Grid-based Model Research Articles

A Study on a Simple Algorithm for Parallel Computation of a Grid-Based One-Dimensional Distributed Rainfall-Runoff Model

This paper presents an algorithm that can efficiently simulate a grid-based one-dimensional distributed rainfall-runoff model by performing parallel computations using flow accumulation values for individual grid cells, which are calculated through an eight flow direction method. This parallel computation algorithm uses information about flow accumulation to automatically find parallel computation target grid cells within the overall area and perform parallel computation on the grid by unit. The Microsoft.NET Parallel class was used to apply and evaluate the parallel computation algorithm independently on two machines. The results showed that the time reduction effect of parallel computation differed for each target domain, because flow accumulation values varied depending on the domain. Parallel computation reduced computation time by around 40% to 78% in virtual domains and around 63% in the real domain compared to sequential computation. The results of this study can be utilized to reduce the computation time of distributed models.

Grid Based Clustering with Intrusion Detection System to Extend Network Lifetime in Mobile ADHOC Network

The key determination of researches on cluster-based Mobile Ad-hoc Networks (MANETs) and the election of consistent energy holding Cluster Head (CH) is to improve the lifetime of the network. Both in real-time as well as non-real-time basis data are collected and spread accordingly. Energy efficiency is said to be the major concern in MANET. Every node in the network supplied with preferred node energy and it will get exhausted whenever node transmits the data. When node runs out of energy transmission may be failed. In order to avoid transmission failure, energy-efficient methods are incorporated in data transmission. In this paper, a clustering method is proposed which is grounded by the Grid-based model. Static sinks in the MANET transmit the data to the transmission area which is distant to the destination that may lead to the energy-hole problem. Hence, an efficient approach is proposed wherein a centralized cluster configuration by the mobile sink is used for Grid-Based Clustering (GBC) and Verification algorithm to achieve improved energy balance to thereby extending network lifetime in the network. The simulation results depict that the GBC and Verification method can effectively increase the network lifetime and energy dissipation.

Macro-HyProS: A new macro-scale hydrologic processes simulator for depression-dominated cold climate regions

This study introduces a new Macro-scale Hydrologic Processes Simulator (Macro-HyProS), tailored for cold climate regions and depression-dominated terrains. Macro-HyProS is a grid-based hydrologic model of a unique structure to deal with hydrologic complexities in depression-dominated cold climate regions. The model runs on a daily time step and incorporates a LEGO-fashion horizontal layout to account for sub-grid land use heterogeneity. On the vertical layout, each grid consists of different bands, each of which is responsible for simulating specific hydrologic processes. Macro-HyProS employs improved methodologies to account for snow accumulation and ablation, depressions, and frozen ground condition. The Red River of the North Basin (RRB) was selected to highlight the capabilities of Macro-HyProS in describing the spatiotemporal complexities associated with depression-dominated cold climate regions. Results indicated that the model provided a satisfactory performance, having the Nash–Sutcliffe efficiency coefficients of 0.56 and 0.72 in the calibration and validation periods, respectively. The modeling results accentuated the impacts of frozen ground, snowmelt, and depressions in the generation of surface runoff over the basin. On average, the RRB experienced 145 days of frozen or partially frozen ground conditions per year, which coincided with early spring rainfall and snowmelt events. Due to the widespread frozen ground conditions over the basin, up to 60% of the generated snowmelt on specific days contributed to surface runoff, whereas infiltration had a smaller portion. Also, the results revealed that the depression-dominated areas acted as “regulators” of surface runoff, particularly in early springs. Macro-HyProS provides spatiotemporal simulations of various hydrologic processes to facilitate a better understanding of macro-scale hydrologic trends.

Development and Demonstration of Grid-based Spatiotemporal Data Model for Monitoring Small Businesses

Development and Demonstration of Grid-based Spatiotemporal Data Model for Monitoring Small Businesses

Development of a Landslide Early Warning System in Indonesia

Landslides are one of the most disastrous natural hazards in Indonesia, in terms of number of fatalities and economic losses. Therefore, Balai Litbang Sabo (BLS) has developed a Landslide Early Warning System (LEWS) for Indonesia, based on a Delft–FEWS (Flood Early Warning System) platform. This system utilizes daily precipitation data, a rainfall threshold method, and a Transient Rainfall Infiltration and Grid-based Regional Slope-stability model (TRIGRS) to predict landslide occurrences. For precipitation data, we use a combination of 1-day and 3-day cumulative observed and forecasted precipitation data, obtained from the Tropical Rainfall Measuring Mission (TRMM) and the Indonesian Meteorological Climatological and Geophysical Agency (BMKG). The TRIGRS model is used to simulate the slope stability in regions that are predicted to have a high probability of landslide occurrence. Our results show that the landslides, which occurred in Pacitan (28 November 2017) and Brebes regions (22 February 2018), could be detected by the LEWS from one to three days in advance. The TRIGRS model supports the warning signals issued by the LEWS, with a simulated factor of safety values lower than 1 in these locations. The ability of the Indonesian LEWS to detect landslide occurrences in Pacitan and Brebes indicates that the LEWS shows good potential to detect landslide occurrences a few days in advance. However, this system is still undergoing further developments for better landslide prediction.

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha−1 vs. 38.95 Mg ha−1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

Effect of storm network simplification on flooding prediction with varying rainfall conditions

In the context of climate change and urban expansion, cities are increasingly vulnerable to floods for increased frequency of rainfall extremes. Timely and accurate flooding prediction is crucial to reduce the losses of life and property. Despite its crucial role in urban hydrologic modelling, storm network, as a key component of urban drainage system, has to be simplified because of both the data availability and computation power. Current literatures have noted the effects of storm network simplification (SNS), while the understanding is limited to certain models and conditions and still far from sufficient. In this study, a grid-based urban hydrologic model was employed to further investigate the effects of SNS on flooding prediction under varying rainfall conditions. The results show that SNS significantly affects both peak flow and total flow volume, while simplification to different degrees may lead to opposite effects. Larger degree of simplification leads to underestimation of flooding magnitude, while smaller degree of simplification results in overestimation of flooding magnitude. More importantly, the simulation bias caused by SNS is further amplified with increased rainfall intensity and peak ratio, especially for higher degree simplifications where the underestimation bias of river discharge can be as large as three times. Through qualitative and quantitative analysis, this study helps to further understand the effects of SNS, and provides some bias estimation for flooding prediction and management in urban areas.

Distinguishing the Relative Contribution of Environmental Factors to Runoff Change in the Headwaters of the Yangtze River

The change in river flows at the basin scale reflects the combined influences of changes in various environmental factors associated with climatic and underlying surface properties. Distinguishing the relative contribution of each of these factors to runoff change is critical for sustainable water resource management, but it is also challenging. The headstream region of the Yangtze River, known as “China’s Water Tower”, has undergone a significant runoff change over the past decades. However, the relative contribution of environmental factors to runoff change is still unclear. Here, we designed a series of detrending experiments based on a grid-based hydrological model to quantify the combined influences of multiple environmental factors on runoff change and the relative contribution of an individual factor to runoff change. The results indicate that changes in climate and vegetation significantly increased water yield in the study basin over the past three decades, and the increase in water yield primarily came from the contribution from the upstream of the basin. On the basin scale, the change in precipitation dominated the runoff change that contributed up to 113.2% of the runoff change, followed by the wind speed change with a contribution rate of −15.1%. Other factors, including changes in temperature, relative humidity, sunshine duration (as a surrogate for net radiation), and albedo (as a surrogate for vegetation) had limited effects on runoff change, and the contribution rate of these factors to runoff change ranged from −5% to 5%. On spatial patterns, the influences of changes in some environmental factors on runoff changes were affected by elevation, particularly for temperature. The rising temperature had mixed effects on runoff change, which generally increased water yield at high altitudes of the basin but decreased water yield at low altitudes of the basin.

Visual Analytics for Regional Economic Environment Factors Based on a Dashboard Design

Abstract. Economic environment is vital for commercial investment, city planning and company strategy planning in urban areas. Mastering the economical trend may help the entrepreneurs, government officers and individuals in their decision-making process. In this study, we explore multiple geo-economic datasets using visual analytics methods for understanding the economic environment. More specifically, we user time-series Gross Domestic Product (GDP) data as an economic indicator of economic development and land use data to support the spatial analysis at a refined geographic scale. The spatiotemporal patterns of the regional economic environment are revealed both qualitatively and quantitatively. The work has a three-fold contributions: (1) we apply a grid-based spatial interpolation model to derive GDP values at a file granularity based on land use data; (2) we design a novel interactive dashboard for the GDP data exploration, which serves as a visual analytical tool between data and users; (3) we combine quantitative analysis with visualizations to strengthen the qualitative analysis. The feasibility of visual analytics methods and the dashboard design are tested in one of the most developed regions, Jiangsu Province, China. Both expected and unexpected economical patterns were extracted.

Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System

Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems.

Demonstrating developments in high-fidelity analytical radiation force modelling methods for spacecraft with a new model for GPS IIR/IIR-M

This paper presents recently developed strategies for high-fidelity, analytical radiation force modelling for spacecraft. The performance of these modelling strategies is assessed using a new model for the Global Positioning System Block IIR and IIR-M spacecraft. The statistics of various orbit model parameters in a full orbit estimation process that uses tracking data from 100 stations are examined. Over the full year of 2016, considering all Block IIR and IIR-M satellites on orbit, introducing University College London’s grid-based model into the orbit determination process reduces mean 3-d orbit overlap values by 9% and the noise about the mean orbit overlap value by 4%, when comparing against orbits estimated using a simpler box-wing model of the spacecraft. Comparing with orbits produced using the extended Empirical CODE Orbit Model, we see decreases of 4% and 3% in the mean and the noise about the mean of the 3-d orbit overlap statistics, respectively. In orbit predictions over 14-day intervals, over the first day, we see smaller root-mean-square errors in the along-track and cross-track directions, but slightly larger errors in the radial direction. Over the 14th day, we see smaller errors in the radial and cross-track directions, but slightly larger errors in the along-track direction.

Interactive Natural Motion Planning for Robot Systems Based on Representation Space

Endowing robot with interactive natural motion planning is of great importance, since user has to get more and more involved for better realization with versatile tasks under increasingly complicated environments. In this paper, studies on interactive natural motion planning are summarized and a general theoretic framework for which is presented in triple-folds. Firstly, the motion planning model is formulated based on representation space, and essential guidelines on planning algorithm selection are also proposed. Then user intention inference, which consists of grid-based intention model and Bayesian filtering based inferring algorithm, is investigated to mitigate the impact of network induced imperfections during human–robot interaction. Finally, for further rejecting various uncertainties, $${\mathcal {H}}_\infty $$ control theory based disturbance observer is proposed. All three algorithmic design procedures are stated in detail and the efficiency of which are presented via existing results, followed by discussions on the future directions.

Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models

Performance of a newly developed semi-distributed (grid-based) hydrological model (satellite-based hydrological model (SHM)) has been compared with another semi-distributed soil and water assessment tool (SWAT)—a widely used hydrological response unit (HRU)-based hydrological model at a large scale (12,900 km2) river basin for monthly streamflow simulation. The grid-based model has a grid cell size of 25 km2, and the HRU-based model was set with an average HRU area of 25.2 km2 to keep a balance between the discretization of the two models. Both the model setups are calibrated against the observed streamflow over the period 1977 to 1990 (with 1976 as the warm-up period) and validated over the period 1991 to 2004 by comparing simulated and observed hydrographs as well as using coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) as statistical indices. Result of SHM simulation (NSE: 0.92 for calibration period; NSE: 0.92 for validation period) appears to be superior in comparison to SWAT simulation (NSE: 0.72 for calibration period; NSE: 0.50 for validation period) for both calibration and validation periods. The models’ performances are also analyzed for annual peak flow, monthly flow variability, and for different flow percentiles. SHM has performed better in simulating annual peak flows and has reproduced the annual variability of observed streamflow for every month of the year. In addition, SHM estimates normal, moderately high, and high flows better than SWAT. Furthermore, total uncertainties of models’ simulation have been analyzed using quantile regression technique and eventually quantified with scatter plots between P (measured data bracketed by the 95 percent predictive uncertainty (PPU) band) and R (the relative length of the 95PPU band with respect to the model simulated values)-values, for calibration and validation periods, for both the model simulations. The analysis confirms the superiority of SHM over its counterpart. Differences in data interpolation techniques and physical processes of the models are identified as the probable reasons behind the differences among the models’ outputs.

Forests as ‘sponges’ and ‘pumps’: Assessing the impact of deforestation on dry-season flows across the tropics

Forests act as ‘pumps’ through their evapotranspiration (Etot) and as ‘sponges’ by enhancing soil infiltration capacity and moisture retention. Tropical deforestation and poor post-forest land management generally result in lower Etot, but also reduce infiltration. Strongly diminished infiltration is typically accompanied by enhanced overland flow and can cause reduced groundwater recharge and baseflows. A grid-based land surface hydrological model (W3RA–LUM) was tailored to incorporate the trade-offs between the ‘pump’ and ‘sponge’ effects to investigate where deforestation can be expected to have the greatest impacts on dry-season flows. Streamflow sensitivity analyses for scenarios with or without full forest cover and/or good versus poor surface infiltration were performed for: (i) selected tropical catchments with documented changes in streamflow after deforestation; and (ii) the tropics at large (23.5°N to 35°S, to include important seasonal montane forests). The catchment sensitivity analyses showed that W3A–LUM captured the streamflow response to imposed deforestation and changes in surface conditions reasonably well. For the tropics as a whole, an increase in mean annual streamflow of 18% was obtained for forest conversion (to pasture) only, versus 26% after an additional imposed reduction in surface infiltration. Much of the inferred flow increases concerned water-limited regions. A reduction in dry-season flows was predicted for nearly one-fifth of all grid cells (despite lower Etot after forest conversion) after impaired infiltration, marking potential 'hot spots' of hydrological change after deforestation and reduced infiltration due to surface degradation. The affected grid cells shared the following key characteristics: (i) strong seasonality; (ii) high infiltration capacity under forested conditions; (iii) sufficient wet-season precipitation to recharge deep soil- and groundwater stores; (iv) sufficient soil water storage under forested conditions to ‘carry over’ infiltrated wet-season rainfall into the subsequent dry season; and (v) slow groundwater recession maintaining baseflow throughout the dry season.Our results demonstrate that forest removal in highly seasonal tropical catchments, whilst typically increasing mean annual water yield, can indeed decrease dry-season flows, depending on pre- and post-forest removal surface conditions and groundwater response times.

Understanding the Water–Food–Energy Nexus for Supporting Sustainable Food Production and Conserving Hydropower Potential in China

Optimizing water–food–energy (WFE) relations has been widely discussed in recent years as an effective approach for formulating pathways towards sustainable agricultural production and energy supply. However, knowledge regarding the WFE nexus is still largely lacking, particularly beyond the conceptual description. In this study, we combined a grid-based crop model (Python-based Environmental Policy Integrated Climate—PEPIC) with a hydropower scheme based on the Distributed Biosphere Hydrological (DBH) model to investigate the WFE interplays in China concerning irrigated agricultural production and hydropower potential. The PEPIC model was used to estimate crop yields and irrigation water requirements under various irrigated cropland scenarios, while the DBH model was applied to simulate hydrological processes and associated hydropower potential. Four major crops, i.e. maize, rice, soybean and wheat, were included for the analyses. Results show that irrigation water requirements present high values (average about 400 mm yr-1) in many regions of northern China, where crop yields are much higher on irrigated land than on rainfed land. However, agricultural irrigation has largely reduced hydropower potential up to 50% in some regions due to the substantial withdrawal of water from streams. The Yellow River basin, the Hai River basin, and the Liao River basin were identified as the hotspot regions concerning the WFE interactions and tradeoffs. Further expansion the irrigated cropland would increase the tradeoffs between supporting sustainable food production and conserving hydropower potential in many parts of China. The results provide some insights into the WFE nexus and the information derived is useful for supporting sustainable water management, food production while conserving the potential for hydropower generation in China.

The effective hydrological neighborhood: A new concept to formulate harvest area constraints

Forest managers have struggled to develop several measures to mitigate the impacts of logging operations on soil erosion. Two measures have consisted in considering slope restrictions and limiting the size of clearcut harvest areas in timber harvest scheduling models by means of adjacency constraints. However, these constraints build on an externally predefined limit on harvest area valid to all stands regardless of their topographic features. Thus, we present a methodology to formulate maximum harvest area constraints for each stand on the basis of its topographic features and hydrological connectivity. Using a grid-based digital elevation model, we first introduce the concept of the “Effective Hydrological Neighborhood” and propose a method to compute it through the analyses of water flow directions and the LS topographic factor of the USLE. Then, we develop a recursive procedure to compute for each stand the maximum harvest area in any single period and in consecutive periods of the planning horizon. Finally, we formulate a set of maximum harvest area constraints for each stand. The methodology is applied to a eucalyptus plantation in northwestern Spain. The constraints can then be considered in strategic timber harvest scheduling models along with other constraints. In this way, the harvest plans derived at this planning level can be compatible with later solutions considering hydrological connectivity concerns at the tactical level.

Rendezvous Planning for Multiple AUVs With Mobile Charging Stations in Dynamic Currents

Operation of autonomous underwater vehicles (AUVs) in large spatiotemporal missions is currently challenged by onboard energy resources requiring manned support. With current methods, AUVs are programmed to return to a static charging station based on a threshold in their energy level. Although this approach has shown success in extending the operational life, it becomes impractical due to interruption of AUV operation and loss of energy needed to return to charging station. It is also not practical for large networks due to shortage of charging stations. We introduce mobile onsite power delivery, which will fundamentally change the range and duration of underwater operations. This letter presents a mission planning method to generate mobile charger trajectories, given pre-defined working AUV trajectories, considering environmental constraints such as currents and obstacles. The problem is formulated as a multiple generalized traveling salesman problem, which is then transformed into a traveling salesman problem. Energy cost in dynamic currents is integrated with a path planning algorithm using a grid-based environment model. A scheduling strategy extends the problem over multiple charging cycles. Simulation results show that the planning method significantly improves mission success and energy expenditure. Field experiments in Lake Superior using two types of AUVs, an unmanned surface vessel, and a manned support vessel validate the feasibility of the planned trajectories for long-term marine missions.

Dominant Influencing Factors of Groundwater Recharge Spatial Patterns in Ergene River Catchment, Turkey

Groundwater is of great significance in sustaining life on planet earth. The reliable estimation of groundwater recharge is the key understanding the groundwater reservoir and forecasting its potential accessibility. The main objective of this study was to assess the groundwater recharge and its controlling factors at the Ergene river catchment. A grid-based water balance model was adopted to determine the spatially distributed long-term groundwater recharge and other water budget components, relying upon the hydro-climatic variables, land-use, soil, geology, and relief of the investigated area. The model calculations were performed for the hydrological reference horizon of 20 years at a spatial resolution of 100 × 100 m. The base flow index (BFI) separation concept was applied to split up the simulated total runoff into groundwater recharge and direct runoff. Subsequently, the statistical methods of Pearson product–moment correlation and principal component analysis (PCA) were combined for identifying the dominant catchment and meteorological factors influencing the recharge. The average groundwater recharge over the investigated area amounts to 95 mm/year. The model validation and statistical analysis indicate that the difference between simulated and observed total runoff and recharge values is generally under 20% and no significant inconsistency was observed. PCA indicated that recharge is controlled, in order of significance, by land-use, soil, and climate variables. The findings of this research highlight the key role of spatial variables in recharge determination. In addition, the generated outputs may contribute to groundwater resource management in the Ergene river catchment.

PKA17-A Coarse-Grain Grid-Based Methodology and Web-Based Software for Predicting Protein pK a Shifts.

We have developed and tested PKA17, a coarse-grain grid-based model for predicting protein pK a shifts. Our pK a predictor is currently deployed via a website interface. We have carried out parameter fitting using 442 Asp, Glu, His, Lys, and Arg residues for which experimental results are available in the literature. PROPKA software has been used for benchmarking. The average unsigned error and root-mean-square deviation (RMSD) have been found to be 0.628 and 0.831 pH units, respectively, for PKA17. The corresponding results with PROPKA are 0.761 and 1.063 units. We have assessed the robustness of the developed PKA17 methodology with a number of tests and have also explored the possibility of using a combination of PROPKA and PKA17 calculations in order to improve the accuracy of predicted pK a values for protein residues. We have also once again confirmed that protein acidity constants are influenced almost entirely by residues in the immediate spatial proximity of the ionizable amino acids. The resulting PKA17 software has been deployed online with a web-based interface at http://users.wpi.edu/~jpcvitkovic/pka_calc.html. © 2019 Wiley Periodicals, Inc.

Development and application of the soil moisture routing (SMR) model to identify subfield-scale hydrologic classes in dryland cropping systems using the Budyko framework

Crop yield patterns at the field level in dryland agricultural systems often trace back to the spatial structure of water storage and subsurface flow paths. These lateral flow paths create zones of saturation that promote nutrient loss from runoff and leaching beneath the root zone. Despite its clear importance, a spatially explicit representation of landscape hydrology is frequently missing from site-specific crop and fertilizer management plans. This work uses the grid-based water balance model, SMR (soil moisture routing), to adapt an established method of hydrologic classification, the Budyko framework, for small agricultural watersheds. The model was evaluated with spatially distributed soil moisture data and surface runoff measurements in four annually cropped fields located across the eastern Palouse region of the inland Pacific Northwest. The Budyko framework, modified to include change in root zone water storage and lateral flow, was combined with a separate indicator of yield potential to form a dual-threshold classification scheme for variable-rate fertilizer management that balances agronomic benefit with environmental impact. Each field was divided into zones based on crop yield and nitrogen loss potentials, which link to the hydrologic functioning of the landscape through soil water storage and subsurface redistribution, respectively. Results showed large variability in the annual water balance assessed within individual fields and between fields. The greatest variability was observed in the two eastern-most fields where the wettest zone of each had water supply 172% and 137% that of the dry zone. Depth to a hydraulic restrictive layer and topography were identified as primary landscape controls on the water supply (precipitation plus net lateral flow minus change in water storage) in these fields. Moreover, the steep climate gradient in the region created field-to-field variability in water balance with the ratio of evapotranspiration to precipitation ranging from 91% in the western-most field to approximately 70% in each of the fields to the east. The clearest choice is for growers to apply more fertilizer in areas with high water storage capacity and high crop yield and where fertilizer loss through leaching and runoff is minimal. Less fertilizer should be applied in areas with low water storage capacity that frequently saturate. Areas with high storage capacity and frequent saturation involve a tradeoff between yields and minimizing fertilizer loss that may be best served by splitting fertilizer application to avoid excessive over-winter leaching losses. Even though remote sensing and variable-rate applicators are at the frontiers of precision agriculture, landscape hydrology underlies all dryland crop production and should accordingly be looked to for inspiration in the design of cropping systems.