Consuelo C. Romero

Assessment of erosion hotspots in a watershed: Integrating the WEPP model and GIS in a case study in the Peruvian Andes

This paper presents a case study in assessment of erosion hotspots in an Andean watershed. To do this, we made use of an interface called Geospatial Modelling of Soil Erosion (GEMSE): a tool that integrates Geographical Information Systems (GIS) with the Water Erosion Prediction Project (WEPP) model. Its advantages are: (i) it is independent of any special GIS software used to create maps and to visualize the results; (ii) the results can be used to produce response surfaces relating outputs (e.g. soil loss, runoff) with simple inputs (e.g. climate, soils, topography); (iii) the scale, resolution and area covered by the different layers can be different among them, which facilitates the use of different sources of information. The objective of this paper is to show GEMSEs performance in a specific case study of soil erosion in La Encanada watershed (Peru) where the hillslope version of WEPP has been previously validated. Resulting runoff and soil loss maps show the spatial distribution of these processes. Though these maps do not give the total runoff and soil loss at the watershed level, they can be used to identify hotspots that will aid decision makers to make recommendations and plan actions for soil and water conservation.

Reanalysis of a global soil database for crop and environmental modeling

There is an increased need for detailed soil information that can be used for applications of crop and environmental modeling. The goal of this project was to conduct a reanalysis of the ISRIC-WISE 1.1 Soil Profile Dataset. As part of the procedures, the soil reanalysis database was fitted to the standard formats of the International Consortium for Agricultural Systems Application (ICASA). Thus, the soil reanalysis database tailors dynamic crop models such as the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). During the reanalysis, the physical and chemical parameters of the soil profiles were revised and estimated, where necessary and possible, using pre-established ranges given by the literature and correlations among other more stable variable. To evaluate each of the 3404 reanalyzed soil profiles, the CSM-CERES-Maize model was run for a standard crop management scenario using both the original and the new improved soil databases. Nine hundred seventy-eight soil profiles were considered to be not useful during the reanalysis due to missing values for one or more critical variables and were, therefore, not considered for quality control procedures. A pre-diagnostic for only nitrogen and soil organic carbon in the original dataset showed 70% and 5% of missing values respectively. A sensitivity analysis based on crop simulations comparing the original and the reanalyzed soil databases, showed that 1294 soil profiles yielded different results due to improvement of either the original data or improved conversion procedures. The details and considerations for detecting missing and erroneous values and for estimating soil variable values are presented in this paper for further use. The final soil reanalysis global database contains 3404 soil profiles and is available at https://harvestchoice.wufoo.com/forms/download-wisol.

Estimation and Analysis of Irrigation in Single-Family Homes in Central Florida

AbstractThere are several studies that have estimated how much water is used for residential irrigation, principally at the national level, with outputs varying from 30 to 64% of total household potable water use. A methodology to estimate irrigation from potable use data in central Florida is presented in this paper. Monthly potable water billing records of single-family homes for the City of Tampa Water Department (TWD) and Orange County Utilities (OCU), Florida, were available from 2003 to 2007. Basic indoor water use at the household scale was estimated using two methods: the minimum month method and the per capita method. A range of impervious surfaces values (5, 15, and 20% from total green area) was considered to estimate the irrigable area. Irrigation was estimated on a monthly basis as the difference of total water use minus the estimated indoor water use, divided by the irrigable area. The estimated irrigation values were compared to a monthly theoretical irrigation requirement calculated by a d...

Are Landscapes Over-Irrigated in Central and Southwest Florida? A Spatial-Temporal Analysis of Observed Data

Outdoor water use is one of the components of household water use whose main purpose is irrigation. There are several previous studies that have estimated how much water is used for residential irrigation purposes, principally at the national level. However, estimates can vary from 30% to 64% as the percentages of total water use in a household that would be devoted for irrigation purposes. We established a methodology to estimate irrigation considering a range of impervious surfaces (5%, 15%, and 20%) and two established methods to estimate basic indoor water use, at the household scale. Monthly water billing records for Tampa and Orlando, Florida, were available from 2003 to 2007. These values were compared with a theoretical irrigation requirement calculated by a daily soil water balance equation to assess if homeowners were over-irrigating. Orlando showed higher values of estimated irrigation, compared to Tampa. Up to 76% of homeowner utility accounts over irrigated in Orlando, when the per capita method (to estimate indoor water use) was used. In Tampa, an average of 35% of households over irrigated. There was an inverse correlation between rainfall and estimated irrigation in Orlando, but not in Tampa.

Assessing crop yield simulations driven by the NARCCAP regional climate models in the southeast United States

A set of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models is used in crop modeling systems to assess economically valuable agricultural production in the southeast United States, where weather/climate exerts strong impact on agriculture. The maize/peanut/cotton yield amounts for the period of 1981–2003 are obtained in a regularly gridded (~20 km) southeast U.S. using (a) observed, (b) a reanalysis, and (c) the NARCCAP Phase I multimodel data set. It is shown that the regional-climate model-driven crop yield amounts are better simulated than the reanalysis-driven ones. Multimodel ensemble methods are then adopted to examine their usefulness in improving the simulation of regional crop yield amounts and are compared to each other. The bias-corrected or weighted composite methods combine the crop yield ensemble members better than the simple composite method. In general, the weighted ensemble crop yield simulations match marginally better with the observed-weather-driven yields compared to those of the other ensemble methods.