Daily Precipitation Time Series Research Articles

Stochastic downscaling of gridded precipitation to spatially coherent subgrid precipitation fields using a transformed Gaussian model

AbstractClimate impact models often require unbiased point‐scale observations, but climate models typically provide biased simulations at the grid scale. While standard bias adjustment methods have shown to generally perform well at adjusting climate model biases, they cannot overcome the gap between grid‐box and point scale. To overcome this limitation, combined bias adjustment and stochastic downscaling methods have been developed. These methods, however, are single‐site methods and cannot represent spatial dependence. Here we propose a multisite stochastic downscaling method that can be applied to bias‐adjusted climate model output for generating spatially coherent time series of daily precipitation at multiple stations, conditional on the driving climate model. The method is based on a transformed truncated multivariate Gaussian model and can also be used to downscale to a full field at finer‐grid resolution. An evaluation for stations across selected catchments in Austria demonstrates the good performance of the stochastic model at representing marginal, temporal and spatial aspects of daily precipitation, including extreme events.

Relationship between Precipitation Characteristics at Different Scales and Drought/Flood during the Past 40 Years in Longchuan River, Southwestern China

In this study, the temporal and spatial patterns of rainfall in the Longchuan River basin from 1977 to 2017 were analyzed, to assess the feature of precipitation. Based on the daily precipitation time series, the Lorenz curve, precipitation concentration index (PCI), precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to evaluate the precipitation distribution characteristics. The PCI, PCD and PCP in five categories, defined by the fixed thresholds, were proposed to investigate the concentrations, and the average values indicated the higher concentrations in the higher intensities. The indices showed strong irregularity of daily and monthly precipitation distributions in this basin. The decrease in the PCD revealed an increase in the proportion of precipitation in the dry season. The rainy days of slight precipitation in the upper and lower basins with significant downward trends (−13.13 d/10 a, −7.78 d/10 a) led to longer dry spells and an increase in the risk of drought, even severe in the lower area. In the upper basin, the increase in rainfall erosivity was supported by the upward trend in the PCIw of heavy precipitation and the simple daily intensity index (SDII) of extreme precipitation. Moreover, the PCP of light precipitation, moderate precipitation, and heavy precipitation concentrated earlier at the end of July. The results of this study can provide beneficial reference information to water resource planning, reservoir operation, and agricultural production in the basin.

Quantification of impacts between 1.5 and 4\xa0\xb0C of global warming on flooding risks in six countries

We project climate change induced changes in fluvial flood risks for six global warming levels between 1.5 and 4 °C by 2100, focusing on the major river basins of six countries. Daily time series of precipitation, temperature and monthly potential evapotranspiration were generated by combining monthly observations, daily reanalysis data and projected changes in the five CMIP5 GCMs also selected in the ISI-MIP fast track project. These series were then used to drive the HBV hydrological model and the CaMa-Flood hydrodynamic model to simulate river discharge and flood inundation. Our results indicate that return periods of 1 in 100-year floods in the late twentieth century (Q100-20C) are likely to decrease with warming. At 1.5 °C warming, 47%, 66%, 27%, 65%, 62% and 92% of the major basin areas in Brazil, China, Egypt, Ethiopia, Ghana and India respectively experience a decrease in the return period of Q100-20C, increasing to 54%, 81%, 28%, 82%, 86% and 96% with 4 °C warming. The decrease in return periods leads to increased number of people exposed to flood risks, particularly with 4 °C warming, where exposure in the major river basin areas in the six countries increases significantly, ranging from a doubling (China) to more than 50-fold (Egypt). Limiting warming to 1.5 °C would avoid much of these increased risks, resulting in increases ranging from 12 to 1266% for the 6 countries.

Quantile estimation of probability distributions for maximum daily precipitation and short time series of observational data for engineering design

Quantile estimation of probability distributions for maximum daily precipitation and short time series of observational data for engineering design

Quality control and filling of daily temperature and precipitation time series in Colombia

Quality control and filling of daily temperature and precipitation time series in Colombia

Global daily 1\u2009km land surface precipitation based on cloud cover-informed downscaling

High-resolution climatic data are essential to many questions and applications in environmental research and ecology. Here we develop and implement a new semi-mechanistic downscaling approach for daily precipitation estimate that incorporates high resolution (30 arcsec, ≈1 km) satellite-derived cloud frequency. The downscaling algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. We apply the method to the ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a daily gridded precipitation time series in 1 km resolution for the years 2003 onward. Comparison of the predictions with existing gridded products and station data from the Global Historical Climate Network indicates an improvement in the spatio-temporal performance of the downscaled data in predicting precipitation. Regional scrutiny of the cloud cover correction from the continental United States further indicates that CHELSA-EarthEnv performs well in comparison to other precipitation products. The CHELSA-EarthEnv daily precipitation product improves the temporal accuracy compared with a large improvement in the spatial accuracy especially in complex terrain.

A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB > EBK > OK > RBF > DIB > IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions.

Simulation of the impact of climate change on runoff and drought in an arid and semiarid basin (the Hablehroud, Iran)

This study evaluates the impact of climate change (CC) on runoff and hydrological drought trends in the Hablehroud river basin in central Iran. We used a daily time series of minimum temperature (Tmin), maximum temperature (Tmax), and precipitation (PCP) for the baseline period (1982–2005) analysis. For future projections, we used the output of 23 CMIP5 GCMs and two scenarios, RCP 4.5 and RCP 8.5; then, PCP, Tmin, and Tmax were projected in the future period (2025–2048). The GCMs were weighed based on the K-nearest neighbors algorithm. The results indicated a rising temperature in all months and increasing PCP in most months throughout the Hablehroud river basin's areas for the future period. The highest increase in the Tmin and Tmax in the south of the river basin under the RCP 8.5 scenario, respectively, was 1.87 °C and 1.80 °C. Furthermore, the highest reduction in the PCP was 54.88% in August under the RCP 4.5 scenario. The river flow was simulated by the IHACRES rainfall-runoff model. The annual runoff under the scenarios RCP 4.5 and RCP 8.5 declined by 11.44% and 13.13%, respectively. The basin runoff had a downward trend at the baseline period; however, it will have a downward trend in the RCP 4.5 scenario and an upward trend in the RCP 8.5 scenario for the future period. This study also analyzed drought by calculating the streamflow drought index for different time scales. Overall, the Hablehroud river basin will face short-term and medium-term hydrological drought in the future period.

Identification of droughts and heatwaves in Germany with regional climate networks

Abstract. Regional climate networks (RCNs) are used to identify heatwaves and droughts in Germany and two subregions for the summer half-years and summer seasons of the period 1951 to 2019. RCNs provide information for whole areas (in contrast to the point-wise information from standard indices), the underlying nodes can be distributed arbitrarily, they are easy to construct, and they provide details otherwise difficult to access, like temporal and spatial extent and localisation of extreme events; this makes them suitable for the statistical analysis of climate model output. The RCNs were constructed on the regular 0.25∘ grid of the E-OBS data set. The season-wise correlation of the time series of daily maximum temperature Tmax and precipitation were used to construct the adjacency matrix of the networks. Based on the results of a sensitivity study, we used the edge density, which increases significantly during extreme events, as the main metrics to characterise the network structure. The standard indices for comparison were the Effective Drought Index and Effective Heat Index (EDI and EHI), respectively, based on the same time series and complemented by other published data. Our results show that the RCNs are generally able to identify severe and moderate extremes and can differentiate between regions and seasons.

Optimal water allocation at different levels of climate change to minimize water shortage in arid regions (Case Study: Zayandeh-Rud River Basin, Iran)

Climate change is one of the most important factors influencing the future of the world's environment. The most important impacts of climate change are changes in water supply and demand in different regions of the world. In this study, different climate change patterns in two RCP4.5 and RCP8.5 emission scenarios (RCP: Representative Concentration Pathway), were adopted for the Zayandeh-Rud River Basin, Iran, through weighting of GCMs (General Circulation Models). These climate change patterns are including ideal, medium, and critical patterns. Using the LARS-WG model (Long Ashton Research Station Weather Generator), the outputs of the GCMs were downscaled statistically and the daily temperature and precipitation time series were generated from 2020 to 2044. Then, based on this information, the inflow volume into the Zayandeh-Rud Reservoir was predicted by the IHACRES model (Identification of unit Hydrograph and Component flows from Rainfall, Evaporation and Streamflow) and the agricultural water demand was also estimated based on future evapotranspiration. Finally, using GAMS (General Algebraic Modeling System) software, water resources in this basin were allocated based on the basic management scenario (B) and the water demand management scenario (D). The results showed that the average monthly temperature will increase by 0.6 to 1.3 °C under different climate change patterns. On the other hand, on the annual basis, precipitation will decrease by 6.5 to 31% and inflow volume to the Zayandeh-Rud Reservoir will decrease by 21 to 38%. The results also showed that the water shortages based on the baseline management scenario (B) will be between 334 and 805 MCM (Million Cubic Meters). These range of values varies between 252 and 787 MCM in the water demand management scenario (D). In general, the water shortage can be reduced in the Zayandeh-Rud River Basin with water demand control, but complete resolution of this problem in this region requires more integrated strategies based on a sustainable development, such as a fundamental change in the cropping pattern, prevention of population growth and industrial development.

Flood severity along the Usumacinta River, Mexico: Identifying the anthropogenic signature of tropical forest conversion

Anthropogenic activities are altering flood frequency-magnitude distributions along many of the world’s large rivers. Yet isolating the impact of any single factor amongst the multitudes of competing anthropogenic drivers is a persistent challenge. The Usumacinta River in southeastern Mexico provides an opportunity to study the anthropogenic driver of tropical forest conversion in isolation, as the long meteorological and discharge records capture the river’s response to large-scale agricultural expansion without interference from development activities such as dams or channel modifications. We analyse continuous daily time series of precipitation, temperature, and discharge to identify long-term trends, and employ a novel approach to disentangle the signal of deforestation by normalising daily discharges by 90-day mean precipitation volumes from the contributing area in order to account for climatic variability. We also identify an anthropogenic signature of tropical forest conversion at the intra-annual scale, reproduce this signal using a distributed hydrological model (VMOD), and demonstrate that the continued conversion of tropical forest to agricultural land use will further exacerbate large-scale flooding. We find statistically significant increasing trends in annual minimum, mean, and maximum discharges that are not evident in either precipitation or temperature records, with mean monthly discharges increasing between 7% and 75% in the past decades. Model results demonstrate that forest cover loss is responsible for raising the 10-year return peak discharge by 25%, while the total conversion of forest to agricultural use would result in an additional 18% rise. These findings highlight the need for an integrated basin-wide approach to land management that considers the impacts of agricultural expansion on increased flood prevalence, and the economic and social costs involved.

A modified Multifractal Detrended Fluctuation Analysis (MFDFA) approach for multifractal analysis of precipitation

Multifractal Detrended Fluctuation Analysis (MFDFA) is an efficient method to investigate the long-term correlations of the power law of non-stationary time series, in which the elimination of local trends usually depends upon a fixed-constant polynomial order. In this paper, we propose a flexible set of polynomial and trigonometric functions to better detect, and correctly model, hidden local trends in the time series at different scales. We introduce the Multifractal Detrended Fluctuation Analysis with Polynomial and Trigonometric functions (MFDFAPT) method via optimal model selection from an optimization framework. The performance of MFDFAPT is assessed with extensive numerical experiments based on the multifractal binomial cascade process, fractional Brownian movements, and fractional Gaussian noises. MFDFAPT shows better performance than MFDFA in the approximation of the Hurst index, and correctly determines the scalar behavior in stationary and non-stationary series. Additionally we apply MFDFAPT to detect and characterize the scalar properties of the daily precipitation time series in meteorological stations of Tabasco, México. Our results confirm previous indications that the general Hurst exponent depends on the physiographic characteristics of the study area, and that fractal dimension correctly characterizes the series of daily precipitations in tropical regions.

Climatic Trends in Different Bioclimatic Zones in the Chitwan Annapurna Landscape, Nepal

The Chitwan Annapurna Landscape (CHAL) is the central part of the Himalayas and covers all bioclimatic zones with major endemism of flora, unique agro-biodiversity, environmental, cultural and socio-economic importance. Not much is known about temperature and precipitation trends along the different bioclimatic zones nor how changes in these parameters might impact the whole natural process, including biodiversity and ecosystems, in the CHAL. Analysis of daily temperature and precipitation time series data (1970–2019) was carried out in seven bioclimatic zones extending from lowland Terai to the higher Himalayas. The non-parametric Mann-Kendall test was applied to determine the trends, which were quantified by Sen’s slope. Annual and decade interval average temperature, precipitation trends, and lapse rate were analyzed in each bioclimatic zone. In the seven bioclimatic zones, precipitation showed a mixed pattern of decreasing and increasing trends (four bioclimatic zones showed a decreasing and three bioclimatic zones an increasing trend). Precipitation did not show any particular trend at decade intervals but the pattern of rainfall decreases after 2000AD. The average annual temperature at different bioclimatic zones clearly indicates that temperature at higher elevations is increasing significantly more than at lower elevations. In lower tropical bioclimatic zone (LTBZ), upper tropical bioclimatic zone (UTBZ), lower subtropical bioclimatic zone (LSBZ), upper subtropical bioclimatic zone (USBZ), and temperate bioclimatic zone (TBZ), the average temperature increased by 0.022, 0.030, 0.036, 0.042 and 0.051 °C/year, respectively. The decade level temperature scenario revealed that the hottest decade was from 1999–2009 and average decade level increases of temperature at different bioclimatic zones ranges from 0.2 to 0.27 °C /decade. The average temperature and precipitation was found clearly different from one bioclimatic zone to other. This is the first time that bioclimatic zone level precipitation and temperature trends have been analyzed for the CHAL. The rate of additional temperature rise at higher altitudes compared to lower elevations meets the requirements to mitigate climate change in different bioclimatic zones in a different ways. This information would be fundamental to safeguarding vulnerable communities, ecosystem and relevant climate-sensitive sectors from the impact of climate change through formulation of sector-wise climate change adaptation strategies and improving the livelihood of rural communities.

Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.

A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series

We present and assess a method to estimate missing values in daily precipitation time series for the Mediterranean island of Crete. The method involves a quantile mapping methodology originally developed for the bias correction of climate models’ output. The overall methodology is based on a two-step procedure: (a) assessment of missing values from nearby stations and (b) adjustment of the biases in the probability density function of the filled values towards the existing data of the target. The methodology is assessed for its performance in filling-in the time series of a dense precipitation station network with large gaps on the island of Crete, Greece. The results indicate that quantile mapping can benefit the filled-in missing data statistics, as well as the wet day fraction. Conceptual limitations of the method are discussed, and correct methodology application guidance is provided.

Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms

Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here we propose super learning (a type of ensemble learning) by combining 10 machine learning algorithms. We apply the proposed algorithm in one-step ahead forecasting mode. For the application, we exploit a big dataset consisting of 10-year long time series of daily streamflow, precipitation and temperature from 511 basins. The super learner improves over the performance of the linear regression algorithm by 20.06%, outperforming the "hard to beat in practice" equal weight combiner. The latter improves over the performance of the linear regression algorithm by 19.21%. The best performing individual machine learning algorithm is neural networks, which improves over the performance of the linear regression algorithm by 16.73%, followed by extremely randomized trees (16.40%), XGBoost (15.92%), loess (15.36%), random forests (12.75%), polyMARS (12.36%), MARS (4.74%), lasso (0.11%) and support vector regression (-0.45%). Based on the obtained large-scale results, we propose super learning for daily streamflow forecasting.

Analysis and Modeling of Precipitation Intermittency by Compound Markov‐DARMA Models

AbstractIntermittency in precipitation is analyzed by a new class of stochastic models consisting of the product of two binary stochastic models. The ingoing models in these compound models are confined to a one‐parameter white noise model and either a first‐order or a second‐order autoregressive model, which is described by two or three parameters, respectively. The two resulting second‐order compound models have three and four parameters, respectively. Expressions of these parameters are derived, as well as equations with which to assess the parameters in the ingoing models based on the parameters in the compound models. The compound models are shown to differ considerably from the usual Markov models and discrete autoregressive moving average (DARMA) models. Daily binary precipitation time series from Copenhagen, Denmark, and Alice Springs, Australia, are analyzed by depicting the Markovian transition probabilities in probability trees with time lags of up to 5 days, resulting in 32 branches. Each tree is a kind of signature for the time series considered, but only a few of the branches, typically 3–4, are significant. Furthermore, the significant parts of the trees are rather skewed due to the intermittency of the precipitation process. This skewness makes the application of usual Markov and DARMA models less effective, whereas the new compound models better explain the observations. Based on one of the two new models, explicit but approximate expressions for the cumulative distribution (or the return period) of the durations of wet and dry spells are derived and compared to the observations from Copenhagen.

Climatological aspects and changes in temperature and precipitation extremes in Viçosa-Minas Gerais.

Extreme climatic events and their hazards have strong impact on society. Urban areas in Brazil are especially vulnerable to the impact of such events due to their rapid growth and inappropriate infrastructure. Viçosa is a mid-sized city in Southeastern Brazil that has been experiencing issues associated with urban expansion and population growth since the 1960s. Thus, this study aims to identify patterns of extreme climate events in Viçosa based on daily temperature and precipitation time series (1968-2017). Homogeneity tests were carried out in order to identify breaking points in these climate variables. Climate trends were analyzed through Mann-Kendall test and their magnitude was checked based on Sen's slope. Results have evidenced statistically significant and increasing trends in annual minimum temperature since the 1990s. Moreover, statistically significant breaking points in extreme temperature indices have shown increasing number of warm days, and decreasing number of cold nights, in both annual and seasonal analyses. Extreme climatic events have been observed more often in recent years, mainly in the number of consecutive dry days and maximum and heavy precipitation days. Based on results, Viçosa experiences warmer conditions throughout the year, whereas more (less) torrential rainfall events have been occurring during Summer (Winter).

Bivariate analysis of drought duration and severity for irrigation planning

In this study, the effectiveness of a bivariate analysis of agricultural drought characteristics for irrigation planning was evaluated. The case study was conducted in locations in central Italy and was based on olive crops which are widely grown in that area under rainfed or deficit irrigation regimes. For each locality, the available time series of daily precipitation and maximum and minimum temperatures were used to simulate the daily soil water dynamics (SWt) for olive crops. The simulation was performed assuming 10 irrigation strategies, different for both the volume and date of the interventions. By applying the Theory of Runs to SWt, with a threshold corresponding to the readily available water, the agricultural drought events in the time series were identified and characterised by their duration D (days) and severity S (i.e. the cumulative evapotranspiration deficit in mm) for each locality and strategy.A 2-parameter Gamma distribution was fitted to both D and S, whilst their dependence structure was modelled by a Gumbel copula. The evaluation of the best irrigation strategy in each locality was obtained by comparing the contour lines of the pairs (d,s) having a 10-year return period for the condition D ≥ d AND S ≥ s. Results showed a great influence of both climate and irrigation strategy on the joint distribution of D and S. Some strategies, despite a very similar behaviour in the severity, differed a lot in terms of the associated values of duration, thus denoting different dynamics of water stress (more or less intense). Among the single-irrigation strategies, the optimal one was usually represented by an application at the end of June. Among the double-irrigation strategies, the optimal one involved two interventions, one at the end of June and one at the end of July. These results were also discussed taking into account the corresponding probability of consecutive severe drought events.

Impact of Seasonal Changes of Precipitation and Air Temperature on Clay Excavation

Excavation of clay soil is one of the most important economic branches in the northern part of Croatia. The impact of clay soil in Croatia compared to the global exploitation fields of clay soil is negligible. Modern methods of clay excavation during winter months due to negligible amounts are not profitable. Therefore, it is important to optimize clay soil excavation throughout the year to increase the efficiency of exploitation and increase profits. In the case of large amounts of precipitation (rain), clay absorbs water and becomes grain. For this reason, access to the exploitation field and excavation itself becomes impossible. Air temperature also plays an important role in excavation. Long-lasting low air temperatures below 0 °C during the winter months result in clay frost. As a result, excavation cannot occur at that time. The paper describes a new method of modeling the precipitation and air temperature on the exploitation fields of clay in Northwest Croatia on the exploitation fields of Cukavec and Cukavec II. The method involves the calculation of the drought index and use of the rescaled adjusted partial sums (RAPS) statistical method and its application on a time series of total daily precipitation and average daily temperatures as a climatic indicator of any observed area. Using this process, it is possible to determine the time period of the year when clay soil can be excavated.