Surrogate Measures for Providing High Frequency Estimates of Total Suspended Solids and Total Phosphorus Concentrations1

Spackman Jones, Amber, David K. Stevens, Jeffery S. Horsburgh, and Nancy O. Mesner, 2010. Surrogate Measures for Providing High Frequency Estimates of Total Suspended Solids and Total Phosphorus Concentrations. Journal of the American Water Resources Association (JAWRA) 1‐15. DOI: 10.1111/j.1752‐1688.2010.00505.x Abstract: Surrogate measures like turbidity, which can be observed with high frequency in situ, have potential for generating high frequency estimates of total suspended solids (TSS) and total phosphorus (TP) concentrations. In the semiarid, snowmelt‐driven, and irrigation‐regulated Little Bear River watershed of northern Utah, high frequency in situ water quality measurements were recorded in conjunction with periodic chemistry sampling. Site‐specific relationships were developed using turbidity as a surrogate for TP and TSS at two monitoring locations. Methods are presented for employing censored data and for investigating categorical explanatory variables (e.g., hydrologic conditions). Turbidity was a significant explanatory variable for TP and TSS at both sites, which differ in hydrologic and water quality characteristics. The relationship between turbidity and TP was stronger at the upper watershed site where TP is predominantly particulate. At both sites, the relationships between turbidity and TP varied between spring snowmelt and base flow conditions while the relationships between TSS and turbidity were consistent across hydrological conditions. This approach enables the calculation of high frequency time series of TP and TSS concentrations previously unavailable using traditional monitoring approaches. These methods have broad application for situations that require accurate characterization of fluxes of these constituents over a range of hydrologic conditions.     

Coupling PCSWMM and WASP to Evaluate Green Stormwater Infrastructure Impacts to Storm Sediment Loads in an Urban Watershed

We coupled rainfall–runoff and instream water quality models to evaluate total suspended solids (TSS) in Wissahickon Creek, a mid‐sized urban stream near Philadelphia, Pennsylvania. Using stormwater runoff and instream field data, we calibrated the model at a subdaily scale and focused on storm responses. We demonstrate that treating event mean concentrations as a calibration parameter rather than a fixed input can substantially improve model performance. Urban stormwater TSS concentrations vary widely in time and space and are difficult to represent simply. Suspended and deposited sediment pose independent stressors to stream biota and model results suggest that both currently impair stream health in Wissahickon Creek. Retrofitting existing detention basins to prioritize infiltration reduced instream TSS loads by 20%, suggesting that infiltration mitigates sediment more effectively than detention. Infiltrating stormwater from 30% of the watershed reduced instream TSS loads by 47% and cut the frequency of TSS exceeding 100 mg/L by half. Settled loads and the frequency of high TSS values were reduced by a smaller fraction than suspended loads and duration at high TSS values. A widely distributed network of infiltration‐focused projects is an effective stormwater management strategy to mitigate sediment stress. Coupling rainfall–runoff and water quality models is an important way to integrate watershed‐wide impacts and evaluate how management directly affects urban stream health.     

A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R² values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R² values of 0.29, 0.51, and 0.46 and SVR had R² values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.     

Estimating Sediment and Nutrient Loads in Four Western Lake Superior Streams

Total suspended solids (TSS) and total phosphorus (TP) have been shown to be strongly correlated with turbidity in watersheds. High‐frequency in situ turbidity can provide estimates of these potential pollutants over a wide range of hydrologic conditions. Concentrations and loads were estimated in four western Lake Superior trout streams from 2005 to 2010 using regression models relating continuous turbidity data to grab sample measures of TSS and TP during differing flow regimes. TSS loads estimated using the turbidity surrogate were compared with those made using FLUX software, a standard assessment technique based on discharge and grab sampling for TSS. More traditional rating curve methodology was not suitable because of the high variability in the particulates vs. discharge relationship. Stream‐specific turbidity and TSS data were strongly correlated (r² = 0.5 to 0.8; p < 0.05) and less so for TP (r² = 0.3 to 0.7; p < 0.05). Near‐continuous turbidity monitoring (every 15 min) provided a good method for estimating both TSS and TP concentration, providing information when manual sample collection was unlikely, and allowing for detailed analyses of short‐term responses of flashy Lake Superior tributaries to highly variable weather and hydrologic conditions while the FLUX model typically resulted in load estimates greater than those determined using the turbidity surrogate, with 17/23 stream years having greater FLUX estimates for TSS and 18/23 for TP.     

Estimation of Suspended‐Sediment Concentration From Total Suspended Solids and Turbidity Data for Kentucky, 1978‐19951

Williamson, Tanja N. and Charles G. Crawford, 2011. Estimation of Suspended‐Sediment Concentration From Total Suspended Solids and Turbidity Data for Kentucky, 1978‐1995. Journal of the American Water Resources Association (JAWRA) 47(4):739‐749. DOI: 10.1111/j.1752‐1688.2011.00538.x Abstract: Suspended sediment is a constituent of water quality that is monitored because of concerns about accelerated erosion, nonpoint contamination of water resources, and degradation of aquatic environments. In order to quantify the relationship among different sediment parameters for Kentucky streams, long‐term records were obtained from the National Water Information System of the U.S. Geological Survey. Suspended‐sediment concentration (SSC), the parameter traditionally measured and reported by the U.S. Geological Survey, was statistically compared to turbidity and total suspended solids (TSS), two parameters that are considered surrogate data. A linear regression of log‐transformed observations was used to estimate SSC from TSS; 72% of TSS observations were less than coincident SSC observations; however, the estimated SSC values were almost as likely to be overestimated as underestimated. The SSC‐turbidity relationship also used log‐transformed observations, but required a nonlinear, breakpoint regression that separated turbidity observations ≤6 nephelometric turbidity units. The slope for these low turbidity values was not significantly different than zero, indicating that low turbidity observations provide no real information about SSC; in the case of the Kentucky sediment record, this accounts for 30% of the turbidity observations.     

Human Influences on Water Quality in Great Lakes Coastal Wetlands

A better understanding of relationships between human activities and water chemistry is needed to identify and manage sources of anthropogenic stress in Great Lakes coastal wetlands. The objective of the study described in this article was to characterize relationships between water chemistry and multiple classes of human activity (agriculture, population and development, point source pollution, and atmospheric deposition). We also evaluated the influence of geomorphology and biogeographic factors on stressor-water quality relationships. We collected water chemistry data from 98 coastal wetlands distributed along the United States shoreline of the Laurentian Great Lakes and GIS-based stressor data from the associated drainage basin to examine stressor-water quality relationships. The sampling captured broad ranges (1.5–2 orders of magnitude) in total phosphorus (TP), total nitrogen (TN), dissolved inorganic nitrogen (DIN), total suspended solids (TSS), chlorophyll a (Chl a), and chloride; concentrations were strongly correlated with stressor metrics. Hierarchical partitioning and all-subsets regression analyses were used to evaluate the independent influence of different stressor classes on water quality and to identify best predictive models. Results showed that all categories of stress influenced water quality and that the relative influence of different classes of disturbance varied among water quality parameters. Chloride exhibited the strongest relationships with stressors followed in order by TN, Chl a, TP, TSS, and DIN. In general, coarse scale classification of wetlands by morphology (three wetland classes: riverine, protected, open coastal) and biogeography (two ecoprovinces: Eastern Broadleaf Forest [EBF] and Laurentian Mixed Forest [LMF]) did not improve predictive models. This study provides strong evidence of the link between water chemistry and human stress in Great Lakes coastal wetlands and can be used to inform management efforts to improve water quality in Great Lakes coastal ecosystems.     

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630–690 nm), band 5 (1550–1750 nm) and the ratio between bands 1 (450–520 nm) and 4 (760–900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520–600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.     

Quality and Quantity of Suspended Particles in Rivers: Continent-Scale Patterns in the United States

Suspended solids or sediments can be pollutants in rivers, but they are also an important component of lotic food webs. Suspended sediment data for rivers were obtained from a United States–wide water quality database for 622 stations. Data for particulate nitrogen, suspended carbon, discharge, watershed area, land use, and population were also used. Stations were classified by United States Environmental Protection Agency ecoregions to assess relationships between terrestrial habitats and the quality and quantity of total suspended solids (TSS). Results indicate that nephelometric determinations of mean turbidity can be used to estimate mean suspended sediment values to within an order of magnitude (r² = 0.89). Water quality is often considered impaired above 80 mg TSS L^–1, and 35% of the stations examined during this study had mean values exceeding this level. Forested systems had substantially lower TSS and somewhat higher carbon-to-nitrogen ratios of suspended materials. The correlation between TSS and discharge was moderately well described by an exponential relationship, with the power of the exponent indicating potential acute sediment events in rivers. Mean sediment values and power of the exponent varied significantly with ecoregion, but TSS values were also influenced by land use practices and geomorphological characteristics. Results confirm that, based on current water quality standards, excessive suspended solids impair numerous rivers in the United States.     

Measuring Suspended‐Sediment Concentration and Turbidity in the Middle Mississippi and Lower Missouri Rivers Using Landsat Data

The Middle Mississippi River (MMR) and lower Missouri River (MOR) provide critical navigation waterways, ecological habitat, and flood conveyance. They are also directly linked to processes affecting geomorphic and ecological conditions in the lower MR and Delta. For this study, a method was developed to measure suspended‐sediment concentration (SSC) and turbidity along the MMR and the lower MOR using Landsat imagery. Data from nine United States Geological Survey water‐quality monitoring stations were used to create a model‐development dataset and a model‐validation dataset. Concurrent gaging data were identified for available Landsat images to generate the datasets. Surface‐reflectance filters were developed to eliminate images with cirrus cloud coverage or vessel traffic. Using the filtered model‐development dataset, unique reflectance‐SSC and reflectance‐turbidity models were developed for three Landsat sensors: Landsat 8 Operational Land Imager, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 4–5 Thematic Mapper. Coefficient of determination values for the models ranged from 0.72 to 0.88 for the model‐development dataset. The model‐validation dataset was used to evaluate the performance of the models and had coefficient of determination values ranging from 0.62 to 0.79.     

THE NEED FOR HIGH RESOLUTION TIME SERIES DATA TO CHARACTERIZE HAWAIIAN STREAMS1

ABSTRACT: Streams in the Hawaiian Islands differ from many streams on the U.S. mainland presenting unique challenges to investigators attempting to characterize Hawaiian streams. Hawaiian streams are short; watersheds are small and steep; and rain events are usually short in duration but intense. As a result, most streams in Hawai'i are flashy. Time scales for storm hydrographs in Hawai'i are on the order of hours instead of days and flash flooding is a common hazard. To characterize the streams we were investigating, we found it necessary to obtain streamflow and water quality measurements at relatively short time intervals. While this resolution resulted in large sometimes onerous quantities of data, we would have otherwise missed certain phenomena, such as 60‐fold flow changes in 15 minutes or 30‐fold turbidity changes in five minutes. Even at five‐minute intervals, we found that attempts to predict TSS using a relationship obtained from in situ turbidity were not always satisfactory. Depending on the precision required, either higher resolution measurements or in vitro turbidity measurements of the TSS samples might be necessary. Finally, these high resolution measurements enabled us to observe other cyclical events that might have been missed if the measurement intervals were greater than one hour.     

Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

Temporal and Spatial Turbidity Patterns Over 30 Years in a Managed Forest of Western Washington1

Abstract: Forest practices have progressively changed over the last 30 years in the Pacific Northwest to address water quality concerns. There have been some assessments of these new management practices made at a site scale but very few studies have attempted to evaluate their efficacy at reducing cumulative sediment production at a watershed scale. Such an evaluation is difficult due to the spatial and temporal variability in sediment delivery and transport processes. Due to this inherent variability, detecting a response to management changes requires a long‐term data record. We utilized a water quality dataset collected over 30 years at four locations in the Deschutes River watershed (western Washington) to assess trends in turbidity and whether sediment control procedures implemented over this time period had any detectable influence. The sample sites ranged from small headwater streams (2.4 and 3.0 km²) to the mainstem of the Deschutes River (150 km²). Declining trends in turbidity were detected at all the permanently monitored sites. The mainstem Deschutes River site, which integrates sediment processes from the entire study watershed, showed dramatic declines in turbidity even with continued active forest management. For the small basins, logging and road construction occurred in the 1970s and 1980s and turbidity declined thereafter, achieving prelogging levels by 2000. There are no temporal trends in flow that could be responsible for the observed trends in turbidity. Our results suggest that increased attention to reducing sediment production from roads and minimizing the amount of road runoff reaching stream channels has been the primary cause of the declining turbidity levels observed in this study.     

Predicting runoff of suspended solids and particulate phosphorus for selected Louisiana soils using simple soil tests

This study was conducted to evaluate the relationships among total suspended solids (TSS) and particulate phosphorus (PP) in runoff and selected soil properties. Nine Louisiana soils were subjected to simulated rainfall events, and runoff collected and analyzed for various parameters. A highly significant relationship existed between runoff TSS and runoff turbidity. Both runoff TSS and turbidity were also significantly related to runoff PP, which on average accounted for more than 98% of total P (TP) in the runoff. Runoff TSS was closely and positively related to soil clay content in an exponential fashion (y=0.10e0.01x, R2=0.91, P<0.001) while it was inversely related to soil electrical conductivity (EC) (y=0.02 x(-3.95), R2=0.70, P<0.01). A newly-devised laboratory test, termed "soil suspension turbidity" (SST) which measures turbidity in a 1:200 soil/water suspension, exhibited highly significant linear relationships with runoff TSS (y=0.06x-4.38, R2=0.82, P<0.001) and PP (y=0.04x+2.68, R2=0.85, P<0.001). In addition, SST alone yielded similar R2 value to that of combining soil clay content and EC in a multiple regression, suggesting that SST was able to account for the integrated effect of clay content and electrolytic background on runoff TSS. The SST test could be used for assessment and management of sediment and particulate nutrient losses in surface runoff.     

Assessing the value of information for water quality management in the North Sea

Global Earth Observation (GEO) is one of the most important sources of information for environmental resource management and disaster prevention. With budgets for GEO increasingly under pressure, it is becoming important to be able to quantify the returns to informational investments. For this, a clear analytical framework is lacking. By combining Bayesian decision theory with an empirical, stakeholder-oriented approach, this paper attempts to develop such a framework. The analysis focuses on the use of satellite observations for Dutch water quality management in the North Sea. Dutch water quality management currently relies on information from 'in situ' measurements but is considering extending and deepening its information base with satellite observations. To estimate returns to additional investments in satellite observation, we analyze the added value of an extended monitoring system for the management of eutrophication, potentially harmful algal blooms and suspended sediment and turbidity in the North Sea. First, we develop a model to make the potential contribution of information to welfare explicit. Second, we use this model to develop a questionnaire and interpret the results. The results indicate that the expected welfare impact of investing in satellite observation is positive, but that outcomes strongly depend on the accuracy of the information system and the range of informational benefits perceived.     

Remote sensing water observation for supporting Lake Victoria weed management

This paper aims to assess the suitability of remote sensing for enhancing the management of water body resources and for providing an inexpensive way to gather, on a wide area, weed infestation extent and optical parameter linked to the water body status. Remotely sensed satellite images and ancillary ground true data were used to produce land cover maps, trough classification techniques, and water compounds maps, applying radiative transfer models. The study proposed within the framework of the cooperation between Italian Foreign Affair Ministry (through the University of Rome) and Kenyan Authorities has been carried out on the Kenyan part of the Lake Victoria. This lake is one of the largest freshwater bodies of the world where, over the last few years environmental challenges and human impact have perturbed the ecological balance affecting the biodiversity. The objective of this research study is to define the thematic products, retrievable from satellite images, like weed abundance maps and water compound concentrations. These products, if provided with an appropriate time frequency, are useful to identify the preconditions for the occurrence of hazard events like abnormal macrophyte proliferation and to develop an up-to-date decision support system devoted to an apprised territory, environment and resource management.     

RESOLUTION OF IMPACTS OF RUNOFF EVENTS ON A WATER SUPPLY RESERVOIR WITH A ROBOTIC MONITORING NETWORK1

A robotic water quality monitoring network is used to resolve the coupled patterns of a natural tracer, specific conductance (SC), and metrics of light scattering and turbidity for Schoharie Creek and downstream Schoharie Reservoir, with particular emphasis on the impacts of runoff events. Strong relationships between these parameters and streamflow, and the propensity for this tributary to plunge in the reservoir in summer and fall based on its lower temperature, are reported. The entry of this stream, the primary tributary, into the reservoir as a turbid density current during runoff events is depicted as distinct and vertically coincident subsurface SC minima and peaks in measures of light scattering. The magnitudes of these signatures imparted to the reservoir's water column are demonstrated to be strongly dependent on the magnitude of the runoff event. The time course of the diminishment of these signatures and longitudinal differences in turbidity within the reservoir are described. The documented patterns of SC and metrics of light scattering provided by the robotic monitoring network offer a rare opportunity to support development and testing of a turbidity model with the necessary attributes of fine temporal and spatial resolution.     

Modeled Summer Background Concentration of Nutrients and Suspended Sediment in the Mid‐Continent (USA) Great Rivers1

Angradi, Ted R., David W. Bolgrien, Matt A. Starry, and Brian H. Hill, 2012. Modeled Summer Background Concentration of Nutrients and Suspended Sediment in the Mid‐Continent (USA) Great Rivers. Journal of the American Water Resources Association (JAWRA) 48(5): 1054‐1070. DOI: 10.1111/j.1752‐1688.2012.00669.x Abstract: We used regression models to predict summer background concentration of total nitrogen (N), total phosphorus (P), and total suspended solids (TSS), in the mid‐continent great rivers: the Upper Mississippi, the Lower Missouri, and the Ohio. From multiple linear regressions of water quality indicators with land use and other stressor variables, we determined the concentration of the indicators when the predictor variables were all set to zero — the y‐intercept. Except for total P on the Upper Mississippi River, we could predict background concentration using regression models. Predicted background concentration of total N was about the same on the Upper Mississippi and Lower Missouri Rivers (430 μg l^?1), which was lower than percentile‐based values, but was similar to concentrations derived from the response of sestonic chlorophyll a to great river total N concentration. Background concentration of total P on the Lower Missouri (65 μg l^?1) was also lower than published and percentile‐based concentrations. Background TSS concentration was higher on the Lower Missouri (40 mg l^?1) than the other rivers. Background TSS concentration on the Upper Mississippi (16 mg l^?1) was below a threshold (30 mg l^?1) designed to protect aquatic vegetation. Our model‐predicted concentrations for the great rivers are an attempt to estimate background concentrations for water quality indicators independent from thresholds based on percentiles or derived from stressor‐response relationships.     

Water quality assessment using remote sensing techniques: Medrano Creek, Argentina

Two spectral bands of the visible spectrum [0.45-0.52 microm (Blue), 0.52-0.60 microm (Green)] of satellite images obtained by LANDSAT 7 ETM+ have been used in this study to follow the contaminated waters of Medrano Creek when it flows into Río de la Plata River. The former is one of the five fresh watercourses going through the Metropolitan Area of Buenos Aires, Argentina, where 13 million people live. Previous studies have shown that the water quality of Rio de la Plata at the outlet of Medrano Creek has decreased more than 50% as a source of water for human consumption. The non-treated effluents of the textile industry probably affect the water quality. We have developed a model that predicts the water quality index (WQI) of surface waters in the study area and uses linear regression analysis. The model has been validated using a data set of 12 physicochemical parameters obtained during the last 3 years. The potentiality of using satellite images was confirmed by the results: (a) to trace the organic contamination (associated with dyes) in freshwater systems and (b) as tools for decision making in the management of water resources.     

Quality of Water from Hand Dug Wells in Onitsha Metropolitan Areas of Nigeria

Summary Wide Range of human and anthropogenic activities impinges on the urban environment in most cities in Nigeria, to degrade the quality of water resources. Some of these activities (daily discharge of solid and liquid waste, industrial effluent, and physo-chemical weathering processes) are discharged directly on sources of water supply to the populace of Onitsha. Based on this problem, this paper examines the quality of water from hand-dug wells in Onitsha. 15 wells water samples were collected during the month of January, July, and August with the aid of sterilized containers. And sent to the laboratory for analyses, results showed that wells that are located close to dumpsites are more polluted in terms of turbidity, TSS, CaCo₂ conductivity salinity acidity, Pb, Fe and bacteriological quality of the well water. Also most of the hand-dug well water qualities are unsafe for human consumption, because they are generally above the WHO maximum acceptable threshold. Since this source of water form one of the major source of water to the inhabitants, there is need for purification proper disposal of waste, public education of the health implication of waste/impure water and other management measures.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part I: A Review of Metrics and Adjoint Methods

Irrigation management consists of many components. In this work we review and recommend rainfall forecast performance metrics and adjoint methodologies for the use of predictive weather data within the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE estimates crop water uses to optimize irrigation scheduling. WISE and its components, input requirements, and related software design issues are discussed. The use of predictive weather allows WISE to consider economic opportunity‐costs of decisions to defer water application if rainfall is forecast. These capabilities require an assessment of the system uncertainties and use of weather prediction performance probabilities. Rainfall forecasts and verification performance metrics are reviewed. In addition, model data assimilation methods and adjoint sensitivity concepts are introduced. These assimilation methods make use of observational uncertainties and can link performance metrics to space and time considerations. We conclude with implementation guidance, summaries of available data sources, and recommend a novel adjoint method to address the complex physical linkages and model sensitivities between space and time within the irrigation scheduling physics as a function of soil depth. Such tool improvements can then be used to improve water management decision performance to better conserve and utilize limited water resources for productive use. 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.