Precipitation Series Research Articles

Monthly precipitation prediction in Luoyang city based on EEMD-LSTM-ARIMA model.

At present, the method of using coupled models to model different frequency subseries of precipitation series separately for prediction is still lacking in the research of precipitation prediction, thus in this paper, a coupled model based on Ensemble Empirical Mode Decomposition (EEMD), Long Short-Term Memory neural network (LSTM) and Autoregressive Integrated Moving Average (ARIMA) is proposed for month-by-month precipitation prediction. The monthly historical precipitation data of Luoyang City from 1973 to 2021 were used to build the model, and the modal components of different frequencies obtained by EEMD decomposition were divided into high-frequency series part and low-frequency series part using the Permutation Entropy (PE) algorithm, the LSTM model is used to predict the high-frequency sequence part, while the ARIMA model is used to predict the low-frequency sequence part. Monthly precipitation forecasts are obtained by superimposing the results of the two models. Finally, the predictive performance is evaluated using several assessment metrics. The indicators show that the model predictive performance outperforms the EMD-LSTM (Empirical Mode Decomposition), EEMD-LSTM, EEMD-ARIMA combined models and the single models, and the model has high confidence in the prediction results of future precipitation.

Evaluating homogeneity and trends in extreme daily precipitation indices in a semiarid region of Brazil

A regional investigation of precipitation extremes could help the local authorities to develop strategies against potential climatic disasters, especially in regions of large climate vulnerability. In this context, this study focused on the homogeneity and trend analysis of eleven extreme precipitation indices from a daily rainfall dataset of rain gauges of the state of Ceará, located in the north of Northeast Brazil, from 1974 to 2018 at annual and seasonal time scales. The data were first submitted to gap-filling and quality control processes. Homogeneity, trend, and correlation coefficient were performed subsequently. Homogeneity results showed that most of the precipitation series were classified as “useful.” Significant break years in the series agreed well with moderate and very strong El Niño and La Niña events, suggesting a further investigation of this possible connection. Wet and dry day precipitation indices mainly indicated a decrease in the rainfall regime and an increase in dry days, mainly in the central-eastern, northwestern, and southern regions of the Ceará, especially during the annual time scale and the rainy season. The Locally Weighted Scatterplot Smoothing (LOWESS) curve showed changes in almost all series during the 1980s and 1990s, coinciding with the homogeneity breaks and years of severe droughts that strongly hit the region. Correlation coefficients were strong and significant between rainfall total index and the other precipitation indices.

Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps

ABSTRACT This study compared the performance of seven gap-filling methods in daily streamflow and precipitation data and assessed the maximum number of gap days on which the methods perform well. Random (occurring randomly in the data series) and sequential (consecutive days with missing record) gaps were considered. Results show that the type of gaps affects the performance of the methods for gap-filling streamflow and precipitation data. Concerning random gaps, the best methods for streamflow were autoregressive integrate moving average and spline interpolation. For precipitation, the best methods were inverse distance weighting and linear regression (LR). Regarding sequential gaps, LR and multiple regression perform well in gap-filling up to 60 consecutive days in streamflow series. The other methods perform well up to 15 consecutive gap days. For precipitation series, the methods performed well up to seven consecutive missing days. For longer gaps (15, 30, 45 and 60 days), the methods performed poorly.

Investigation of innovative trend approaches (ITA with significance test and IPTA) comparing to the classical trend method of monthly and annual hydrometeorological variables: a case study of Ankara region, Turkey

Abstract The difficulty of monitoring hydrometeorological trends remains relevant because of the importance of climate change. In this study, innovative polygon trend analysis (IPTA), new graphical methods, have recently been presented, Mann-Kendall and innovative trend analysis (ITA) with significance tests are employed at monthly and annual hydrometeorological data for Ankara province in Turkey. Finally, sequential Mann-Kendall (SQ-MK), CUSUM, and standard normal homogeneity test (SNHT) are applied to detect any abrupt changes in annual time series. The results indicate that ITA with significance test, IPTA, and MK capture precipitation trends in 83, 94, and 0.2% of all months (3 stations × 12 months), temperature trends in 86, 75, and 22% of all months, relative humidity trends in 80, 80, and 30% of all months, and evapotranspiration series in 91, 80, and 47% of all months. These findings suggest that the ITA with significance test and IPTA are more sensitive than the MK test, with the precipitation series being the most sensitive and the evapotranspiration series being the least sensitive. SQ-MK, CUSUM, and SNHT tests on station 17664 were successful in detecting a change in annual evapotranspiration in 2005. The annual total evaporation series have been rising since 2005, according to the SQ-MK test.

Assessment of the standard precipitation frequency estimates in the United States

Study regionThe Conterminous United States Study focusThe NOAA Atlases have provided the standard precipitation frequency estimates (PFEs) for over two decades in the United States, but they are losing that status due to climate change. This study evaluates the Atlases compared to new PFEs developed based on the Automated Surface Observing System and Regional Frequency Analysis (ASOS-RFA) as a benchmark and examines a radar-based precipitation product as a data source eligible for developing the post-Atlas. New hydrological insights for the regionThe stationarity assumption of precipitation was highlighted as a significant factor in increasing the uncertainties in the Atlases. The Atlases explicitly tended to underestimate PFEs compared to the ASOS-RFA. The difference between the Atlases and the ASOS-RFA increases as the return period increases and the duration decreases. The age of the Atlases correlated with the difference, suggesting that recent observations diverge from older estimates. The radar-based PFEs are well-matched with the ASOS-RFA at frequent return periods, but the uncertainties increased as the return period increased. This is because the conventional bias correction was limited in improving the annual maximum series of precipitation. Lastly, we need to pay careful attention to an increase in precipitation frequency estimates shorter than 12-hr durations.

Using a Simple Methodology to Assess the Acceleration in Daily Precipitation Extreme Events in the São Paulo Metropolitan Region

This article analyses a near-centennial time series of daily precipitation in the Metropolitan Region of São Paulo, Brazil, in order to quantify the detectable increase in intensity and/or frequency of extreme events. This area is the most populated in the southern hemisphere, and heavy or extreme precipitation events, mainly those related with hydro-meteorological disasters, have important effects on its society. Indexes derived from daily precipitation data through a simple methodological approach are able to quantify changes at decadal and annual time scales. The analysis was carried out for five thresholds, i.e., daily precipitation higher than 50, 60, 70, 80, and 90 mm. The indexes exhibited statistically trends in both precipitation intensity and frequency for all thresholds, indicating significant changes in daily extreme events in the study period.

Extreme Months: Multidimensional Studies in the Carpathian Basin

In addition to the one-dimensional mathematical statistical methods used to study the climate and its possible variations, the study of several elements together is also worthwhile. Here, a combined analysis of precipitation and temperature time series was performed using the norm method based on the probability distribution of the elements. This means, schematically speaking, that each component was transformed into a standard normal distribution so that no element was dominant. The transformed components were sorted into a vector, the inverse of the correlation matrix was determined and the resulting norm was calculated. Where this norm was at the maximum, the extreme vector, in this case the extreme month, was found. In this paper, we presented the results obtained from a joint analysis of the monthly precipitation and temperature time series for the whole territory of Hungary over the period 1871–2020. To do this, multidimensional statistical tests that allowed the detection of climate change were defined. In the present analysis, we restricted ourselves to two-dimensional analyses. The results showed that none of the tests could detect two-dimensional climate change on a spatial average for the months of January, April, July and December, while all the statistical tests used indicated a clear change in the months of March and August. As for the other months, one or two, but not necessarily all tests, showed climate change in two dimensions.

A Process for removing toxic heavy metals to produce the high purity NH4H2PO4 and KH2PO4 from a crude phosphoric acid

The use of chemical fertiliser, in particular a phosphate, in the agricultural sector is an essential in a modern production of crops. However, low grade fertilisers containing toxic heavy metals could be accumulated in soil and introduced into our food chain eventually. For sustainability, it is important to use the high purity fertilisers in the agricultural sector. In this context, we propose a hydrometallurgical process to remove impurities, including toxic heavy metals, to produce the high purity monoammonium phosphate (MAP) and monopotassium phosphate (MKP) from a crude phosphoric acid (approx. 50% P2O2). The proposed process is incorporated with a series of precipitations, ion exchange, activated carbon adsorption, and evaporating to crystallise the final product. All impurities in final products were removed by over 90% and controlled under marketable specifications. As and Cr were removed further below to less than 1 mg/L in the final solution for adjusting the capacity of carbon was identified. Optimised conditions identified were then utilised in 40 L size pilot-scale reactors and produced the high purity MAP and MKP, respectively. The proposed process is expected to contribute for producing the high purity MAP and MKP by relatively simple technique and cost-effective than a conventional solvent extraction process from the crude phosphoric acid. The use of high purity phosphate fertilisers can aid sustainability for the agricultural industry by reducing accumulating the toxic heavy metals in soil in a wide sense.

Thermal regime of the Grigoriev ice cap and the Sary-Tor glacier in the inner Tien Shan, Kyrgyzstan

Abstract. The thermal regime of glaciers and ice caps represents the internal distribution of ice temperatures. An accurate knowledge of the thermal regime of glaciers and ice caps is important to understand their dynamics and response to climate change and to model their evolution. Although the assumption is that most ice masses in the Tien Shan are polythermal, this has not been examined in appropriate detail so far. In this research, we investigate the thermal regime of the Grigoriev ice cap and the Sary-Tor glacier, both located in the inner Tien Shan in Kyrgyzstan, using a 3D higher-order thermomechanical ice flow model. Input data and boundary conditions are inferred from a surface energy mass balance model, a historical air temperature and precipitation series, ice thickness measurements and reconstructions, and digital elevation models. Calibration and validation of the englacial temperatures are performed using historical borehole measurements on the Grigoriev ice cap and radar measurements for the Sary-Tor glacier. The results of this study reveal a polythermal structure of the Sary-Tor glacier and a cold structure of the Grigoriev ice cap. The difference is related to the larger amount of snow (insulation) and refreezing meltwater (release of latent heat) for the Sary-Tor glacier, resulting in higher surface layer temperature, especially in the accumulation area, which is subsequently advected downstream. Further, ice velocities are much lower for the Grigoriev ice cap, with consequent lower horizontal advection rates. A detailed analysis concerning the influence of temperature and precipitation changes at the surface reveals that the thermal structure of both ice bodies is not constant over time, with recent climate change causing increasing ice temperatures in higher areas. The selected ice masses are representative examples of the (inner) Tien Shan glaciers and ice caps. Therefore, our findings and the calibrated parameters can be generalised, allowing improved understanding of the dynamics and future evolution of other glaciers and ice caps in the region.

Wavelet transform-based trend analysis of streamflow and precipitation in Upper Blue Nile River basin

Study regionUpper Blue Nile River Basin, Ethiopia Study focusThis study aims to determine possible trends in monthly, seasonal and annual precipitation and streamflow series. The Discrete Wavelet Transform (DWT) was used to obtain detailed information about the time-frequency conditions of six meteorological and five streamflow stations during 1984–2014. The Mann-Kendall test was also applied to the original and the DWT components to determine which periodicities are dominant in reproducing the observed trends. The combined DWT and Mann-Kendall test has not been explored in the UBNRB context. We used three criteria to determine a suitable wavelet, decomposition level, and signal extension mode. From these criteria, the bias ratio between the wavelet approximation series and the original time series was proposed and explored in this study. New hydrological insights for the regionResults show that almost all the precipitation stations experience an insignificant but increasing trend, whereas the streamflow stations exhibit an increasing trend at a 5% significance level. The combined use of DWT and the Mann-Kendall test show that periodic events up to 4 years are more influential in affecting the observed trends.

An improved estimator of Shannon entropy with applications to systems with memory

We investigate the memory properties of discrete sequences built upon a finite number of states. We find that the block entropy can reliably determine the memory for systems modeled as Markov chains of arbitrary finite order. Further, we provide an entropy estimator that remarkably gives accurate results when correlations are present. To illustrate our findings, we calculate the memory of daily precipitation series at different locations. Our results are in agreement with existing methods being at the same time valid in the undersampled regime and independent of model selection.

Climate variability impacts on runoff projection under quantile mapping bias correction in the support CMIP6: An investigation in Lushi basin of China

In this study, we modelled the XAJ (Xin’anjiang) hydrologic model parameters for the Lushi basin of China, an important tributary of the Yellow River. Time series of daily precipitation, evaporation, and runoff data from 1976 to 1995 were used to calibrate the model, while the data from 1996 to 2000 were used for validation. Thereafter, the future climate variability was projected from 2021 to 2100 for 0.22° grids under CMIP6 CORDEX-East-Asia (Coordinated Regional Climate Downscaling Experiment-East-Asia) RCP2.6 and RCP8.5 climate scenarios. The non-parametric quantile mapping (QM) bias correction method was used to minimize the large differences between climate variability directly derived from the regional circulation model (RCM) and the historical observations, and the results were also compared with the baseline period (1976–2000). Overall, when compared to the baseline case, results showed a decline in the annual precipitation by 7.22 % and 5.01 %, while an increase was observed for the annual evaporation by 2.03 % and 3.58 % under RCP2.6 and RCP8.5 climate scenarios in the 21st century. Annual runoff projection by XAJ model indicate reduction, with 9.3 % and 31.2 % fall for short-term (2021–2040), 31.5 % and 41.2 % fall for mid-term (2051–2070) and 32.3 % and 77.4 % fall for long-term (2081–2100), respectively. The extreme streamflow projects increase during the dry period (November to June) with 76.5 m3/s and 84.6 m3/s under RCP2.6 and RCP8.5 compared to 45.6 m3/s from the baseline period. In contrast, there is a decrease during the wet period (July to October) by 390 m3/s and 237 m3/s compared to 405 m3/s in the baseline data of the same period. Such projections become more pronounced toward the end of the 21st century, and the reduction is likely to continue if future climate projections occur. The hydrological parameters can be applied to adjacent and similar landscape basins for disaster forecasting and early warning. The QM method is effective in bias correction. The findings can also be used for flood and drought mitigation planning and water resources management in the Yellow River Basin.

Historical Trends Associated with Annual Temperature and Precipitation in Aegean Turkey, Where Are We Heading?

The trend analysis of annual temperature (daily average) and total precipitation has been conducted for 14 stations located in the Aegean Region, Turkey. The Sen, Spearman’s rho, and Mann-Kendall test methods are used in the detection of the historical trends in the region. The Pettitt test is also implemented to find the significance of the trend, while the Theil-Sen approach is applied to detect the change point(s) in the time series. Findings of the following study indicate that both precipitation and temperature time series in the selected stations depict statistically significant trends with increasing nature. The rate of increase in precipitation and temperature by the Theil-Sen test is found to be 4.2–7.9 mm/year and 0.20–0.35 °C/decade, respectively. It is also found that the turn points of the temperature trends determined by the Pettitt test occurred in 1998 for all the stations. According to the results, the magnitude of the extreme events would change in the future, which may help in conceptualizing the framework and the resilience of the infrastructures against climate change.

Moisture Transport and Contribution to the Continental Precipitation

Understanding the water cycle change under a warming climate is essential, particularly the ocean to land moisture transport, which affects the precipitation over land areas and influences society and the ecosystem. Using ERA5 data from 1988 to 2020, the time series of moisture transport and the trend across the boundary of each continent, including Eurasia, Africa, North America, South America, Antarctic, Australia, and Greenland, have been investigated. The inflow and outflow sections of the moisture have been identified for each continent. The trends of moisture convergence over Eurasia, Africa, North America, and Antarctic are all positive, with the values of 2.59 ± 3.12, 2.60 ± 3.17, 12.98 ± 2.28, and 0.32 ± 0.47 (in 106 kg/s/decade), respectively, but only the trend over North America is statistically significant at a 0.1 significance level. The moisture convergence trend of −0.59 ± 3.63 (in 106 kg/s/decade) over South America is negative but insignificant. The positive trend of 0.10 ± 0.35 (in 106 kg/s/decade) over Greenland is very weak. The precipitation, evaporation, and moisture convergence are well balanced at middle and low latitudes, but the combination of moisture convergence and evaporation is systematically lower than the precipitation over Antarctic and Greenland. Contributions of evaporation and moisture convergence (or transport) to the continental precipitation vary with the continent, but the moisture convergence dominates the precipitation variability over all continents, and the significant correlation coefficients between the anomaly time series of continental mean moisture convergence and precipitation are higher than 0.8 in all continents.

The role of teleconnection in the occurrence probability of extreme precipitation over China based on extreme value theory

Non-stationarity of extreme climate events has been reported worldwide in recent decades, and traditional stationary analysis methods are no longer sufficient to properly reveal the occurrence probability of climate extremes. Based on the 0.25°C × 0.25°C gridded precipitation data (i.e., CN05.1), stationary and non-stationary models of generalized extreme value (GEV) and generalized Pareto (GP) distributions are adopted to estimate the occurrence probability of extreme precipitation over China during 1961–2018. Low-frequency oscillation (LFO) indices, such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO), are included as time-varying covariates in the non-stationary GEV and GP models. Results illustrate that the occurrence probability of extreme precipitation estimated from the stationary GEV and GP distributions shows a significant increasing trend in northwestern and southeastern China, and the opposite trend in southwestern, central, and northeastern China. In comparison with stationary model, the fitness of extreme precipitation series is improved for both the GEV and GP distributions if these LFO indices are used as time-varying covariates. Positive ENSO, IOD and PDO tend to cause negative anomalies in the occurrence probability of extreme precipitation in northeastern China and Tibet Plateau, and positive anomalies in southern China. Positive NAO and SAM phases mainly tend to cause positive anomalies in southern China. The circulation patterns of extreme precipitation anomalies associated with these LFO indices are discussed from aspects of precipitable water, vertical integrated moisture transport, 500-hPa geopotential height and 850-hPa wind field.

Evaluation of change factor methods in downscaling extreme precipitation over India

Flood is one of the most prevalent natural disasters, and extreme precipitation events are the principal cause of most flooding. Understanding climate change impacts on extreme precipitation is vital for sustainable infrastructure development and water resources management. There are different methods to downscale precipitation to local scale from the Global Climate Models’ simulations available at coarse resolution. The change factor (CF) method is one of those widely used methods for downscaling extreme precipitation. However, there are many variations in the CF method, and no study is available reporting the best CF method for downscaling extreme precipitation until today. Consequently, this study aims to evaluate different variants of four change factor methods, namely Additive Constant Scaling (ACS), Additive Daily Scaling (ADS), Multiplicative Constant Scaling (MCS) and Multiplicative Daily Scaling (MDS), for simulating extreme precipitation over India. Further, the optimum size of the baseline period for all four change factor methods and the optimum number of change factors for the daily scaling method are also evaluated. A total of 80 variants of four change factor methods, namely ACS, ADS, MCS and MDS, are formulated by varying the size of the baseline period and the number of change factors in the daily scaling method. Change factor methods are evaluated in terms of their capability in downscaling the Annual Maximum (AM) precipitation series and four extreme precipitation series extracted with Peaks Over Threshold (POT) method. The results indicate that the daily scaling methods (ADS and MDS) are the best change factor method for more than 75% (3713 out of 4948) of IMD grids for downscaling extreme precipitation. It is also observed that the variants of the daily scaling method having 100 change factors are alone the best change factor method for 35% (1708 out of 4948) of IMD grids for downscaling extreme precipitation. However, change factor methods having a smaller number of change factors (including constant scaling variants) are consistent compared to the variants of the daily scaling method having 100 change factors.

New modelling approach to optimize rainwater harvesting system for non-potable uses and groundwater recharge: A case study from Israel

• A rainwater harvesting system from a building in Israel was used as a case study. • Precipitation, runoff coefficient and non-potable consumption were measured. • Measured data used to model and assess the efficiency of the system. • Results are stochastic, allowing to assess variability of the system performance. Countries with dry climates must optimize the use of natural water sources. Rainwater Harvesting (RWH) is a viable water source for non-potable uses. Besides, rainwater can also be used for managed aquifer recharge (MAR). This work discusses the combined use of RWH for toilet flushing and MAR. A mass-balance simulation was used to estimate efficiency of the combined system in a building in Israel equipped with a RWH system used for MAR only. This enabled the use of actual water consumption patterns and the development of a runoff function using rainfall and runoff measurements. By the novel use and the integration of measured dynamic data into the model, the results are stochastic and the variability and uncertainty of the performance of the RWH system could be assessed. Non-potable water savings were ∼20% for the expected cost-effective tank size, and in this case ∼40% of the rainwater was used for MAR. The stochastic approach of the combined system proved the importance of the non-potable consumption value as well as precipitation series. Finally, it is shown that the potable water savings from using rainwater for toilet flushing would represent an annual CO 2 emissions reduction of 267 kg.

Stochastic Downscaling of Hourly Precipitation Series From Climate Change Projections

AbstractStochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for nonstationary conditions informed by the global climate model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly pressure change events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multivariable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100‐year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast United States. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the Northeast United States. This SPG provides physically plausible weather ensembles for water resource studies involving climate change.

Precipitation variation reconstructed based on tree-ring width data for the past 399 years in the eastern Yinshan Mountains, China

Based on the tree-ring increment cores of Pinus tabuliformis collected from the eastern Yinshan Mountains, the tree-ring width chronology was developed. The correlation coefficients were calculated between the chronology and monthly mean temperature and monthly precipitation during the instrumental period of AD 1952-2007. The results showed that the highest correlation was found between the total precipitation from September of previous year to June of current year and the chronology (r=0.73, n=56, P<0.01). Based on the correlation, the September of previous year to June of current year total precipitation variation was reconstructed in the eastern Yin-shan Mountains during the past 399 years (AD 1619-2017). The reconstruction explained 54.9% of the variation in the total precipitation from September of previous year to June of current year for the calibration period (AD 1952-2007). Both the 'leave-one-out' cross validation and split-period validation showed that the model was relatively robust, with sufficient skills of estimation and high reliability. At the decadal scale, there were four wet periods (AD 1619-1663, AD 1705-1711, AD 1945-1963, and AD 1979-2017)) and four dry periods (AD 1734-1767, AD 1786-1814, AD 1839-1867, and AD 1888-1942) in the past 399 years. Among those periods, the AD 1979-2017 was the wettest period, and AD 1888-1942 was the longest dry duration with the driest period at the late 1920s. Results of power spectral analysis revealed cyclic fluctuations of precipitation series on 2-7 years and 125 years. In addition, the comparison with other reconstructions and spatial correlation analysis indicated that the reconstructed precipitation series well represented regional scale precipitation variation.

The Age of Evapotranspiration: Lower‐Bound Constraints From Distributed Water Fluxes Across the Continental United States

AbstractUnlike streamflow, which can be sampled in aggregate at the catchment outlet, evapotranspiration (ET) is spatially dispersed, challenging large‐scale age estimation. Here, we introduce an approach for constraining the age of ET via mass balance and present the minimum flux‐weighted age of ET across the continental United States using distributed, publicly available water flux data sets. The lower‐bound constraint on ET age can be calculated by assuming that ET is preferentially sourced from the most recent precipitation through a last‐in, first‐out algorithm. From 2012 to 2017, ET was at least several months old across large areas of the western continental United States, including in Mediterranean and (semi‐)arid climate zones and shrub and evergreen needleleaf plant communities. The primary limitation of this approach is that it provides only a minimum flux‐weighted average age to satisfy the mass balance of outgoing fluxes; true ET fluxes are composed of distributions of ages and may be composed of much older water. The primary advantage of the approach is that flux time series of precipitation and ET are sufficient to constrain ET age, and model parameterization is unnecessary. ET ages can be used to validate tracer‐aided and modeling approaches and inform studies of biogeochemistry, water‐rock interactions, and plant water sourcing under drought.