Antecedent Precipitation Index Research Articles

Soil Moisture Estimation Using GOES-VISSR Infrared Data: A Case Study with a Simple Statistical Method

Five days of clear sky observations of Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudoindependent tests in which the test day is held out of the regression is 0.71. It is shown that a depletion coefficient of 0.92, when used to compute antecedent precipitation index (API), produces the best correlation between API and soil moisture as inferred from GOES thermal infrared data. By averaging daily predicted values over the 5-day rain-free case study period, 92 percent of the variance of the morning surface temperature change is explained by a simple multiple linear regression with all independent variables, or, alternatively, 85 percent of the observed variance in API is explained. It is concluded that this approach can distinguish at least four classes of soil wetness, but the necessity for measurement of surface advection may limit its usefulness in remote areas.

Correlations between Nimbus-7 Scanning Multichannel Microwave Radiometer Data and an Antecedent Precipitation Index

Passive microwave brightness temperatures from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) can be used to infer the soil moisture content over agricultural areas such as the southern Great Plains of the United States. A linear regression analysis between three transforms of the five dual polarized SMMR wavelengths of 0.81, 1.36, 1.66, 2.80 and 4.54 cm and an antecedent precipitation index representing the precipitation history showed correlation coefficients greater than 0.90 for pixel aggregates of 25-50 km. The use of surface air temperatures to approximate the temperature of the emitting layer was not required to obtain high correlation coefficients between the transforms and the antecedent precipitation index.

Kalman filter estimation model in flood forecasting

Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.

Regional‐Scale Estimates of Surface Moisture Availability from GOES Infrared Satellite Measurements1

AbstractA method for obtaining remotely a measure of surface moisture, called moisture availability, is derived here using satellite infrared surface temperature measurements in conjunction with a numerical boundary layer model. Results are presented largely in the form of a statistical comparison between rainfall (antecedent precipitation index) and soil moisture patterns. Moisture availability patterns were determined from Geostationary Operational Environmental Satellite (GOES) thermal infrared temperature measurements for 12 days during the dry summers of 1978 and 1980 over eastern Kansas and southeastern Nebraska. Representative analyses of moisture availability are presented, along with a summary of results for all cases. A significant correlation was found between M and the antecedent precipitation (API) for virtually all 12 days analyzed. The relationship between M and API can be expressed as a log‐linear one: API (cm) = 0.32 (2a, where n = a + bM and a ∼ 0.9 and b ∼ 2.2. Agreement between M and API was poorest on the smallest scales, for reasons which are discussed in the text. The relatively narrow range of API values over which M is sensitive to changes in rainfall suggest that the infrared method is most suitable for deriving surface moisture availability over plains or semiarid vegetation and where there are large horizontal precipitation gradients. The accuracy of the method may permit inference of percent soil moisture over the rooting depth of plants to within plus or minus 10 to 20%, at least when averaged over a regional‐scale domain.

SIMULATING SOIL WATER RECESSION COEFFICIENTS FOR AGRICULTURAL WATERSHEDS1

ABSTRACT: The antecedent precipitation index (API) has been a useful indicator of soil moisture conditions for watershed runoff calculations, and recent attempts to correlate this index with spaceborne microwave observations have been fairly successful. The prognostic equation for soil moisture used in some of the atmospheric general circulation models (GCM) together with Thomthwaite‐Mather parameterization of actual evapotranspiration leads to API equations. The recession coefficient for API is found to depend on climatic factors as contained in potential evapotranspiration and to depend on soil texture as reflected by field capacity and permanent wilting point. A recently developed model for global insolation is used with climatological data for Wisconsin to simulate the annual trend of the recession coefficient. Good quantitative agreement is shown with the observed trends at Fennimore and Colby watersheds in Wisconsin. This study suggests that API could be a unifying concept for watershed and atmospheric general circulation modeling.

Case study: estimating antecedent precipitation index from Heat Capacity Mapping Mission day thermal infrared data

ABSTRACT A study was conducted to determine if surface soil moisture conditions can be estimated from day Heat Capacity Mapping Mission thermal infrared data. From 13 to 29 July 1978, pasture day surface temperatures were related to corresponding antecedent precipitation index (API) values over the Washita basin in central Oklahoma. Moisture variations across the basin were high as a result of a storm which passed over areas of the basin on 21 July. At given raingauge sites, day pasture surface temperature differences between two dates were strongly related to API immediately after a storm (r2 = 0.68). The thermal infrared/API relationship was evident up to 7 days after a storm.

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

ABSTRACTLarge area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R2= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R2 value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.

An analysis of the processes of river bank erosion

Measurements of erosion have been made on sections of river banks in Devon, England over a period of 2.5 yr. Erosion pins were used and found to be satisfactory. Data of the hydrological and meteorological conditions during each erosion period were collected; these were incorporated into a stepwise multiple regression analysis of the conditions controlling the amount and distribution of river bank erosion. Field observations are shown to aid considerably in understanding the processes of removal of material. Two main methods of bank erosion are identified, corrasion and slumping, and these appear to be associated with the influence of river flow levels and antecedent precipitation conditions, respectively. Antecedent precipitation index emerges as the variable providing highest explanation of the erosion characteristics but the diversity of results from different sites illustrates the complex combinations of conditions related to erosion events. A tentative grouping of the sites according to the types and amounts of erosion and the variables explaining their distribution is suggested.

Method for determining baseflow adjustments to synthesized peaks produced from the US Geological Survey rainfall-runoff model / Méthode pour la détermination des ajustements du débit de base aux écoulements de pointe produits par le modèle précipitation-écoulement US Geological Survey

Abstract A technique for determining baseflow adjustments to synthesized peaks produced from the US Geological Survey's rainfall-runoff model is presented. The antecedent precipitation index (API) is determined for each storm used in the calibration of a rainfall-runoff station. The antecedent precipitation index is related to the baseflow by a best fit straight line which can be represented by an equation. This equation is used to determine baseflow adjustments when using longterm rainfall data to synthesize peaks.

Using a Desk-Top Computer for an On-Line Flood Warning System

The paper deals with the development of an adaptive model that is applicable to real-time forecasting of hydrologic processes. The rainfall-runoff process is considered here. In this model the discharge was modeled as autoregressive with past discharges and a moving average representation on the precipitation. The model makes use of the Constrained Linear Systems (CLS) technique to split the precipitation into two rainfall inputs by using a threshold based on an antecedent precipitation index. This technique can be thought of as a piecewise linearization of a nonlinear process. The real-time forecasting model is a time invariant linear state model where the state variables, discharge and rainfall, are estimated by the Kalman filtering algorithm and the unknown model parameters by using the instrumental variables approach. This technique was applied in a case study using data from the Ombrone River Basin, Italy, and was implemented on a small desk-top computer.

DEVELOPMENT OF A STORM RUN-OFF PREDICTION MODEL WITH SIMULATED TEMPORAL RAINFALL DISTRIBUTION

The Yamuna catchment up to Kalanur is studied. The representative character of limited pluviograph data was determined using the storm index concept. After establishing a relationship between the limited pluviograph data and areal rainfall data, the possible effects of areal rainfall variation on the use of limited data was studied. A simulated temporal rainfall pattern for the area was then worked out and applied in the unit graph analysis, which led to the development of interesting relationships between rainfall intensity, Antecedent Precipitation Index and Phi-Index on the one hand and the API-IL relationships on the other. These two put together have given rise to a reasonable peak trend prediction diagram for the Yamuna catchment up to Kalanur.

Infiltration and Antecedent Precipitation

A simple, inexpensive, and expedient method is presented for determining the initial infiltration capacity (\If\do\N), the decay rate of infiltration (\ik), and the constant infiltration capacity (\If\dc\N) to be used in Horton’s equation for a certain soil and for any antecedent precipitation. The method consists of performing a series of infiltration tests using flooding-type test tube infiltrometers for different antecedent precipitation conditions. Horton’s equation is fitted to the available data, thus obtaining values of \If\do\N, \If\dc\N and \ik for each test. Daily rainfall records are used, starting at least 2 months prior to the infiltration tests, to determine the antecedent precipitation index values corresponding to each test. Curves of antecedent precipitation index plotted against Horton’s constants provide the designer with the proper values of \If\do\N and \ik for the soil on which infiltration tests were performed for any specified antecedent condition.

Initial Loss for Flood Estimation and Forecasting

Estimation of a flood from a design storm is made difficult by the need to estimate losses from the storm. The amount of initial loss is usually fixed in some arbitrary fashion. It is shown that potential initial loss may be estimated on any day from a simple antecedent precipitation index (API) model. The model provides an index of watershed wetness. The evapotranspiration of moisture from the watershed is represented by a variable API recession factor which is related to monthly evaporation, temperature or a sine curve which approximates the variation of monthly temperature. Initial loss is related to this API. Median initial loss which, it is suggested, is required for design may be estimated given daily rainfall data and the API—initial loss relationship. For eastern New South Wales, Australia, median initial loss is shown to be related to watershed area and mean annual rainfall. For ungaged watersheds sufficient information is available for an API—initial loss relationship to be estimated.

Continuous hydrograph synthesis with an API‐type hydrologic model

The U. S. ESSA Weather Bureau Hydrologic Research and Development laboratory has developed a complete hydrologie model utilizing an antecedent precipitation index (API) type rainfall‐runoff relation to compute surface runoff. With increasing demand for continuous river forecasts as well as flood forecasts, it is necessary to have a model that will predict all components of flow as functions of observable independent parameters on a continuous basis. To formulate the model, existing and proved techniques were used where possible and new techniques developed as necessary. The model consists of four basic parts: a relation for computing ground‐water recession, a method of computing the ground‐water flow hydrograph as a function of the direct runoff hydrograph, an API‐type rainfall‐runoff relation, and a unit hydrograph. The rainfall‐runoff relation is of the incremental type, yielding a runoff computation for each 6‐hour period rather than computing the total storm runoff. This has been accomplished through the inclusion of a new parameter, retention index. Two important features of the model are the ease of adjusting parameters to observed flow and the sequential development of the four basic parts with a minimum of interaction.

The estimation of runoff from rainfall for New Brunswick watersheds

Prediction equations have been derived for forecasting runoff volume for regions within the Province of New Brunswick. Five basins were selected so as to provide a regionally representative distribution over the province. The prediction equations are based on the storm rainfall, antecedent precipitation index, base flow and week number in which the storm occurred. Statistical methods were used to obtain the least-squares multiple linear regression equation, correlation coefficient, and the standard error for each of the techniques used for the watersheds. The number of storms varied from 8 to 23 for the basins studied. The standard error of the optimum prediction equations for runoff ranged from 0.065 inches to 0.212 inches and the multiple correlation coefficient ( R) varied from 0.556 to 0.963. The results of the regression equations developed for one basin were extended to a neighbouring basin of similar hydrological characteristics but with only recent streamflow records.

Antecedent Retention Indexes Predict Soil Moisture

Two models for calculating antecedent moisture index values are evaluated by comparisons with measured soil moisture. One model, based on the traditional antecedent precipitation index equation, depletes the index values exponentially. The second model depletes the index values by an evapotranspiration-soil moisture relation. Index values computed by the traditional exponential model with recommended K values of 0.88 and 0.92 compare poorly with measured soil moisture. Derived K values show a distinct seasonal trend and range from 0.98 in the spring and fall to 0.92 in the summer when representing the 0- to 12-in. soil depth, and 0.98 to 0.96 when representing the 0- to 36-in. soil depth. Index values by both models compare favorably with available soil moisture when derived K and PET values are used. The K and PET values are related to pan evaporation to facilitate their prediction at other locations.

API nomogram

The Antecedent Precipitation Index nomogram was designed as a computational aid for the hydrologist who uses 0.9 Antecedent Index [Kohler and Linsley, 1951; Linsley, Kohler, and Paulus, 1958]. Especially useful during the development of flood forecast procedures, it can be used for API computations during rainfall-runoff forecasting. It is general in that it can be used with precipitation data from any station, and its upper limit is believed to be high enough for nearly any area in the United States. By use of the nomogram, the 0.9 factor and the addition of the daily rainfall are included in a single step.

Determinação da velocidade de infiltração da água no solo, por meio de diagramas de pluviografos e limnígrafos

Neste trabalho são apresentados os valores da velocidade de infiltração, obtidos com os dados de perdas por erosão dos talhões de comprimento de rampa, da Estação Experimental de Conservação do Solo, em Clarinda, Iowa, nos Estados Unidos. Usando os dados de intensidades de chuva e enxurrada, a velocidade de infiltração foi determinada pelo método gráfico de Sharp e Holtan para as 10 maiores chuvas, com apreciável enxurrada, que ocorreram durante um período de cinco anos. As 10 chuvas foram selecionadas com base na sua duração, intensidade e época do ano. Os problemas do cálculo da velocidade de infiltração com os dados de talhões de perdas por erosão e algumas das limitações dos valores determinados são apresentados.