Runoff Scenarios Research Articles

Effect of temporal sampling of precipitation on hydrologic model calibration

Although the effects of spatial and temporal variability of precipitation on hydrologic modeling results have been well established, there has been very little attention given to sampling impacts on model calibration. To examine the effects of temporal sampling of rainfall, a combination of numerical simulations and calibration experiments have been carried out. A “true” runoff scenario, which is used for calibration of models using longer sampling periods, is established using very high resolution precipitation data and parameters derived from the physical properties of the soils. Three point infiltration models were chosen to isolate the effects of temporal sampling. A simple empirical model, a more complex empirical model, and a physics‐based model were tested and compared. Numerical simulations using parameters based directly on soil properties demonstrated an undersimulation of runoff as the precipitation signal is smoothed at lower sampling frequencies. Calibration of selected parameters may compensate for this undersimulation effect. This impact becomes more pronounced for higher infiltration capacities, as calibration is required to compensate for greater underestimation of runoff. The adjustments that are necessary to compensate for the sampling effect illustrate how some of the physical meaning of the parameters is lost in the calibration process. Since the calibrated model parameters correspond to physical properties of the system, these results demonstrate that it may not be possible to make a priori parameter estimates based solely on physical properties of the system or to use parameters calibrated using data with different temporal or spatial sampling.

Modelling the benefits of soil water conservation using the PARCH model—a case study from a semi-arid region of Kenya

Field experiments in the semi-arid regions of Kenya have shown that soil water conservation techniques can result in increased maize grain yields. The degree of benefit in a particular season is dependant on the rainfall amount and distribution. However, the results of field experiments are limited to a few years of observations at specific locations and it is therefore difficult to make generalized conclusions about the benefits in years of differing rainfall patterns.The PARCH model has been calibrated for Katumani Composite ‘B’—a local variety of maize used in the Machakos district of Kenya—and validated against observed grain and dry matter yields from experimental plots. Historical daily rainfall data were collected for Katumani Research Station, in the semi-arid region of Kenya, during the period 1961 to 1994. Seasonal totals for the short rains (October to February) and long rains (February to August) were calculated and nine years were selected as representative of ‘wet’, ‘average’ and ‘dry’ seasons for the long and short rains, respectively.The PARCH model was then used to simulate the maize grain yield under three soil water conservation scenarios: (i) a typical situation where 30% of rainfall above a 15 mm threshold is lost as runoff, (ii) runoff control, where all rainfall infiltrates, and (iii) runoff harvesting, which results in 60% extra ‘rainfall’ for rains above 15 mm. The soil was taken to be a sandy clay loam which is typically found in the region. Two planting densities of 4·4 and 8·8 plants m−2were used to simulate normal and high levels of management. Planting dates were determined from the 30% runoff scenario and were fixed for the other scenarios to avoid confounding the results.The results showed that runoff control and runoff harvesting produce significant yield increases in ‘average’ years in both the long rains and the short rains. However, in ‘dry’ years there were only small yield increases in the short rains and negligible benefit in the long rains. In ‘wet’ years there were no significant yield increases due to water conservation in either season.Clearly, these results are a simplification of the real situation where water conservation strategies may allow earlier planting or be accompanied by increased planting densities, both of which may result in yield increases. However, this work demonstrates the usefulness of appropriate crop growth models in evaluating a wider range of crop management strategies under a realistic range of climatic conditions than would be possible in the field.

Natural fertilisation of the marine environment — modelling of the Glomma flood 1995

Abstract The flood in the Norwegian river Glomma in May-June 1995 was among the largest ones during this century. Besides being devastating for man and buildings, it implied an extra supply of nutrients to the Skagerrak. This paper will focus on the possible effects this natural fertilisation could have had on the primary production in the receiving water. The NORWegian ECOlogical Model system (NORWECOM) has been used to quantifY this effect. The model has been run three times with different runoff scenarios to isolate the effects of the flood. To investigate the dispersion and dilution of the water from Glomma, this water has been labelled in the model. The model results have also been compared with a set of field data obtained during the period. During the flood the model gives a significant change in primary production over large areas of the Skagerrak, and all extra nutrients added from the flooded rivers were consumed by the algae. However, the flood seems to have only a small impact on the annual production.

Australian runoff scenarios from A runoff‐climate model

AbstractData of changes in precipitation and evaporation as computed from the GISS GCM for three scenarios of transient levels in trace gas concentration were used as input into the runoff‐climate model of Wigley and Jones to estimate changes in annual runoff regimes for three regions in Australia: Murray‐Darling (south‐east), Lake Eyre (central), and Timor Sea (northern) Drainage Divisions. All regions were calculated to have significant increases in mean annual runoff regardless of the GISS scenarios, due chiefly to the combination of intense aridity (e.g. low runoff ratios) and substantial changes in precipitation and evaporation as predicted by the GISS GCM. Also, attributes of the runoff‐climate model are related to their applicability and accuracy in runoff prediction.