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.     

Mountain range specific analog weather forecast model for northwest Himalaya in India

Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya). The model searches past similar cases from historical dataset of reference observatory in each mountain range based on current situation. The searched past similar cases of each mountain range are used to draw weather forecast for that mountain range in operational weather forecasting mode, three days in advance. The developed analog weather forecast model is tested with the independent dataset of more than 717 days (542 days for Pir Panjal range in HP) of the past 4 winters (2003–2004 to 2006–2007). Independent test results are reasonably good and suggest that there is some possibility of forecasting weather in operational weather forecasting mode employing analog method over different mountain ranges in NW-Himalaya. Significant difference in overall accuracy of the model is found for prediction of snow day and no-snow day over different mountain ranges, when weather is predicted under snow day and no-snow day weather forecast categories respectively. In the same mountain range, significant difference is also found in overall accuracy of the model for prediction of snow day and no-snow day for different areas. This can be attributed to their geographical position and topographical differences. The analog weather forecast model performs better than persistence and climatological forecast for day-1 predictions for all the mountain ranges except Karakoram range in NW-Himalaya. The developed analog weather forecast model may help as a guidance tool for forecasting weather in operational weather forecasting mode in different mountain ranges in NW-Himalaya.     

中国东部降水的极端特性及其气候特征分析

基于1960—2017年观测数据分析了中国东部降水极端特性的地区差异、季节和气候学特征及变化格局,探讨了与全球变化和区域气候变率的关联性。结果表明,极端性降水的演化与降水均值或总量的气候型态、降水集中性和持续性密切关联,尤其雨带迁移和雨型演替是影响极端性降水地区差异与时空演变的根本因素。气候变化背景下,中国东部极端性降水强度和频次变化存在较好的协同一致性,近60年来在长江以南,强度加大的地区极端性降水亦趋于频发。同时,两者季节特征和地区差异明显。春季东北地区及华北北部极端性降水强度和频次均有明显增加。近60年来夏季极端性降水强度和频次的趋势变化在长江以南均以增加为主,以北以下降为主。秋季极端性降水强度和频次在华北地区亦呈增加趋势。冬季华南和江南地区极端性降水强度和频次趋势变化均以增加为主。华北地区及以北和内蒙古的西部冬季极端性降水强度增加显著,但频次变化不明显。而东北地区北部冬季极端性降水在强度减小的情形下,其频次仍趋显著增加。特别是中国降水主要集中在夏季,自1980年代以来中国东部夏季多雨带南移,雨型以北方型和中间型占优,转换为以长江型和华南型为主,多雨带的极端性降水群发性强,影响指数显著增加。此外,太平洋年代际振荡(PDO)暖位相及ENSO暖事件期间,长江以北夏季极端性降水的影响指数会显著降低。而东亚夏季风的减弱则有利于长江中下游等地区夏季极端性降水的频发和群发,极端性降水强度加大,其影响的危险性趋于增强。      

Climate-driven trends in mean and high flows from a network of reference stations in Ireland

Abstract

This paper introduces a reference hydrometric network for Ireland and examines the derived flow archive for evidence of climate-driven trends in mean and high river flows. The Mann-Kendall and Theil-Sen tests are applied to eight hydroclimatic indicators for fixed and variable (start and end date) records. Spatial coherence and similarities of trends with rainfall suggest they are climate driven; however, large temporal variability makes it difficult to discern widely-expected anthropogenic climate change signals at this point in time. Trends in summer mean flows and recent winter means are at odds with those expected for anthropogenic climate change. High-flow indicators show strong and persistent positive trends, are less affected by variability and may provide earlier climate change signals than mean flows. The results highlight the caution required in using fixed periods of record for trend analysis, recognizing the trade-off between record length, network density and geographic coverage.

Editor Z.W. Kundzewicz; Associate editor H. Lins

Citation Murphy, C., Harrigan, S., Hall, J., and Wilby, R.L., 2013. Climate-driven trends in mean and high flows from a network of reference stations in Ireland. Hydrological Sciences Journal, 58 (4), 755–772.     

Temporal correlations and clustering of landslides

This paper examines temporal correlations and temporal clustering of a proxy historical landslide time series, 2255 reported landslides 1951–2002, for an area in the Emilia‐Romagna Region, Italy. Landslide intensity is measured by the number of reported landslides in a day (D_L) and in an ‘event’ (S_event) of consecutive days with landsliding. The non‐zero values in both time series D_L and S_event are unequally spaced in time, and have heavy‐tailed frequency‐size distributions. To examine temporal correlations, we use power‐spectral analysis (Lomb periodogram) and surrogate data analysis, confronting our original D_L and S_event time series with 1000 shuffled (uncorrelated) versions. We conclude that the landslide intensity series D_L has strong temporal correlations and S_event has likely temporal correlations. To examine temporal clustering in D_L and S_event, we consider extremes over different landslide intensity thresholds. We first examine the statistical distribution of interextreme occurrence times, τ, and find Weibull distributions with parameter γ << 1·0 [D_L] and γ < 1·0 [S_event]; thus D_L and S_event each have temporal correlations, but S_event to a lesser degree. We next examine correlations between successive interextreme occurrence times, τ. Using autocorrelation analysis applied to τ, combined with surrogate data analysis, we find for D_L linear correlations in τ, but for S_event inconclusive results. However, using Kendall's rank correlation analysis we find for both D_L and S_event the series of τ are strongly correlated. Finally, we apply Fano Factor analysis, finding for both D_L and S_event the timings of extremes over a given threshold exhibit a fractal structure and are clustered in time. In this paper, we provide a framework for examining time series where the non‐zero values are strongly unequally spaced and heavy‐tailed, particularly important in the Earth Sciences due to their common occurrence, and find that landslide intensity time series exhibit temporal correlations and clustering. Many landslide models currently are designed under the assumption that landslides are uncorrelated in time, which we show is false. Copyright © 2010 John Wiley & Sons, Ltd.     

Persistence of Summer Sea Surface Temperature Anomalies in the Midlatitude North Pacific and Its Interdecadal Variability

The present study investigates the persistence of summer sea surface temperature anomalies(SSTAs) in the midlatitude North Pacific and its interdecadal variability. Summer SSTAs can persist for a long time(approximately 8–14 months)around the Kuroshio Extension(KE) region. This long persistence may be strongly related to atmospheric forcing because the mixed layer is too shallow in the summer to be influenced by the anomalies at depths in the ocean. Changes in atmospheric circulation, latent heat flux, and longwave radiation flux all contribute to the long persistence of summer SSTAs. Among these factors, the longwave radiation flux has a dominant influence. The effects of sensible heat flux and shortwave radiation flux anomalies are not significant. The persistence of summer SSTAs displays pronounced interdecadal variability around the KE region, and the variability is very weak during 1950–82 but becomes stronger during 1983–2016. The changes in atmospheric circulation, latent heat flux, and longwave radiation flux are also responsible for this interdecadal variability because their forcings on the summer SSTAs are sustained for much longer after 1982.     

Analysis of long-term precipitation changes in West Bengal,India: An approach to detect monotonic trends influenced by autocorrelations

The precipitation of the entire Indian sub-continent is primarily driven by the monsoon wind system, which throws a tough challenge to model changes due to its natural variabilities. Additionally, the unique geographical location and diverse climatic condition of the Indian state of West Bengal (WB) accelerates this challenge even more. Such a situation can be more complicated if the assumption of randomness in the Mann-Kendall (MK) test is not taken care of. The present study inspects the dynamics of precipitation using the original MK test along with its three modified versions in WB. The modified versions are considered to incorporate all the significant autocorrelation coefficient (ACC) along with the short and long term persistence (STP and LTP) in the time series. Results showed that the presence of significant ACC was more common than the LTP behavior. The STP approach produced similar results to the original MK test, while the LTP approach reduced the number of significant trends. When the series was free from the scaling effect, consideration of all significant ACC gave better result in comparison to the STP approach. The annual precipitation is decreasing in large areas of WB, while the coastal areas are receiving increasing trends, which can intricate the estuarine and coastal processes. The Sub-Himalayan region and the western parts of WB are experiencing significant falling trend in monsoon season. Such a decreasing trend can enhance drought vulnerabilities, especially in the western parts of WB. However, the other three seasons (pre-monsoon, post-monsoon, and winter) witness the maximum number of non-significant trends.     

Erosion,deposition, and the persistence of soil organic matter: mechanistic considerations and problems with terminology

The effect of erosional detachment, transport, and deposition of topsoil on the stock of soil organic matter (SOM) and its association with soil minerals has been a focus of a growing number of studies. A particularly lively debate is currently centered on the questions of whether terrestrial sedimentation of previously eroded SOM may constitute a relevant sink for atmospheric carbon dioxide (CO₂), and how ‘stable’ such carbon (C) might be on multidecadal timescales. In this commentary, we illustrate how redistribution of eroded SOM within a landscape can create situations that are not adequately described by the jargon commonly used to characterize C turnover dynamics. We argue that more quantitative and scientifically rigorous categories are needed to describe soil C turnover and to promote the development of innovative, numerical models of C dynamics in landscapes characterized by significant mass movement. Copyright © 2013 John Wiley & Sons, Ltd.     

Seasonal and spatial variations in the scaling and correlation structure of streamflow data

Seasonal and spatial variability in scaling, correlation and wavelet variance parameter of daily streamflow data were investigated using 56 gauging stations from five basins located in two different climate zones. Multifractal temporal scaling properties were detected using a multiplicative cascade model. The wavelet variance parameter yielded persistence properties of the streamflow time series. Seasonal variations were found to be significant in that winter and spring seasons where large‐scale frontal events are dominant showed higher long‐term correlations and less multifractality than did summer and fall seasons. Coherent spatial variations were apparent. The Neches River basin located in a subtropic humid climate zone exhibited high persistence and long‐term correlation as well as less multifractality as compared with other basins. It is found that larger drainage areas tend to have smaller multifractality and higher persistence structure, and this tendency becomes apparent in regions that receive large amounts of precipitation and decreases towards arid regions. Copyright © 2012 John Wiley & Sons, Ltd.     

基于人工神经网络的热带气旋路径预报试验

利用气候持续性因子,分别采用神经网络法及最小二乘回归法,建立西北太平洋地区12、24、36和48h热带气旋路径预报模型。通过对1992～2002年资料的试报,人工神经网络方法优于回归方法,且这种优势在预报时效较长时更明显。人工神经网络法48h的预报平均绝对误差比回归法减小27．56km,预报水平提高7％。