Precipitation Evaporation Research Articles

Atmospheric Circulation as a Factor Contributing to Increasing Drought Severity in Central Europe

AbstractLong‐lasting and severe droughts seriously threaten agriculture, ecosystems, and society. Summer 2018 in central Europe was characterized by unusually persistent heat and drought, causing substantial economic losses, and became a part of a several years long dry period observed across this region. This study assesses the magnitude of the recent drought within a long‐term context and links the increased drought severity to changes in atmospheric circulation. Temporal variability of drought conditions since the late 19th century was analyzed at seven long‐term stations distributed across the Czech Republic using the Palmer Drought Severity Index and the Standardized Precipitation Evaporation Index. The Palmer Z Index and a variation of the Standardized Precipitation Evaporation Index were used to study rapidly emerging short‐term droughts and to link these episodes to atmospheric circulation. Changes in circulation were analyzed through circulation types calculated from flow strength, direction and vorticity in mean sea level pressure data from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis for 1948–2018. Increasing drought severity across the Czech Republic with record‐low values of the drought indices during 2015–2018 was found. The trend was distinctive in both vegetation (April–September) and cold (October–March) periods. The tendency toward more severe droughts in recent decades was linked to changes in frequency of dry and wet circulation types, highlighting the important role of atmospheric circulation in regional climate. It remains an open question whether the significantly increasing frequency of dry circulation types in the vegetation period is related to climate change, or rather represents multidecadal climate variability.

Chemical evolution and evaporation of shallow groundwaters discharging from a gold mine, southern New Zealand

Waste rock piles at the Macraes gold mine in southern New Zealand expose abundant fresh schist to alteration reactions that principally involve pyrite, chlorite, and calcite, yielding alkaline waters (pH~8) with elevated dissolved loads. Pyrite oxidation has resulted in dissolved sulfate concentrations rising up to ~3000 mg/L over 10 years, and this high sulfate is a potential environmental issue in waters leaving the site. Up to 600 mg/L dissolved Mg2+ results from chlorite dissolution. The site has a semi-arid climate and evaporative precipitation of sulfate minerals could contribute to an engineered water treatment system. However, the evolved waters are saturated or supersaturated with respect to carbonate minerals and spontaneous precipitation of aragonite is widespread, thereby limiting Ca-sulfate (gypsum) precipitation. Localised precipitation of Mg-sulfate (epsomite) also occurs, but this redissolves during rain events. Compositionally different waters seeping from the mine tailings impoundment have >2500 mg/L dissolved sulfate, and elevated Na+, K+ and Cl− from addition of reagents in the processing system. These seepage waters have also undergone interaction with waste rocks in the impoundment walls. The seep waters are approximately saturated with respect to carbonate minerals, but no spontaneous carbonate precipitation has occurred over two years of observations. Ephemeral evaporative epsomite and Na–Mg sulfate (bloedite) precipitates form locally. Geochemical modelling suggests that evaporative precipitation of gypsum has potential to remove sulfate from both types of water, especially if carbonate precipitation was suppressed by lowering the water pH to ~6 in an engineered treatment system that involves substantial evaporation.

Assessment of the potential impacts of climate change on the hydrology at catchment scale: modelling approach including prediction of future drought events using drought indices

In this study, the Distributed Catchment-Scale Model, DiCaSM, was used to study the impact of climate change on the hydrology of the Eden catchment, north east of Scotland. As a first step, the model was successfully calibrated and validated for a 42 years period. The DiCaSM model was then used to study the impact of climate change on the water availability. Data from the UKCP09 Climate change scenarios for the 2010–2039, 2040–2069 and 2070–2099 periods, considering three gas emission scenarios (low, medium and high), were applied. The results indicated that the greatest decrease in streamflow and groundwater recharge was projected to happen under the high emission scenarios towards the end of the century, i.e. between 2070 and 2099. This would mainly be due to the summers becoming drier. Meanwhile, the projected increase in winter precipitation did not contribute much towards groundwater recharge due the projected increases in evapotranspiration and soil moisture deficit.The following drought indices were calculated and were found to be effective in predicting different types of droughts: the Standardized Precipitation Index, SPI, and the Standardized Precipitation Evaporation Index, SPEI, the Reconnaissance Drought Index, RDI, the modified adjusted RDI, the Soil Moisture Deficit, SMD and the Wetness Index, WI. The findings of the study have broader implications in water resources management considering the future changes in climate.

Detection and attribution of lake water loss in the semi‐arid Mongolian Plateau—A case study in the Lake Dalinor

AbstractExtensive lake shrinkages have been observed in the semi‐arid Mongolian Plateau over the past several decades, causing a great challenge to the local socio‐economic sustainability and ecological security. However, issue on the dominant drivers of this water loss has not been well addressed. In this work, variations in water area and water level of the Lake Dalinor during the period 1984–2018 were tracked using the satellite altimetry and Landsat images. Dominant drivers for the derived variations were then investigated based on the Standard Precipitation Evaporation Index (SPEI) analysis and the Vegetation Interface Processes model. Results showed that both the water area and water level displayed a two‐stage variation with a slightly increasing trend before 2000 and a drastic decline since 2000 (p < 0.01). The SPEI analysis revealed a close relationship between water level variations and the cumulative water anomalies at seasonal and annual scales. The model‐based attribution analysis suggested that climate change and anthropogenic impacts contributed 76.5% and 23.5% of the observed declining trend, respectively. Climate warming and drying dominated the water level decline since 2000, while the large‐scale conversion from the natural grassland to the seeded pasture might have accelerated this water loss process. This study confirmed the prominent impacts of climate warming and drying on the lake desiccation in the semi‐arid Mongolian Plateau and highlighted the gradually intensifying impacts of human activities such as grassland conversion on the lake water budget.

Drought-related hot summers: A joint probability analysis in the Iberian Peninsula

Droughts and hot extremes are major sources of risk to several socio-economic activities and their impacts are expected to increase under future global warming. Moreover, the simultaneous or sequential occurrence (compound events) of different climate extremes may lead to the amplification of the associated impacts. Even though the latest efforts in assessing hot and dry extremes and their interactions, the development of models describing the joint behavior of climate extremes is still a challenge. To contribute to the understanding of these compound events, we propose to assess the probability of extremely hot summer days in the Iberian Peninsula (IP) being preceded by drought events in spring and early summer, based on their joint probability distribution through copula theory. The precursor drought hazard was characterized by the Standardized Precipitation Evaporation Index (SPEI) for the months of May, June and July for different timescales (3-, 6- and 9-months). The Number of Hot Days per month (NHD) summed over the months of July and August were considered for modelling. The dependence structure between SPEI and NHD was very well identified for the most of the IP's regions by asymmetrical copulas with upper tail dependence (except in northwestern regions), suggesting that compound hot and dry extremes are strongly associated. The results show that the transition from previous wet to dry regimes increases substantially the probability of exceeding summer NHD extreme values, depending on the region and the drought timescale and target month. The results suggest a spatial heterogeneity over the IP when characterizing the influence of water deficits on following summer extreme temperatures, whereas northeastern, western and central regions were found to be the regions more prone to summer hot extremes induced by dryness, in contrast to southwestern, northwestern and southeastern regions (depending on the month and the timescale). This study provides estimates of the probability of drought-related hot extremes in the summer of the IP for different regions, which could be an important tool for responsible authorities to mitigate the impacts magnified by the interactions between the different hazards.

Cold Pool Responses to Changes in Soil Moisture

AbstractThis study examines the role of soil moisture in modulating convective cold pool properties in an idealized modeling framework that uses a cloud‐resolving model coupled to an interactive land surface model. Five high‐resolution simulations of tropical continental convection are conducted in which the initial soil moisture is varied. The hundreds of cold pools forming within each simulation are identified and composited across space and time using an objective cold pool identification algorithm. Several important findings emerge from this analysis. Lower soil moisture results in greater daytime heating of the surface, which produces a deeper, drier subcloud layer. As a result, latent cooling through the evaporation of precipitation is enhanced, and cold pools are stronger and deeper. Increased propagation speed, combined with wider rain shafts, results in wider cold pools. Finally, the rings of enhanced water vapor that surround each cold pool when soil is wet disappear when the soil moisture is reduced, due to the suppression of surface latent heat fluxes. Instead, short‐lived “puddles” of enhanced water vapor permeate the cold pools. The results are nonlinear in that the properties of the cold pools in the two driest‐soil simulations depart substantially from the cold pool properties in the three simulations initialized with wetter soil. The dividing line between the resulting wet‐soil and dry‐soil regimes is the permanent wilting point. Below the permanent wilting point, transpiration is subdued due to a sharp increase in water stress. These results emphasize the role of land surface‐boundary layer‐cloud interactions in modulating cold pool properties.

Tree-ring lignin proxies in Larix gmelinii forest growing in a permafrost area of northeastern China: Temporal variation and potential for climate reconstructions

The hydrogen and carbon stable isotope ratios (δ2HLM and δ13CLM values) of tree-ring lignin methoxy groups have recently been recognized as valuable palaeoclimate indicators. However, the environmental factors but also sample preparation processes that might cause variations of δ2HLM and δ13CLM values have been not fully explored. Furthermore, the temporal dynamics of wood lignin content on both isotopes hasn’t been investigated. To investigate the effects of total lignin content, removal of lipids and differences between individual and pooled tree-ring series on isotopic values, we analyzed ring samples of dominant Larix gmelinii trees from a typical permafrost region of northeastern China. We also examined relationships between δ2HLM and δ13CLM values with annual-resolution lignin content and climatic parameters over the common period of overlap 1901 to 2013. We found that the inter-tree variability between the individual and pooled isotopic chronologies was consistent, and the removal of lipids did not change the results of stable isotope measurements. The total lignin content shifted between the heartwood and sapwood, whereas the content of guaiacyl monomers (G-lignin), the only donor of methoxy groups, remained constant. Correlations analysis between lignin proxies and climate variables indicated that the heartwood total lignin content was positively correlated with the intrinsic water-use efficiency, temperature and vapor-pressure deficit (VPD), but negatively with the early-growing-season standardized precipitation evaporation index. Whilst δ2HLM values were positively correlated with the growing season temperature (May to August) and VPD, particularly in August, δ13CLM series showed insignificant correlation. Therefore, we suggest that the total lignin content and δ2HLM values of tree rings (both individual and pooled series) are suitable for tracking environment dynamics, as they can reveal the climate responses of tree growth.

Event-Based Rainfall Interception Modeling in a Cerrado Riparian Forest—Central Brazil: An Alternative Approach to the IS Method for Parameterization of the Gash Model

Although being the second largest biome in South America, the Cerrado biome is understudied in terms of hydrological modeling of the interception processes of its different vegetation types. To better understand its net precipitation components, high-temporal-resolution throughfall data were used to parameterize the Gash Analytical Model on a per storm basis. Two different methods (Single Event Regression—SI, and Particle Swarm Optimization—PSO) for parametrizing the Gash model were tested. Simulated throughfall from 35 events showed the Nash coefficient ranging from 0.779 to 0.989, confirming the good phenomenological approach of the Gash model. One of the most important results presented in this work was the analysis of the variability of the parameters. Considering the PSO method, we observed a mean free throughfall (ρ) of 0.17, with a confidence interval ranging from 0.16 to 0.18. The storage (S) confidence intervals ranged from 1.11 mm to 1.62 mm, with a mean value of 1.37 mm. The observed saturation depth (P′G) mean value was 1.66 mm, with a confidence interval from 1.35 mm to 1.97 mm. Finally, the mean evaporation precipitation rate (E/R) was of 0.20, with confidence bands from 0.15 to 0.24. Overall, there were observed higher losses from the Cerrado compared to other Brazilian biomes such as the Amazon and Atlantic Forest. These findings can contribute to the parameterization of hydrological models applied to the Brazilian Cerrado.

Drought Impacts on Vegetation in Southeastern Europe

We evaluated the response of vegetation’s photosynthetic activity to drought conditions from 1998 to 2014 over Romania and the Republic of Moldova. The connection between vegetation stress and drought events was assessed by means of a correlation analysis between the monthly Standardized Precipitation Evaporation Index (SPEI), at several time scales, and the Normalized Difference Vegetation Index (NDVI), as well as an assessment of the simultaneous occurrence of extremes in both indices. The analysis of the relationship between drought and vegetation was made for the growing season (from April to October of the entire period), and special attention was devoted to the severe drought event of 2000/2001, considered as the driest since 1961 for the study area. More than three quarters (77%) of the agricultural land exhibits a positive correlation between the two indices. The sensitivity of crop areas to drought is strong, as the impacts were detected from May to October, with a peak in July. On the other hand, forests were found to be less sensitive to drought, as the impacts were limited mostly to July and August. Moreover, vegetation of all land cover classes showed a dependence between the sign of the correlation and the elevation gradient. Roughly 60% (20%) of the study domain shows a concordance of anomalously low vegetation activity with dry conditions of at least 50% (80%) in August. By contrast, a lower value of concordance was observed over the Carpathian Mountains. During the severe drought event of 2000/2001, a decrease in vegetation activity was detected for most of the study area, showing a decrease lasting at least 4 months, between April and October, for more than two thirds (71%) of the study domain.

Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia

Droughts can cause significant damage to agriculture and water resources, leading to severe economic losses and loss of life. One of the most important aspect is to develop effective tools to forecast drought events that could be helpful in mitigation strategies. The understanding of droughts has become more challenging because of the effect of climate change, urbanization and water management; therefore, the present study aims to forecast droughts by determining an appropriate index and analyzing its changes, using climate variables. The work was conducted in three different phases, first being the determination of Standard Precipitation Evaporation Index (SPEI), using global climatic dataset of Climate Research Unit (CRU) from 1901–2018. The indices are calculated at different monthly intervals which could depict short-term or long-term changes, and the index value represents different drought classes, ranging from extremely dry to extremely wet. However, the present study was focused only on forecasting at short-term scales for New South Wales (NSW) region of Australia and was conducted at two different time scales, one month and three months. The second phase involved dividing the data into three sample sizes, training (1901–2010), testing (2011–2015) and validation (2016–2018). Finally, a machine learning approach, Random Forest (RF), was used to train and test the data, using various climatic variables, e.g., rainfall, potential evapotranspiration, cloud cover, vapor pressure and temperature (maximum, minimum and mean). The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Regarding this, the performance of the testing period was conducted by using statistical metrics, Coefficient of Determination (R2) and Root-Mean-Square-Error (RMSE) method. The performance of the model showed a considerably higher value of R2 for both the time scales. However, statistical metrics analyzes the variation between the predicted and observed index values, and it does not consider the drought classes. Therefore, the variation in predicted and observed SPEI values were analyzed based on different drought classes, which were validated by using the Receiver Operating Characteristic (ROC)-based Area under the Curve (AUC) approach. The results reveal that the classification of drought classes during the validation period had an AUC of 0.82 for SPEI 1 case and 0.84 for SPEI 3 case. The study depicts that the Random Forest model can perform both regression and classification analysis for drought studies in NSW. The work also suggests that the performance of any model for drought forecasting should not be limited only through statistical metrics, but also by examining the variation in terms of drought characteristics.

Nanomineralogy of evaporative precipitation of efflorescent compounds from coal mine drainage

Efflorescent nanophases (NPs) are found as a transitory accumulation of potentially hazardous elements (PHEs), particularly in tropical climates. The central objective of this study was to investigate the distribution of PHEs with NPs through the evaporative formation structures (EFS) of enormously PHEs-rich coal-mine drainages (CMD). The EFS were studied in natural coal mine drainage for five months in order to determine their geochemical and ecological structures and to assess their position in the reduction of PHEs in nature. The largest coal-fired power plant in South America, located in south Brazil, is used as an example of such a problem. In this work, a novel methodology for the analysis of PHEs in CMD precipitates is proposed for this affected coal area. The analytical method, combining X-Ray Diffraction (XRD) and advanced electron microscopies, shows the importance of nanomineralogy in understanding different circumstances of coal contamination. Several ultrafine-nanoparticles (UNPs) were identified in the sampled soils and river sediments together with the PHEs. A decrease in PHEs was identified in association with UNPs. However, further investigations are required with regard to the mobility of PHEs in water, atmosphere, soils, and sediments. The EPS was thoroughly studied, acquiring suitable understanding with investigational facts for Ca and Fe-sulphates, pickeringite, and several amorphous phases.

Skill of large-scale seasonal drought impact forecasts

Abstract. Forecasting of drought impacts is still lacking in drought early-warning systems (DEWSs), which presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (logistic regression and random forest) to predict drought impacts with lead times up to 7 months ahead. The observed and forecasted hydrometeorological drought hazards – such as the standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and standardized runoff index (SRI) – were obtained from the The EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) DEWS. Reported drought impact data, taken from the European Drought Impact Report Inventory (EDII), were used to develop and validate drought impact functions. The skill of the drought impact functions in forecasting drought impacts was evaluated using the Brier skill score and relative operating characteristic metrics for five cases representing different spatial aggregation and lumping of impacted sectors. Results show that hydrological drought hazard represented by SRI has higher skill than meteorological drought represented by SPI and SPEI. For German regions, impact functions developed using random forests indicate a higher discriminative ability to forecast drought impacts than logistic regression. Moreover, skill is higher for cases with higher spatial resolution and less lumped impacted sectors (cases 4 and 5), with considerable skill up to 3–4 months ahead. The forecasting skill of drought impacts using machine learning greatly depends on the availability of impact data. This study demonstrates that the drought impact functions could not be developed for certain regions and impacted sectors, owing to the lack of reported impacts.

Saharan Hot and Dry Sirocco Winds Drive Extreme Fire Events in Mediterranean Tunisia (North Africa)

With hot and dry summers, the Mediterranean basin is affected by recurrent fires. While drought is the major driver of the seasonal and inter-annual fire distribution in its northern and mildest climate conditions, some extreme fire events are also linked to extreme winds or heat waves. The southern part of the Mediterranean basin is located at the driest range of the Mediterranean bioclimate and is influenced by Saharan atmospheric circulations, leading to extreme hot and dry episodes, called Sirocco, and potentially acting as a major contributor to fire hazard. The recently created fire database for Tunisia was used to investigate the ±10-day pre- and post-fire timeframe of daily weather conditions associated with fire events over the 1985–2006 period. Positive anomalies in minimum and maximum temperatures, negative anomalies in air relative humidity, and a preferential south-eastern wind during fire events were identified, which were characteristic of Sirocco winds. +7 °C anomalies in air temperature and −30% in relative air humidity were the critical thresholds for the most extreme fire conditions. In addition, meteorological anomalies started two days before fire events and lasted for three days after for large fires >400 ha, which suggests that the duration of the Sirocco event is linked with fire duration and final fire size. Lastly, the yearly number of intense Sirocco events better explained the inter-annual variability of burned area over the 1950–2006 period than summer drought based on Standardized Precipitation Evaporation Index (SPEI) indices.

Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches

Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901–2010 and tested for nine years (2011–2018), using three different performance metric scores (coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R2 value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological variables and drought incidences for the NSW region.

Potentiality of Waste-to-Energy Sector Coupling in the MENA Region: Jordan as a Case Study

Population growth, urbanization, and changes in lifestyle have led to an increase in waste generation quantities. The waste management system in the Middle East and North Africa (MENA) region is still considered an adolescent system, while developed countries have made great progress in this field, including regulation, financing, administration, separation at source, recycling, and converting waste to energy. At the same time, in the MENA region, the best performance of the recycling process is around 7–10% of total waste. Nowadays, many developed countries like Germany are shifting from waste management to material flow systems, which represent the core of a circular economy. Also, it should be stated here that all countries that have a robust and integrated waste management system include waste-to-energy (W-to-E) incineration plants in their solutions for dealing with residual waste, which is still generated after passing through the entire treatment cycle (hierarchy). Therefore, this paper illustrates the potentiality of embedding waste incineration plants in the MENA region, especially in large cities, and addressing the economic and financial issues for the municipalities. Cities in these countries would like to build and operate waste treatment plants; however, municipalities do not have the sustainable investment and operating costs. The solution is to maximize the income from the output, such as energy, recycling materials, etc. In addition, the MENA region is facing another dilemma, which is water scarcity due to climate change, increasing evaporation, and reduction of precipitation. This research illustrates a simulated model for a waste incineration plant in the MENA region. The EBSILON 13.2 software package was used to achieve this process. Furthermore, the simulated plant applies the concept of waste-to-energy-to-water, so that not only is waste converted to energy but, by efficient usage of multi-stage flash (MSF) technology, this system is able to generate 23 MWe of electric power and 8500 m3/day of potable water. A cost analysis was also implemented to calculate the cost of thermal treatment of each ton of municipal solid waste (MSW) during the life span of the plant. It was found that the average cost of treatment over 30 years would be around US$39/ton.

A Design of Experiment (DoE) based approach for development and optimization of nanosuspensions of telmisartan, a BCS class II antihypertensive drug

BackgroundThe present study was aimed to enhance the solubility and dissolution of BCS class II drug, telmisartan (TEM), by nanoformulation approach. Several attempts were made to develop a nanosuspension by bottom-up and top-down techniques. In our study, we found in situ nanoamorphization technique to be incompatible and hence was not selected for further development of nanoformulation. Bottom-up techniques such as anti-solvent precipitation and emulsification solvent evaporation methods failed to reduce the size of the drug to nanoform by HPMC E15 and PVP K-25 at 1500–2000 rpm but resulted in micron-sized particles.ResultsHowever, the acid-base neutralization method has produced nanosuspension with a particle size of 243.9 nm and 0.119 PDI. Formulation and analytical development were carried out by statistical factorial design using the Design-Expert software (version 11.0). The nanosuspensions remained stable even after 90 days without any aggregations with particle size 338.1 nm and PDI 0.146. Zeta potential of optimized formulation was found to be − 16.2 mV. Drug content and its release were estimated by the developed and validated in-house HPLC method. In vitro drug diffusion studies on the optimized formulation have shown a drug release of 82.6% by the end of 3 h, whereas plain drug suspension has shown only 42.8% release, indicating a 2-fold increase of drug diffusion with nanosuspension. Ex vivo drug permeation studies performed using excised goat gastric mucosa revealed much faster permeation of TEM from nanosuspension than the plain drug suspension.ConclusionsHence, from the results, it can be concluded that TEM, when formulated by acid-base neutralization method as a nanosuspension, leads to enhanced solubility, dissolution, and stability.

Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models

Forecasting extreme hydrological events is critical for drought risk and efficient water resource management in semi-arid environments that are prone to natural hazards. This study aimed at forecasting drought conditions in a semi-arid region in north-eastern South Africa. The Standardized Precipitation Evaporation Index (SPEI) was used as a drought-quantifying parameter. Data for SPEI formulation for eight weather stations were obtained from South Africa Weather Services. Forecasting of the SPEI was achieved by using Generalized Additive Models (GAMs) at 1, 6, and 12 month timescales. Time series decomposition was done to reduce time series complexities, and variable selection was done using Lasso. Mild drought conditions were found to be more prevalent in the study area compared to other drought categories. Four models were developed to forecast drought in the Luvuvhu River Catchment (i.e., GAM, Ensemble Empirical Mode Decomposition (EEMD)-GAM, EEMD-Autoregressive Integrated Moving Average (ARIMA)-GAM, and Forecast Quantile Regression Averaging (fQRA)). At the first two timescales, fQRA forecasted the test data better than the other models, while GAMs were best at the 12 month timescale. Root Mean Square Error values of 0.0599, 0.2609, and 0.1809 were shown by fQRA and GAM at the 1, 6, and 12 month timescales, respectively. The study findings demonstrated the strength of GAMs in short- and medium-term drought forecasting.

Characteristics and trends of flash droughts in Spain, 1961-2018.

Flash droughts are characterized by rapid onset and intensification, as well as major environmental and agricultural impacts. In this study, we developed an objective method for identifying flash droughts using the standardized evaporation precipitation index (SPEI) based on a short time scale (1-month) and high-frequency data (weekly). The identification of flash droughts was focused on the development phase, anomalous decreases in index values in a short time period (4 weeks), and the magnitude of the events. The method was applied to mainland Spain and the Balearic Islands using a high spatial resolution gridded dataset for the period 1961-2018. For this period of 58 years, we characterized the occurrence of flash droughts and showed that for Spain, there was a large spatial and temporal variability in their frequency, with more occurring in the northwest than in the central and southern regions. The northern regions, where a higher frequency of flash droughts was found, showed negative trends in the frequency of flash droughts, while the regions subject to fewer flash drought events showed generally positive trends. We investigated the relative frequency of flash droughts affecting the study regions and found that they are a common phenomenon, as 40% of all droughts were characterized by rapid development. The findings of this study have important implications for drought assessment, monitoring, and mitigation.

Vertical Temperature Structure Associated with Evaporation of Stratiform Precipitation in Idealized WRF Simulations

AbstractA recent study based on observations has shown that after precipitation over tropical oceans rather shallow temperature structures occur in the lower troposphere and that their magnitude depends on climatological low- to midtropospheric humidity. As any process that produces temperature perturbations in the lower troposphere can be of great significance for the formation of atmospheric deep convection, the vertical temperature structure associated with evaporation of stratiform precipitation and its sensitivity to low- to midtropospheric humidity are studied by conducting three-dimensional, high-resolution, idealized simulations with the Advanced Research version of the Weather Research and Forecasting (WRF) Model. In the simulations, rainwater with mixing ratio and number concentration characteristic of stratiform precipitation associated with mesoscale convective systems is added in a large round area at roughly 560 hPa. Evaporative cooling and subsidence warming below result in a cold anomaly at roughly 560–750 hPa and, especially, a warm anomaly at roughly 750–900 hPa. The cold-over-warm anomalies are stronger with smaller low- to midtropospheric relative humidity in the initial conditions, with the maximum magnitude of the warm anomaly ranging between 0.7 and 1.2 K. The temperature anomalies propagate to the environment and still remain present after precipitation stops. The results show that evaporation of stratiform precipitation alone can lead to temperature structures, which are on the same order of magnitude as the observed ones, that potentially inhibit subsequent convection by increasing convective inhibition. Therefore, the representation of microphysical processes affecting the location, amount, and vertical and horizontal distribution of stratiform precipitation and its evaporation in numerical models requires special attention.

Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe

Meteorological and agricultural information coupled with remote sensing observations has been used to assess the effectiveness of satellite-derived indices in yield estimations. The estimate yield models generated by both the regression (MLR) and Bayesian network (BBN) algorithms and their levels of predictive skill were assessed. The enhanced vegetation index (EVI2), soil water index (SWI), standardized precipitation evaporation index (SPEI) have been considered predictors for three rainfed crops (maize, sunflower and grapevine) grown in 37 districts in the Republic of Moldova (RM). We used the weekly EVI2, which was collected by MODIS instruments aboard the Terra satellite with a 250m × 250m spatial resolution and aggregated for each district during the 2000–2018 period. We also used the weekly SWI, which was collected from the ASCAT instruments with a 12 km x 12 km spatial resolution and aggregated for each district at the topsoil (0–40 cm; SWI-12) and the root-zone layer (0–100 cm; SWI-14) during 2000–2018. The multiscalar SPEI during 1951–2018 farming years proved to be a significant addition to the remote sensing indices and led to the development of a model that improved the yield assessment. The study also summarized (i) the optimal time window of satellite-derived SWIi and EVI2i for yield estimation, and (ii) the capability of remotely sensed indices for representing the spatio–temporal variations of agricultural droughts. We developed statistical soil-vegetation-atmosphere models to explore drought-related yield losses. The skill scores of the sunflower MLR and BBN models were higher than those for the maize and grape models and were able to estimate yields with reasonable accuracy and predictive power. The accurate estimation of maize, sunflower and grapevine yields was observed two months before the harvest (RMSE of ∼1.2 tha-1). Despite the fact that summer crops (maize, sunflower) are able to develop a root system that uses the entire root zone depth, however, the SWI-12 had the stronger correlation with crop yield, then SWI-14. This explains much better the fit between yields of the crops and SWI-12, which represents soil moisture anomaly in the key rooting layer of soil. In any case, all summer crops showed negative correlations with each of the remote sensing soil moisture indices in the early and middle of the growing season, with SWI-12 performing better than SWI-14. Based on the crop-specific soil moisture model, we found that topsoil moisture declines in the most drought-susceptible crop growth stages, which indicates that RM is a good candidate for studying drought persists as main driver of rainfed yield losses in the south-eastern Europe.