Journal of Hydrologic Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Restoration activities in the Central Valley of California and elsewhere require accurate evapotranspiration information, which can then be used for a wide variety of surface and subsurface hydrologic evaluations. However, directly measuring evapotranspiration can be difficult or impossible depending on the evaluation’s time frame. Transferability of measured evapotranspiration in time and space is also necessary but typically requires a weather-based reference. For nonagricultural vegetation, there is at present time no standard reference, which makes the evaluation of a variety of vegetation types from different sources difficult and time-consuming. This paper examines several methods used to estimate evapotranspiration from native vegetation, including the use of vegetation coefficients ($K_v$). Vegetation coefficients are based on a standardized reference and are computed as the ratio of vegetation evapotranspiration (${\mathrm{ET}}_v$) to the grass reference evapotranspiration (${\mathrm{ET}}_o$). These monthly $K_v$ values are used to compute the long-term (for this study, 1922–2009) average ${\mathrm{ET}}_v$ for vegetation types documented to exist in California’s Central Valley prior to the arrival of the first European settlers in the mid-18th century. For vegetation that relies on precipitation and soil moisture storage, a calibrated daily soil–water balance with a dual crop coefficient approach was used to compute evapotranspiration regionally over the time frame.

Sustainable mitigation of soil erosion and peak discharge from hilly urban watersheds in an ecologically and economically sound manner is a formidable challenge. This paper applies a linear programming optimization model for determining the best combination of ecological management practices that control sediment and peak discharge within permissible limits at a minimum cost. The linear programming model equations were solved with simplex method using a computer program. The model was applied to a hilly watershed of a rapidly developing city located in the northeastern part of India. The sediment yield and peak discharge were determined by well known equations. A mapping program was employed for extracting topographic parameters of the watershed. The best combination of three possible ecological management practices—grass, garden, and detention pond—was then established. Sensitivity analysis was performed for the assessment of physical parameters. While the sensitivity to rainfall intensity was observed to be high, the sensitivity to rainfall erodibility factor, soil erodibility factor, slope length factor, and runoff coefficient were each found to be moderate.

Precipitation estimates from Doppler weather radar (DWR) provide much better spatial resolution as compared to rain gauges and are therefore becoming more popular in hydrological applications. However, traditional estimates of precipitation from radar-measured reflectivity (e.g., $Z=a{R}^b$) are deterministic and thus do not offer any information about the uncertainty associated with the estimate. However, the radar scans may contain significant errors that propagate to the rainfall estimates. This gives rise to the need for the probabilistic estimates of rainfall. This paper proposes a copula-based approach to obtain the joint cumulative distribution function (CDF) of reflectivity ($Z$) and precipitation ($R$) from which the conditional CDF of precipitation is determined. Three copulas are implemented, and the temporal and spatial transferability of each model is evaluated using different measures of performance. It is established that the precipitation estimates are better than those obtained from the four different existing methods, including the traditional approach. In addition, uncertainty estimates are also available from the proposed approach. Though the mean (50th quantile) of the probabilistic estimates does not correspond well to the gauge rainfall data, availability of the estimates at various confidence levels makes the proposed copula-based approach suitable for different applications.

Efforts to prevent temperature increases in streams and rivers have led to regulation of the maximum permissible temperature for wastewater discharges. Hyporheic discharge treatment, where warm wastewater is allowed to infiltrate into the soil and percolate toward the stream, has been proposed as an alternative management practice, potentially providing increased streamflow at decreased temperature. To assess the feasibility of such a system, a pilot study was performed in which groundwater level, temperature, and chemistry were monitored to characterize percolation from a 0.15-ha infiltration wetland. The temperature patterns suggested that water seepage rates exceeded $1.6{\mathrm{md}}^{-1}$ near the wetland, while the chemistry data indicated that infiltrated water had laterally percolated more than 100 m during the study period. The water level and temperature measurements were also used to calibrate a software model. Observations and simulations showed that the local water table became directly connected to the wetland. As a result, much of the water infiltrated through the wetland perimeter rather than through the wetland center, which implies that specific infiltration rates will decrease with increasing wetland size. This theory was tested using two approaches: (1) numerical simulation of a large-scale (5.5 ha) infiltration wetland; and (2) a water budget performed on an adjacent low-lying, inundated section of the floodplain, which due to its topography was considered to be an accurate approximation of the performance of a large-scale system. The water budget approach indicated a mean seepage velocity of $0.08{\mathrm{md}}^{-1}$, which was essentially equal to the predicted seepage velocity for a large-scale infiltration wetland of $0.077{\mathrm{md}}^{-1}$.

Flow prediction is one of the most important issues in modern hydrology. In this study, a statistical tool, stepwise-clustered hydrological inference (SCHI) model, was developed for daily streamflow forecasting. The SCHI model uses cluster trees to represent the nonlinear and complex relationships between streamflow and multiple factors related to climate and watershed conditions. It allows a great deal of flexibility in watershed configuration. The proposed model was applied to the daily streamflow forecasting in the Xiangxi River watershed, China. The correlation coefficient for calibration (1991–1995) was 0.881, and that for validation (1996–1998) was 0.771. Nash–Sutcliffe efficiencies for calibration and validation were 0.768 and 0.577, respectively. The results were compared to those of a conventional process-based model, and it was found that the SCHI model had a superior performance. The results indicate that the proposed model could provide not only reliable and efficient daily flow prediction but also decision alternatives through analyzing the end nodes of the cluster tree under uncertainties. This study is a first attempt to predict daily flow using stepwise-cluster analysis.

Water balance modeling is commonly used to quantify the impacts of climate change on water availability over a region or a catchment. Under climate change, significant variability in precipitation and evapotranspiration would dramatically affect the catchment water balance. Changes in soil and vegetation also have large impacts on water resources. However, current water balance modeling is mainly dependent on precipitation, while evapotranspiration is a fixed proportion of precipitation. Also, the interaction of the various phases of rainfall-runoff transformation within the soil is not fully computed. This paper for the first time investigates the combined effects of precipitation and evapotranspiration on the water balance of three typical forested and farmed catchments in the Waikato basin of New Zealand. A conceptual lumped water-soil model is employed to simulate the land phase of the hydrological cycle including soil moisture and ground water recharge from rainfall and evapotranspiration at catchment scale for both historical and future time slices. Observation data from 1971 to 2000 are used for model calibration. Future data up to year 2090 is obtained from a model. Future simulations are projected accordingly. The results show that changes in precipitation and especially potential evapotranspiration have a large impact on daily streamflow even though they do not much affect runoff volume. Streamflow is projected to dramatically decrease in 2030, 2060, and 2090 in the grassed catchments, while an inconsiderable reduction is found in the forested catchment. Even though bias correction is used to improve the accuracy of the potential evapotranspiration and the resulting catchment runoff, other errors are addressed but not yet resolved. They originate from regional climate model (RCM) outputs, scenarios, data observation, and interpretation as well as model performance.

This study examines the effects that the spatial configuration of flood retention ponds have on the reduction of flood peaks across different spatial scales in the catchment. A continuous simulation approach is used to investigate how different spatial organizations, storage capacities, and release capacities of retention ponds alter the downstream flood frequency curve. The simulation experiment involves a small (${\sim 30\mathrm{km}}^2$) hypothetical catchment, a 1,000 year-long randomly generated rainfall time series and a distributed rainfall-runoff model that simulates the transport of water along the drainage network. Moreover, a hypothetical order four Mandelbrot-Viseck tree is used as a generic template of river network branching. Three important insights emerge from this study’s theoretical exercise. First, flood retention ponds that are placed in the upstream section of the catchment and that are configured in parallel offer better flood control than either ponds that are configured in series along the main stem or a single, bigger pond that is located near the catchment outlet. Second, for ponds that are configured in series and have different flood release or storage capacities, the maximum reduction of peak discharges of low exceedance probability at the catchment outlet is achieved when the upstream pond is emptied first or has a larger storage capacity than the downstream pond. Third, the effect that retention ponds have on flood frequency diminishes in the downstream direction as the proportion of unregulated subcatchments that contribute to the peak discharge at the outlet also increases. This means that the flood mitigation benefits of flood retention ponds are primarily local, which underscores the added value of distributed flood retention ponds in terms of their ability to disperse the flood control benefits across the catchment.

At low sample size, sampling error may be reduced by pooling multiple gauge records. This creates an error component due to heterogeneity, the degree to which the pooled regional data’s quantile estimates are different from the true at-site quantiles. Heterogeneity statistics attempt to quantify the degree to which error is added due to regional heterogeneity. They are justified through elucidation of a so-called reasonable proxy relationship with error caused by heterogeneity and through the ability of heterogeneity thresholds to detect heterogeneous regions. In this paper, previous findings regarding three heterogeneity statistics $H_1–H_3$ are revisited; a previous finding that $H_1$ is superior to $H_2$ and $H_3$ is amended based on simulation experiments and upon enumeration of all possible regionalizations of a small gauge dataset across time scales from daily to monthly. Thresholds defined based on $H_1$ are shown to be $4\times$ too high for application to $H_2$ and new thresholds are derived for $H_2$. Two nonparametric heterogeneity statistics are tested and found to achieve only the unsatisfactory performance level of $H_3$.

In the last decades, several physically based hydrological modeling approaches of various complexities have been developed that solve shallow water equations or their approximations using various numerical methods. Users of the model may not necessarily know the different hypotheses underlying these development and simplifications, and it might therefore be difficult to judge if a code is well adapted to their objectives and test case configurations. This paper aims to compare the predictive abilities of different models and evaluate potential gain by using an advanced numerical scheme for modeling runoff. Four different codes are presented, each based on either shallow water or kinematic wave equations, and using either the finite volume or finite difference method. These four numerical codes are compared with different test cases, allowing to emphasize their main strengths and weaknesses. Results show that, for relatively simple configurations, kinematic wave equations solved with the finite volume method represent an interesting option. Nevertheless, as it appears to be limited in case of discontinuous topography or strong spatial heterogeneities, for these cases they advise the use of shallow water equations solved with the finite volume method.

A new method is introduced to simulate air compression and counterflow during infiltration into a shallow water table. The method retains the simplicity of Green and Ampt but with one added term, the air pressure resistance below the wetting front. The method differs from existing methods because it couples air compression and counterflow flux dynamically with infiltration. The new method is original and simple, yet it allows the extension of Green and Ampt to simulate infiltration into shallow water environments or a shallow impervious zone where soil-air pressurization is likely to develop. In shallow water table environments, it is shown that neglecting counterflow of air will cause a rapid decline in infiltration due to unrealistic buildup of air pressure below the wetting front. Accounting for air compression and counterflow seemed necessary to bridge between Hortonian (infiltration excess) and Dunne (saturation excess) mechanisms of runoff.

Maximum precipitation (MP) was estimated by means of a regional atmospheric model over three watersheds in northern California [(1) the American River watershed (ARW), (2) the Yuba River watershed (YRW), and (3) the Upper Feather River watershed (UFRW)], based on the reconstruction and analyses of the historical severe storms that were recorded over these target watersheds, and where the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data were available for regional atmospheric modeling of the historical storms. Since severe storm events are mainly caused by atmospheric rivers (ARs) in northern California, the contribution of an AR on precipitation over each of the targeted watersheds is maximized to estimate the 72-h MP. For this purpose, the moisture of ARs is maximized after shifting the regional atmospheric model’s atmospheric boundary conditions (BCs) in space with respect to latitude and longitude so that the AR strikes each of the targeted watersheds in the optimized location. To maximize the moisture of the AR, the relative humidity at the boundaries of the modeling domain is set at 100%. The 72-h MPs that were estimated by this method are 738, 690, and 513 mm over the ARW, YRW, and UFRW, respectively. The estimated MP values are respectively 1.74, 1.50, and 1.36 times larger than the historical maximum values at the previously mentioned basins.

A semidistributed (DPHMRS) and a fully distributed (MISBA) physically based hydrologic models were applied to the Blue River Basin (BRB) of Oklahoma in a multiyear mode using next-generation radar (NEXRAD) precipitation data. DPHMRS generally oversimulated low flows of BRB during the validation stage partly because rainfall events based on radar precipitation data are likely too high. However, by adjusting the NEXRAD data based on Mesonet gauge measurements, the Nash-Sutcliffe efficiency ($E_f$) in the validation run is significantly improved from $-0.16$ to 0.46. The model performance of DPHMRS also depends on the subbasin resolution, and it was found that DPHMRS can optimally model BRB with seven subbasins. Goodness-of-fit statistics of streamflow simulated by both models demonstrate better performance of MISBA compared to DPHMRS in calibration ($E_f$ improved from 0.53 to 0.82) and validation ($E_f$ improved from 0.46 to 0.83) stages. This difference is partly due to spatially distributed information of soil, land use, and precipitation data for BRB that are partially averaged out in the subbasin framework of DPHMRS, while such information are better retained in the fully distributed framework of MISBA. It seems a fully distributed hydrologic model can more fully take advantage of the spatially distributed information of input data for BRB than a semidistributed model if such detailed data are available. However, this may not be necessarily true for river basins where either data are limited, or where river basins have fairly uniform land use and nonmountainous terrain characteristics.

In this paper, the generalized likelihood uncertainty estimation (GLUE) methodology, which is widely applied in the field of hydrology, is used for testing and predictive uncertainty estimation in the application of the Xinanjiang rainfall-runoff (XAJ-RR) model for estimating monthly stream flow (Nangao reservoir) catchment in China. However, one of the drawbacks of using the GLUE method is the definition of the likelihood function, which reflects the behavior of the hydrological model. Although there are different formulations of likelihood functions in the literature, most previous research focused on the application of the GLUE method with the likelihood function of Nash-Sutcliffe (NS) efficiency. In this respect, to illustrate the impact of the selection of likelihood functions on the results of the GLUE method, the authors adopted four likelihood functions: NS, normalized absolute error (NAE), index of agreement (IoA), and Chiew and McMahon (CM). The main findings of the study are that (1) the parameter uncertainty is more sensitive to the choice of the likelihood functions than the uncertainty in the model prediction by the GLUE method; (2) the parameters of the XAJ-RR model with NS had less uncertainty compared to those of NAE, IoA, and CM; (3) the uncertainty bounds showed slight differences from various likelihood functions; and (4) the computational efficiency of the GLUE method based on likelihood function IoA was much better because the IoA likelihood function corresponded to narrower uncertainty bounds, higher bracketing of observations, and the best maximum value of likelihood functions. Thus, this study confirms the importance of the likelihood function selection in the application of GLUE to the uncertainty assessment of the XAJ-RR model.

Hydrologic fluxes in mountainous watersheds are particularly important as these areas often provide a significant source of freshwater for more arid surrounding lowlands. The state of Colorado in the United States comprises a principal snow catchment area, with all major headwater river basins in Colorado providing substantial water flows to surrounding western and midwestern states. The ability to represent and quantify hydrologic processes controlling the generation and movement of water in headwater basins of Colorado therefore has significant implications for effective management of water resources in the western United States under varying climatic and land-use conditions. In the research reported in this paper, hydrologic modeling was applied to four snow-dominated, mountainous basins of Colorado [i.e., the river basins of (1) Cache la Poudre, (2) Gunnison, (3) San Juan, and (4) Yampa] to evaluate the relevance of specific hydrologic components (i.e., evapotranspiration, snow processes, groundwater processes, surface runoff, and so on) in the complex, high-elevation watersheds. The soil and water assessment tool (SWAT) model was calibrated and tested for multiple river locations within each basin using monthly naturalized flows over the 1990–2005 period. The model was able to adequately simulate streamflows at all locations within the four basins. Monthly patterns of precipitation, snowfall, evapotranspiration (ET), and total water yield were similar for all the basins, while subsurface lateral flow was the dominant hydrologic pathway, contributing between 64 and 82% to gross basin water yields on an average annual basis. Overall, results indicated the strong influence of snowmelt and groundwater processes on amounts and timing of streamflows in the study basins. Hence, enhanced representation of these processes may be essential to improve hydrological estimation using computer software in snowmelt-driven mountainous basins. In particular, examination of monthly streamflow residuals indicated that the normality and independence of model residuals, which are often assumed in parameter estimation and uncertainty analysis, were not always satisfied.

In this study, the Weiss approach to derive the CF (the conversion factor to convert the fixed-interval annual maximum rainfall into the true-interval one) was examined and revised to consider the rainfall temporal distribution. As examples, several rainfall temporal distribution models currently being used in the rainfall-runoff analysis, along with several simple distributions such as triangular or pentagonal, were considered to derive the CF. The resulting CFs were then compared with the CFs estimated by analyzing the observed rainfall data, both in Korea and in several other countries, such as the United States, the United Kingdom, Australia, and New Zealand. The findings from this study can be summarized as follows. First, the effect of the temporal distribution of rainfall is very significant on the estimation of the CF. The CF for the impulse rainfall was the smallest at 1.0, and that for the uniformly-distributed rainfall was the highest at 1.333. Second, the CFs derived for the temporal distribution models considered in this study were higher than the empirical CFs used worldwide. Finally, it was found that, among simple distributions and temporal distribution models analyzed in this study, the quadratic functional form and the Keifer and Chu method provide the most similar CF value to the empirical CF values used in many countries mentioned above.

This study develops an original methodology for forecasting real-time reservoir inflow hydrographs during typhoons, taking advantage of meteoro-hydrological methods such as analysis of typhoon hydrographs, numerical typhoon track forecasts, statistic typhoon central impulse-based quantitative precipitation forecasts model based on a real-time revised approach (TCI-RTQPF), real-time recurrent learning neural network (RTRLNN), and adaptive network-based fuzzy inference system (ANFIS). To derive the inflow hydrograph induced by interaction between typhoon rain bands, terrain, and monsoons, the inventive novel ensemble numerical-statistic impulse techniques are employed. The inflow during peak flow, inflection, and direct runoff ending (DRE) periods (impulse signal) are used for the deriving process. The hydrograph analysis is used to examine the mechanism between typhoon center location, wind field, precipitation, and the inflow hydrograph, and to establish the evaluation methods. Additionally, a novel total inflow forecast model is developed using image hashing and ANFIS for selecting optimal derived hydrograph. The experiment is conducted in the Tseng-Wen Reservoir basin, Taiwan. Results demonstrate that the wind field–based and moving dynamics–based approach of typhoon can effectively evaluate the time of peak flow, inflection point, and DRE incorporating terrain and monsoon effects. The effective functions for deriving impulse signal include blended polynomial, exponential, and power functions, and for deriving inflow hydrograph, multinomial Gaussian functions. Finally, the real-time experimental outcomes show that the proposed innovative practical methodology can accurately forecast the real-time reservoir inflow hydrograph that the average error of Typhoon Krosa is 7.81% within 32 h average forecasted lead time, and Typhoon Morakot, 9.78% within 79 h forecasted lead time.

The rainfall-runoff model, WinTR-20, uses subdivision to simulate runoff behavior for complex watersheds exhibiting heterogeneous conditions or storage. It has been shown by others that subdivision generally causes the predicted peak discharge to increase, though the underlying processes are often obscured by watershed complexity. This study instead focuses on a simplified, theoretical watershed, systematically comparing the unsubdivided watershed with a two-subbasin model in order to determine the most sensitive factors. Peak discharge sensitivity is evaluated with respect to (1) series subdivision with varying total area, (2) parallel subdivision with varying proportional area, (3) parallel subdivision with varying curve number, and (4) parallel subdivision with simultaneously varying area and curve numbers. Peak discharge is most sensitive to differences in curve number, which controls both the runoff volume and peak timing. Serial subdivision was found to produce a significant high peak discharge, regardless of relative area, while parallel subdivision produced a smaller and more variable effect, either increasing or decreasing peak flow based on the area ratio. Using these subdivision sensitivities, general guidelines are presented for the rational subdivision in rainfall-runoff modeling. For example, subdivision is recommended when subarea curve numbers differ by more than five and the relative sizes of subareas influence the effects of discretization.

Land use–land cover change (LULC) has considerable impacts on hydrologic response at the watershed level. Quantitative assessment of LULC impacts on runoff generations is vital for water resources development. The soil and water assessment tool (SWAT) model was used to study the effect of LULC change on streamflows. In addition to this, the present study proposed a newly developed flow-routing model called runoff coefficient routing model (RCRM). This new model is simple and requires limited data, such as precipitation, LULC and streamflows as compared to other models, which require meteorological and many more input data. The Nethravathi River basin was selected for testing the RCRM model with the SWAT model to study land use–land cover change on streamflows. The SWAT model and RCRM model have been calibrated for 2001–2005 and validated for 2006–2009 daily data. Results have shown that the simulated streams are well correlated with observed streamflows with a coefficient of correlation ($R^2$) equal to 0.82 in calibration and 0.68 in validation period. Whereas, the RCRM model results have shown $R^2$ of 0.81 and 0.66 in the calibration and validation period. Finally, the SWAT and RCRM results were compared. It is observed that the results of the RCRM model have shown a good agreement with SWAT model results of $R^2$ equal to 0.99 and 0.98, respectively, in the calibration and validation period. The sensitivity analysis was also carried out based on Latin hypercube one factor-at-a-time (LH-OAT) method using the SWAT model and found 11 sensitive parameters out of 28 parameters. Model performance was carried out using the Nash–Sutcliffe model efficiency coefficient (NSE) and found 0.81 for calibration and 0.62 for the validation period in the SWAT model. RCRM has NSE of 0.79 and 0.63. The response of the streamflows for the year 2013 was simulated from the calibrated model. The results showed that the observed streamflows have shown good correlation with simulated streamflows with $R^2$ values of 0.86 and NSE of 0.81. From the results, it is concluded that the runoff shows early response in the year 2013 compared to the year 2003. This is mainly due to changes in LULC, which shows the conversion of forest to agricultural area and increase in built-up area from 2003 to 2013. The effect of LULC change on the hydrological model parameters were calculated and observed a decrease in evapotranspiration (ET) of about 4.5%, an increase in runoff of about 0.9%, and an increase in groundwater of about 1.12%. In conclusion, the proposed RCRM in the present study simulates streamflows at par with the SWAT model with only few input data. Hence, the newly developed RCRM model would be used to study streamflows responses to LULC changes.

The hydrology of the Yellow River source region is expected to be affected by coming climate change. This will have repercussions for the 110 million basin inhabitants. Consequently, precipitation, temperature, and streamflow trends and periodicities during the last 50 years were investigated to identify significant changes in time and space over the study area. Results showed that mean annual temperature increased for all stations and it had an accelerated increasing trend during the last decade. Mean annual precipitation trends varied depending on the station; however, they were generally slightly decreasing. Annual streamflow decreased markedly, especially from the 1990s, but showed recovery during recent years. Statistically significant changes in trend occurred for temperature in 1998 and for streamflow in 1990. Based on the streamflow change point, seasonal analysis results showed that precipitation mainly decreased during the summer monsoon period (July–September) and temperature increased throughout the year. Corresponding to the weakened monsoon period the average runoff depth is decreasing by $0.74\mathrm{mm}/\mathrm{year}$ over the whole area. Statistically significant 2- to 4-year periodicities for mean areal precipitation and temperature occurred over the area. For streamflow, an even stronger 8-year periodicity was revealed from the end of the 1960s to the beginning of the 1990s. Frequency analysis investigated the magnitudes of mean annual precipitation and discharge corresponding to a given frequency. Hydroclimatic trends and linkages at each subbasin were investigated to further improve the understanding of observed streamflow changes. The investigated results have important implications for future water availability in the Yellow River source region.

The rain garden is one of the most commonly used, low-impact development (LID) devices. Rain gardens are designed to reduce runoff volume, delay peak flow, and improve water quality in stormwater. A rain garden consists of surface and subsurface components: The surface component is a storage basin that stores the water quality capture volume (WQCV), whereas the subsurface structure is formed with a two-layered filtering medium. The drain time is the most important design parameter for the rain garden’s operation. A short drain time will lead to an inadequate residence time for the sedimentation process, whereas a long drain time will result in potential hazards to the public. A new rain garden usually has a high infiltration capacity through its filtering layers. Over years of service, the clogging effect will slow down its infiltration rate. To improve the performance of a rain garden through its life cycle, it is recommended that a cap orifice be installed at the outlet of the perforated underdrain pipe. Such a cap orifice can be adjusted to achieve the targeted flow release, according to the stage of rain garden’s life cycle. This paper presents a numerical algorithm that predicts the infiltrating process through the unsaturated and saturated sand-mix zones underneath a rain garden. With field data, the calibration parameters for the numerical model are identified to be hydraulic conductivity and initial soil moisture content. The numerical procedure developed in this paper provides a quantifiable guidance on how to turn the cap orifice up and down to satisfy the design condition. This numerical procedure can be further incorporated into a web-based remote control system that can assist the engineer to operate a rain garden at a distance.