Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 1. Impact on peak and extreme rainfall values

A noble approach of stochastic rainfall generation that can account for inter-annual variability of the observed rainfall is proposed. Firstly, we show that the monthly rainfall statistics that is typically used as the basis of the calibration of the parameters of the Poisson cluster rainfall generators has significant inter-annual variability and that lumping them into a single value could be an oversimplification. Then, we propose a noble approach that incorporates the inter-annual variability to the traditional approach of Poisson cluster rainfall modeling by adding the process of simulating rainfall statistics of individual months. Among 132 gage-months used for the model verification, the proportion that the suggested approach successfully reproduces the observed design rainfall values within 20 % error varied between 0.67 and 0.83 while the same value corresponding to the traditional approach varied between 0.21 and 0.60. This result suggests that the performance of the rainfall generation models can be largely improved not only by refining the model structure but also by incorporating more information about the observed rainfall, especially the inter-annual variability of the rainfall statistics.     

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.     

Flow regime shifts in the Little Piney creek (US)

Non-stationarity of climate drivers and soil-use strongly affects the hydrologic cycle, producing significant inter-annual and multi-decadal fluctuations of river flow regimes. Understanding the temporal trajectories of hydrologic regimes is a key issue for the management of freshwater ecosystems and the security of human water uses. Here, long-term changes in the seasonal flow regime of the Little Piney creek (US) are analyzed with the aid of a stochastic mechanistic approach that expresses analytically the streamflow distribution in terms of a few measurable hydroclimatic parameters, providing a basis for assessing the impact of climate and landscape modifications on water resources. Mean rainfall and streamflow rates exhibit a pronounced inter-annual variability across the last century, though in the absence of clear sustained drifts. Long-term modifications of streamflow regimes across different periods of 2 and 8 years are likewise significant. The stochastic model is able to reasonably reproduce the observed 2-years and 8-years regimes in the Little Piney creek, as well as the corresponding inter-annual variations of streamflow probability density. The study evidences that a flow regime shift occurred in the Little Piney creek during the last century, with erratic regimes typical of the 30s/40s that had been progressively replaced by persistent flow regimes featured by more dumped streamflow fluctuations. Causal drivers of regime shift are identified as the increase of the frequency of events (a byproduct of climate variability) and the decrease of recession rates (induced by a decrease of cultivated lands). The approach developed offers an objective basis for the analysis and prediction of the impact of climate/landscape change on water resources.     

Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida

Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study.     

Weather and seasonal climate prediction for flood planning in the Yangtze River Basin

This paper describes the use of numerical weather and climate models for predicting severe rainfall anomalies over the Yangtze River Basin (YRB) from several days to several months in advance. Such predictions are extremely valuable, allowing time for proactive flood protection measures to be taken. Specifically, the dynamical climate prediction system (IAP DCP-II), developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP), is applied to YRB rainfall prediction and flood planning. IAP DCP-II employs ensemble prediction with dynamically conditioned perturbations to reduce the uncertainty associated with seasonal climate prediction. IAP DCP-II was shown to successfully predict seasonal YRB summer flooding events based on a 15-year (1980–1994) hindcast experiment and the real-time prediction of two summer flooding events (1999 and 2001). Finally, challenges and opportunities for applying seasonal dynamical forecasting to flood management problems in the YRB are discussed.     

Variations and statistical probability characteristic analysis of extreme precipitation events under climate change in Haihe River Basin,China

The magnitude and frequency of regional extreme precipitation events may have variability under climate change. This study investigates the time–space variability and statistical probability characteristics of extreme precipitation under climate change in the Haihe River Basin. Hydrological alteration diagnosis methods are implemented to detect the occurrence time, style and degree of alteration such as trend and jump in the extreme precipitation series, and stationarity and serial independence are tested prior to frequency analysis. Then, the historical extreme precipitation frequency and spatio‐temporal variations analyses are conducted via generalized extreme value and generalized Pareto distributions. Furthermore, the occurrence frequency of extreme precipitation events in future is analysed on the basis of the Fourth Assessment Report of the Intergovermental Panel on Climate Change multi‐mode climate models under different greenhouse gases emission scenarios (SRES‐A2, A1B and B1). Results indicate that (1) in the past, alteration of extreme precipitation mainly occurred in the area north of 38°N. Decreasing trends of extreme precipitation are detected at most stations, whereas jump alteration is not obvious at most stations. (2) Spatial variation of estimated extreme precipitation under different return periods shows similarity. Bounded by the Taihang Mountain–Yan Mountain, extreme rainfall in the Haihe River Basin gradually reduces from the southeast to the northwest, which is consistent with the geographical features of the Haihe River Basin. (3) In the future, extreme precipitation with return period 5–20 years accounts for a significant portion of the total occurrence times. The frequency of extreme precipitation events has an increase trend under A1B and A2 scenarios. The total occurrence times of extreme precipitation under A1B senario are not more than that under B1 senario until the 2030s. Copyright © 2012 John Wiley & Sons, Ltd.     

Simulation of future climate scenarios with a weather generator

Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.     

Hydroclimatology of the Upper Madeira River basin: spatio-temporal variability and trends

Rising in the Andes, the Madeira River drains the southwestern part of the Amazon basin, which is characterized by high geographical, biological and climatic diversity. This study uses daily records to assess the spatio-temporal runoff variability in the Madeira sub-basins. Results show that inter-annual variability of both discharge and rainfall differs between Andean and lowland tributaries. High-flow discharge variability in the Andean tributaries and the Guaporé River is mostly related to sea surface temperature (SST) in the equatorial Pacific in austral summer, while tropical North Atlantic (TNA) SST modulates rainfall and discharge variability in the lowlands. There also is a downward trend in the low-flow discharge of the lowland tributaries which is not observed in the Andes. Because low-flow discharge values at most lowland stations are negatively related to the SST in the tropical North Atlantic, these trends could be explained by the warming of this ocean since the 1970s.

EDITOR A. Castellarin

ASSOCIATE EDITOR A. Viglione     

Simulating hourly rainfall occurrence within an equatorial rainforest,Borneo Island / Simulation de l'occurrence de pluie horaire au sein de la forêt équatoriale,Ile de Bornéo

Abstract

The ability to simulate characteristics of the diurnal cycle of rainfall occurrence, and its evolution over the seasons is important to the forecasting of hydrological impacts resulting from land-use and climate changes within the humid tropics. This stochastic modelling study uses a generalized linear model (GLM) solution to second-order Markov chain models, as these discrete models are better at describing binary occurrence processes on an hourly time-scale than continuous-time approaches such as stochastic state-space models. We show that transition probabilities derived by the Markov chain method need to be time-varying rather than stationary to simulate the evolution of the diurnal cycle of rainfall occurrence over a Southeast Asian monsoon sequence. The conceptual and pragmatic links between discrete diurnal processes and continuous processes occurring over seasonal periods are thereby simulated within the same model.     

Hydrography and plankton temporal variabilities at different time scales in the southwest lagoon of New Caledonia: A review

The New Caledonia SW lagoon is wide (5–20 nautical miles) and semi-closed. It is influenced by both the open ocean and the high island within a meteorological context subject to seasonal, inter-annual and longer term variations. The short-term variability (>1 day) of meteorological, hydrographical and planktonic parameters is illustrated by a 5-month long time series and is linked to local or remote wind, and precipitation. Seasonal and inter-annual variabilities, inferred from a 10-year long station by spectral analysis, appear clearly for all parameters. Seasonality is the main scale of variability as the island lies near the tropic of Capricorn. Inter-annual variability of a 3–4 year periodicity is poorly related to the Southern oscillation index (an equatorial climatic index), stressing the need for a separate tropical index. Long term trends appear on several parameters but their reliability depends on the length of the records. Considering only the longest records (1958–2005), surface temperature appears to have increased since the end of the 1960s in Noumea area. Finally, as a result of greater terrestrial influence, shallower depths, and longer water turnover times close to shore, the temporal variability amplitude decreases from the shore to the barrier reef.     

Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 2. Impact on watershed response variables

This study analyzes how the stochastically generated rainfall time series accounting for the inter-annual variability of rainfall statistics can improve the prediction of watershed response variables such as peak flow and runoff depth. The modified Bartlett–Lewis rectangular pulse (MBLRP) rainfall generation model was improved such that it can account for the inter-annual variability of the observed rainfall statistics. Then, the synthetic rainfall time series was generated using the MBLRP model, which was used as input rainfall data for SCS hydrologic models to produce runoff depth and peak flow in a virtual watershed. These values were compared to the ones derived from the synthetic rainfall time series that is generated from the traditional MBLRP rainfall modeling. The result of the comparison indicates that the rainfall time series reflecting the inter-annual variability of rainfall statistics reduces the biasness residing in the predicted peak flow values derived from the synthetic rainfall time series generated using the traditional MBLRP approach by 26–47 %. In addition, it was observed that the overall variability of the peak flow and run off depth distribution was better represented when the inter-annual variability of rainfall statistics are considered.     

Analysis of agricultural drought in Iiuni,Eastern Kenya: application of a Markov model

In semi‐arid Kenya, episodes of agricultural droughts of varying severity and duration occur. The occurrence of these agricultural droughts is associated with seasonal rainfall variability and can be reflected by seasonal soil moisture deficits that significantly affect crop performance and yield. The objective of this study was to stochastically simulate the behaviour of dry and wet spells and rainfall amounts in Iiuni watershed, Kenya. The stochastic behaviour of the longest dry and wet spells (runs) and largest rainfall amounts were simulated using a Markov (order 1) model. There were eight raingauge stations within the watershed. The entire analysis was carried out using probability parameters, i.e. mean, variance, simple and conditional probabilities of dry and rain days. An analysis of variance test (ANOVA ) was used to establish significant differences in rainfall characteristics between the eight stations. An analysis of the number of rain days and rainfall amount per rain day was done on a monthly basis to establish the distribution and reliability of seasonal rainfall. The graphic comparison of simulated cumulative distribution functions (Cdfs) of the longest spells and largest rainfall amounts showed Markovian dependence or persistence. The longest dry spells could extend to 24 days in the long rainy season and 12 in the short rainy season. At 50% (median) probability level, the largest rainfall amounts were 91 mm for the long rainy season and 136 mm for the short rainy season. The short rains were more reliable for crop production than the long rains. The Markov model performed well and gave adequate simulations of the spells and rainfall amounts under semi‐arid conditions. Copyright © 2004 John Wiley & Sons, Ltd.     

天气尺度瞬变扰动的物理分解原理

大气变量可以在时空域内物理分解成四个部分.前两个是纬圈-时间平均的对称部分和时间平均的非对称部分,分别由太阳辐射和海陆分布热力调节的季节变化引起,并形成规则的逐日气候.第三部分是由年际和季节内的热带海洋或极地热力强迫引起的纬圈平均瞬变对称扰动,可形成大气变量的行星尺度指数循环.第四部分是一些复杂的天气尺度瞬变非对称扰动.大气变量中的逐日天气尺度瞬变扰动,可以用于指示区域持续性的干旱、暴雨、低温和热浪等极端天气事件.天气尺度瞬变扰动天气图能在极端天气事件的预报中发挥应有的作用.     

Response of hydrological processes to land‐cover and climate changes in Kejie watershed,south‐west China

Land‐cover/climate changes and their impacts on hydrological processes are of widespread concern and a great challenge to researchers and policy makers. Kejie Watershed in the Salween River Basin in Yunnan, south‐west China, has been reforested extensively during the past two decades. In terms of climate change, there has been a marked increase in temperature. The impact of these changes on hydrological processes required investigation: hence, this paper assesses aspects of changes in land cover and climate. The response of hydrological processes to land‐cover/climate changes was examined using the Soil and Water Assessment Tool (SWAT) and impacts of single factor, land‐use/climate change on hydrological processes were differentiated. Land‐cover maps revealed extensive reforestation at the expense of grassland, cropland, and barren land. A significant monotonic trend and noticeable changes had occurred in annual temperature over the long term. Long‐term changes in annual rainfall and streamflow were weak; and changes in monthly rainfall (May, June, July, and September) were apparent. Hydrological simulations showed that the impact of climate change on surface water, baseflow, and streamflow was offset by the impact of land‐cover change. Seasonal variation in streamflow was influenced by seasonal variation in rainfall. The earlier onset of monsoon and the variability of rainfall resulted in extreme monthly streamflow. Land‐cover change played a dominant role in mean annual values; seasonal variation in surface water and streamflow was influenced mainly by seasonal variation in rainfall; and land‐cover change played a regulating role in this. Surface water is more sensitive to land‐cover change and climate change: an increase in surface water in September and May due to increased rainfall was offset by a decrease in surface water due to land‐cover change. A decrease in baseflow caused by changes in rainfall and temperature was offset by an increase in baseflow due to land‐cover change. Copyright © 2009 John Wiley & Sons, Ltd.     

A hybrid stochastic-weather-generation method for temporal disaggregation of precipitation with consideration of seasonality and within-month variations

Linking atmospheric and hydrological models is challenging because of a mismatch of spatial and temporal resolutions in which the models operate: dynamic hydrological models need input at relatively fine temporal (daily) scale, but the outputs from general circulation models are usually not realistic at the same scale, even though fine scale outputs are available. Temporal dimension downscaling methods called disaggregation are designed to produce finer temporal-scale data from reliable larger temporal-scale data. Here, we investigate a hybrid stochastic weather-generation method to simulate a high-frequency (daily) precipitation sequence based on lower frequency (monthly) amounts. To deal with many small precipitation amounts and capture large amounts, we divide the precipitation amounts on rainy days (with non-zero precipitation amounts) into two states (named moist and wet states, respectively) by a pre-defined threshold and propose a multi-state Markov chain model for the occurrences of different states (also including non-rain days called dry state). The truncated Gamma and censored extended Burr XII distributions are then employed to model the precipitation amounts in the moist and wet states, respectively. This approach avoids the need to deal with discontinuity in the distribution, and ensures that the states (dry, moist and wet) and corresponding amounts in rainy days are well matched. The method also considers seasonality by constructing individual models for different months, and monthly variation by incorporating the low-frequency amounts as a model predictor. The proposed method is compared with existing models using typical catchment data in Australia with different climate conditions (non-seasonal rainfall, summer rainfall and winter rainfall patterns) and demonstrates better performances under several evaluation criteria which are important in hydrological studies.     

变化环境下近60年西江流域径流时空演变特征

气候变化叠加人类活动的双重影响下,西江流域的河川径流发生了不同程度的改变,重新认识和掌握变化环境下的径流时空演变规律对流域的科学管理具有重要意义。基于西江流域干支流7个控制性水文站近60年的日径流资料,综合极点对称模态分解法(ESMD)、Mann-Kendall检验、R/S分析、小波分析等方法,从年代、年、季和年内多个时间尺度对径流的时空演变特征进行分析,研究结果表明:年代尺度下,西江流域径流丰枯交替、变化悬殊,1970s与1990s径流丰沛,2010s径流偏枯,降雨影响着径流的丰枯变化,流域上、中游更易发生干旱与洪涝灾害;春、夏、秋、冬季径流振荡周期依次为2～7、15～20、28～29 a,3～5、7～10、20 a,2～3、6～8、12～15 a和3～8、12～15、20 a,均呈现年际与年代的双重周期特征,IMF1的年际振荡在径流变化中占主导地位;受降雨与水库调蓄作用的影响,年、夏、秋季径流呈下降趋势,预测下降变化具有持续性,春、冬季径流整体呈上升趋势。空间内上游的变化趋势更显著;年和季尺度径流在1980年后突变增多,尤其集中于2000—2010年间,人类活动与气候变化是造成西江...     

Temporal and spatial variability of annual and seasonal rainfall over Ethiopia

Abstract

Characterization of the seasonal and inter-annual spatial and temporal variability of rainfall in a changing climate is vital to assess climate-induced changes and suggest adequate future water resources management strategies. Trends in annual, seasonal and maximum 30-day extreme rainfall over Ethiopia are investigated using 0.5° latitude?×?0.5° longitude gridded monthly precipitation data. The spatial coherence of annual rainfall among contiguous rainfall grid points is also assessed for possible spatial similarity across the country. The correlation between temporally coinciding North Atlantic Multidecadal Oscillation (AMO) index and annual rainfall variability is examined to understand the underlying coherence. In total 381 precipitation grid points covering the whole of Ethiopia with five decades (1951–2000) of precipitation data are analysed using the Mann-Kendall test and Moran spatial autocorrelation method. Summer (July–September) seasonal and annual rainfall data exhibit significant decreasing trends in northern, northwestern and western parts of the country, whereas a few grid points in eastern areas show increasing annual rainfall trends. Most other parts of the country exhibit statistically insignificant trends. Regions with high annual and seasonal rainfall distribution exhibit high temporal and spatial correlation indices. Finally, the country is sub-divided into four zones based on annual rainfall similarity. The association of the AMO index with annual rainfall is modestly good for northern and northeastern parts of the country; however, it is weak over the southern region.

Editor Z.W. Kundzewicz; Associate editor S. Uhlenbrook

Citation Wagesho, N., Goel, N.K., and Jain, M.K. 2013. Temporal and spatial variability of annual and seasonal rainfall over Ethiopia. Hydrological Sciences Journal, 58 (2), 354–373.     

Seasonal ensemble generator for radar rainfall using copula and autoregressive model

Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.     

Statistical downscaling of reference evapotranspiration in Haihe River Basin: applicability assessment and application to future projection

Future changes in reference evapotranspiration (ET₀) are of increasing importance in assessing the potential impacts on hydrology and water resources systems of more pronounced climate change. This study assesses the applicability of the Statistical Downscaling Model (SDSM) in projecting ET₀, and investigates the seasonal and spatial patterns of future ET₀ based on general circulation models (GCMs) across the Haihe River Basin. The results indicate that SDSM can downscale ET₀ well in term of different basin-averaged measures for the HadCM3 and CGCM3 GCMs. HadCM3 has a much superior capability in capturing inter-annual variability compared to CGCM3 and thus is chosen as the sole model to assess the changes in future ET₀. There are three homogeneous sub-regions of the Haihe River Basin: Northwest, Northeast and Southeast. Change points are detected at around 2050 and 2080 under the A2 and B2 scenarios, respectively. The Northwest is revealed to have a slight to strong increase in ET₀, while the Northeast and the Southeast tend to experience a pattern change from decrease to increase in ET₀.

EDITOR M.C. Acreman

ASSOCIATE EDITOR J. Thompson     

Nonstationary modeling of a long record of rainfall and temperature over Rome

A long record (1862–2004) of seasonal rainfall and temperature from the Rome observatory of Collegio Romano are modeled in a nonstationary framework by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). Modeling analyses are used to characterize nonstationarities in rainfall and related climate variables. It is shown that the GAMLSS models are able to represent the magnitude and spread in the seasonal time series with parameters which are a smooth function of time. Covariate analyses highlight the role of seasonal and interannual variability of large-scale climate forcing, as reflected in three teleconnection indexes (Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Mediterranean Index), for modeling seasonal rainfall and temperature over Rome. In particular, the North Atlantic Oscillation is a significant predictor during the winter, while the Mediterranean Index is a significant predictor for almost all seasons.