Rainfall Runoff Relationships for a Cloud Forest Watershed in Central America: Implications for Water Resource Engineering1

Caballero, Luis A., Alon Rimmer, Zachary M. Easton, and Tammo S. Steenhuis, 2012. Rainfall Runoff Relationships for a Cloud Forest Watershed in Central America: Implications for Water Resource Engineering. Journal of the American Water Resources Association (JAWRA) 48(5): 1022‐1031. DOI: 10.1111/j.1752‐1688.2012.00668.x Abstract: Understanding the basic relationships between rainfall and runoff is vital for effective management and utilization of scarce water resources. Especially, this is important in Central America with widespread potable water shortage during the dry months of the monsoon. Potential good water sources are cloud forests, but little information concerning its potential is available to water supply engineers. Our objective is to define rainfall‐runoff‐base flow relationships for a cloud forest catchment. Flumes were installed for measuring river flow in four subwatersheds in La Tigra National Park, Honduras. One of the four watersheds was a 636‐ha subwatershed (WS1) with 60% cloud forest coverage. Precipitation averaged 1,130 mm/yr over the entire basin. About half of the total rainfall became runoff for the cloud forest watershed whereas, for the adjacent undisturbed forested watershed, the total discharge was <20% of the amount of precipitation. Infiltration rates were generally greater than rainfall rates. Therefore, most rainfall infiltrated into the soil, especially in the upper, steep, and well‐drained portions of the watershed. Direct runoff was generated from saturated areas near the river and exposed bedrock. This research provides compelling evidence that base flow is the primary contributor to streamflow during both wet and dry seasons in cloud forest catchments. Protecting these flow processes over time is critical for the sustained provision of potable water.     

ESTIMATING RUNOFF VOLUMES FROM URBAN AREAS1

ABSTRACT: Estimations of runoff volumes from urban areas can be made by the equation Q = a A σ(P_e– b), where Q is the runoff volume, a is the part of the total area A Contributing to runoff, P_e is the rainfall amount for a single event, and b is the initial rainfall losses. For the evaluation of a and b, rainfall/runoff measurements were made in five areas of sizes between 0.035 km² and 1.450 km². By linear regression analysis of rainfall volumes versus runoff volumes, the initial rainfall losses were found to vary from 0.38 mm to 0.70 mm for the different areas. The parts of the areas contributing to runoff were found to be proportional to the impermeable parts of the mas. The equation is applicable in urban areas with well defined paved surfaces and roofs and with a negligible amount of runoff from permeable areas.     

FLOOD-RUNOFF FORECASTING WITH HEC1F1

HEC1F is a computer program for making short- to medium-term forecasts of uncontrolled flood runoff. The program employs unit hydrographs and hydrologic routing to simulate runoff from a subdivided basin. Estimates of future rainfall can be accommodated. Runoff parameters for gaged headwater subbasins can be estimated (optimized) in real time. Blending of calculated with observed hydrographs can be performed. HEC1F is a component of an on-line software system that includes capability for data acquisition and processing, precipitation analysis, streamflow forecasting, reservoir system analysis, and graphical display of data and simulation results. The conceptual framework for HEC1F is described, and application of the program is illustrated.     

Ensemble Streamflow Forecast: A GLUE‐Based Neural Network Approach1

Abstract: While training a Neural Network to model a rainfall‐runoff process, generally two aspects are considered: its capability to be able to describe the complex nature of the processes being modeled and the ability to generalize so that novel samples could be mapped correctly. The general conclusion is that, the smallest size network capable of representing the sample distribution is the best choice, as far as generalization is concerned. Oftentimes input variables are selected a priori in what is called an explanatory data analysis stage and are not part of the actual network training and testing procedures. When they are, the final model will have only a “fixed” type of inputs, lag‐space, and/or network structure. If one of these constituents was to change, one would obtain another equally “optimal” Neural Network. Following Beven and others' generalized likelihood uncertainty estimate approach, a methodology is introduced here that accounts for uncertainties in network structures, types of inputs, and their lag‐space relationships by looking at a population of Neural Networks rather than target in getting a single “optimal” network. It is shown that there is a wide array of networks that provide “similar” results, as seen by a likelihood measure, for different types of inputs, lag‐space, and network size combinations. These equally optimal networks expose the range of uncertainty in streamflow predictions and their expected value results in a better performance than any of the single network predictions.     

EFFECTS OF STORM PATTERNS ON RUNOFF HYDROGRAPHS1

ABSTRACT: As an alternative to the conventional single-peak design storms commonly used in hydrologic practice, a large number of Southeastern Pennsylvania storm events were selected from hourly U.S. National Oceanographic and Atmospheric Administration (NOAA) records, and their temporal distributions were analyzed. From these recorded events, design storms of a typical distribution were developed for storm durations between 6 and 18 hours. All of these generated design storms have two or more peaks. The conventional single peak as well as the “typical” multi-peak storms were then applied to a simulated watershed. It was found that the multi-peak storms consistently produced more dispersed hydrographs with lower runoff peaks than the conventional single peak storms.     

SHORT-TERM FORECASTING OF SNOWMELT RUNOFF USING ARMAX MODELS1

ABSTRACT: Time series models of the ARMAX class were investigated for use in forecasting daily riverflow resulting from combined snowmelt/rainfall. The Snowmelt Runoff Model (Martinec-Rango Model) is shown to have a form similar to the ARMAX model. The advantage of the ARMAX approach is that analytical model identification and parameter estimation techniques are available. In addition, previous forecast errors can be included to improve forecasts and confidence limits can be estimated for the forecasts. Diagnostic checks are available to determine if the model is performing properly. Finally, Kalman filtering can be used to allow the model parameters to vary continuously to reflect changing basin runoff conditions. The above advantages result in improved flow forecasts with fewer model parameters.     

FLOOD PLAIN MODELING1

Computer simulation of river basin hydrology has rapidly progressed from an interesting academic exercise to a practical engineering procedure of increasing utility. Mathematical models of the many interrelated processes which occur in a basin have been developed along with efficient numerical procedures for their solution. The present paper is concerned with a particular model which has been used to describe the transformation of a temporally and spatially varying rainfall into a time history of stage and discharge on a flood plain. Although developed principally as a model of the physical processes involved, it is envisioned that the model can serve as one component of an information system for flood plain planning and management. The model consists of the following elements: (i) a rainfall simulation which generates stochastic inputs to the model according to specified rainfall statistics, (ii) a catchment-runoff model which converts the rainfall to surface runoff, (iii) a flood stage model which converts the surface runoff to time histories of flood stage and discharge. The model has been used to investigate the effect of various structural flood control measures in a basin and, in particular, to establish frequency-stage information for each of these. Of particular interest in development of the model have been recurring and partially unanswered questions relative to the proper balance among availability of data for use in the model, data requirements of the model and the objectives of the outputs produced by the model.     

AN ADDITIONAL ANALYSIS OF PEAK-VOLUME RELATIONS AND STANDARDIZATION PROCEDURES1

ABSTRACT: Much has been written about the linear relationship in log space between the runoff volume of a hydrograph and the peak discharge. Three versions of this relation (an original and two standardizations) have been presented and recommended by various authors. In this paper, the standardized equations are compared to the original relationship and the behavior of the coefficient of determination (r²) in each case is discussed. It is shown that the r² of the standardized equations is increased or decreased relative to that of the original relation based upon the magnitude of the original slope. Further implications of these relationships are discussed and demonstrated using a data base of 90 watersheds and over 1,200 separate flood hydrographs.     

三峡库区降雨径流水体中铜、锰、镍、锌、铅含量及分布特征

本文按时段和随机采集了三峡库区降雨径流水体样品,测定了水体中Ｃｕ、Ｍｎ、Ｎｉ、Ｚｎ、Ｐｂ含量,统计结果表明：Ｍｎ、Ｚｎ、Ｐｂ在径流水体中的含量趋于平均,变异系数较小。随产流时间的变化,水体中５种元素的变化规律不明显