MODEL DEVELOPMENT FOR CONJUNCTIVE USE STUDY OF THE SAN JACINTO BASIN,CALIFORNIA1

ABSTRACT: A two-layered confined-unconfined numerical model for flow and mass transport is developed for the San Jacinto Basin. The model structure is determined by the geological structure of the Basin and model parameters are calibrated using 20 years of historical records. The total number of historical head observations used for the flow model calibration is 1,117 and the total number of the estimated parameters is 91. The two-layered transport model is also calibrated using historical water quality records. Sensitivity analysis of the flow model shows that only 68 parameters (out of a total of 91) are relatively sensitive and reliable. However, the unreliable parameters (23 of them) are found to be insensitive and thus not significant to the prediction and management of conjunctive use of surface water and ground water. The developed flow model has been used to study the two proposed artificial recharge scenarios for the San Jacinto Basin. We have found that during a relatively dry condition, an artificial recharge rate of 80 acre-ft/day can be achieved during the recharge period October through January. However, for a relatively wet condition, only 80 percent of the proposed rate can be effectively stored in the Basin during these months.     

LINEAR PROGRAMS FOR NONLINEAR HYDROLOGIC ESTIMATION1

ABSTRACT: The minimization of the sum of absolute deviations and the minimization of the absolute maximum deviation (mini-max) were transformed into equivalent linear programs for the estimation of parameters in a transient and linear hydrologic system. It is demonstrated that these two methods yield viable parameter estimates that are globally optimal and reproduce properly the timing and magnitude of hydrologic events and associated variables such as total runoff. The two linear estimation methods compared favorably with the popular least-squares nonlinear estimation method. The generality of the theoretical developments shows that linear program equivalents are adequate competitors of nonlinear methods of hydrologic estimation and parameter calibration.     

Oracle-based optimization applied to climate model calibration

In this paper, we show how oracle-based optimization can be effectively used for the calibration of an intermediate complexity climate model. In a fully developed example, we estimate the 12 principal parameters of the C-GOLDSTEIN climate model by using an oracle-based optimization tool, Proximal-ACCPM. The oracle is a procedure that finds, for each query point, a value for the goodness-of-fit function and an evaluation of its gradient. The difficulty in the model calibration problem stems from the need to undertake costly calculations for each simulation and also from the fact that the error function used to assess the goodness-of-fit is not convex. The method converges to a ‘best fit’ estimate over 10 times faster than a comparable test using the ensemble Kalman filter. The approach is simple to implement and potentially useful in calibrating computationally demanding models based on temporal integration (simulation), for which functional derivative information is not readily available.     

APPLICATION OF THE BASINS DATABASE AND NPSM MODEL ON A SMALL OHIO WATERSHED1

ABSTRACT: The purpose of this study was to evaluate the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) watershed management system. BASINS data were used with the NPSM model to predict discharge and sediment concentrations at the outlet of a 103 km² Ohio watershed. It was concluded that the NPSM model should always be calibrated but only a few of the parameters provided with BASINS needed to be calibrated. For a three‐year study period, there was a 2 percent underestimation of discharge using area weighted precipitation values and a 25 percent overestimation using the single station data in BASINS. A comparison of observed and predicted monthly discharge resulted in an r² of 0.86 with area‐weighted precipitation and an r² of 0.74 with the single station data. Calibrating the model to substantially improve sediment predictions was unsuccessful and we concluded that a calibration period of one year was too short. For the three‐year study period, the r² for sediment was 0.36 with a slope of 0.37 and an intercept of 18.8 mg/l. The mean observed and predicted sediment concentrations were 27.1 mg/l and 22.6 mg/l, respectively.     

SWAT‐LUT: A Desktop Graphical User Interface for Updating Land Use in SWAT

Long‐term simulations of agricultural watersheds have often been done assuming constant land use over time, but this is not a realistic assumption for many agricultural regions. This paper presents the soil and water assessment tool (SWAT)‐Landuse Update Tool (LUT), a standalone, user‐friendly desktop‐based tool for updating land use in the SWAT model that allows users to process multi‐year land use data. SWAT‐LUT is compatible with several SWAT model interfaces, provides users with several options to easily prepare and incorporate land use changes (LUCs) over a simulation period, and allows users to incorporate past or emerging land use categories. Incorporation of LUCs is expected to provide realistic model parameterization and scenario simulations. SWAT‐LUT is a public domain interface written in Python programming language. Two applications at the Fort Cobb Reservoir Experimental Watershed located in Oklahoma and pertinent results are provided to demonstrate its use. Incorporating LUCs related to implementation of recommended conservation practices over the years reduced discharge, evapotranspiration, sediment, total nitrogen, and total phosphorus loads by 59%, 9%, 68%, 53%, and 88%, respectively. The user’s manual is included in this article as Supporting Information. The SWAT‐LUT executable file and an example SWAT project with three land use rasters and the user’s manual are available at the United States Department of Agriculture‐Agricultural Research Service Grazinglands Research Laboratory website under Software. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

亚甲基蓝分光光度法测定硫化物校准曲线斜率、截距参考值研究

通过近三年实验室采用亚甲基蓝分光光度法测定硫化物校准曲线制作的一系列原始数据的收集,并通过数理统计方法对校准曲线、-余标准偏差、斜率扩展不确定度、截距扩展不确定度开展了详细讨论,得出亚甲基蓝分光光度法测定硫化物校准曲线斜率(b)参考范围为:0.019 1±0.00044,截距(a)参考值范围:0.003 3±0.005 0.此结果对实验室分析人员判定硫化物项目校准曲线制作是否合格具有一定的参考价值.     

质量块对动态力传感器校准的影响特性

校准质量块是动态力传感器校准中的关键部件,其加速度分布的均匀性直接影响动态力传感器的校准准确度,采用有限元分析方法结合实验对比方法,研究了质量块形状尺寸、密度、弹性模量及比模量等参数对质量块加速度分布均匀性的影响,给出了各因素的影响特性。实验结果表明,使用重质合金质量块可提高动态力传感器的校准准确度。     

鄱阳湖流域面源污染负荷模拟与氮和磷时空分布特征

基于气象、土壤、土地利用、数字高程模型和农业管理等数据基础上,对输入鄱阳湖赣江、抚河、信江、饶河、修水等"五河"上的7个水文站的径流、泥沙和面源氮(N)和磷(P)污染负荷进行参数的敏感性分析,利用实测数据对参数校准和验证,通过SWAT模型对2003—2012年十年间入湖的径流、泥沙和面源N、P污染负荷进行了模拟.2003—2012年面源总氮(TN)、总磷(TP)、硝氮(NO-3-N)、有机氮(ON)和有机磷(OP)面源污染负荷入湖特征呈现出:时间变化上,年际间变化大、年内集中在4—7月入湖,鄱阳湖N、P污染负荷主要来自于面源污染,入湖面源TN组份中NO-3-N所占比重较高,TP组份中OP所占比重较高的特征;空间分布上,"五河"中赣江流量和流域面积最大,流域各项面源N、P污染物入湖量最大;修水流量和流域面积最小,流域各项面源N、P污染物入湖量最小的特征.     

Integrating Modeling and Field Experiments to Evaluate Impacts of Vegetative Practices on Ponderosa Pine Watersheds1

Abstract: Determining watershed response to vegetation treatment has been the subject of numerous hydrologic studies over the years. However, generalizing the information obtained from traditional paired‐watershed studies to other watersheds in a region is problematic because of the empirical nature of such studies and the context dependence of hydrologic responses. This paper addresses the issue of generalizing hydrologic information through integration of process‐based modeling and field observations from small‐scale watershed experiments. To this end, the results from application of a process‐based model were compared with the results from small‐scale watershed experiments in ponderosa pine forests of Arizona. The model simulated treatment impacts reasonably well when compared to the traditional paired‐watershed approach. However, the model tended to overestimate water yields during periods of low flow, and there was a significant difference between the two approaches in the estimation of treatment impacts during the first four years following treatment. The results indicate that the lumped‐parameter modeling approach used here may be limited in its ability to detect small changes, and tends to overestimate changes that occur immediately following treatment. It is concluded that watershed experiments can be highly informative due to their direct examination of cause‐effect relationships, while process‐based models are useful for their processing power and focus on functional relationships. The integrated use of both watershed experiments and process‐based models provides a way to generalize hydrologic information, illuminate the processes behind landscape treatment effects, and to generate and test hypotheses.     

Calibration Catchment Selection for Flood Regionalization Modeling1

Wan Jaafar, Wan Zurina, and Dawei Han, 2012. Calibration Catchment Selection for Flood Regionalization Modeling. Journal of the American Water Resources Association (JAWRA) 48(4): 698‐706. DOI: 10.1111/j.1752‐1688.2012.00648.x Abstract: There are two unsolved problems in flood regionalization model development related to the quantity and quality of calibration catchments: (1) how many calibration catchments should be used? and (2) how to select the calibration catchments? This study explores these two questions through a case study on the median annual maximum flood (QMED) model in the United Kingdom. It has been found that the chance of developing a good QMED model decreases significantly when the number of calibration catchments drops below a critical number (e.g., 60 in the case study). However, no significant improvement is achieved if the number of calibration catchments is above it. This number could be used as a benchmark for choosing randomly selected calibration catchments. Across a broad range of calibration catchment numbers, there are good and poor calibrated models regardless of calibration catchment numbers. High quality models could be developed from a small number of calibration catchments and also poor models from a large number of calibration catchments. This indicates that the number of calibration catchments may not be the dominating factor for developing a high quality regionalization model. Instead, the information content could be more important. The study has demonstrated that the standard deviation values between the best and poorest groups are distinctive and could be used in choosing appropriate calibration catchments.