Changes In Precipitation Intensity Research Articles

The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Analysis of sediment provenance using geochemical and isotopic data for a post-LGM sediment core from the western Great Rann of Kachchh, India: Implications for climate control on source regions

This study addresses the nature and causes of post-Last Glacial Maxima (LGM) sedimentation in the Western Great Rann of Kachchh, Gujarat, India. Specifically, we report geochemical and isotopic data (87Sr/86Sr and 143Nd/144Nd) for a 46.5 m sediment core from the Nara River bed. These data indicate that up to the beginning of the Holocene, sedimentation mostly comprised contributions from monsoon-dominated rivers of Western Himalaya. Later in the early Holocene, a shift towards Indus-derived sediments occurred, with a minor reversal at ~7 ka, attributed to the rejuvenation of the Ghaggar-Hakra channel. A renewed contribution from the Indus River routed through the Nara River and through the Kori Creek (mouth of the Nara River) occurred during the mid- to late Holocene. Persistent availability of freshwater during the mid- to late Holocene helped sustain early Iron Age and medieval cultures in the region. However, significant decline in the hydrological system anchored in the shifting Indus River through the Nara channel after the medieval period, likely caused the gradual collapse of these cultures. Our study is important as it demonstrates that, if the sediment source is from regions of contrasting climatic domains (for example, monsoon-dominated western Himalaya rivers and the westerlies-dominated Indus River), temporal changes in precipitation intensities exert a first order control in modulating the sediment provenances.

Evaluating future flood risk mitigation capacities in major cities across China

Rapid urbanization and climate change have significantly altered urban hydrological processes. Previous research has highlighted their adverse effects on escalating risk of urban flood. Nevertheless, few assessments have quantified how future urban flood risk mitigation capacities evolve under various urban expansion and climate change scenarios. This study simulates urban expansion of major cities across China using future urban-regional environment simulation model at 30-m resolution under shared socioeconomic pathways scenarios (SSP). Concurrently, future precipitation intensity changes are assessed based on climate projection data. Finally, the urban flood risk mitigation capacity impacted by urban expansion and climate change is evaluated using the soil conservation service model. The results reveal: (1) The sizes of major cities are projected to expand 30% by 2050, and the north and east China are primary urban growth poles. (2) Major cities' precipitation intensity increase 0.4% in SSP126, but decline 5.4% and 2.3% in SSP245 and SSP585. (3) The flood risk mitigation capacities of major cities decrease 2.7% in SSP126 and 2.1% in SSP585, but increase 0.2% in SSP245. Most cities experiencing capacity decrease are located in the north and east China. These results provide valuable insights for the formulation of future urban flood adaptation and mitigation strategies.

Non-uniform changes of daily precipitation in China: Observations and simulations

Daily precipitation of different intensities is expected to change differently in response to global warming. Based on station observations and simulations from the latest climate models, we investigated the non-uniform features of changes in daily precipitation frequency, intensity and amount over China. Results show that western China experiences an overall wetting trend across the spectrum of precipitation intensity, while eastern China exhibits negative trends in light-to-moderate precipitation and positive trends in heavy-to-extreme precipitation with respect to precipitation frequency and amount. Changes in precipitation intensity do not show a spatially consistent pattern of intensification in most intensity spectra, but exhibit the most pronounced intensification in heavy-to-extreme precipitation. Interestingly, changes in precipitation frequency dominate changes in the amount of precipitation for each intensity level, particularly for the spatial patterns. Although climate models show limited skills in reproducing the magnitudes of these observed changes, they show skills in simulating the sign of the changes. Also, they reasonably reproduce the observed non-uniform patterns of daily precipitation changes, especially for changes in the contributions from different intensity levels to annual total precipitation on average over the whole country. The evaluation of current climate models in simulating daily precipitation changes as a function of precipitation intensity suggests that improvement in the detection and attribution of precipitation changes in China can be gained by dividing daily precipitation into different categories.

The Role of Changbai Mountain in an Extreme Precipitation Event in Liaoning Province, China

Based on the half-hourly Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data product (0.1°), the fifth-generation European Center for Medium-Range Weather Forecasting atmospheric reanalysis dataset (ERA5), sounding data, and the Weather Research and Forecasting Model (WRF-ARW), this study explored the developmental process of a typical extreme precipitation event in Liaoning Province on 2 June 2021. This study focused on the impact of Changbai Mountain on this precipitation process and its corresponding physical mechanisms. The research findings revealed that Changbai Mountain significantly affected the precipitation event in three main aspects: blocking drag, forcing uplift, and leeside convergence. The blocking drag caused by the mountain topography led to an extension in the duration of heavy rainfall. The dynamic lifting and leeside convergence associated with the mountainous terrain also substantially increased the amount of precipitation. Furthermore, the topography hindered the movement of the Bohai Bay cold pool and enhanced the intensity of the cold pool, contributing to the sustained extreme precipitation in Liaoning Province. Lastly, the terrain sensitivity experiment demonstrated that when the height of Changbai Mountain was reduced, the convergence uplift, moisture condensation, and cold pool intensity were weakened, leading to significant changes in precipitation intensity and spatial distribution. These findings further confirm the crucial role of Changbai Mountain in the occurrence and development of local precipitation in Liaoning Province.

Variations in diversity, composition, and species interactions of soil microbial community in response to increased N deposition and precipitation intensity in a temperate grassland

BackgroundGlobal climate change has resulted in precipitation regimes exhibiting an increasing trend in rainfall intensity but a reduction in frequency. In addition, nitrogen (N) deposition occurs simultaneously in arid and semi-arid regions. Microbial biomass, diversity, composition, and species interactions are key determinants of ecological functions. We examined the effects of changes in precipitation intensity and N addition on the soil bacterial and fungal communities in a semi-arid grassland in Inner Mongolia, China.MethodsThe microbial biomass (bacterial PLFAs and fungal PLFAs) was determined through phospholipid fatty acid (PLFA) analysis, and microbial diversity (Shannon index and evenness index) was determined with high-throughput sequencing (16S and ITS). Species interactions were determined using a molecular ecological network analysis. The relationships between microbial community (bacterial community and fungal community) and environmental variables were examined by Mantel tests.ResultsWe found that N addition decreased fungal PLFA under moderate, high, and extreme precipitation intensity treatments and increased fungal community complexity under the high precipitation intensity treatment. Furthermore, N addition increased bacterial diversity under moderate and high precipitation intensity treatments. N addition caused greater environmental stress to the fungal community, which was dominated by deterministic processes.ConclusionsThe effects of N deposition on soil bacterial and fungal communities were altered by precipitation intensity. The changes in soil bacterial and fungal communities were different, implying that composition and functional traits adapt differently to projected global changes at a regional scale.

Moving statistic method (MSM) for quantification of changes in precipitation intensity and dry periods caused by climate change and demonstration on major U.S. cities

Moving statistic method (MSM) for quantification of changes in precipitation intensity and dry periods caused by climate change and demonstration on major U.S. cities

Long-term forecast of flow dynamics of Chirchik basin

Global climate change is undoubtedly one of the main threats to the world community among existing environmental problems. This problem has a particularly severe impact on water resources. Changes in hydrological processes or flows in river basins directly or indirectly affect the dynamics of changes in gross domestic product (GDP) in this region. Chirchik basin provides about 16% of GDP of the republic, and more than 70% of water resources are used for irrigation of crops [1-5]. For this reason, any factors affecting hydrological processes and flow dynamics in the river basin, especially climate change, immediately affect all aspects of life in the region, including the social level of society and economic stability. Assessing the impact of climate differences on river basin flow dynamics is important for ensuring sustainable agricultural productivity for river basins in the future, as well as reducing the ecological and environmental impacts of climate change. This situation calls for urgent and concerted action in several areas: technology, infrastructure, politics, economy, ecology and environmental protection. The article assessed the impact of changes in precipitation intensity on stream dynamics based on the Global Circulation Model (GCM) coordination scenarios, RCP4.5 and RCP8.5 for the years 2030, 2050 and 2070 using the delta approach method. The results of the study provided strong evidence for changes in stream dynamics in the Chirchik basin for the near and far future.

More frequent consecutive extreme precipitation in the dry regions of China

AbstractPrecipitation extremes occurring on consecutive days may be of crucial importance for the formation of extensive and long‐lasting flooding. Changes in such consecutive extreme precipitation (CEP) events between different regions (i.e., dry and wet regions) still remain unclear in China, which may result in different impacts on human livelihoods and ecosystems. Here, the changes in CEP frequency between dry and wet regions of China were studied by utilizing an observation‐based gridded precipitation dataset. We further determined the driving factors of the changes in CEP frequency by separating the effects of precipitation intensification and temporal clustering of daily extreme precipitation. Our results showed that the CEP frequency in dry regions increased faster (9.73%·decade−1) compared with wet regions (1.14%·decade−1) during the last ~60 years. The increasing precipitation intensity primarily (over 90%) resulted in the increases of CEP events. However, changes in the temporal clustering of daily extreme precipitation can benefit the effects of the changes in precipitation intensity in dry regions but can reverse these effects in wet regions. In dry regions, the regression relationship of precipitation intensity and temperature (7.38%·°C−1) was stronger than that in wet regions (3.44%·°C−1), suggesting that the same magnitude of warming would cause greater precipitation intensity and consequently cause more frequent CEP events in dry regions. Therefore, as the intensification of precipitation and the increasing temporal clustering of daily extreme precipitation, more CEP events may increase the flooding risk in dry regions over China with a stronger warming signal.

Deciphering China's Complex Pattern of Summer Precipitation Trends

AbstractObservations show that summer precipitation in China has undergone pronounced changes, resulting in an enigmatic “north‐south drying‐wetting” pattern in eastern China that is of great concern for socio‐economic development. Scientific consensus on the mechanisms that are responsible for this pattern of change has not yet been achieved. We show that this complex pattern of summer total precipitation trends observed in China since the 1960s is overwhelmingly the result of changes in daily precipitation frequency, rather than being the result of changes in precipitation intensity or the frequency of synoptic circulation patterns favorable to precipitation. Changes in precipitation intensity, which are very likely due to anthropogenic greenhouse gas forcing, contribute a relatively homogeneous wetting tendency across the country while changes due to synoptic circulation change are weak. The changes in daily precipitation frequency that drive the observed patterns of change may be due to aerosols, but improved process understanding will be required to resolve that question and enable reliable projections of regional scale precipitation change in China and elsewhere.

Future changes in extreme precipitation in central Asia with 1.5–4°C global warming based on Coupled Model Intercomparison Project Phase 6 simulations

AbstractQuantifying future precipitation changes in central Asia (CA), one of the largest semiarid‐to‐arid regions in the world, is increasingly attracting attention. Extreme precipitation changes in CA have been studied based on global climate models (GCMs) in the Coupled Model Intercomparison Project (CMIP). However, quantitative information regarding projected changes in extreme precipitation in CA under the 1.5–4°C global warming scenario is lacking; the potential advantages of the latest‐generation GCMs (i.e., CMIP6) in characterizing precipitation in CA have not been investigated. Thus, in this study, we evaluated the models' overall performance in reproducing the spatiotemporal pattern of four extreme precipitation indices and further performed an observationally constrained projection of changes in extreme precipitation indices for 1.5–4°C global warming based on a weighted multimodel ensemble. Regarding homologous models, CMIP6 models exhibited limited improvement relative to their earlier versions in CMIP5. We project that extreme precipitation indices in CA will increase approximately linearly as global warming increases, except for the consecutive dry days (CDD) index. The changes in precipitation intensity and accumulation exhibit robust consistency between models, whereas the signal of CDD changes is masked by the noise produced by intermodel uncertainties. The changes in the average annual accumulated and maximum 1‐day precipitation relative to the reference period (1981–2010) in CA are 12.0 and 14.2% at 3°C global warming (similar to late‐century warming projected based on current mitigation policies), respectively. Moreover, we demonstrate the advantage of the weighted scheme over the traditional unweighted scheme for multimodel ensemble projection.

Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.

Robinia pseudoacacia Seedlings Are More Sensitive to Rainfall Frequency Than to Rainfall Intensity

Climate change causes the global redistribution of precipitation, yet little is known about the effects of the changes in precipitation intensity and frequency on the seedlings of wood trees in warm temperate forests. In this study, we focused on the effects of variability in both the intensity and frequency of water supply on the physiological traits, biomass, and growth of an important plantation wood species, Robinia pseudoacacia. In the greenhouse, we exposed R. pseudoacacia seedlings to three rainfall intensity and three rainfall frequency treatments. The results from the 62-day experiment revealed that lower rainfall intensity and frequency significantly reduced the photosynthetic performance, growth, and biomass of the tree seedlings. In lower rainfall intensity and frequency conditions, the seedlings had improved water absorption and utilization by increasing the water use efficiency and root shoot ratio, and reduced water consumption by defoliating the compound leaves of the lower crown. More importantly, we found that R. pseudoacacia seedlings were more sensitive to rainfall frequency than to rainfall intensity. Therefore, our results suggest that increasing the irrigation water, especially irrigation frequency, could better facilitate the survival and growth of R. pseudoacacia seedlings and eventually promote the process of vegetation restoration in the future global climate change context.

Bayesian Deep Learning for Passive Microwave Precipitation Type Detection

Precipitation is one of the primary drivers of earth’s water and energy cycles. While its role in the climate system is generally well-understood, our ability to accurately capture the spatio-temporal distribution of precipitation is limited. Satellite-borne passive microwave (PMW) radiometers and the associated research on their precipitation retrievals provide a highly accurate understanding of global total precipitation. Still, these precipitation retrievals are prone to large systematic errors at all spatial and temporal scales given their inability to accurately relate changes in precipitation intensity to the changes in radiometric signatures caused by variability in cloud system structure. Central to generating estimates of precipitation is proper classification of precipitation type, that is, convective or stratiform. This work presents a study on using a Bayesian deep learning (BDL) to help mitigate this problem by accurately classifying precipitation type and providing uncertainty in the classification. Specifically, it adopts a Bayesian form of Residual Networks (ResNet) architectures to extract the information from PMW observations vectors and identify these structural differences of cloud systems while providing, per pixel, classification uncertainty estimates. Benchmarked ResNet architectures in a deterministic configuration achieve accuracies above 86% on the classification task for precipitation type, while a Bayesian configuration of the same architectures reaches accuracy above 90%. Most importantly, uncertainty estimates are predicted by the Bayesian configuration to accompany each individual output value. This allows end-users to use them to calibrate the output by filtering the least certain predictions. The performance of the classifier, therefore, can be further improved depending on the application needs.

Analysis of convective and stratiform precipitation characteristics in the summers of 2014–2019 over Northwest China based on GPM observations

The research on three-dimension of precipitation is far less than sufficient due to limited satellite observation over Northwest China. In this study, the characteristics of convective and stratiform precipitation, and the relationships between the two types of precipitation and several atmospheric parameters for the summers of 2014–2019 over Northwest China are explored based on GPM observations, ERA5 reanalysis products and Integrated Global Radiosonde Archive dataset. Stratiform precipitation is dominant over Northwest China and four typical regions, i.e., the Tianshan Mountain (region A), Tarim Basin (region B), Qilian Mountain (region C) and eastern part of Northwest China (region D), are selected. The storm top height of convective precipitation is 2–3 km higher than that of stratiform precipitation, and the highest value (near 16 km) occurs in region C and the lowest value (about 10 km) occurs in region B. Moreover, convective and stratiform precipitation profiles over Northwest China are similar to those over East China. Below 4 km, the largest rain rate appears in region A for convective precipitation and in region D for stratiform precipitation, and the maximum latent heat appears at 4–6 km. The peak frequency of convective precipitation occurs in the afternoon among four regions, whereas the diurnal cycles for stratiform precipitation display a bimodal pattern with the two peaks that more likely occur in the morning and late afternoon. Furthermore, the precipitation intensity changes with total column water vapor and follows an approximate quadratic function in four regions. The precipitation conversion rate and CAPE of convective precipitation are evidently larger than those of stratiform precipitation. A pattern of convergence in the lower troposphere and divergence in the upper troposphere appears, which is conducive to the development of precipitation except region B where downdrafts prevail. Additionally, the most significant warming and humidification exhibit in the middle and lower troposphere respectively during precipitation. This study not only displays the features of convective and stratiform precipitation over Northwest China from the perspective of satellite remote sensing, but also the patterns of atmospheric parameters during precipitation would provide a reference for numerical simulation over arid-semiarid regions of China.

Pooling Data Improves Multimodel IDF Estimates over Median-Based IDF Estimates: Analysis over the Susquehanna and Florida

AbstractTraditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.

УМОВИ ФОРМУВАННЯ ПОВЕРХНЕВОГО СТОКУ В МЕЖАХ УРБАНІЗОВАНИХ ТЕРИТОРІЙ ПРИ ВИПАДАННІ ДУЖЕ СИЛЬНИХ ТА НЕБЕЗПЕЧНИХ ОПАДІВ

The research investigates atmospheric precipitation, which according to Ukrainian national regulation has reached the criteria of heavy (≥ 50 mm at ≤ 12 hours) and dangerous (15 – 49 mm at ≤ 12 hours). A total of 98 cases of heavy precipitation (2005 – 2018), and 14 cases of dangerous precipitation (2017 – 2018) were analyzed. The research focuses on the formation conditions and volumes of surface runoff that forms on various types of underlying surface. To obtain a statistically valid classification, a cluster analysis of heavy and dangerous precipitation was carried out. The analysis allowed to distinguish three blocks or clusters, one of which corresponds to the type “mix” of heavy and slight precipitations during the development of frontal stratus clouds with so-called “submerged or flooded” convection, the second cluster corresponds to the type “heavy precipitations”, the third cluster is similar to the first cluster, but is marked by a decrease in the intensity of processes. The degree of connection between the intensity of heavy precipitation and the height of the clouds top was investigated. As a result, it was found that there is an inverse relation between the values. The physical features of the processes of cloud and precipitation formation was considered. The exceptional role of convective clouds in individual frontal massifs and also in the form of “submerged or flooded convection” in the massifs of stratus clouds in the formation of significant volumes of rainwater on the underlying surface was noted. The structure of the temporal changes in precipitation intensity was established: the maximum intensity values, the time of their occurrence, the availability and number of amplification waves and their temporal parameters. On the basis of the obtained indicators, the quantity of the surface runoff for determined processes and for amplification periods were calculated. As a result, it was found that the surface runoff, which formed in the built-up areas, exceeds the surface runoff from the moderately built-up area with almost no artificial pavement by 100 – 300 %.

EFFECT OF DROUGHT STRESS ON YIELD AND QUALITY TRAITS OF COMMON WHEAT DURING GRAIN FILLING STAGE

Wheat (T. aestivum) has a crucial role for human diet especially in developing countries. Changes in precipitation intensity, amounts and patterns restrict wheat growth and productivity under rainfed conditions. Thus, assessment of drought effects during growth stages of wheat on grain yield and quality traits has substantial importance. Grain filling stage, coincides with early spring when the rainfall pattern highly variable, was considered in this study to evaluate effects of drought conditions on yield and quality of 16 wheat genotypes and determine superior varieties. Drought treatment inhibited plant height (5.5 %), 1000 grain weight (9.2 %) and grain yield (17.7 %) while harvest index increased (8.5 %). However, there was no significant effect of drought conditions on grains number spike-1 and spike numbers m-2. Protein content increased (31.6 %) in all genotypes, while the Zeleny sedimentation significantly decreased (8.2 %) with drought treatments during both growing seasons. Cultivars Pandas and Meta had higher grain yield under drought stress in both years whereas Line-28 and Pandas had better quality properties.

Quantifying the Location Error of Precipitation Nowcasts

In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Δt ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Δt. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t − 1 to t (LK-Lin1) and t − 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models.

Assessing the Maximum Possible Water Discharge of the Zeya River on the Zeya HPP Site

The problem of estimating the probable maximum values of the inflow to the Zeya reservoir on the basis of determining maximum probable precipitation and results from simulating the flood runoff formation is considered. The maximum probable daily precipitation amounts were determined by the hydrometeorological method using the data of eight-term station-based meteorological observations of dew point temperature and precipitation amounts. Maximum daily precipitation values were converted to those for the travel time of the flood wave via the channel system (seven days for the Zeya basin) using the transition coefficient obtained by a statistical processing of precipitation amounts of different durations. To calculate the possible maximum flood, a rain graph with 1-day sampling intervals was constructed according to the type of change in precipitation intensity during the maximum flood observed in 2013. Precipitation was recalculated to the runoff in terms of the deterministic model developed by the Hydrologic Engineering Center (USA) with due regard for the formation characteristic of heavy floods in the settings of permafrost. The calculated limiting value of the inflow was refined using the Bayesian method of constructing a probability curve taking into account the upper limit of the distribution and the specified determination error of the desired characteristic. The values obtained for the maximum flow of the Zeya are compared with its estimates available to date.