Robust inferences on climate change patterns of precipitation extremes in the Iberian Peninsula

This work presents a methodology to make statistical significant and robust inferences on climate change from an ensemble of model simulations. This methodology is used to assess climate change projections of the Iberian daily-total precipitation for a near-future (2021–2050) and a distant-future (2069–2098) climates, relatively to a reference past climate (1961–1990).Climate changes of precipitation spatial patterns are estimated for annual and seasonal values of: (i) total amount of precipitation (PRCTOT), (ii) maximum number of consecutive dry days (CDD), (iii) maximum of total amount of 5-consecutive wet days (Rx5day), and (iv) percentage of total precipitation occurred in days with precipitation above the 95th percentile of the reference climate (R95T). Daily-total data were obtained from the multi-model ensemble of fifteen Regional Climate Model simulations provided by the European project ENSEMBLES. These regional models were driven by boundary conditions imposed by Global Climate Models that ran under the 20C3M conditions from 1961 to 2000, and under the A1B scenario, from 2001 to 2100, defined by the Special Report on Emission Scenarios of the Intergovernmental Panel on Climate Change.Non-parametric statistical methods are used for significant climate change detection: linear trends for the entire period (1961–2098) estimated by the Theil-Sen method with a statistical significance given by the Mann-Kendall test, and climate-median differences between the two future climates and the past climate with a statistical significance given by the Mann-Whitney test. Significant inferences of climate change spatial patterns are made after these non-parametric statistics of the multi-model ensemble median, while the associated uncertainties are quantified by the spread of these statistics across the multi-model ensemble. Significant and robust climate change inferences of the spatial patterns are then obtained by building the climate change patterns using only the grid points where a significant climate change is found with a predefined low uncertainty.Results highlight the importance of taking into account the spread across an ensemble of climate simulations when making inferences on climate change from the ensemble-mean or ensemble-median. This is specially true for climate projections of extreme indices such CDD and R95T. For PRCTOT, a decrease in annual precipitation over the entire peninsula is projected, specially in the north and northwest where it can decrease down to 400 mm by the middle of the 21st century. This decrease is expected to occur throughout the year except in winter. Annual CDD is projected to increase till the middle of the 21st century overall the peninsula, reaching more than three weeks in the southwest. This increase is projected to occur in summer and spring. For Rx5day, a decrease is projected to occur during spring and autumn in the major part of the peninsula, and during summer in northern Iberia. Finally, R95T is projected to decrease around 20% in northern Iberia in summer, and around 15% in the south-southwest in autumn.     

中国区域地理、地形因子对降水分布影响的估算和分析

不同与以往基于最小二乘的多元线性回归方法,本文首次尝试将新型的第二代回归分析方法——偏最小二乘回归分析方法应用到中国区域的降水建模中．利用区域内394个气象观测站建站到2000年45年(及以上)的降水资料,建立了一个简单的年、季降水量和地理、地形因子(包括纬度、经度、地形高程、坡度、坡向和遮蔽度)的关系模型,估算了区域降水量中地理、地形的影响部分,并分析了这种影响的特征．结果表明,用此方法建立的模型能够解释70%以上的因变量的变异,相关系数基本都在0.84以上,经交叉有效性检验,模型的回归效果较显著．分析表明,在多元线性回归不适用的情况下,本文基于偏最小二乘法的简单模型能够比较准确地定性、定量地再现实际降水分布．     

The Use of Radar and Gauge Measurements to Estimate Areal Precipitation for Several Czech River Basins

Two methods estimating areal precipitation for selected river basins in the Czech Republic are compared. The methods use radar precipitation (the radar-derived precipitation estimate based on column maximum reflectivity) and data from 81 on-line rain gauges routinely provided by the Czech Hydrometeorological Institute. Data from a dense network of climatological rain gauges (the average inter-station distance is approximately 8 km), the measurements of which are not available in real time, are utilized for the verification. The mean areal precipitation, which is used as the ground truth, is obtained by the weighted interpolation of the dense rain gauge network. The accuracy of the methods is evaluated by the root-mean-square-error.The first, pixel-related method merges radar precipitation with rain gauge data to obtain adjusted pixel values. The adjusting procedure combines radar and gauge values in one variable that is interpolated into all radar pixels. The adjusted pixel precipitation is calculated from radar precipitation and from the value of the combined variable. The areal estimates are determined by adding the corresponding pixel values. The second method applies a linear regression model to describe the relationship between the areal precipitation (dependent variable) and its estimates, which are determined from (i) non-adjusted radar precipitation and (ii) on-line rain gauge measurements interpolated into pixels. Classical linear regression, ridge regression and robust regression models are tested.Both the methods decrease the average areal error in comparison with the reference method, which uses the on-line rain gauge data only. The decrease is about 10% and 15% for the pixel-related and regression methods, respectively. When the estimates of the pixel-related method are included as predictors into the regression method then the improvement of accuracy is almost 25%.     

Forecasting annual precipitation to improve the operation of dams in the Comahue region,Argentina

ABSTRACT

This paper attempts to design statistical models to forecast annual precipitation in the Neuquen and Limay river basins in the Comahue region of Argentina. These forecasts are especially useful as they are used to better organize the operation of hydro-electric dams, the agriculture in irrigated valleys and the safety of the population. In this work, multiple linear regression statistical models are built to forecast mean annual rainfall over the two river basins. Since the maximum precipitation occurs in the winter (June–August), forecasting models have been developed for the beginning of March and for the beginning of June, just before the rainy season starts. The results show that the sea-surface temperatures of the Indian and Pacific oceans are good predictors for March models and explain 42.8% of the precipitation index variance. The efficiency of the models increases in June, adding more predictors related to the autumn circulation.     

MOS based forecast of 6-hourly area precipitation

A statistical post-processing methodology for application to numerical weather prediction (NWP) model outputs for precipitation forecast is proposed. The post-processing is based on the model output statistics approach. The statistical relationships are described by the multiple linear regression model, which is complemented by an iteration procedure to further correct the regression outputs. Prognostic fields of the ALADIN/LACE (Aire Limitée Adaptation Dynamique Développement InterNational/Limited Area Modelling in Central Europe) NWP model are used for the forecast of 6-hourly areal precipitation amounts at 15 river basins. The NWP model integration starts at 00UTC and forecasts are calculated for lead times of +12, +18, +24 and +30 hours. The post-processing models are developed separately for each lead time and for separate warm (April to September) and cool (October to March) seasons. The forecasts are focused on large precipitation amounts. Using all the combinations, data from four years (1999–2002) are divided into calibration data (3 years), where the models are developed, and verification data. The models are evaluated by examining the root-mean-square error (RMSE), bias, and correlation coefficient (CC) on the verification data samples. The results show that the additional iteration procedure increases the forecast accuracy for a given range of precipitation amounts and simultaneously does not deteriorate the bias, a situation which can arise when negative regression outputs are set to zero. The post-processing method improves the forecast of the NWP model in terms of RMSE and CC. For large precipitation amounts during the summer season, the decrease of RMSE reaches 10% to 20% depending upon the applied method of verification. For the cool season, the decrease is somewhat smaller (7% to 15%).     

Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed

Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.     

Homogeneity analysis of Turkish meteorological data set

The missing value interpolation and homogeneity analysis were performed on the meteorological data of Turkey. The data set has the observations of six variables: the maximum air temperature, the minimum air temperature, the mean air temperature, the total precipitation, the relative humidity and the local pressure of 232 stations for the period 1974–2002. The missing values on the monthly data set were estimated using two methods: the linear regression (LR) and the expectation maximization (EM) algorithm. Because of higher correlations between test and reference series, EM algorithm results were preferred. The homogeneity analysis was performed on the annual data using a relative test and four absolute homogeneity tests were used for the stations where non‐testable series were found due to the low correlation coefficients between the test and the reference series. A comparison was accomplished by the graphics where relative and absolute tests provided different outcomes. Absolute tests failed to detect the inhomogeneities in the precipitation series at the significance level 1%. Interestingly, most of the inhomogeneities detected on the temperature variables existed in the Aegean region of Turkey. It is considered that theseinhomogeneities were mostly caused by non‐natural effects such as relocation. Because of changes at topography at short distance in this region intensify non‐random characteristics of the temperature series when relocation occurs even in small distances. The marine effect, which causes artifical cooling effect due to sea breezes has important impact on temperature series and the orograhpy allows this impact go through the inner parts in this region. Copyright © 2010 John Wiley & Sons, Ltd.     

Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors

Three downscaling models, namely the Statistical Down‐Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS‐WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS‐WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS‐WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry‐spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry‐spell lengths of the LARS‐WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months. Copyright © 2006 John Wiley & Sons, Ltd.     

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34′10.92″N, 88°22′10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.     

Vegetation and cloud cover shape semi-arid carbonate landform development

We study the relationships among precipitation, vegetation, and morphological characteristics of watersheds draining either side of the Dhofar Mountains in southeastern Oman to understand the geomorphic signature of water availability in a semi-arid carbonate landscape. Water availability is expressed in terms of vegetation and cloud cover. The integral and the statistical moments of the hypsometric curve were used to determine whether hyper-arid, inland-draining watersheds are significantly different from seasonally wet watersheds on the coast side of the mountain range. We demonstrate that the vegetation and cloud cover are correlated, with locations with longer cloud periods also having a longer period with a vegetation canopy. The analysis shows that the hypsometric curve and its statistical moments capture the morphological difference between wet watersheds shaped by groundwater sapping and dry watersheds with fluvial morphology. Specifically, the curves exhibit two shapes: watersheds with more vegetation and cloud cover are characterized by higher convexity, and those with less vegetation and cloud cover are characterized by higher concavity. A variance analysis of cloud cover, vegetation, and hypsometric integral shows that they are significantly different between the wet and dry watersheds. The link between hydrology and morphology is not strong at the scale of a single watershed, but it is significant when the watersheds are aggregated in zones. The statistical moments of the hypsometric curve in the range of values of the integral and skewness show good separation between watersheds dominated by sapping and fluvial erosion processes. We can separate the watersheds draining the mountain range in two distinct groups on the basis of their bimodal hydrological and morphological characteristics. Our findings support other studies that hypothesize a trade-off from chemical- to mechanical-dominated denudation in carbonate terranes as precipitation decreases.     

Spatial interpolation schemes of daily precipitation for hydrologic modeling

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.     

The impact of land use change on soil water holding capacity and river flow modelling in the Nakambe River, Burkina-Faso

The annual hydrological regime of the Nakambe River shows substantial changes during the period 1955–1998 with a shift occurring around 1970. From 1970 to the mid-1990s, despite a reduction in rainfall and an increase in the number of dams in the basin, average runoff and maximum daily discharges increased. This paper reviews the hydrological behaviour of the Nakambe River from 1955 to 1998 and examines the potential role of land use change on soil water holding capacity (WHC) in producing the counter-intuitive change in runoff observed after 1970. We compare the results of two monthly hydrological models using different rainfall, potential evapotranspiration and WHC data sets. Model simulations with soil WHC values modified over time based upon historical maps of land use, are compared against simulations with a constant value for WHC. The extent of natural vegetation declined from 43 to 13% of the total basin area between 1965 and 1995, whilst the cultivated areas increased from 53 to 76% and the area of bare soil nearly tripled from 4 to 11%. The total reduction in WHC is estimated to range from 33 to 62% depending on the method used, either considering that the WHC values given by the FAO stand for the environmental situation in 1965 or before. There is a marked improvement in river flow simulation using the time-varying values of soil WHC. The paper ends with a discussion of the role of other factors such as surface runoff processes and groundwater trends in explaining the hydrological behaviour of the Nakambe River.     

Frequency based imputation of precipitation

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method.     

Daily extreme precipitation and trends over China

Based on daily precipitation data of more than 2000 Chinese stations and more than 50 yr, we constructed time series of extreme precipitation based on six different indices for each station: annual and summer maximum(top-1) precipitation,accumulated amount of 10 precipitation maxima(annual, summer; top-10), and total annual and summer precipitation.Furthermore, we constructed the time series of the total number of stations based on the total number of stations with top-1 and top-10 annual extreme precipitation for the whole data period, the whole country, and six subregions, respectively. Analysis of these time series indicate three regions with distinct trends of extreme precipitation:(1) a positive trend region in Southeast China,(2) a positive trend region in Northwest China, and(3) a negative trend region in North China. Increasing(decreasing)ratios of 10–30% or even 30% were observed in these three regions. The national total number of stations with top-1 and top-10 precipitation extremes increased respectively by 2.4 and 15 stations per decade on average but with great inter-annual variations.There have been three periods with highly frequent precipitation extremes since 1960:(1) early 1960 s,(2) middle and late 1990 s,and(3) early 21 st century. There are significant regional differences in trends of regional total number of stations with top-1 and top-10 precipitation. The most significant increase was observed over Northwest China. During the same period, there are significant changes in the atmospheric variables that favor the decrease of extreme precipitation over North China: an increase in the geopotential height over North China and its upstream regions, a decrease in the low-level meridional wind from South China coast to North China, and the corresponding low moisture content in North China. The extreme precipitation values with a50-year empirical return period are 400–600 mm at the South China coastal regions and gradually decrease to less than 50 mm in Northwest China. The mean increase rate in comparison with 20-year empirical return levels is 6.8%. The historical maximum precipitation is more than twice the 50-year return levels.     

Estimating design probable maximum precipitation using multifractal methods and comparison with statistical and synoptically methods case study: Basin of Bakhtiari Dam

The probable maximum precipitation which is defined as the maximum precipitation at a particular location for a given duration is used as a design criterion for major dams. The assumptions of deterministic consideration and an upper limit to probable maximum precipitation have been repeatedly criticized by hydrologists. Nowadays, multifractal method which strongly contains physical bases can be used to improve the probable maximum precipitation. In this research, the universal multifractal model was used to estimate the design probable maximum precipitation for specified exceedence probability in basin of Bakhtiari Dam, southwest Iran, and its results were compared with statistical and synoptically methods. The results revealed that the return period of statistical and synoptically probable maximum precipitation, estimated for the different durations, are about 10⁹ and 10³–10⁴ years, respectively; also, over periods ranging from 1 to 7 days, the ratios of design probable maximum precipitations, estimated based on multifractal method for return period of 10³–10⁹ years, to statistical and synoptically probable maximum precipitation estimates ranged from 0.61 to 1.1 and 1.33 to 2.37, respectively. These results indicated that the multifractal method can be used to reasonably estimate the probable maximum precipitation.     

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.     

Potential impact of climate change on flooding in the Upper Great Miami River Watershed,Ohio, USA: a simulation-based approach

Abstract

This study aims to assess the potential impact of climate change on flood risk for the city of Dayton, which lies at the outlet of the Upper Great Miami River Watershed, Ohio, USA. First the probability mapping method was used to downscale annual precipitation output from 14 global climate models (GCMs). We then built a statistical model based on regression and frequency analysis of random variables to simulate annual mean and peak streamflow from precipitation input. The model performed well in simulating quantile values for annual mean and peak streamflow for the 20th century. The correlation coefficients between simulated and observed quantile values for these variables exceed 0.99. Applying this model with the downscaled precipitation output from 14 GCMs, we project that the future 100-year flood for the study area is most likely to increase by 10–20%, with a mean increase of 13% from all 14 models. 79% of the models project increase in annual peak flow.

Citation Wu, S.-Y. (2010) Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach. Hydrol. Sci. J. 55(8), 1251–1263.     

Influence of calibration methodology on ground water flow predictions

We constructed a numerical model of transient ground water flow and solute transport for a portion of the Biscayne Aquifer in Florida, and calibrated the model with three different combinations of data from a 193-day period: head (h) data alone, data on h and ground water discharge to a canal (q), and data on h, q, and ground water chloride concentration (C). We used each of the three calibrated models to predict h and q during a 182-day test period separate from the calibration period. All three calibrated models predicted h equally well during the test period (r = 0.95, where r = 1 indicates perfect agreement between measured and simulated values), though the model calibrated on h alone had significantly different parameter values than the other two models. Predictions of q during the test period depended on calibration methodology; models calibrated with multiple targets simulated q more accurately than the model calibrated on h alone (r = 0.79 compared to r = 0.49). Based on the results of these simulations, we conclude: (1) Post-calibration prediction is important in assessing the value of different data types in automated calibration; (2) inverse-solution uniqueness is not a requirement for accurate h predictions; (3) relatively simple models can predict with reasonable accuracy transient ground water flow in a complex aquifer, and parameters governing this prediction can be estimated by nonlinear regression methods that incorporate both h and q data; (4) addition of C data to the calibration did not improve model predictive capacity because the information in the C data was similar to that in the q data, from the perspective of model calibration (the subsurface chemical signal in question was controlled mainly by seepage of high-chloride canal water into the low-chloride ground water system).