Assessment of future precipitation indices in the Shikoku region using a statistical downscaling model

In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0–15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios.     

Evaluation of multiple stochastic rainfall generators in diverse climatic regions

Long term synthetic precipitation data are useful for water resources planning and management. Commonly stochastic weather generator (SWG) models are useful to produce synthetic time series of unlimited length of weather data based on the statistical characteristics of observed weather at a given location. However, it is difficult to find a single model which works best for all weather (climate) patterns. The objective of this study is to evaluate five different SWG models namely CLIGEN, ClimGen, LARS-WG, RainSim and WeatherMan to generate precipitation at three diverse climatic regions: a Mediterranean climate of western USA, temperate climate of eastern Australia and tropical monsoon region in northern Vietnam. The performance of SWG models to generate precipitation characteristics (i.e., precipitation occurrence; wet and dry spell; and precipitation intensity on wet days) varies between three selected climatic regimes. It was observed that the second order Markov chain (ClimGen and WeatherMan) performed well for all three selected regions in generating precipitation occurrence statistics. All models are able to simulate the ratio of wet/dry spell lengths with respect to observed precipitation. The RainSim performed well in reproducing wet/dry spell lengths in comparison to other models for wetter regions in Australia and Vietnam. ClimGen and WeatherMan are the two best models in simulating precipitation in the western USA, followed by CLIGEN and LARS. Similarly, ClimGen and WMAN are the two best models for synthetic precipitation generation for eastern Australian and northern Vietnam stations, but CLIGEN performs poorly over these regions. All SWG model performed differently with respect to climatic regimes, therefore careful validation is required depending on the weather pattern as well as its application in different water resources sectors. Although our findings are preliminary in nature, however, in order to generalize the performance of SWG’s in a given climate type, it is recommended that more number of stations needs to be evaluated in future studies.     

Multivariate properties of extreme precipitation events in the Pearl River basin,China: Magnitude,frequency, timing,and related causes

Daily precipitation/temperature data collected at 74 weather stations across the Pearl River basin of China (PRBC), for the years 1952–2013, were used to analyse extreme precipitation (EP) processes at annual and seasonal scales in terms of precipitation magnitude, occurrence rates, and timing. Peak‐over‐threshold sampling, modified Mann‐Kendall trend tests, and Poisson regression model were utilized in this study. Causes driving the observed statistical behaviours of EP were investigated, focusing particularly on the impacts of temperature change and the El Niño–Southern Oscillation (ENSO). EP events, which occur mainly during April and September, are most frequent in June. At an annual scale, they are subject to relatively even interannual distributions during the wet season. Significant trends were observed in the magnitude, frequency, and timing of EP events during the dry seasons, although no such trends were seen during the wet seasons. Seasonal shifts in EP can easily trigger sudden flood or drought events and warming temperatures, and ENSO events also have significant impacts on EP processes across the PRBC, as reflected by their increased magnitude and frequency in the western PRBC and decreased precipitation magnitudes in the eastern PRBC during ENSO periods. These results provide important evidence of regional hydrological responses to global climate changes in terms of EP regimes in tropical and subtropical zones.     

Simulating precipitation in the Northeast United States using a climate-informed K-nearest neighbour algorithm

Decadal prediction using climate models faces long-standing challenges. While global climate models may reproduce long-term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate-informed K-nearest neighbour (K-NN)-based stochastic modelling approach to capture the long-term trend and variability while replicating intra-annual statistics. The climate-informed K-NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near-term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate-informed K-NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K-NN weather generator and ISIMIP-2b GFDL general circulation model bias-corrected output in a cross-validation mode. Results indicate that the climate-informed K-NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within-year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra-annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near-term streamflow, which in turn can be used for short-term water resources planning and management.     

Weather observations on Whistler Mountain during five storms

A greater understanding of precipitation formation processes over complex terrain near the west coast of British Colombia will contribute to many relevant applications, such as climate studies, local hydrology, transportation, and winter sport competition. The phase of precipitation is difficult to determine because of the warm and moist weather conditions experienced during the wintertime in coastal mountain ranges. The goal of this study is to investigate the wide range of meteorological conditions that generated precipitation on Whistler Mountain from 4–12 March 2010 during the SNOW-V10 field campaign. During this time period, five different storms were documented in detail and were associated with noticeably different meteorological conditions in the vicinity of Whistler Mountain. New measurement techniques, along with the SNOW-V10 instrumentation, were used to obtain in situ observations during precipitation events along the Whistler mountainside. The results demonstrate a high variability of weather conditions ranging from the synoptic-scale to the macro-scale. These weather events were associated with a variation of precipitation along the mountainside, such as events associated with snow, snow pellets, and rain. Only two events associated with a rain–snow transition along the mountainside were observed, even though above-freezing temperatures along the mountainside were recorded 90 % of the time. On a smaller scale, these events were also associated with a high variability of snowflake types that were observed simultaneously near the top of Whistler Mountain. Overall, these detailed observations demonstrate the importance of understanding small-scale processes to improve observational techniques, short-term weather prediction, and longer-term climate projections over mountainous regions.     

On the computation of area probabilities based on a spatial stochastic model for precipitation cells and precipitation amounts

A main task of weather services is the issuing of warnings for potentially harmful weather events. Automated warning guidances can be derived, e.g., from statistical post-processing of numerical weather prediction using meteorological observations. These statistical methods commonly estimate the probability of an event (e.g. precipitation) occurring at a fixed location (a point probability). However, there are no operationally applicable techniques for estimating the probability of precipitation occurring anywhere in a geographical region (an area probability). We present an approach to the estimation of area probabilities for the occurrence of precipitation exceeding given thresholds. This approach is based on a spatial stochastic model for precipitation cells and precipitation amounts. The basic modeling component is a non-stationary germ-grain model with circular grains for the representation of precipitation cells. Then, we assign a randomly scaled response function to each precipitation cell and sum these functions up to obtain precipitation amounts. We derive formulas for expectations and variances of point precipitation amounts and use these formulas to compute further model characteristics based on available sequences of point probabilities. Area probabilities for arbitrary areas and thresholds can be estimated by repeated Monte Carlo simulation of the fitted precipitation model. Finally, we verify the proposed model by comparing the generated area probabilities with independent rain gauge adjusted radar data. The novelty of the presented approach is that, for the first time, a widely applicable estimation of area probabilities is possible, which is based solely on predicted point probabilities (i.e., neither precipitation observations nor further input of the forecaster are necessary). Therefore, this method can be applied for operational weather predictions.     

Stochastic modelling of seasonal and yearly rainfalls with low-frequency variability

Stochastic rainfall models are important for many hydrological applications due to their appealing ability to simulate synthetic series that resemble the statistical characteristics of the observed series for a location of interest. However, an important limitation of stochastic rainfall models is their inability to preserve the low-frequency variability of rainfall. Accordingly, this study presents a simple yet efficient stochastic rainfall model for a tropical area that attempts to incorporate seasonal and inter-annual variabilities in simulations. The performance of the proposed stochastic rainfall model, the tropical climate rainfall generator (TCRG), was compared with a stochastic multivariable weather generator (MV-WG) in various aspects. Both models were applied on 17 rainfall stations at the Kelantan River Basin, Malaysia, with tropical climate. The validations were carried out on seasonal (monsoon and inter-monsoon) and annual basis. The third-order Markov chain of the TCRG was found to perform better in simulating the rainfall occurrence and preserving the low-frequency variability of the wet spells. The log-normal distribution of the TCRG was consistently better in modelling the rainfall amounts. Both models tend to underestimate the skewness and kurtosis coefficient of the rainfall. The spectral correction approach adopted in the TCRG successfully preserved the seasonal and inter-annual variabilities of rainfall amounts, whereas the MV-WG tends to underestimate the variability bias of rainfall amounts. Overall, the TCRG performed reasonably well in the Kelantan River Basin, as it can represent the key statistics of rainfall occurrence and amounts successfully, as well as the low-frequency variability.     

Implications for groundwater recharge from stable isotopic composition of precipitation in Hawai'i during the 2017–2018 La Niña

The project captured a subset of the hydrological cycle for the tropical island of O'ahu, linking precipitation to groundwater recharge and aquifer storage. We determined seasonal storm events contributed more to aquifer recharge than year-round baseline orographic trade wind rainfall. Hydrogen and oxygen isotope values from an island-wide rain collector network with 20 locations deployed for 16 months and sampled at 3-month intervals were used to create the first local meteoric water line for O'ahu. Isotopic measurements were influenced by the amount effect, seasonality, storm type, and La Niña, though little elevation control was noted. Certain groundwater compositions from legacy data showed a strong similarity with collected precipitation from our stations. The majority of these significant relationships were between wet season precipitation and groundwater. A high number of moderate and heavy rainfall days during the dry season, large percentage of event-based rainfall, and wind directions outside of the typical NE trade wind direction were characteristics of the 2017–2018 wet season. This indicates that the majority of wet season precipitation is from event-based storms rather than typical trade wind weather. The deuterium-excess values provided the strongest evidence of a relationship between groundwater and different precipitation sources, indicating that this may be a useful metric for determining the extent of recharge from different rain events and systems.     

Implementing quality control on a random number stream to improve a stochastic weather generator

For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non‐uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30‐year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness‐of‐fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process‐based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.     

Statistical corrections of spatially interpolated missing precipitation data estimates

Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd.     

Impact of climate change on water resources in southern Taiwan

This study investigates the impact of climate change on water resources in southern Taiwan. The upstream catchment of Shin-Fa Bridge station in the Kao-Pen Creek basin was the study area chosen herein. The historical trends of meteorological variables, such as mean daily temperature, mean daily precipitation on wet days, monthly wet days, and the transition probabilities of daily precipitation occurrence in each month, at the Kao-Hsiung meteorological station, near the catchments were detected using a non-parametric statistical test. The trends of these meteorological variables were then employed to generate runoff in future climatic conditions using a continuous rainfall–runoff model. The analytical results indicate that the transition probabilities of daily precipitation occurrence significantly influence precipitation generation, and generated runoff for future climatic conditions in southern Taiwan was found to rise during the wet season and decline during the dry season.     

Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications

Stochastic weather generators are widely used in hydrological, environmental, and agricultural applications to simulate weather time series. However, such stochastic models produce random outputs hence the question on how representative the generated data are if obtained from only one simulation run (realization) as is common practice. In this study, the impact of different numbers of realizations (1, 25, 50, and 100) on the suitability of generated weather data was investigated. Specifically, 50 years of daily precipitation, and maximum and minimum temperatures were generated for three weather stations in the Western Lake Erie Basin (WLEB), using three widely used weather generators, CLIGEN, LARSWG and WeaGETS. Generated results were compared with 50 years of observed data. For all three generators, the analyses showed that one realization of data for 50 years of daily precipitation, and maximum and minimum temperatures may not be representative enough to capture essential statistical characteristics of the climate. Results from the three generators captured the essential statistical characteristics of the climate when the number of realizations was increased from 1 to 25, 50 or 100. Performance did not improve substantially when realizations were increased above 25. Results suggest the need for more than a single realization when generating weather data and subsequently utilizing in other models, to obtain suitable representations of climate.     

Rainfall derivatives pricing with an underlying semi-Markov model for precipitation occurrences

Weather derivatives represent a new and particular kind of contingent claim which shares a specific underlying weather index. These derivatives are written for different temperature indices, hurricanes, frost, snowfall and rainfall, and they are available for several cities. Our paper focuses on rainfall derivatives. In order to price this kind of derivatives, we have to model daily rainfall sequences at a specific location. For this purpose, we adopt a non-homogeneous parametric semi-Markov model to describe the rainfall occurrences, and a mixture of exponential distributions for rainfall amounts. The underlying Markov process has the obvious two states: dry and wet. In addition, dry and wet sequences are estimated by using best-fitting techniques. The model parameters are determined thanks to classical log-likelihood maximization. We finally price some rainfall contracts issued by the Chicago Mercantile Exchange through Monte Carlo simulation. The numerical applications and the parameter estimations are carried out using real data.     

Climate Change Projections Based on GCM-Simulated Daily Data

Three-dimensional general circulation models (GCMs) are 'state-of-the-art' tools for projecting possible changes in climate. Scenarios constructed for the Czech Republic are based on daily outputs of the ECHAM-GCM in the central European region. Essential findings, derived from validating, procedures are summarized and changes in variables between the control and perturbed experiments are examined. The resulting findings have been used in selecting the most proper methods of generating climate change projections for assessing possible hydrological and agricultural impacts of climate change in selected exposure units. The following weather variables have been studied: Daily extreme temperatures, daily mean temperature, daily sum of global solar radiation, and daily precipitation amounts. Due to some discrepancies revealed, the temperature series for changed climate conditions (2×CO ₂ ) have been created with the help of temperature differences between the control and perturbed runs, and the precipitation series have been derived from an incremental scenario based on an intercomparison of the GCMs' precipitation performance in the region. Solar radiation simulated by the ECHAM was not available and, therefore, it was generated using regression techniques relating monthly means of daily extreme temperatures and global radiation sums. The scenarios published in the paper consist of monthly means of all temperatures, their standard deviations, and monthly means of solar radiation and precipitation amounts. Daily weather series, the necessary input to impact models, are created (i) by the additive or multiplicative modification of observed weather daily series or (ii) by generating synthetic time series with the help of a weather generator whose parameters have been modified in accord with the suggested climate change scenarios.     

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.     

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station‐scale hydrological variables from large‐scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K‐nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue‐type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright © 2011 John Wiley & Sons, Ltd.     

Climate change effects on hydrological system conditions influencing generation of storm runoff in small Alpine catchments

Floods and debris flows in small Alpine torrent catchments (<10 km²) arise from a combination of critical antecedent system state conditions and mostly convective precipitation events with high precipitation intensities. Thus, climate change may influence the magnitude–frequency relationship of extreme events twofold: by a modification of the occurrence probabilities of critical hydrological system conditions and by a change of event precipitation characteristics. Three small Alpine catchments in different altitudes in Western Austria (Ruggbach, Brixenbach and Längentalbach catchment) were investigated by both field experiments and process‐based simulation. Rainfall–runoff model (HQsim) runs driven by localized climate scenarios (CNRM‐RM4.5/ARPEGE, MPI‐REMO/ECHAM5 and ICTP‐RegCM3/ECHAM5) were used in order to estimate future frequencies of stormflow triggering system state conditions. According to the differing altitudes of the study catchments, two effects of climate change on the hydrological systems can be observed. On one hand, the seasonal system state conditions of medium altitude catchments are most strongly affected by air temperature‐controlled processes such as the development of the winter snow cover as well as evapotranspiration. On the other hand, the unglaciated high‐altitude catchment is less sensitive to climate change‐induced shifts regarding days with critical antecedent soil moisture and desiccated litter layer due to its elevation‐related small proportion of sensitive areas. For the period 2071–2100, the number of days with critical antecedent soil moisture content will be significantly reduced to about 60% or even less in summer in all catchments. In contrast, the number of days with dried‐out litter layers causing hydrophobic effects will increase by up to 8%–11% of the days in the two lower altitude catchments. The intensity analyses of heavy precipitation events indicate a clear increase in rain intensities of up to 10%.     

A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278.  https://doi.org/10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32.  https://doi.org/10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability.