Daily Precipitation Amounts Research Articles

Recent Precipitation Trends and Floods in the Colombian Andes

This study aims to identify spatial and temporal precipitation trends by analyzing eight extreme climate indices of rainfall in the High Basin of the Cauca River in Southwestern Colombia from 1970 to 2013. The relation between historical floods and El Niño Southern Oscillation (ENSO) is also analyzed. Results indicate that in general, the reduction of precipitation, especially in the center of the basin with negative annual and seasonal trends in intensity indices, namely, the annual maximum 1-day precipitation amount (RX1day) and annual maximum 5-day precipitation amount (RX5day). Sixty-four percentage of the stations exhibit an increasing trend in September–October–November in the consecutive dry days. In December–January–February interval, positive trends in most of the stations is noted for total precipitation and for the number of wet days with rainfall greater than or equal to 1 mm. The findings also show that sea surface temperature (SST) in the equatorial Pacific is statistically correlated (r) with indices of extreme precipitation (r ≥ −0.40). However, the effect of ENSO is evident with a time lag of 2–3 months. These results are relevant for forecasting floods on a regional scale, since changes in SST of the equatorial Pacific may take place 2–3 months ahead of the basin inundation. Our results contribute to the understanding of extreme rainfall events, hydrological hazard forecasts and climate variability in the Colombian Andes.

Analysis of Precipitation and Temperature Extremes over the Muda River Basin, Malaysia

Trends in precipitation and temperature extremes of the Muda River Basin (MRB) in north-western Peninsular Malaysia were analyzed from 1985 to 2015. Daily climate data from eight stations that passed high quality data control and four homogeneity tests (standard normal homogeneity test, Pettitt test, Buishand range test, and von Neumann ratio test) were used to calculate 22 Expert Team on Climate Change Detection and Indices (ETCCDI) extreme indices. Non-parametric Mann–Kendall, modified Mann–Kendall and Sens’ slope tests were applied to detect the trend and magnitude changes of the climate extremes. Overall, the results indicate that monthly precipitation tended to increase significantly in January (17.01 mm/decade) and December (23.23 mm/decade), but decrease significantly in May (26.21 mm/decade), at a 95% significance level. Monthly precipitation tended to increase in the northeast monsoon, but decrease in the southwest monsoon. Mann–Kendall test detected insignificant trends in most of the annual climate extremes, except the extremely wet days (R99p), mean of maximum temperature (TXmean), mean of minimum temperature (TNmean), cool days (TX10p), cool nights (TN10p), warm days (TX90p) and warm nights (TN90p) indices. The number of heavy (R10mm), very heavy (R20mm), and violent (R50mm) precipitation days changed at magnitudes of 0~2.73, −2.14~3.33, and −1.67~1.29 days/decade, respectively. Meanwhile, the maximum 1-day (Rx1d) and 5-day (Rx5d) precipitation amount indices changed from −10.18 to 3.88 mm/decade and −21.09 to 24.69 mm/decade, respectively. At the Ampangan Muda station, TNmean (0.32 °C/decade) increased at a higher rate compared to TXmean (0.22 °C/decade). The number of the cold days and nights tended to decrease, while an opposite trend was found in the warmer days and nights.

Nature of extreme precipitation over Ukraine in the 21st century

The article examines the state of precipitation over the territory of Ukraine over recent decades. Through the example of central months of the seasons differences in monthly and average daily precipitation amounts for the period of 2000-2014 are shown. Within the most territory of Ukraine summer precipitation is almost twice as high as spring and autumn one. During all seasons the greatest amount of precipitation is observed in the Carpathian region. Distribution of average long-term precipitation values over the rest of the territory coincides in spring, summer and autumn: the highest precipitation values are observed in the western and north-western parts and decrease to the south-east.
 The article studies a yearly precipitation rate at low-land and mountain meteorological stations. It proposes to separate criteria of precipitation extremality depending on the regions. All extreme daily precipitation can be divided into the following categories: > 20-30 mm / day, > 30-50 mm / day, > 50 mm / day. Each category of extreme precipitation has its a certain economic risk, but the third class can cause not only economic risks, but also risks associated with human life and activities. The distinct feature of the present-day precipitation consists in redistribution of precipitation in the middle of the months, when a daily precipitation rate increases together with intervals between heavy rains.
 In order to analyze the changes of precipitation regime, the approach of dividing the rates of monthly precipitation amount by the rates of extreme and non-extreme precipitation is proposed. A comparative analysis of daily precipitation in different seasons and over different climatic periods was also carried out. The article studies the proportion of daily precipitation of up to 15 mm and the one exceeding 15 mm forming a part of monthly rates of precipitation over the territory of Ukraine. In January, rainfalls exceeding 15 mm make up from 5-10 % of the total amount of monthly precipitation, except the Carpathian region and the southwestern regions of Ukraine where those exceed 20-25 %. In spring, the amount of rainfalls increases and its percentage of the monthly precipitation amount is around 20 % over most of the regions. Until summer, the amount of rainfalls increases and in July its percentage is 50-70 %. Until autumn, the amount of those starts decreasing, however, the percentage of rainfalls is almost twice as high as in spring, and for most of the regions it is about 30-40 %. Such breakdown of the monthly precipitation rates into two components allows determination during a period in question of precipitation amounts we have each month.
 The maximum daily precipitation amounts serve as an important indicator of the precipitation regime which shows the potential danger from extreme precipitation. For different regions the threshold values of the upper limit of rainfalls taken as a maximum daily value for the period of 2000-2014 differ. In winter and spring time, the limit of rainfalls amount per day usually hits 20-30 mm for the most territory of the country. At the same time there are certain areas where the limit values of the daily rainfalls rate reach 40-50 mm. The most significant rainfalls are observed in summer. Despite the fact that the territory of such rainfalls is quite patchy, nevertheless, those areas where precipitation rate over one day may reach 70 mm are the most vulnerable and have high risks for human life and activities. In autumn, the threshold values are 30-40 mm.
 The breakdown of the rates of monthly precipitation amount into extreme and non-extreme ones allows determination in future of whether the precipitation regime changes because of extreme or non-extreme values. Also, in the long run, a comparative analysis of the rates of showers and weak rainfalls in the late 20th and early 21st centuries can be carried out and a tendency of precipitation regime seasonal change over the next decade can be obtained which will help us to identify vulnerable regions suffering from extreme precipitation rates.

Projection in Extreme Climate Events and uncertainty analysis in the Source Area of the Yellow River for the Next Three Decades

The spatial and temporal characteristics of extreme temperature and precipitation events were studied by using extreme climate indices based on the daily outputs of five general climate models (GCMs), including maximum temperature, minimum temperature and precipitation, in the period of 2021-2050 under the representative concentration pathway 4.5 (RCP4.5) scenario. The results show that compared with the period of 1971-2000, Warmest day and Warm days (the warm event) could increase by 1.4°C and 12.5d, respectively, and Frost days and Cool nights (the cold event) could decrease by 24.5d and 6.8d, respectively. The coldest night could increase by 2.1°C, the diurnal temperature range could decrease by 0.1°C. The precipitation frequency and intensity indices (Number of heavy precipitation days, Simple daily intensity indices, Max 1-day precipitation amount and Max 5-day precipitation amount) could increase by 1.1d, 3.1%, 8.4% and 6.3%, respectively. The very wet days could increase by 24.6% and the consecutive dry days could decrease by 12.3d. But, the spatial distributions of changes in extreme climate events are uneven. Although projected changes are still uncertain to some extent, the trends among different GCMs show a good consistency.

Anomalous holiday precipitation over southern China

Abstract. The Chinese Spring Festival (CSF, also known as the Chinese New Year or Lunar New Year) is the most important festival in China. Lunar New Year's Day (LNYD) is the first day of the Lunar New Year. Traditionally, the CSF holiday begins a couple of days before LNYD and ends on lantern day, lasting for approximately 2 weeks. In this paper, based on the long-term station observations from 1979 to 2012, the precipitation during the holiday over southern China (108–123° E and 21–33° N, 155 stations) tends to be lower than that before and after the holiday. The mean precipitation frequency anomaly from the fourth day to the sixth day after LNYD (i.e., days [+4, +6]) decreases by −7.4 %. Simultaneously, the daily precipitation amount experiences a reduction of −0.62 mm day−1 during days [+2, +5]. The holiday precipitation anomalies are strongly linked to the anomalies of relative humidity (ΔRH) and cloud cover. The station observations of the ΔRH show an evident decrease from day +2 to day +7, and a minimum appears on days [+4, +6], with a mean of −3.9 %. The ΔRH vertical profile displays significant drying below approximately 800 hPa. Between 800 and 1000 hPa, the mean ΔRH is −3.9 %. The observed station daytime low cloud cover (LCC) evidently decreases by −6.1 % during days [+4, +6]. Meanwhile, the ERA-Interim daily LCC also shows a comparable reduction of −5.0 %. The anomalous relative humidity is mainly caused by the decreased water vapor in the lower-middle troposphere. Evident negative specific humidity anomalies persist from day −3 to day +7 in the station observations. The average specific humidity anomaly for days [+4, +6] is −0.73 g kg−1. When the precipitation days are excluded, the anomaly remains significant at −0.46 g kg−1. A significant water vapor deficit is observed in the lower troposphere below 700 hPa. Between 800 and 1000 hPa, the mean specific humidity drops by −0.70 g kg−1. This drier lower-middle troposphere is due to anomalous northerly winds, which are closely related to the cyclonic circulation anomaly over the northwestern Pacific. The time-lag correlation demonstrates that approximately 1 week after a lower temperature occurs over eastern China, a stronger cyclone is observed over the western Pacific. The possible mechanism needs further clarification through elaborate observation and numerical modeling.

Assessing the Use of Satellite-Based Estimates and High-Resolution Precipitation Datasets for the Study of Extreme Precipitation Events over the Iberian Peninsula

An assessment of daily accumulated precipitation during extreme precipitation events (EPEs) occurring over the period 2000–2008 in the Iberian Peninsula (IP) is presented. Different sources for precipitation data, namely ERA-Interim and ERA5 reanalysis by the European Centre for Medium-Range Weather Forecast (ECMWF) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), both in near-real-time and post-real-time releases, are compared with the best ground-based high-resolution (0.2° × 0.2°) gridded precipitation dataset available for the IP (IB02). In this study, accuracy metrics are analysed for different quartiles of daily precipitation amounts, and additional insights are provided for a subset of EPEs extracted from an objective ranking of extreme precipitation during the extended winter period (October to March) over the IP. Results show that both reanalysis and multi-satellite datasets overestimate (underestimate) daily precipitation sums for the least (most) extreme events over the IP. In addition, it is shown that the TRMM TMPA precipitation estimates from the near-real-time product may be considered for EPEs assessment over these latitudes. Finally, it is found that the new ERA5 reanalysis accounts for large improvements over ERA-Interim and it also outperforms the satellite-based datasets.

A Stochastic Precipitation Generator Conditioned by a Climate Index

AbstractThis work presents a stochastic daily precipitation generator that incorporates a climate index to reflect the associated, seasonally varying, influence on simulated precipitation statistics. The weather generator is based on a first-order, two-state Markov chain to simulate the occurrence of daily precipitation and a gamma distribution to compute the nonzero daily precipitation amounts. Therefore, it has four parameters that are, in turn, allowed to vary daily following an autoregressive linear model in Gaussian space that simulates the parameters’ deviations from their climatological seasonal cycle. This model is forced by the independently predicted evolution of a climate index and captures how the model parameters and, therefore, precipitation are gradually shifted by the associated climate signal. In this case, the Niño-3.4 index is used to account for the influence of the El Niño–Southern Oscillation (ENSO) phenomenon on precipitation in Uruguay. However, the methodology is general and could be readily transferable to indices of other climate modes or downscaling algorithms for seasonal climate prediction. The results show that the proposed methodology successfully captures the ENSO signal on precipitation, including its seasonality. In doing so, it greatly reduces the underestimation of the seasonal and interannual precipitation variability, a well-known limitation of standard weather generators termed the “overdispersion” phenomenon. This work opens interesting opportunities for the application of seasonal climate forecasts in several process-based models (e.g., crop, hydrological, electric power system, water resources), which may be used to inform the decision-making and planning processes to manage climate-related risks.

The role of anthropogenic aerosols in future precipitation extremes over the Asian Monsoon Region

The role of anthropogenic aerosols in future projections (up to 2100) of summertime precipitation and precipitation extremes over the Asian monsoon region is investigated, by comparing two sets of the Community Earth System Model (CESM1) large ensemble simulations under the Representative Concentration Pathway 8.5 scenario (RCP8.5) and the corresponding scenario with aerosol fixed at 2005 levels (RCP8.5_FixA). The model is verified to be performing well in capturing present-day (1986–2005) climate and precipitation extremes. Our results suggest that the Asian monsoon region would become progressively warmer and wetter in the future under RCP8.5, while precipitation extremes will be significantly aggravated due to anthropogenic aerosol mitigation, particularly over East Asia. Specifically, aerosol reductions are found to shift the distribution of precipitation mean and extremes to larger values. For example, aerosol reductions would result in an increased likelihood of extreme precipitation (e.g. the maximum consecutive 5-day precipitation amount) and related disasters. Sensitivities of changes in precipitation mean and extremes to local warming from aerosol reductions are much larger than that from greenhouse gas increases. This is particularly important over East Asia in accordance with larger magnitudes of aerosol reductions compared to South Asia. Finally, by investigating the response of the climate system to aerosol changes, our findings demonstrate that aerosol induced precipitation changes would be dominated by aerosol–radiation–cloud forcing over northern East Asia and aerosol forcing induced large-scale circulation anomalies over southern East and South Asia.

Detection of future changes in trends and scaling exponents in extreme short-term rainfall at selected stations in Slovakia

Abstract This paper analyses projected changes in short-term rainfall events during the warm season (April – October) in an ensemble of 30 regional climate model (RCM) simulations. The analysis of trend changes and changes in scaling exponents was done for the Hurbanovo, Bratislava, Oravská Lesná, and Myjava stations in Slovakia. The characteristics of maximum rainfall events were analysed for two scenario periods, one past and one future (1960–2000 and 2070–2100) and compared to the characteristics of the actual observed events. The main findings from the analysis show that 60-min short-term events for most of the RCM simulations will either increase or remain constant. On the other hand, the depths and intensities of daily events are projected to increase significantly; in some cases they were found to be ten times larger. Trends in future events at the Hurbanovo station were found to be insignificant. In the other stations positive trends in future rainfall events prevail, except for daily rainfall at the Myjava station, which shows a negative trend. Using results from the selected simulations, the scaling exponents estimated are on average lower than the exponents of the data observed. On the other hand, due to the higher daily precipitation amounts in the future seen to all the scenarios, the downscaled values of short-term rainfall at all the stations analysed might be considerably higher in the future horizons, which could subsequently affect future design rainfall values for engineering designs.

Assessing the Robustness of Future Extreme Precipitation Intensification in the CMIP5 Ensemble

AbstractA warming climate is expected to intensify extreme precipitation, and climate models project a general intensification of annual extreme precipitation in most regions of the globe throughout the twenty-first century. We investigate the robustness of this future intensification over land across different models, regions, and seasons and evaluate the role of model interdependencies in the CMIP5 ensemble. Strong similarities in extreme precipitation changes are found between models that share atmospheric physics, turning an ensemble of 27 models into around 14 projections. We find that future annual extreme precipitation intensity increases in the majority of models and in the majority of land grid cells, from the driest to the wettest regions, as defined by each model’s precipitation climatology. The intermodel spread is generally larger over wet than over dry regions, smaller in the dry season compared to the wet season and at the annual scale, and largely reduced in extratropical compared to tropical regions and at the global scale. For each model, the future increase in annual and seasonal maximum daily precipitation amounts exceeds the range of simulated internal variability in the majority of land grid cells. At both annual and seasonal scales, however, there are a few regions where the change is still within the background climate noise, but their size and location differ between models. In extratropical regions, the signal-to-noise ratio of projected changes in extreme precipitation is particularly robust across models because of a similar change and background climate noise, whereas projected changes are less robust in the tropics.

Computation of rainfall erosivity from daily precipitation amounts

Rainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases.

Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity

AbstractParties to the United Nations Framework Convention on Climate Change have agreed to hold the “increase in global average temperature to well below 2°C above preindustrial levels and to pursue efforts to limit the temperature increase to 1.5°C.” Comparison of the costs and benefits for different warming limits requires an understanding of how risks vary between warming limits. As changes in risk are often associated with changes in exposure due to projected changes in local or regional climate extremes, we analyze differences in the risks of extreme daily temperatures and extreme daily precipitation amounts under different warming limits. We show that global warming of 2°C would result in substantially larger changes in the probabilities of the extreme events than global warming of 1.5°C. For example, over the global land area, the probability of a warm extreme that occurs once every 20 years on average in the current climate is projected to increase 130% and 340% at the 1.5°C and 2.0°C warming levels, respectively (median values). Moreover, the relative changes in probability are larger for rarer, more extreme events, implying that risk assessments need to carefully consider the extreme event thresholds at which vulnerabilities occur.

Assessment of satellite-based precipitation estimates over Paraguay

Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.

Temporal and Spatial Variation in Precipitation Seasonality for Kansas

A considerable amount of research work on climate change is being done to examine what has happened in the recorded past and what might happen throughout the remainder of the 21st Century. While the data for trends in the temperature record tend to be more straightforward, what has and will happen with precipitation change is less clear. However, there is the suggestion that wet places will get wetter and dry places with become more arid.Research by others suggests that extreme daily precipitation amounts in Kansas are trending upward. This research addresses the observational record of change in precipitation seasonality, a little-studied aspect of global climate variability and the related impact on moisture delivery. Monthly precipitation data were summed to provide seasonal totals and seasonal percentages of the annual total were calculated. Using data for the nine Climate Divisions within Kansas for the period from 1895–2014, the results from this analysis indicate that much of Kansas has a winter-dry climate. Over the period of record, there has been a tendency for the spring months of March, April, and May to gain a greater share of the annual total. Geographic variation across the state indicates a west-to-east gradient with greater seasonality in the west and a greater increase in spring and summer precipitation in eastern Kansas.

Nonlinear response of precipitation to climate indices using a non‐stationary Poisson‐generalized Pareto model: case study of southeastern Canada

ABSTRACTQuantile estimates are generally interpreted in association with the return period concept in practical engineering. To do so with the peaks‐over‐threshold (POT) approach, combined Poisson‐generalized Pareto distributions (referred to as PD‐GPD model) must be considered. In this article, we evaluate the incorporation of non‐stationarity in the generalized Pareto distribution (GPD) and the Poisson distribution (PD) using, respectively, the smoothing‐based B‐spline functions and the logarithmic link function. Two models are proposed, a stationary PD combined to a non‐stationary GPD (referred to as PD0‐GPD1) and a combined non‐stationary PD and GPD (referred to as PD1‐GPD1). The teleconnections between hydro‐climatological variables and a number of large‐scale climate patterns allow using these climate indices as covariates in the development of non‐stationary extreme value models. The case study is made with daily precipitation amount time series from southeastern Canada and two climatic covariates, the Arctic Oscillation (AO) and the Pacific North American (PNA) indices. A comparison of PD0‐GPD1 and PD1‐GPD1 models showed that the incorporation of non‐stationarity in both POT models instead of solely in the GPD has an effect on the estimated quantiles. The use of the B‐spline function as link function between the GPD parameters and the considered climatic covariates provided flexible non‐stationary PD‐GPD models. Indeed, linear and nonlinear conditional quantiles are observed at various stations in the case study, opening an interesting perspective for further research on the physical mechanism behind these simple and complex interactions.

Wieloletnie tendencje warunków klimatycznych w Szymbarku na podstawie wybranych wskaźników = Long-term climatic trends in Szymbark (Poland), based on selected indicators

The aim of this study has been to analyse long-term (1971–2015) changes in climatic conditions in the foothills and Beskid region of the Western Carpathian Mountains (South-Eastern Poland), on the basis of selected geoindicators calculated by reference to average annual and monthly air temperatures and precipitation. Climatic conditions were analysed for the Szymbark Research Station of the Institute of Geography and Spatial Organization of the Polish Academy of Sciences (IGSO PAS). Average daily air temperature and daily amounts of precipitation were referred to. The average annual temperature over the 45 years (1971–2015) was 8.1°C. The annual temperature ranged from 6.3°C (1980) to 9.9°C (2014). In the multi-year period analysed, there was an increase in the average annual temperature of about 0.4°C/10 years, which testifies to an intensification of continental climatic characteristics, confirming the course of and trends of two geoindicators of climate, i.e. oceanism after Marsz (Oc) and continentalism after Gorczynski (KG). This increased average annual temperature has the effect of shifting the onset of the thermal pre-winter period (5-0° C) to the end of the year, while also increasing the length of the growing season, by an average of 5 days/10 years. The multi-year period analysed has also witnessed a decrease in the number of cold months. The average annual precipitation was 834 mm (1971–2015). This contrasts with the maximum amount (of 1164 mm) noted in 1974, as well as the minimum (of 535 mm) recorded in 1982. Overall, annual rainfall increased, by 16.2 mm/10 years. However, the rainfall geoindicator (LAN) showed a downward trend due to the increased the average annual air temperature. Equally, the pluviometric geoindicator P showed an increased trend influenced by ever-greater annual amplitude of temperature. Based on the classifications of monthly temperature and precipitation, it was find an increased trend of warm and wet months, as associated with a reduced incidence of cold and dry months. The values of indicators calculated for the Szymbark Research Station in 1971–2015 are similar to those recorded in other mountainous areas in Europe.

Rainfall erosivity and extreme precipitation in the Netherlands

In order to assess the rainfall erosivity of the Netherlands, several parameters which describe distribution, concentration, and variability of precipitation were used (the annual amount of precipitation, the precipitation concentration index and the modified Fournier index), as well as eleven extreme precipitation indices (maximum1-day precipitation amount, maximum 5-day precipitation amount, simple daily intensity index, number of heavy precipitation days, number of very heavy precipitation days, number of days above 25 mm, consecutive dry days, consecutive wet days, very wet days, extremely wet days, and annual total wet-day precipitation). The precipitation data for calculating the above mentioned parameters is obtained from the Royal Netherlands Meteorological Institute for the period 1957–2016. Based on statistical analysis and the calculated values, the results have been presented with the Geographic Information System (GIS) to point out the most vulnerable parts of the Netherlands with regard to pluvial erosion. This study presents the first results of combined rainfall erosivity and extreme precipitation indices for the investigated area. Trend analysis implies a shift from being largely in the low erosivity class to being completely in the moderate erosivity class in the future, thus indicating an increase in rainfall erosivity. Furthermore, the observed precipitation extremes suggest that both the amount and the intensity of precipitation are increasing. The results of this study suggest that the climate conditions in the Netherlands are changing, and that this change might have a negative influence on the rainfall erosivity of the country.

Сравнительный анализ гидрометеорологических наблюдений Росгидромета и автономного измерительного комплекса «Майма» в бассейне р. Майма (Горный Алтай)

The paper contains the comparative analysis results of the rows of hydro/meteorological observations over 2017 in the Mayma River basin according to the Rosgidromet data (a hydro/post in the village of Mayma and a meteorological station in the village of Kyzyl-Ozek) and an autonomous gauging system (AK «Mayma») developed in Russian Academy of Sciences Siberian Branch Institute for Climatic and Ecological Systems Monitoring. We have found that in spite of the distance between the observation points («Mayma» is 4 km from the Kyzyl-Ozek meteorological station and 22 km from the Mayma village hydro/post) dynamics of changes of the Mayma River water level, air temperature and precipitation amount according to Rosgidromet data and the AK «Mayma» data agrees. We established a close correlation between rows of daily observations of the water level and air temperature (correlation indices 0.996 and 0.929, respectively). Correlation between observation rows of daily precipitation amount is weaker (correlation index 0.713), this is caused by spatial variations of precipitation distribution. Based on the conducted investigation results, we have made a conclusion about representativeness of the AK «Mayma» observation data. Reliability of observations, autonomous character of operations and possibility to transmit the observation operative data over some distance make «Mayma» gauging system a suitable instrument for the Mayma River basin hydro/meteorological status monitoring. It is possible to use the obtained hydro/meteorological information to forecast the river level regime and to provide operative data for executive bodies, units of Ministry of Emergencies and the population.

Statistical analysis of daily precipitation in the Bydgoszcz region

The aim of the research has been to provide a statistical analysis of precipitation in the Bydgoszcz region based on the results of the measurements taken at the Experiment Station of the UTP University of Science and Technology in Bydgoszcz, located at Mochle, about 20 km away from the city centre. The paper analyses the daily total precipitation throughout 43 years (1971—2013). The analysis demonstrated a high dependence of the indicators studied on the month, confirming the annual pattern typical for the transitional climate of the temperate zone. In general, it shows an advantage of the amount and variation, and less considerably — the daily precipitation frequency in summer months, as compared with the winter months. The distribution of the probability of the daily precipitation amount for each month turned out to be compliant with gamma distribution, which allows for a potential variation in the future.

Adjustment of a weather generator to represent actual rain erosivity in the northern Black Forest – Germany

To estimate the impact of highly erosive precipitation on existing and planned forest infrastructure we deem the Forest Service WEPP Interfaces, based on the Water Erosion Prediction Project (WEPP), feasible.As a first step towards testing WEPP and especially the implemented weather generator CLIGEN for conditions in Germany we evaluated the application of CLIGEN to calculate rain erosivities from generated time series. CLIGEN parameters were taken from time series of up to 17years with 10-minute resolution from three sites in the Northern Black Forest, southwestern Germany. We assessed a rain kinetic energy function for this region from field measurements with a laser disdrometer to compare CLIGEN performance using the local function to using common kinetic energy functions.We showed that running CLIGEN with unaltered input parameters is not suitable to model climatic conditions and erosivity indices in the Northern Black Forest. R-factors from unaltered model runs deviated extremely from observed R-factors, resulting in just one third of observed values. Model performance and parameter uncertainties do not benefit much from the use of a site specific kinetic energy function. Differences in model errors and sensitivities compared to a well-established kinetic energy function remain negligible. However, model output was improved by empirical calibration of input data. Virtual best parameter sets for best model results could be identified.To reproduce observed rain erosivities the input parameters of CLIGEN have to be manipulated to model a precipitation regime where daily precipitation amounts and maximum precipitation intensities are higher at a lower number of rainy days.We also identified the input parameters to which the model is most sensitive to when manipulated, i.e. precipitation amount and frequency, and maximum peak precipitation. These parameters are especially important when implementing future climate change scenarios.