Spatial Variability of Soil Phosphorus in Relation to the Topographic Index and Critical Source Areas
Trevor PageP. M. HaygarthKeith BevenA. JoynesTrisha ButlerChris KeelerJim FreerPhilip N. OwensGavin Wood
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ABSTRACT A measure of soil P status in agricultural soils is generally required for assisting with prediction of potential P loss from agricultural catchments and assessing risk for water quality. The objectives of this paper are twofold: (i) investigating the soil P status, distribution, and variability, both spatially and with soil depth, of two different first‐order catchments; and (ii) determining variation in soil P concentration in relation to catchment topography (quantified as the “topographic index”) and critical source areas (CSAs). The soil P measurements showed large spatial variability, not only between fields and land uses, but also within individual fields and in part was thought to be strongly influenced by areas where cattle tended to congregate and areas where manure was most commonly spread. Topographic index alone was not related to the distribution of soil P, and does not seem to provide an adequate indicator for CSAs in the study catchments. However, CSAs may be used in conjunction with soil P data for help in determining a more “effective” catchment soil P status. The difficulties in defining CSAs a priori, particularly for modeling and prediction purposes, however, suggest that other more “integrated” measures of catchment soil P status, such as baseflow P concentrations or streambed sediment P concentrations, might be more useful. Since observed soil P distribution is variable and is also difficult to relate to nationally available soil P data, any assessment of soil P status for determining risk of P loss is uncertain and problematic, given other catchment physicochemical characteristics and the sampling strategy employed.Keywords:
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The analysis of baseflow contribution is very significant in Korea because most rivers have high variability of streamflow due to the monsoon climate. Recently, the importance of such analysis is being more evident especially in terms of river management because of the changing pattern of rainfall and runoff resulted from climate change. Various baseflow separation methods have been developed to separate baseflow from streamflow. However, it is very difficult to identify which method is the most accurate way due to the lack of measured baseflow data. Moreover, it is inappropriate to analyze the annual baseflow contribution for Korean rivers because rainfall patterns varies significantly with the seasons. Thus, this study compared the baseflow contributions at various time-scales (annual, seasonal and monthly) for the 4 major river basins through BFI (baseflow index) and suggested baseflow contribution of each basin by the BFI ranges searched from different baseflow separation methods (e.g., BFLOW, HYSEP, PART, WHAT). Based on the comparison of baseflow contributions at the three time scales, this study showed that the baseflow contributions from the monthly and seasonal analysis are more reasonable than that from the annual analysis. Furthermore, this study proposes that defining BFI with its range is more proper than a specific value for a watershed, considering the difference of BFIs between various baseflow separation methods.
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In 2003, Reilly and Kroll examined the baseflow correlation method at river sites throughout the United States. The current study reexamines Reilly and Kroll’s baseflow correlation experiment by investigating the use of different performance metrics, experimental parameters, and model assumptions that were not investigated by Reilly and Kroll. The goal of this study is to provide additional guidance on how to implement the baseflow correlation method in practice. The results confirm that baseflow measurements should be obtained during low flow seasons and as far as possible from runoff events. When one has only five baseflow measurements at the low-flow partial-record site, the correlation coefficient between baseflows at gauged and low-flow partial-record sites should be at least 0.9; when the number of baseflow measurements is 10 or more, the method performs adequately if the correlation coefficient is greater than 0.6. The performance of the baseflow correlation method improves as the number of baseflow measurements increases, but levels off dramatically when one has more than 10 measurements.
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Catchment storage capacity is an important factor in the determination of catchment sensitivity to climate variability. Quantification of catchment sensitivity is in turn important in the regional assessment of the effects of possible climate change. In the present paper, an empirical regional model is proposed that quantifies catchment sensitivity as the ratio of present maximum reservoir storage to catchment storage capacity. Catchment storage capacity is defined theoretically using readily available catchment variables. Present maximum reservoir storage in a catchment, as determined from recession analysis, is expressed as a fraction of catchment storage capacity; the fraction defines catchment sensitivity and depends on storage capacity and annual net precipitation. Average annual conditions for present maximum reservoir storage and average annual net precipitation are used to test the developed model. Although the study used data from only 15 catchments in the Upper Loire region in France, the model proved statistically valid. Storage capacity calculated with the model compares favourably with the baseflow index and a storage index defined in previous research. Values of storage capacity are probable with respect to reported water resources in the area. With the model catchment sensitivity can easily be assessed. Flood or drought prone catchments can be identified as well as a catchment's sensitivity to a catchment-type transition (baseflow versus direct flow dominated catchments). © 1998 John Wiley & Sons, Ltd.
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Baseflow plays an important role in water security,food security,water resource assessment and investigation,and water allocation.Baseflow is mainly the recharges from groundwater.The characteristics of recharge and discharge of groundwater are very important to efficient groundwater management and sustainable development,and are vital to the control of pollution.Runoff of the source regions of the Yellow River accounts for more than 35% of the total ruhoff of the Yellow River basin.Baseflow is a very important water supply source in the low flow season and also an important supply cource in the headwaters,hence,it's essential to estimate baseflow in this area.There are numerous baseflow separation methods such as analytic methods and graphic methods,but they have their own shortcomings and can't be applied extensively.In this study,two physically based and two parameters based baseflow separation methods-Kalinin method and digital filiter method-were selected.First the Kalinin baseflow separation method was improved based on the climate condition of the study area and a new procedure was developed and then the sensitivity analysis was conducted for the two methods.The results show that the baseflow separated by digital filter baseflow separation method is very sensitive to the parameter's variation,and the filter parameter β is the control factors of the baseflow.With the increase of β the separated baseflow decreases and vice versa.The sensitivity(analysis) curves are very alike as to the runoff of different years.The baseflow separated by the modified Kalinin method is strongly affected by the runoff hydrograph,generally,the baseflow increases with the increase of recession coefficient and is not sensitive to the variation of parameters.
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The purpose of this study is to identify and assess the change of baseflow and climatic impacting factors in the source regions of the Yellow River,based on the analyses of long hydrological time series(1956-2000) from four subbasins of the source regions of the Yellow River and the whole source regions of the Yellow River.Kalinin baseflow separation technique has been improved based on the characteristics of climate and streamflow of the study regions,then applied to estimate baseflow.Statistical method is adopted in order to investigate the effect of climate factors on baseflow.Annual mean baseflow in the source regions of the Yellow River is 13.246 billion m3,accounting for more than 60% of the total runoff of the area interested.Annual baseflow is in direct proportion to annual precipitation.The sharp rise in temperature in the 1990s decreased baseflow significantly.The impacts of climate factors on baseflow are different in different subcatchment.In subbasin above Huangheyan Hydrologic Station,which is relative cold and arid with annul mean temperature of-3.84 °C and the water area accounted for 7.95% of the subbasin,both temperature and precipitation nearly had no direct impacts on baseflow on annual time scale.The increasing temperature thaws frozen soil more rapid,thus lowers the groundwater table and lake water level,hence decreases water supply of baseflow from groundwater and lake water.In subbasin between Huangheyan and Jimai Hydrologic Station baseflow is influenced both by precipitation and temperature but the response rate of baseflow to precipitation is more rapid than temperature.In subbasin between Jimai and Maqu Hydrologic Stations precipitation and temperature area two key factors impacting baseflow,but the effect of precipitation is more pronounced than temperature,while in subbasin between Maqu and Tangnag Hydrologic Station precipitation is the only climate factor affecting baseflow in short term.The factors inducing serious decrease of baseflow in the 1990s are also investigated into.
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ABSTRACT: Man‐made lakes have significant impacts on the hydrologic conditions in the watershed in which they are built. This paper examines the nature of the impact upon baseflow by comparing baseflow conditions at the outlet of the lakes with those elsewhere in the watershed. Situated in the upper reaches of a small watershed, the lakes studied have only a minor effect upon the magnitude of baseflow discharge, increasing it slightly from October to January, and decreasing it from May to September. Baseflow quality is substantially affected. Natural dissolved ions, as represented by magnesium, are generally decreased in concentration and total load by the lakes. Road salt related inons are substantially increased in both concentration and total load in the baseflow. Surface runoff stored in the lakes is extremely enriched in salt in the winter, and the storage capacity of the lakes is sufficient to maintain winter salt concentrations in the baseflow near the lakes until summer. The storage effect also tends to damp out seasonal fluctuations in baseflow chloride content which are extreme in suburban watersheds. The difference in quality between the lake and non‐lake baseflows and the linear distance needed for complete mixing are used as measures of the magnitude and distal extent of the lake effect on baseflow quality.
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作为流速及流水量的一个部件， baseflow 为调整河流动的季节的分发并且稳定水供应是批评的。在西北中国的干旱区域的水资源主要从多重集水在高山那能被变化影响气候，陆地盖子，土壤和地质的因素。当众多的研究在流速及流水量上被做了时，在高山的河系统的 baseflow 的系统的分析是惊吓。基于历史的每日的流速及流水量数据和 baseflow 分离的自动化数字过滤器方法，这研究调查了降雨喂的河系统的陆上的流动，流速及流水量和 baseflow 的自记水位计的特征， snowmelt，冰河融化或这些的混合物。这研究也计算了衰退常数和 65 个河系统的 baseflow 索引。当衰退常数与 0.018 的一个平均数是 0.00340.0728 时， baseflow 索引与 0.57 的一个平均数是 0.270.79。进一步，枪兵关联分析证明 baseflow 索引显著地与集水被相关气候的因素(例如，降水和温度) ，地志的因素(例如，举起和斜坡) 并且含水土层性质由衰退常数代表了。多重回归分析显示因素在学习区域解释了 baseflow 索引的 65% 可变性。
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