Delayed Evacuation after a Disaster Because of Irrational Prediction of the Future Cumulative Precipitation Time Series under Asymmetry of Information

This study investigated biased prediction of cumulative precipitation, using a variety of patterns of histories of cumulative precipitation, to explore how such biased prediction could delay evacuation or evacuation orders. The irrationality in predicting the future of cumulative precipitation was examined to obtain insights into the causes of delayed evacuation or evacuation orders using a simulated prediction of future cumulative precipitation based on the cumulative precipitation history. Anchoring and adjustment, or availability bias stemming from asymmetry of information, was observed in the prediction of cumulative precipitation, and found to delay evacuation or evacuation orders.

Hydrometeorological analysis of the 12 and 13 September 2019 widespread flash flooding in eastern Spain

On 12 and 13 September 2019, a long-lasting heavy precipitation episode (HPE) affected the València, Murcia and Almería regions in eastern Spain. Observed rainfall amounts were close to 500 mm in 48 h, being the highest cumulative precipitation registered in some rain-gauges for the last century. Subsequent widespread flash flooding caused seven fatalities and estimated economical losses above 425 million EUR. High-resolution precipitation estimates from weather radar observations and flood response from stream-gauges are used in combination with a fully-distributed hydrological model to examine the main hydrometeorological processes within the HyMeX program. This HPE was characterized by successive, well-organized convective structures that impacted a spatial extent of 7500 km2, with rainfall amounts equal or larger than 200 mm. The main factors driving the flood response were quasi-stationarity of heavy precipitation, very dry initial soil moisture conditions and large storage capacities. Most of the examined catchments exhibited a dampened and delayed hydrological response to cumulative precipitation: Until runoff thresholds were exceeded, infiltration-excess runoff generation did not start. This threshold-based hydrological behaviour may impact the shape of flood peak distributions, hindering strict flood frequency statistical analysis due to the generally limited lengths of data records in arid and semi-arid catchments. As an alternative, simple scaling theory between flood magnitude and total rainfall amount is explored.

Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

Performance Evaluation of Effective Drought Prediction Using Machine Learning

There has been much research recently to improve the prediction of drought, but the frequency and pattern of drought displays an irregular time series that limits its predictability, making it difficult to predict with only a single model, and high-level predictions cannot be made even when many models are applied. Therefore, many studies have been conducted to improve predictions by using explanatory variables such as precipitation, temperature, sunshine duration, and air volume as input data. The purpose of this study is to devise a method for predicting drought using the Standard Precipitation Evaporation Index (SPEI), which represents a complex and difficult time series drought index using climate data for weather phenomena. The Standard Precipitation Evaporation Index is a method of calculating the cumulative precipitation by excluding the cumulative evaporation amount from the cumulative precipitation using precipitation and evapotranspiration data, and the evaporation amount is calculated using the monthly heat index method. The Meteorological Agency evaluated meteorological drought using SPI6, which is a 6-month cumulative precipitation standard, and applied it to machine learning based on monthly data and daily data SPEI6 in this study. As a result, ANN monthly data R2 was 0.488 in Andong and 0.533 in Mungyeong, Gumi 0.594, SVR 0.452, 0.496, 0.564, RF 0.355, 0.467, 0.524, and the daily data are ANN 0.923, 0.919, 0.915, SVR 0.925, 0.923, 0.896, RF 0.915, 0.915, 0.797, and the daily data SPEI at all points. It was confirmed that high prediction was obtained when machine learning was applied to these methods.

Projections of rainfall erosivity over Italy exploiting EURO-CORDEX ensemble

<p>Erosion processes are caused by a combination of predisposing factors (slope, intrinsic soil properties), accelerating factors (removal of vegetation cover, altered soil properties due e.g. to fires, overgrazing, tillage) and triggering factors (water – from rain and rivers – and wind). While the first components are rather unchanging (or changing slowly) at the human time scale, the last two must deal with the consequences of global changes. Indeed, modifications in land use, land management and climate have strong feedbacks so that, from one side, lands are more and more overexploited, degraded and exposed to erosion and, on the other side, over these lands, the frequency, magnitude, duration and timing of triggering events could deviate from their “normal” conditions.</p><p>According to the well-known RUSLE soil loss estimation model, the triggering effect of rainfall for sheet and rill erosion is accounted for by means of the so-called rainfall erosivity or “R-factor”. R-factor consists of the annual summation of the erosive power of relevant storm events, averaged over a significant period of observation. For each storm event, computation of R-factors requires high-resolution rainfall information for the evaluation of the maximum rainfall intensity occurring over a time window of 30 min during the rainfall event. Due to the generally limited access to sub-hourly precipitation observations, a number of empirical models relating R-factor to easily accessible climate, physical and geographical covariates, such as rainfall data at coarse aggregation levels, have been developed for different areas of the world.</p><p>As concerns Italy, a novel empirical model is proposed relating rainfall erosivity to cumulative precipitation, elevation and latitude. Such model, calibrated for a significant selection of relevant rain gauges with available sub-hourly data, showed a good accordance with observations and a large amount of explained variance at the annual scale, with promising results also at the monthly level. The model was effectively extended to cover the whole Italian Country for the period 1981-2010 by means of gridded rainfall datasets retrievable in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS), with limited performance loss, exploring the feasibility of Copernicus products for erosion-related assessments. Although affected by limitations, the proposed model is particularly suitable for applications involving future rainfall projections since it explicitly accounts for monthly cumulative precipitation as the only climate covariate, differently for other proposed methodologies also including rainfall-related variables with higher temporal resolution, whose future trends cannot be robustly evaluated with current climate modelling tools. In the present research an ensemble of twelve future rainfall projections included in the Euro-Cordex initiative, bias-adjusted by means of the ERA5-Land reanalysis dataset, is considered to account for the uncertainties coming from the use of multiple projections. The proposed approach provides a unique example of rainfall erosivity dataset accounting for a wide ensemble of bias-adjusted rainfall projections resulting from different General Circulation Models/Regional Climate Models coupling, for multiple Representative Concentration Pathway scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) and different future horizons (near, 2021-2050, and far, 2051-2080, future).</p>

Development of a Multiple Linear Regression Model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

176 Application of growing degree day to predict diet crude protein in rangeland beef cows

We used a fecal near infrared spectroscopy (FNIRS) calibration for cattle diet crude protein (CP) to evaluate the efficacy of growing degree day (GDD) as a remotely-sensed method to monitor grazing animal nutrition. Composite fecal samples representing a herd of 24 cross bred beef cows grazing native range pastures in southwest Texas were collected along with GDD and precipitation data from April 2018 to September 2019. Regression analyses were performed to determine relationships between FNIRS-predicted diet CP and GDD within year and growing season. In 2018, FNIRS-predicted diet CP ranged from a minimum of 7.05% in August to a maximum of 9.69% in July. 2018 cumulative precipitation was 28% and 94% of the 20-year average for January-April and May-August, respectively. In 2019, FNIRS-predicted diet CP ranged from a minimum of 6.85% in September to a maximum of 12.01% in May. 2019 cumulative precipitation was 74% and 102% of the 20-year average for January-April and May-August, respectively. There were no significant (P > 0.1) simple linear relationships identified between FNIRS-predicted diet CP and GDD. There were, however, cubic exponential relationships identified in both 2018 (y = 7E-10x3 - 5E-06x2 + 0.0106x + 2.9603; R² = 0.7261; P = 0.1271) and 2019 (y = 1E-09x3 - 6E-06x2 + 0.0062x + 9.2923; R² = 0.7659; P = 0.0493). As expected, perennial range grass phenology/nutritive value (i.e. cattle diet CP) was influenced by accumulation of heat units (i.e. GDD) and precipitation. Although FNIRS is an established non-invasive method to monitor grazing animal nutrition, a remotely-sensed method to accomplish this task, such as GDD, has the potential to facilitate large-scale monitoring of grazing animal nutritional status. Our results indicate that complementary research using data from multiple locations and for more than 2 years is needed to fully evaluate these techniques.

How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data

Historically, snowpack trends have been assessed using one fixed date to represent peak snow accumulation prior to the onset of melt. Subsequent trend analyses have considered the peak snow water equivalent (SWE), but the date of peak SWE can vary by several months due to inter-annual variability in snow accumulation and melt patterns. A 2018 assessment evaluated monthly SWE trends. However, since the month is a societal construct, this current work examines daily trends in SWE, cumulative precipitation, and temperature. The method was applied to 13 snow telemetry stations in Northern Colorado, USA for the period from 1981 to 2018. Temperature trends were consistent among all the stations; warming trends occurred 63% of the time from 1 October through 24 May, with the trends oscillating from warming to cooling over about a 10-day period. From 25 May to 30 September, a similar oscillation was observed, but warming trends occurred 86% of the time. SWE and precipitation trends illustrate temporal patterns that are scaled based on location. Specifically, lower elevations stations are tending to record more snowfall while higher elevation stations are recording less. The largest SWE, cumulative precipitation, and temperature trends were +30 to −70 mm/decade, +30 to −30 mm/decade, and +4 to −2.8 °C/decade, respectively. Trends were statistically significance an average of 25.8, 4.5, and 29.4% of the days for SWE, cumulative precipitation, and temperature, respectively. The trend in precipitation as snow ranged from +/−2%/decade, but was not significant at any station.

Analysis of Agricultural Droughts According to the 2018 Heat Wave in Korea

<p><span>According to the Korea Meteorological Administration, in 2018, Korea's national average temperature and maximum temperature are the highest in 111 years since meteorological observations (1907.10.1.) The highest value was observed since August 1 at </span><span> 39.6 </span>℃ <span>in Seoul. Heatwaves represent the number of days with the highest daily temperature above 33 ° C. The number of heatwaves in 18 years totaled 31.5 days. Heatwaves have a particularly significant effect on the growth and death of field crops. Indeed, 18,254 ha of field crops occurred nationwide. Precipitation in 2018 is higher than normal, but precipitation shortages have occurred due to seasonal and regional variations and local droughts due to the lowest precipitation from mid-July to late August. In particular, there were more rains than normal years at the beginning of farming season (March-May) and the end of farming season (October), but the summer agricultural drought occurred due to less precipitation than the average year-end of July-August. The second shortest rainy season (half of the average year) since 1987 and the rainy season was 72% compared to the average year, some of the reservoirs have caused a serious and severe stage. The country recorded the maximum number of rainfall days on 27th during the period of 7.10 ~ 8.5 days and 43 days on Chungnam. This is believed to have affected the drought occurrence by overlapping with the stage of water-forming, which requires the largest amount of water supply for rice growth. In the case of field crops, irrigation facilities are inferior to paddy fields, so field crop growth is directly related to no rainfall days, and droughts such as deterioration of field crops were recorded nationwide during the maximum rainfall period. Since the end of the rainy season, there have been a total of 22,767 ha droughts, iincluding 2,513 ha of paddy field and 20,254 ha of field crops, due to severe shortages of precipitation and damage to crops caused by heat waves. </span><span>For the 2018 rainfall-based drought frequency analysis, the analysis was based on cumulative precipitation from January to August of 18, and there was a severe shortage of precipitation from mid-July to mid-August, but the cumulative precipitation from January to August is normal. As a result of rainfall-based drought frequency analysis, the drought frequency area was analyzed into two regions for more than 10 years. Based on rainfall in July 2018, drought occurred in most parts of the country due to severe rainfall shortages. For over 200 years, the frequency of drought has been analyzed to 107 counties. As a result of the drought frequency analysis based on the reservoir storage rate in August 2018, there were 45 counties in the drought frequency area for more than 200 years due to the lack of water during the high demand period of rice crop growth period.</span></p><div data-hjsonver="1.0" data-jsonlen="11062"><span>This research was supported by a grant(2019-MOIS31-010) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety(MOIS).</span></div>