations during heavy precipitation events. Using global positioning system (GPS) observations, the wet refractivity field was constructed at a temporal resolution of 30 min for three heavy precipitation events occurring in Hong Kong, China, in 2010–14. The zenith wet delay (ZWD) is shown to be a good indicator in observing the water vapor evolution in heavy rain events. The variabilities of water vapor at five altitude layers (<1000, 1000–2000, 2000–3000, 3000–5000, and >5000 m) were examined. It revealed that water vapor above 3000 m has larger fluctuation than that under 3000 m, though it accounts for only 10%–25% of the total amount of water vapor. The relative humidity fields derived from tomographic results revealed moisture variation, accumulation, saturation, and condensation during the heavy rain events. The water vapor variabilities observed by tomography have been validated using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and radiosonde data. The results positively demonstrated the potential of using water vapor tomographic technique for detecting and monitoring the evolution of heavy rain events.


2021 ◽  
Author(s):  
Haoyang Du ◽  
Manchun Li ◽  
Penghui Jiang ◽  
Haoqing Tang ◽  
Xiaolong Jin ◽  
...  

Abstract Precipitation is critical for maintaining ecosystem stability, especially in arid regions. This study was primarily focused on the changes during the present (i.e., from 1985 to 2005) and future (i.e., from 2040 to 2059) periods in Xinjiang, northwest China. To predict the future climate, the Weather Research and Forecasting model was run in Xinjiang using National Climate Research Center Community Climate System Model version 4 for the mid-21st century under representative concentration pathways 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively). The results indicate that the amount of annual precipitation would increase in the future under RCP4.5 and RCP8.5 in Xinjiang, especially in the mountainous areas. The increase in precipitation was predicted to be much smaller under RCP8.5 than under RCP4.5, except in Southern Xinjiang. Moreover, the increased precipitation predicted in Xinjiang implies that the current humid and warm conditions will continue. In addition, the largest increase in seasonal precipitation was predicted to occur in spring and summer in Tianshan and Northern Xinjiang, whereas this phenomenon will occur in spring and winter in Southern Xinjiang. In addition, it was predicted that daily heavy precipitation events will occur more frequently in various subregions of Xinjiang, although light rain events will remain dominant. Finally, the increase in the frequency of heavy precipitation events was found to be related to the vertically integrated column precipitation, whereas the relative humidity was observed to be closely related to the changes in annual and seasonal precipitation.


2012 ◽  
Vol 15 (2) ◽  
pp. 464-485 ◽  
Author(s):  
Xianwei Wang ◽  
Hongjie Xie ◽  
Newfel Mazari ◽  
Jon Zeitler ◽  
Hatim Sharif ◽  
...  

This study evaluates the Next Generation Weather Radar (NEXRAD) Digital Storm-Total Precipitation product (DSP) by analyzing 30 rain events on the Upper Guadalupe River Basin, Texas, from September 2006 to May 2007. The DSP product provides relatively accurate information on the evolution of rain events at high spatial and temporal resolutions in near-real time. This is particularly important for rainfall estimation of heavy rain events and flash flood forecasting. The DSP's accuracy is comparable to the other NEXRAD product MPE (multisensor precipitation estimator, at hourly resolution and 4 km grid spacing) at both hourly and event total scales for some heavy rain events, although the DSP is inferior to the MPE product for total rainfall of all 30 rain events analyzed, especially for light rain events. The DSP product shows the best agreement with gauges at ranges of 50–150 km from the radar (with mean absolute estimation bias (MAEB) of +15–22% for total rainfall of 30 rain events), while underestimating precipitation at both close ranges (&lt;30 km) and far ranges (&gt;180 km). The DSP product also tends to underestimate (overestimate) precipitation during event growth (dissipation). However, the total rainfall estimate for all rain events over a long period from DSP shows range dependence and is not recommended for calculation of water resource budget.


2019 ◽  
Vol 5 (1) ◽  
pp. 255 ◽  
Author(s):  
Nguyen Tien Thanh

Recently, several precipitation products are released with the improved algorithm to strengthen the performance of precipitation construction and monitoring. These data play a key role in a wide range of hydrological models, water resources modeling and environmental researches. Especially in developing countries like Vietnam, it is challenging to gather data for long-term time series at scales of daily and sub-daily due to the very coarse density of observation station. In order to overcome the problem of data scarcity, this study aims to evaluate the performance of newest multiple precipitation products including Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Climate Prediction Center (CPC) MORPHing Version 1.0 (CMORPH_V1.0), European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis systems (ERA-Interim), Climate Research Unit Time series Version 4.0.1 (CRU TS 4.0.1) and Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources version 2 (APHRODITE) in comparison with measured precipitation for multiple time scales (daily, monthly, seasonal and annual), taking the VuGia-ThuBon (VG-TB) as a pilot basin where climate regime is complex. Seven continuous and four dichotomous statistics are applied to evaluate the precipitation estimates qualitatively at multiple time scales. In addition, specifically, evaluation of spatial distribution of multiple time scales is implemented. The results show lower precipitation estimates in areas of high elevation and higher precipitation estimates over the areas of plain and coastal in comparison with measured precipitation for all considered precipitation data. More importantly, ERA-Interim well captures rain events of heavy rain (50.0-100 mm/day). CMORHPH_V1.0 better reproduces the rain events with little overestimation of light rain (0.6-6 mm/day) than the others. For zero rain events (0-0.6 mm/day), TRMM 3B42 V7 gives the best performance. Furthermore, the cumulative distribution function of APHRODITE well matches the distribution of measured precipitation. All precipitation products completely fail to capture the rain events of extremely heavy rain. More importantly, a formula is proposed to scale and adjust the merged satellite precipitation at a sub-daily scale.


2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
V. Iordanidou ◽  
A. G. Koutroulis ◽  
I. K. Tsanis

Data from a dense network of 69 daily precipitation gauges over the island of Crete and cyclone climatological analysis over middle-eastern Mediterranean are combined in a statistical approach to develop a rain diagnostic model. Regarding the dataset, 0.5 × 0.5, 33-year (1979–2011) European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) is used. The cyclone tracks and their characteristics are identified with the aid of Melbourne University algorithm (MS scheme). The region of interest is divided into a grid mesh and for each grid the probability of rain occurrence from passing cyclones is estimated. Such probability maps are estimated for three rain intensity categories. The probability maps are evaluated for random partitions of the data as well as for selected rain periods. Cyclones passing south of Italy are found to have greater probability of producing light rain events in Crete in contrast to medium and heavy rain events which are mostly triggered by cyclones of southern trajectories. The performance of the probability maps is very satisfactory, recognizing the majority of “affecting” cyclones and rejecting most cyclones that do not trigger rain events. Statistical measures of sensitivity and specificity range between 0.5 and 0.8 resulting in effective forecasting potential.


Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1006 ◽  
Author(s):  
Jiayong Shi ◽  
Fei Yuan ◽  
Chunxiang Shi ◽  
Chongxu Zhao ◽  
Limin Zhang ◽  
...  

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
T. S. Sreekanth ◽  
Hamza Varikoden ◽  
G. Mohan Kumar ◽  
E. A. Resmi

AbstractIn the present study, seven-year-long observations of rain microphysical properties are presented using a ground-based disdrometer located at Braemore; a site on the windward slope of the Western Ghats (WG) over the Indian Peninsula. The annual cycle of rainfall shows a bimodal distribution with a primary peak during summer monsoon and secondary peak during pre-monsoon. Pre-monsoon rain events are less in number but are with high intensity and characterize large raindrops and low number concentration. During summer monsoon, short and less intense rain events with small drops are noticed. Post-monsoon rain is having a long duration less intense events with lower concentration of large raindrops compared to the summer monsoon. In the seasonal variation of mean diameter (Dm) and raindrop concentration (NT) with Rain Intensity (RI), winter and pre-monsoon rains exhibit higher values of Dm and lower values of NT compared to the summer and post-monsoon seasons for all the RI ranges. The mean features of the rain microphysical parameters are also supported by the case studies of rain events. RI, Dm and NT are categorized into different range bins for all the seasons to identify their variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/Light rain has maximum rain duration, and the relative contribution to the rainfall is high from heavy rain type. Winter and pre-monsoon rains are mostly contributed from the larger raindrops (>Dm3), and during summer and post-monsoons it is from Dm2 onwards. The distribution of occurrence frequency of NT and rainfall are similar during all four seasons. NT2 recorded rainfall percentage nearly the same as NT1 in summer monsoon and this also supports large number of raindrops in this season. In RI-Duration analysis, all seasons showed similar distribution, and 90% of total duration is contributed from RI with less than 20 mm h−1.


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