Distribution Patterns Of Precipitation Research Articles

Meteorological disaster disturbances on the main crops in the north‒south transitional zone of China

Global climate change, with warming as its main feature, has altered the spatial-temporal evolution of factors such as precipitation and temperature that can cause meteorological disasters. The complex and changeable climate has led to frequent natural disasters, while the frequency and intensity of extreme climate events have also significantly increased, posing an enormous threat to societal production and human life. As the most important geoecological transitional zone of mainland China, the stability of agricultural production in China’s north–south transitional zone is crucial for ensuring food security under climate change. With the use of daily precipitation and potential evapotranspiration data from 1961 to 2018, this study focused on analysing disturbances such as extreme precipitation and drought disasters at different time scales during the winter wheat and summer maize growing seasons in the north–south transitional zone of China from an agricultural production perspective and attempted to answer the following questions: first, from an agricultural production perspective, what are the temporal and spatial distribution patterns of extreme precipitation and arid climate events in the north–south transitional zone? Second, which areas are at high risk of being disturbed by different types of meteorological disasters and require increased attention? The results indicated that (1) in terms of the overall temporal variation, the degree of extreme precipitation and drought stress faced by agricultural production in the region is decreasing. However, the temporal variation at each station in the north–south transitional zone was not completely consistent with the overall trend, and both increasing and decreasing trends were observed. The sites exhibiting an increase overlapped with typical regions of the north–south transitional zone to varying degrees, indicating that the typical regions represented not only theoretical potential risk areas under climate change but also suffered from meteorological disaster disturbances. (2) The precipitation distribution during the winter wheat growth period in the south–north transitional zone was uneven and varied significantly. High values of extreme precipitation indices during the winter wheat growth period were mainly concentrated in the southern part of the eastern section of the north‒south transitional zone. The precipitation distribution during the summer maize growth period significantly differed, with the highest amount of heavy rain and largest number of rainstorm days concentrated in the southeastern part of the north‒south transitional zone. The spatial distribution of the drought frequency in the north–south transitional zone, as indicated by the monthly standardized precipitation evapotranspiration index (SPEI1), showed that the areas with high total drought frequencies were mainly concentrated in northeast Jiangsu, southeast Henan, and north Anhui, which primarily experienced light drought. The central part of Jiangsu Province exhibited a high frequency of moderate drought, while southern Jiangsu Province and southwestern Shaanxi Province were prone to severe drought. Additionally, southeastern Hebei and eastern Henan were identified as areas with a high frequency of extreme drought. Finally, the central region of Sichuan Province was characterized by both severe and extreme drought conditions. Based on the SPEI12-derived spatial distribution of the drought frequency in the north–south transitional zone, the areas with a high total drought frequency were mainly concentrated in central and eastern Henan, southeast Shaanxi, southeast Shandong, and central Sichuan, which primarily experienced light to moderate drought. The northwestern part of Jiangsu, the southern part of Hebei, and the western part of Shandong are regions with a high frequency of severe drought, while the eastern part of Henan is an area with high frequencies of both severe and extreme drought. (3) High-value areas of extreme precipitation and drought disturbance in the north–south transitional zone overlapped with the edge of the transitional zone to varying degrees. Approximately 63.58% of the north‒south transitional zone of China was characterized by moderate or high stress levels, primarily concentrated along the southern boundary and central core area, and nearly 39.5% of all counties experienced two or more types of disaster stresses.

A Numerical Bio-Geotechnical Model of Pressure-Responsive Microbially Induced Calcium Carbonate Precipitation

The employment of Microbially Induced Calcium Carbonate Precipitation (MICP) is of increasing interest as a technique for environmentally sustainable soil stabilisation. Recent advancements in synthetic biology have allowed for the conception of a pressure-responsive MICP process, wherein bacteria are engineered to sense environmental loads, thereby offering the potential to stabilise specific soil regions selectively. In this study, a 2D smart bio-geotechnical model is proposed based on a pressure-responsive MICP system. Experimentally obtained pressure-responsive genes and hypothetical genes with different pressure responses were applied in the model and two soil profiles were evaluated. The resulting model bridges scales from gene expression within bacteria cells to geotechnical simulations. The results show that both strata and gene expression–pressure relationships have a significant influence on the distribution pattern of calcium carbonate precipitation within the soil matrix. Among the evaluated experimental genes, Gene A demonstrates the best performance in both of the two soil profiles due to the effective stabilisation in the centre area beneath the load, while Genes B and C are more effective in reinforcing peripheral regions. Furthermore, when the hypothetical genes are utilised, there is an increasing stabilisation area with a decreased threshold value. The results show that the technique can be used for soil reinforcement in specific areas.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Analysis of the Impact of Drought Characteristics Change on Natural Ecological Environment in the West of Hexi Corridor

The plant communities and wild animals in the Gobi desert outside the oases in the west of Hexi Corridor are an indispensable and important part of the human living environment. Affected by global warming, the spatial and temporal distribution patterns of precipitation have changed significantly. This article uses daily precipitation data from 6 hydro-logical stations in the middle and lower reaches of the Shule River Basin from 1957 to 2018, and daily precipitation data from 3 meteorological stations from 1980 to 2018. Through research on annual scale precipitation anomaly percentage, effective precipitation days and annual distribution, and drought station frequency ratio and other parameters, it is shown that there is a significant change in precipitation distribution within the year. The distribution of effective precipitation is not conducive to the survival of ecosystems, and seasonal drought is on the rise. The trend of continuous drought in two seasons and three seasons is on the rise, and the evolution of drought has a significant impact on the natural ecological environment. Effective rainfall is a key element in the rise and fall, distribution, and change of natural ecosystems. Studying the impact of changes in drought characteristics on natural ecology is of great significance for actively responding to drought disasters and maintaining basic ecology.

Evolutionary Characteristics of Daytime and Nocturnal Precipitation Heterogeneity in Gansu Province, Northwest China

The diurnal variation in precipitation and its evolution are important foundations for understanding the regional impact of climate change and the parameterization of the model. Based on the daily precipitation data set of 23 national meteorological stations during 1970–2019, the spatial and temporal distribution characteristics of precipitation concentration degree (PCD) and precipitation concentration period (PCP) in Gansu province were evaluated on daytime and nocturnal scales. The results show the following: (1) Annual precipitation ranges from 69.1 ± 24.7 mm to 578.3 ± 96.6 mm, mainly (54.1 ± 2.6%) occurring at night, and the spatial distribution of the nocturnal precipitation rate is positively (r = 0.53, p < 0.01) correlated with annual precipitation; the wetting trend (12.7 mm/10 a, p < 0.01) in the past 50 years is obvious, and is mainly dominated by the frequency of precipitation (r = 0.58, p < 0.001), with both performing better during the day. (2) Most PCD is located between 0.55 and 0.75, showing a basic distribution pattern for daytime greater than nocturnal, higher values, and stronger interannual fluctuations in arid areas; the significant decreasing trend (p < 0.05) of PCD is very clear and highly consistent, especially in the high-altitude area, and the increase in precipitation in the dry season and the improvement in precipitation uniformity in the wet season play a key role. (3) PCP often fluctuates slightly around the 39th–41st pentad, but the general rule that daytime values are smaller than night values and the interannual variability is larger in arid areas also requires special attention; PCP has shown a relatively obvious advance trend in a few regions, but this is because the prominent and complex changes in the monthly precipitation distribution pattern have not been fully reflected. Along with continuous humidification, the decrease in PCD and the advance of PCP are likely to be the priority direction of precipitation evolution in the arid region of Northwest China, especially during the day. These findings provide a new perspective for understanding regional climate change.

Trends in Warm Season Mesoscale Convective Systems Over Asia in 2001–2020

AbstractMesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high‐resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso‐α scales (MαCSs) and meso‐β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower‐tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. The trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower‐tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.

Time distribution pattern and spatial heterogeneity of hourly scale event-based extreme precipitation in China

Extreme precipitation occurs naturally in the form of “events,” and the spatio-temporal distribution pattern of event-based extreme precipitation is of great significance to flood disaster prevention and water resources protection. This study proposed the concept of hourly scale event-based extreme precipitation (HEEP), referring to a precipitation event with at least an hourly precipitation extreme (exceeding the 99th percentile threshold). Then we identified the dominant time distribution pattern (TDP), analyzed the seasonal proportion and distribution, and investigated the spatial distribution of three basic features (total frequency, amount, and duration) of HEEP from 2008 to 2017 in China and its nine major river basins. Results showed that over 90% of the areas in China were dominated by short-duration HEEP (S-HEEP, less than eight hours). While the rest areas were led by long-duration HEEP (L-HEEP, more than eight hours), mainly concentrated on the middle and lower reaches of the Yangtze River and southeast coastal areas of China. Areas dominated by S-HEEP were more than L-HEEP in different seasons except in winter. Three basic features of HEEP showed similar spatial distribution patterns of high in southeastern China and low in northwestern China, indicating HEEP frequently occurred in southeastern China with relatively large amounts and long duration. The concept of HEEP can provide new ideas for regional and global extreme precipitation research.

Evaluation of NEX-GDDP-CMIP6 in simulation performance and drought capture utility over China – based on DISO

Abstract Global climate models (GCMs) are the state-of-the-art tool for understanding climate change and predicting future. However, little research has been reported on the latest NEX-GDDP-CMIP6 product in China. The purpose of this study was to evaluate the simulated performance and drought capture utility of the NEX-GDDP-CMIP6 over China. First, the simulation skills of the 16 GCMs in NEX-GDDP-CMIP6 was evaluated by the 'DISO', a big data evaluation method. Second, the DISO framework for drought identification was constructed by coupling the Correlation Coefficient (CC), False Alarm Rate (FAR) and Probability of Detection (POD). Then, it was combined with SPI and SPEI to evaluate the drought detection capability of NEX-GDPD-CMIP6. The result shows that: (1) NEX-GDPD-CMIP6 can reproduce the spatial distribution pattern of historical precipitation and temperature, which performs well in simulating warming trend but fails to capture precipitation's fluctuation characteristics. (2) The best performing model in precipitation is ACCESS-CM2 (DISO 1.630) and in temperature is CESM2 (DISO 3.246). (3) The 16MME performs better than the best single model, indicating that multi-model ensemble can effectively reduce the uncertainty inherent in models. (4) The SPEI calculated by 16MME identifying drought well in arid, while SPI is recommended for other climate classifications of China.

Direct Shear Characteristics of Enzymatically Cemented Sands

A laboratory study was conducted to investigate the shear strength of bio cemented sands using enzyme-induced carbonate precipitation (EICP) via the direct shear test. Several specimens were prepared using four different application methods of EICP solution, namely spraying, mix-&-compact, percolation, and injection. Potential use of EICP for improving the sand-concrete interaction strength was also studied. Results indicated that the adopted application method of introducing the EICP solution to the soil affects the distribution pattern of CaCO3 precipitation in the soil matrix, thereby shear strength. Each method yielded a different response when sheared compared to untreated sand. The injection method was found to achieve higher shear strength and interparticle cementation under controlled rate and distribution followed by percolation. The spraying and mix-&-compact methods produced specimens with weaker particles' bond and lower shear strength measurement but still showed relatively better performance than the untreated sands. Better cementation of soil grains was achieved in medium sand and when fine and organic materials were removed. Sands response to EICP-treatment was inferred to vary depending on the geotechnical characteristic of the soils. Moreover, the interfacial strength between sand and concrete was found to be enhanced after applying the EICP treatment as it coarsened soil particles.

Enhanced precipitation prediction using DEM‐based predictors and satellite imagery

AbstractOver the last decades, climatic changes have triggered considerable impacts across the globe with detrimental effects on all ecosystems. Given the complexity of topography and climate, Romania is one of the most exposed countries in the South‐Eastern Europe to extreme hydrological events. As a consequence, the spatial distribution of precipitation is of greater importance for future analysis. This study examines the performance of Empirical Bayesian Kriging Regression Prediction (EBKRP) and Geographically Weighted Regression (GWR) to predict the spatial distribution of annual and seasonal precipitation over Romania. Twelve co‐variables derived from topography, and remote sensing products data were used to improve the prediction. The co‐variable selection process was performed prior to the regression model to reduce the complexity and processing time, using the Boruta Algorithm (BA), which is an innovative approach. The performance of BA was compared with Gini Coefficient. Our findings confirmed that 10 co‐variables were relevant to predict annual precipitation and nine for seasonal. The overall prediction of precipitation is more influenced by topography (altitude, slope, surface roughness) and the distance to marine bodies (Black Sea and Adriatic Sea). Cross‐validation and five statistical metrics were applied to assess the performance of the regression models. The results show similar spatial distribution pattern of precipitation, whereas the highest annual precipitation is found in the Carpathian Mountains (EBKRP: 1,399.2 mm, GWR: 1,249.7 mm) and the lowest in the lowlands (EBKRP: 355.9 mm, GWR: 346.8 mm). For seasons, both methods predicted the highest precipitation in summer and lowest in autumn. In all seasons, the precipitation is underestimated by both methods, however, for annual, only GWR does so. Our study revealed GWR as the best method to predict annual and seasonal precipitation over Romania, as it yielded the highest correlation coefficient Spearman (S), Pearson (P), determination coefficient (R2) and lowest mean absolute error (MAE) and root mean square error (RMSE).

Probabilistic-forecasting method for determination of low flow discharge of Pivdennyi Buh, Black Sea area, and Lower Dnieper rivers

The aim of the study is to substantiate the probabilistic-forecasting method for forecasting the low flow discharge and its implementation for the Pivdennyi Buh, Black Sea area and Lower Dnieper rivers, taking into account climatic patterns of precipitation distribution and establishing probabilistic characteristics of low flow discharge in multi-year period. The study area is in a zone of significant risk due to the shortage of water resources, the formation of extremely low runoff in the dry flow period, which requires its definition and forecasting. The methodological basis of forecasts is to solve the equation in determining the components of the low flow of rivers by constructing regional dependences for forecasting the average decade summer-autumn low flow from previous water discharge (in runoff modules), establishing their probabilities water discharge for a number of intakes. Results. The paper substantiates the probabilistic-forecasting method for forecasting low flow discharge (in summer, autumn and winter periods) based on the construction of regional dependences of average decadal runoff modules on previous water discharge for groups of basins of studied rivers taking into account climatic dependences of precipitation distribution in the territory and the establishment of probabilistic characteristics of the low flow water discharge in a multi-year period. The methodology of territorial short-term forecasts of average decade water discharge of low flow of summer, autumn and winter river runoff is assessed as satisfactory with a margin of error of 70 % to 97 %, with a number of members of more than 500 points. To determine the cumulative probability of the forecast values of the average decade water discharge of the summer, autumn and winter dry weather flow, the empirical distribution of the average monthly water discharge in the limited seasons, which are generalized in the basins of the studied rivers of Ukraine, is established. Scientific novelty. For the first time for the zone of insufficient natural water content of rivers the method of territorial forecasts of low flow discharge, determination of their probability of occurrence in a multi-year period and cartographic representation of prognostic values have been developed and practically implemented. The practical importance is the use of forecast modules maps of low flow for spatial monitoring and assessment of low water levels on rivers in the whole region, including ungauged rivers, and their probabilistic characteristics – to assess the possible occurrence of low runoff, even when it reaches values close to the environmental runoff, which are critical for the functioning of the river ecosystem.

Spatiotemporal Patterns of Extreme Precipitation Events over Jhelum River Basin

Climate change has a wide range of impacts on Pakistan’s water resources system, followed by increased magnitude, frequency and intensity of extreme events. Therefore, investigating the variations in extreme climatic events is vital in planning and developing agriculture and water conservation projects. Hence, the present study investigated the spatiotemporal variability of extreme precipitation indices (EPIs) over the Jhelum River basin (JRB) using the Mann-Kendall (MK) and innovative trend analysis (ITA) methods. In addition, variations in the event-based extreme precipitation (EEPs) and time distribution patterns (TDPs) were analyzed because EPIs considered the daily precipitation values only and the combined effect of the extreme events that last for more than 1 day were often neglected. The results show that the fixed and station-related EPIs, i.e., R10, R20, R25, CWD, Prcpt and R95p, exhibited significant decreasing trends at 06, 05, 04, 04, 07 and 05 stations, whereas an increasing trend was evident for consecutive dry days (CDD) at nine stations with a magnitude of 0.33 days/year, indicating a rise in the drought events compared to floods. Moreover, precipitation events with an amount of 40–290 mm, duration of 4–7 days and concentration ratio of 0.7–0.95 were found to be dominant across JRB. Therefore, an increase in the CDD and a decrease in the consecutive wet days (CWD) may change the magnitude, intensity and frequency of extreme precipitation events and consequently pressure the availability of water resources. Therefore, the findings of this study may help understand the precipitation variability over JRB and play a pivotal role in future development projects.

Assessment of Flood Hazard in Climatic Extreme Considering Fluvio-Morphic Responses of the Contributing River: Indications from the Brahmaputra-Jamuna’s Braided-Plain

Climate change is expected to raise river discharge and sea level in the future, and these near-term changes could alter the river flow regime and sedimentation pattern of future floods. Present hazard assessment studies have limitations in considering such morpho-dynamic responses in evaluating flood hazards or risks. Here, we present a multi-model-based approach to quantify such potential hazard parameters influenced by climate change for the most vulnerable communities living on river bars and islands of the Brahmaputra–Jamuna River. River flood-flow and flood wave propagation characteristics are predicted to be affected by changing temporal distribution patterns of precipitation as a result of enhanced global warming. Increased incidences of large multi-peak floods or uncommon floods resulting in long-duration floods driven by sea-level rise may happen as a result of this. To assess it, we have set up a hydromorphic model, Delft3D, for the Brahmaputra–Jamuna River forced by upstream flow, generated from a hydrological model SWAT, over the Brahmaputra basin. The simulations cover moderate, wettest, and driest conditions of the RCP8.5 scenario, and the results reflect the flooding consequences of the near-future, mid-century, and end-century. Floods in the Brahmaputra–Jamuna River are becoming more severe, frequent, and long-lasting, as a result of climate change, and are expected to last until the end of November rather than the current September timeline. While assessing the hazard, we found that the pattern and timing of the flood are as equally important as the peak of the flood, as the river continuously adjusts its cross-sectional area with the flow. The study also demonstrates that, depending on their location/position, climate-induced hazards can affect sand bars/islands disproportionally. The high flood depth, duration, and sedimentation have a significant impact on the sand bars downstream of the river, making them more vulnerable.

Error Characteristic Analysis of Satellite-Based Precipitation Products over Mainland China

Satellite-based precipitation products (SPPs) provide valuable precipitation information for various applications. Their performance, however, varies significantly from region to region due to various data sources and production processes. This paper aims to evaluate four selected SPPs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Gauge-adjusted Global Satellite Mapping of Precipitation (GSMAP-gauge), and Global Precipitation Measurement (GPM)) over mainland China from 2016 to 2019. Both conventional statistical indicators (e.g., correlation coefficients (CC), root mean square error (RMSE), mean absolute error (MAE), relative bias (RB), and Nash–Sutcliffe efficiency (NSE)) and categorical indicators (probability of detection (POD), probability of true detection (POTD), false-alarm rate (FAR), and critical success index (CSI)) are used for quantitative analysis. The results show that: (1) GSMAP-gauge and GPM perform best in reproducing the spatial distribution pattern of precipitation over mainland China, whereas SPPs generally underestimate summer precipitation with a high frequency of no-rain cases. (2) MSWEP has the best capability for recording precipitation events, although some parts of northern China exhibit abnormal overestimations for winter precipitation. (3) All SPPs, especially the PERSIANN-CDR, significantly underestimate the precipitation in the mountainous areas of southwestern China. (4) The GSMAP-gauge and GPM outperformed the other two of the four SPPs, in terms of the probability density function of daily precipitation for cases (PDFc) and the probability density function of daily precipitation for volume (PDFv). Generally, PERSIANN-CDR shows the poorest performance when compared to the other three products. The product’s algorithm for estimating heavy precipitation and mountainous precipitation needs further improvement.

ASSESSMENT OF HUMIDIFICATION OF THE TERRITORY OF VINNITSA REGION BY THE RATIO OF PRECIPITATION AND EVAPORABILITY

The article contains information on the results of the analysis of the distribution of atmospheric precipitation and humidification in the Vinnytsia region. Vinnytsia region is located in the forest-steppe zone, but belongs to an area with unstable humidification with a high probability of dry years. Both in wet and dry years, the amount of precipitation increases from east to west towards the Carpathians.
 To show the patterns of precipitation distribution, published sources and reference books on climate with normative indicators for precipitation for 45 points of the Vinnitsa region were analyzed. The humidification coefficient was calculated in relation to annual precipitation to evaporability.
 Analysis of the geographical distribution of precipitation and humidification showed their dependence on relief, orohydrography and exposure of slopes. At altitudes less than 100 m (in the Dniester river valley), the amount of precipitation is 480-490 mm per year and the humidification coefficient is 0.60-0.62, and on windward slopes they increase to 517-520 mm and 0.62-0.64, respectively. On vistas of 100-150 m, the amount of precipitation increases to 504-520 mm and humidification to 0.63-0.65. For heights of 150-200 m, the average precipitation for the year is 500-510 mm and the humidification coefficient is 0.64. Most observation points are located at absolute altitudes from 200 to 300 m. At altitudes of 200-250 m, the amount of precipitation and humidification increases to 490-520-540-560 mm per year and 0.62-0.67-0.70-0.76. At altitudes of 250-275 m, precipitation amounts to 485-505-530-550 mm per year and humidification coefficients 0.63-0.66-0.73-0.75. The largest values ​ ​ of average annual precipitation and humidification coefficients are observed at altitudes of 275-300 and more than 300 m (510-540-550-560-570 mm and 0.65-0.72-0.76-0.77).
 The northern territory of the region is located in the zone of optimal humidification of typical forest-steppes. Most of the territory is located in the zone of insufficient humidification (less than 0.7-0.65). A coefficient of insufficient humidification typical of the steppe zone (0.65-0.60) is determined for 16 points in areas south of the latitude of the cities of Gaisin and. Mooded Kurilovtsy. In a warm season, there are 3-5 periods without rains lasting 10-11 days. In 25% of cases, the duration of idle periods increases to 11-20 days, in 5% - to 21-30 days and 1-3% cases to 30-50 days or more.
 Thus, the largest amount of precipitation (525-560 mm on average per year) falls in the north and northeast within the Dnieper Upland. In the South Bug Valley, annual rainfall decreases from northwest to southeast to 520-490 mm. On the Podolsk Upland and Vinnytsia Transnistria, the amount of precipitation varies from 500-550 mm per year on watersheds to 490-480 mm in river valleys. In most of the territory south of the latitude of Vinnitsa, humidification is insufficient, and south of the latitude of Gaisin, the humidification coefficient is the same as in the steppe zone (0.64-0.60).
 Keywords: Vinnytsia region, climate, humidification coefficient, precipitation, distribution.

Integrating Blue and Green Water to Identify Matching Characteristics of Agricultural Water and Land Resources in China

The uneven distribution and spatial mismatch between water and land resources (WLR) limit the utilization efficiency of agricultural water and land resources (AWLR), and then pose a threat to food production. There is a critical need to understand their matching characteristics for strategic resource allocation decisions and management. In this study, a holistic index (RSI) derived from resource equivalency analysis (REA) was developed which contributed to examine the abundance or deficiency of agricultural water and land resources (AWLR) based on the conception of generalized water resources (including blue water and green water). Results indicate that the matching degree of AWLR was the lowest in Northeast China, North China and Gansu in Northwest China, while the highest value was in Xinjiang due to the higher blue water use. In north China, other provinces suffered water scarcity except in Jilin, Liaoning, Henan, while all provinces in south China suffered land scarcity except in Tibet and Jiangsu. Meanwhile, the matching of AWLR was not exactly consistent with the distribution pattern of precipitation. The higher proportion of green water resources was not located in southeast China with rich precipitation; it had to do with water management. Too much or little water resources can result in the increasing of blue water use. The proposed approach can be a powerful instrument to implement public policies and management strategy as an attempt to improve the utilization efficiency and enhance the optimize-allocation of AWLR.

A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB > EBK > OK > RBF > DIB > IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions.

Comparison of the temporal distribution pattern of precipitation and the cumulative effect of daily droughts on the local scale in Iran

Comparison of the temporal distribution pattern of precipitation and the cumulative effect of daily droughts on the local scale in Iran

Precipitation Simulation from the Cumulus Convection Parameterization Schemes Based on the WRF Model in the Weihe River Basin, china

Two cumulus convection parameterization schemes, i.e., Kain-Fritsch (K-F) and Grell-3 based on the V4.1.2 Weather Research and Forecasting (WRF) model, were used to simulate the summer precipitation in the Weihe River Basin using hourly precipitation data at the resolution of 5 km and 2 km. the precipitation experiment results at different resolution were compared and analyzed. The root square error and correlation coefficient were used to verify and evaluate the simulation results. The results show that in K-F and Grell-3, the two cumulus model simulations with a resolution of 5 km can explain the spatial distribution pattern of precipitation and the diurnal variation process, and the experimental simulation results are consistent with the actual observations. The summer precipitation simulation in June, July, and August is highly sensitive to the two cumulus parameter schemes of K-F and Grell-3. The simulated precipitation value is higher than the actual value, and the root mean square errors (RMSEs) of K-F and Grell-3 schemes are 5.49-13.29 and 5.69-10.88, respectively. In terms of precipitation simulation, Grell-3 scheme is better than K-F scheme, especially in areas with heavy rainfall. When the resolution is increased to 2 km, the regions with heavy precipitation can be displayed more finely. The influence of water vapor flux and vertical velocity changes on precipitation in the Weihe River is analyzed from two aspects, i.e., water conditions and atmospheric instability. From the simulation results by the K-F scheme, the vertical velocity fluctuations are unstable and strong in the convective area, and the convective rainfall is relatively high. From the simulation results by Grell-3, the vertical velocity is large and increased rapidly, which is conducive to the generation of large-scale heavy precipitation. Both schemes are very sensitive to the simulation of water vapor transport and vertical velocity. This study provides a basis for the research on the adaptability of regional precipitation simulation.

Estimation of Biomass Increase and CUE at a Young Temperate Scots Pine Stand Concerning Drought Occurrence by Combining Eddy Covariance and Biometric Methods

The accurate estimation of an increase in forest stand biomass has remained a challenge. Traditionally, in situ measurements are done by inventorying a number of trees and their biometric parameters such as diameter at the breast height (DBH) and height; sometimes these are complemented by carbon (C) content studies. Here we present the estimation of net primary productivity (NPP) over a two years period (2019–2020) at a 25-year-old Scots pine stand. Research was based on allometric equations made by direct biomass analysis (tree extraction) and carbon content estimations in individual components of sampled trees, combined with a series of stem diameter increments recorded by a network of band dendrometers. Site-specific allometric equations were obtained using two different approaches: using the whole tree biomass vs DBH (M1), and total dry biomass-derived as a sum of the results from individual tree components’ biomass vs DBH (M2). Moreover, equations for similar forest stands from the literature were used for comparison. Gross primary productivity (GPP) estimated from the eddy-covariance measurements allowed the calculation of carbon use efficiency (CUE = NPP/GPP). The two investigated years differed in terms of the sum and patterns of precipitation distribution, with a moderately dry year of 2019 that followed the extremely dry 2018, and the relatively average year of 2020. As expected, a higher increase in biomass was recorded in 2020 compared to 2019, as determined by both allometric equations based on in situ and literature data. For the former approach, annual NPP estimates reached ca. 2.0–2.1 t C ha−1 in 2019 and 2.6–2.7 t C ha−1 in 2020 depending on the “in situ equations” (M1-M2) used, while literature-derived equations for the same site resulted in NPP values ca. 20–30% lower. CUE was higher in 2020, which resulted from a higher NPP total than in 2019, with lower summer and spring GPP in 2020. However, the CUE values were lower than those reported in the literature for comparable temperate forest stands. A thorough analysis of the low CUE value would require a full interpretation of interrelated physiological responses to extreme conditions.