Spatial variation of effective porosity and its implications for discharge in an upland headwater catchment in Scotland

Spatial and temporal measurements of shallow sub-surface soil physical properties were made within a 1 km² upland catchment. The surface soil layer of the catchment was organic rich (>70% organic matter) with a corresponding total porosity of 81%. Monthly point observations of volumetric water content (θ) were combined with point estimates of total porosity () and the porosity <50 μm (_residual), to define the ratio of water filled pore volume:pore volume in pores <50 μm (=θ/_residual). Values of θ/_residual were compared with discharge to test whether mass flow occurred when θ/_residual>1. A correlation between water content and discharge was found, with discharge increasing rapidly when θ/_residual approached unity. Similar relationships between water content and catchment discharge were identified for soil units adjacent to the stream when θ/_residual approached unity. These data suggest that soil pores >50 μm are of crucial importance in determining catchment discharge. Spatial and temporal variations in soil properties related to moisture content of the soil were also observed. Under dry conditions, a clear division based on aspect was noted, the west-facing side of the catchment being wettest. In wetter months, total porosity and soil water content were significantly affected by soil type and the spatial pattern of soil water content was more variable than in the dryer months. The physical quantification of soil properties in the shallow sub-surface layer proved important in explaining different initial changes in discharge from the catchment in response to a rainfall event.     

A groundwater-based, objective-heuristic parameter optimisation method for a precipitation-runoff model and its application to a semi-arid basin

A hydrologic model calibration methodology that is based on groundwater data is developed and implemented using the US Geological Survey's precipitation-runoff modelling system (PRMS) and the modular modelling system (MMS), which performs automatic calibration of parameters. The developed methodology was tested in the Akrotiri basin, Cyprus. The necessity for the groundwater-based model calibration, rather than a typical runoff-based one, arose from the very intermittent character of the runoff in the Akrotiri basin, a case often met in semi-arid regions. Introducing a datum and converting groundwater storage to head made the observable groundwater level the calibration indicator. The modelling of the Akrotiri basin leads us to conclude that groundwater level is a useful indicator for hydrological model calibration that can be potentially used in other similar situations in the absence of river flow measurements. However, the option of an automatic calibration of the complex hydrologic model PRMS by MMS did not ensure a good outcome. On the other hand, automatic optimisation, combined with heuristic expert intervention, enabled achievement of good calibration and constitutes a valuable means for saving effort and improving modelling performance. To this end, results must be scrutinised, melding the viewpoint of physical sense with mathematical efficiency criteria. Thus optimised, PRMS achieved a low simulation error, good reproduction of the historic trend of the aquifer water level evolution and reasonable physical behaviour (good hydrologic balance, Reasonable match of aquifer level evolution, good estimation of mean natural recharge rate).     

Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin

ABSTRACT

The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.     

Integration of transfer function model and back propagation neural network for forecasting storm sewer flow in Taipei metropolis

Typhoons and storms have often brought heavy rainfalls and induced floods that have frequently caused severe damage and loss of life in Taiwan. Our ability to predict sewer discharge and forecast floods in advance during storm seasons plays an important role in flood warning and flood hazard mitigation. In this paper, we develop an integrated model (TFMBPN) for forecasting sewer discharge that combines two traditional models: a transfer function model and a back propagation neural network. We evaluated the integrated model and the two traditional models by applying them to a sewer system of Taipei metropolis during three past typhoon events (NARI, SINLAKU, and NAKR). The performances of the models were evaluated by using predictions of a total of 6 h of sewer flow stages, and six different evaluation indices of the predictions. Finally, an overall performance index was determined to assess the overall performance of each model. Based on these evaluation indices, our analysis shows that TFMBNP yields accurate results that surpass the two traditional models. Thus, TFMBNP appears to be a promising tool for flood forecasting for the Taipei metropolis sewer system. For publication in Stochastic Environmental Research and Risk Analysis.     

ENSO and the natural variability in the flow of tropical rivers

This paper examines the relationship between the annual discharges of the Amazon, Congo, Paran á, and Nile rivers and the sea surface temperature (SST) anomalies of the eastern and central equatorial Pacific Ocean, an index of El Niño-Southern Oscillation (ENSO). Since river systems are comprehensive integrators of rainfall over large areas, accurate characterization of the flow regimes in major rivers will increase our understanding of large-scale global atmospheric dynamics. Results of this study reveal that the annual discharges of two large equatorial tropical rivers, the Amazon and the Congo, are weakly and negatively correlated with the equatorial Pacific SST anomalies with 10% of the variance in annual discharge explained by ENSO. Two smaller subtropical rivers, the Nile and the Paraná, show a correlation that is stronger by about a factor of 2. The Nile discharge is negatively correlated with the SST anomaly, whereas the Paraná river discharge shows a positive relation. The tendency for reduced rainfall/discharge over large tropical convection zones in the ENSO warm phase is attributed to global scale subsidence associated with major upwelling in the eastern Pacific Ocean.     

青藏高原春季土壤湿度与我国长江流域夏季降水的联系及其可能机理

土壤湿度作为陆面过程的重要因子,对局地及邻近地区的大气环流和天气气候有重要影响.青藏高原的土壤湿度观测站点稀少,时间较短,鉴于此,本文使用经过部分观测站点检验的卫星反演数据,研究了春季高原土壤湿度的年际变化与后期夏季我国东部降水的联系和可能机理.结果表明：在全球变暖的背景下,高原土壤湿度总体呈现出显著增加的趋势,去除该线性趋势后,我们定义了一个高原土壤湿度指数TPSMI来定量表征高原土壤湿度的年际变化特征,发现表层、中层、深层的土壤湿度年际变率趋于一致,且春季土壤湿度与夏季土壤湿度显著相关（相关系数可达0.56）.当TPSMI偏大时,即高原东部土壤湿度偏大,而西部偏小时,夏季在高原东部（西部）存在一个潜热（感热）热源,二者共同作用下,在对流层中高层从高原西部经我国大陆直至东北地区激发出一个气旋-反气旋-气旋波列,该波列呈相当正压结构,有利于东北冷涡的加强及冷空气向南爆发;与此同时,南亚高压加强东伸,西太副高西伸加强,低空南方暖湿气流与北方干冷气流在长江流域汇合,伴随着上升运动加强,从而有利于夏季长江流域降水增多;反之,当TPSMI偏小时,夏季长江流域降水减少.     

A 2D finite volume model for bebris flow and its application to events occurred in the Eastern Pyrenees

FLATModel is a 2D finite volume code that contains several original approaches to improve debris-flow simulation. Firstly, FLATModel incorporates a ＂stop-and-go＂ technique in each cell to allow continuous collapses and remobilizations of the debris-flow mass. Secondly, flow velocity and consequently yield stress is directly associated with the type of rheology to improve boundary accuracy. Thirdly, a simple approach for entrainment is also included in the model to analyse the effect of basal erosion of debris flows. FLATMODEL was tested at several events that occurred in the Eastern Pyrenees and simulation results indicated that the model can represent rather well the different characteristics observed in the field.     

A sensitivity analysis of lake water level response to changes in climate and river regimes

Lake water level regimes are influenced by climate, hydrology and land use. Intensive land use has led to a decline in lake levels in many regions, with direct impacts on lake hydrology, ecology and ecosystem services. This study examined the role of climate and river flow regime in controlling lake regimes using three different lakes with different hydraulic characteristics (volume-inflow ratio, CIR). The regime changes in the lakes were determined for five different river inflows and five different climate patterns (hot-arid, tropical, moderate, cold-arid, cold-wet), giving 75 different combinations of governing factors in lake hydrology. The input data were scaled to unify them for lake comparisons. By considering the historical lake volume fluctuations, the duration (number of months) of lake volume in different ‘wetness’ regimes from ‘dry’ to ‘wet’ was used to develop a new index for lake regime characterisation, ‘Degree of Lake Wetness’ (DLW). DLW is presented as two indices: DLW₁, providing a measure of lake filling percentage based on observed values and lake geometry, and DLW₂, providing an index for lake regimes based on historical fluctuation patterns. These indices were used to classify lake types based on their historical time series for variable climate and river inflow. The lake response time to changes in hydrology or climate was evaluated. Both DLW₁ and DLW₂ were sensitive to climate and hydrological changes. The results showed that lake level in high CIR systems depends on climate, whereas in systems with low CIR it depends more on river regime.