Modeling daily soil temperature using data-driven models and spatial distribution

The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro–fuzzy inference system (ANFIS), for estimating daily soil temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily soil temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily soil temperature successfully in this study.     

Predicting daily pan evaporation by soft computing models with limited climatic data

Abstract

Accurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiority of MLP-NNM, KSOFM-NNM, and GEP over MLRM. It was concluded that the soft computing models can be successfully employed for predicting daily PE in south western Iran.

Editor D. Koutsoyiannis     

Revisiting the stratigraphy of the Mesoproterozoic Chhattisgarh Supergroup,Bastar craton,India based on subsurface lithoinformation

In the last 10 years, several teams of geologists from different institutions in India and abroad have vigorously investigated the Chhattisgarh basin (Bastar craton, India). Based on the new results and the lithologs of more than 350 water wells, resistivity and gamma-ray logs, and extensive geological traverses, we present a revised geological map, relevant cross sections, a new comprehensive stratigraphic column and a discussion of the new findings. Major outcomes of this revision are: (1) confirming the existence of two sub-basins (Hirri and Baradwar) and two depocentres; (2) establishing the age of the basin to be essentially Mesoproterozoic; (3) discarding the ‘unclassified Pandaria Formation’ and classifying the package of Pandaria rock units into Chandi, Tarenga, Hirri and Maniari formations in the Hirri sub-basin; (4) accepting the ‘group’ status of the Singhora Group and the newly proposed Kharsiya Groups in the Baradwar sub-basin; (5) establishing an intrabasinal correlation of formations; (6) reappraising the thicknesses of the different formations; and (7) finding that the geometry of the basin is ‘bowl-shaped’, which is compatible with a sag model for the origin and evolution of the basin.     

Post-2004 mega-earthquake temporal velocity variation at Andaman Islands from GPS measurements

Post-mega-earthquake velocity adjustment at Andaman region was found to vary with time. Coordinate repeatability for 5-year span shows changeover from post-seismic to inter-seismic period in between 900 and 1,000 days from the mega-earthquake. Excluding Havlock Island, all sites move from south to north and from east to west. Velocity vectors gradually rotated from the nearly perpendicular orientation after the mega-earthquake to parallel orientation with the subduction interface in later phase. Velocities in India 2005 reference frame indicate the presence of a structural discontinuity between Bedonabad and Chidiatapu at south Andaman, between Padmanavapuram and Kaushalyanagar at middle Andaman and between Aerial Bay and Radhanagar at north Andaman.     

基于模糊集理论的降雨不确定性传播影响研究

基于模糊集理论,耦合遗传算法,量化分析降雨的量级、空间分布和时程分配产生的不确定性对流量模拟的影响。雨量量级的不确定性使用模糊集概念表示,运用遗传算法对时段雨量在时间上进行随机解集,并通过在各子流域上采用不同的时间解集模式以同时考虑降雨时程分配和空间分布不确定性。应用TOPMODEL对资水流域新宁水文站洪水过程进行模拟研究,结果表明,雨量不确定性的传播对洪水预报的影响处于主导地位,降雨时空分布引起的不确定性对洪水模拟的影响次之。此外,通过对1 h和0.5 h解集结果的比较发现,本文中采用1 h作为模拟的时间步长已可以较充分反映雨量的时间变异性。     

Precipitation extremes in the Yangtze River Basin,China: regional frequency and spatial–temporal patterns

Regional frequency analysis and spatial–temporal patterns of precipitation extremes are investigated based on daily precipitation data covering 1960–2009 using the index-flood L-moments method together with some advanced statistical tests and spatial analysis techniques. The results indicate that: (1) the entire Yangtze River basin can be divided into six homogeneous regions in terms of extreme daily precipitation index. Goodness-of-fit test indicates that Pearson type III (PE3, three parameters), general extreme-value (GEV, three parameters), and general normal (GNO, three parameters) perform well in fitting regional precipitation extremes; (2) the regional growth curves for each homogeneous region with 99 % error bands show that the quantile estimates are reliable enough and can be used when return periods are less than 100 years, and the results indicate that extreme precipitation events are highly probable to occur in regions V and VI, and hence higher risk of floods and droughts; and (3) spatial patterns of annual extreme daily precipitation with return period of 20 years indicate that precipitation amount increases gradually from the upper to the lower Yangtze River basin, showing higher risks of floods and droughts in the middle and lower Yangtze River basin, and this result is in good agreement with those derived from regional growth curves.     

Probabilistic forecasting of seasonal drought behaviors in the Huai River basin,China

Results from high resolution 7-km WRF regional climate model (RCM) simulations are used to analyse changes in the occurrence frequencies of heat waves, of precipitation extremes and of the duration of the winter time freezing period for highly populated urban areas in Central Europe. The projected climate change impact is assessed for 11 urban areas based on climate indices for a future period (2021–2050) compared to a reference period (1971–2000) using the IPCC AR4 A1B Scenario as boundary conditions. These climate indices are calculated from daily maximum, minimum and mean temperatures as well as precipitation amounts. By this, the vulnerability of these areas to future climate conditions is to be investigated. The number of heat waves, as well as the number of single hot days, tropical nights and heavy precipitation events is projected to increase in the near future. In addition, the number of frost days is significantly decreased. Probability density functions of monthly mean summer time temperatures show an increase of the 95th percentile of about 1–3 °C for the future compared with the reference period. The projected increase of cooling and decrease of heating degree days indicate the possible impact on urban energy consumption under future climate conditions.