Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus

In a globalizing and rapidly-developing world, reliable, sustainable access to water and food are inextricably linked to each other and basic human rights. Achieving security and sustainability in both requires recognition of these linkages, as well as continued innovations in both science and policy. We present case studies of how Earth observations are being used in applications at the nexus of water and food security: crop monitoring in support of G20 global market assessments, water stress early warning for USAID, soil moisture monitoring for USDA's Foreign Agricultural Service, and identifying food security vulnerabilities for climate change assessments for the UN and the UK international development agency. These case studies demonstrate that Earth observations are essential for providing the data and scalability to monitor relevant indicators across space and time, as well as understanding agriculture, the hydrological cycle, and the water-food nexus. The described projects follow the guidelines for co-developing useable knowledge for sustainable development policy. We show how working closely with stakeholders is essential for transforming NASA Earth observations into accurate, timely, and relevant information for water-food nexus decision support. We conclude with recommendations for continued efforts in using Earth observations for addressing the water-food nexus and the need to incorporate the role of energy for improved food and water security assessment

Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets

Agricultural water use accounts for around 70% of the total water that is withdrawn from surface water and groundwater. We use a new, gridded, global-scale water balance model to estimate interannual variability in global irrigation water demand arising from climate data sets and uncertainties arising from agricultural and climate data sets. We used contemporary maps of irrigation and crop distribution, and so do not account for variability or trends in irrigation area or cropping. We used two different global maps of irrigation and two different reconstructions of daily weather 1963–2002. Simulated global irrigation water demand varied by ∼30%, depending on irrigation map or weather data. The combined effect of irrigation map and weather data generated a global irrigation water use range of 2200 to 3800 km3 a−1. Weather driven variability in global irrigation was generally less than ±300 km3 a−1, globally (\u3c∼10%), but could be as large as ±70% at the national scale

Using the soil and water assessment tool to simulate the pesticide dynamics in the data scarce Guayas River Basin, Ecuador

Agricultural intensification has stimulated the economy in the Guayas River basin in Ecuador, but also affected several ecosystems. The increased use of pesticides poses a serious threat to the freshwater ecosystem, which urgently calls for an improved knowledge about the impact of pesticide practices in this study area. Several studies have shown that models can be appropriate tools to simulate pesticide dynamics in order to obtain this knowledge. This study tested the suitability of the Soil and Water Assessment Tool (SWAT) to simulate the dynamics of two different pesticides in the data scarce Guayas River basin. First, we set up, calibrated and validated the model using the streamflow data. Subsequently, we set up the model for the simulation of the selected pesticides (i.e., pendimethalin and fenpropimorph). While the hydrology was represented soundly by the model considering the data scare conditions, the simulation of the pesticides should be taken with care due to uncertainties behind essential drivers, e.g., application rates. Among the insights obtained from the pesticide simulations are the identification of critical zones for prioritisation, the dominant areas of pesticide sources and the impact of the different land uses. SWAT has been evaluated to be a suitable tool to investigate the impact of pesticide use under data scarcity in the Guayas River basin. The strengths of SWAT are its semi-distributed structure, availability of extensive online documentation, internal pesticide databases and user support while the limitations are high data requirements, time-intensive model development and challenging streamflow calibration. The results can also be helpful to design future water quality monitoring strategies. However, for future studies, we highly recommend extended monitoring of pesticide concentrations and sediment loads. Moreover, to substantially improve the model performance, the availability of better input data is needed such as higher resolution soil maps, more accurate pesticide application rate and actual land management programs. Provided that key suggestions for further improvement are considered, the model is valuable for applications in river ecosystem management of the Guayas River basin

Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use

The Purdue Agro-climatic (PAC) Dataset for The U.S. Corn Belt: Development and Initial Results

This study is a result of a project titled ‘‘Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers”. This paper responds to the project goal to improve farm resiliency and proftability in the U.S. Corn Belt region by transforming existing meteorological dataset into usable knowledge and tools for the agricultural community. A high-resolution agro-climatic dataset that covers the U.S. Corn Belt was built for the U2U project based on a Land Data Assimilation System (LDAS) framework. This data referred to as the Purdue Agro-climatic (PAC) dataset is a gridded, continuous dataset suitable for agrocli- matic and crop model studies over the U.S. Corn Belt. The dataset was created at 4 km, sub- daily spatiotemporal resolution and covers the period of 1981–2014. The dataset includes a range of variables such as daily maximum/minimum temperature, solar radiation, rainfall, evapotranspiration (ET), multilevel soil moisture and soil temperatures. The data were com- pared to feld measurements from Amerifux and the Soil Climate Analysis Network (SCAN), and with coarser but widely used atmospheric regional reanalysis data products. Validations indicate an overall good agreement between this dataset and feld measurements. The agree- ment is particularly high for radiation and temperature parameters and lesser for rainfall and soil moisture data. Despite the differences with observations, the data show improvements over the coarser resolution products and other available models and thus highlights the value of the dataset for agroclimatic and crop model studies. This high-resolution dataset is available to the wider community, and can fll gaps in observed data records and increase accessibility for the agricultural sector, and for conduct- ing variety of if-then assessments

A Land Data Assimilation System (ldas) Based Dataset For Regional Agro-Climatic Assessments

This study is part of a USDA sponsored project ----Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers . The broader objective includes improving farm resilience and profitability in the U.S. Corn Belt region by transforming existing climate/weather data into usable knowledge and tools for the agricultural community. The specific tasks of this research are: (1) Build a high-resolution (4 km, daily) agro-climatic dataset using a Land Data Assimilation System (LDAS). (2) Estimate regional corn yield across the Corn Belt with crop models and the agro-climatic dataset. (3) Evaluate the impacts of climate variability due to El Niño-Southern Oscillation (ENSO) on corn yield in the Corn Belt. Accordingly, a high-resolution (4 km, 1979-2012, daily) agro-climatic dataset across the U.S. Corn Belt has been built using the North America Land Data Assimilation System version 2 (NLDAS2) product. This newly developed dataset includes daily maximum/minimum temperature, precipitation, solar radiation, soil moisture, and soil temperature at four soil depths (0-10 cm, 10-40 cm, 40-100 cm, and 100-200 cm). Validations indicate strong agreement between this dataset and field measurements. The agro-climatic dataset was then used with a Hybrid-Maize crop model to estimate regional corn yield at grid scale. The crop model was first validated at the field and county scale and found to consistently overestimate yields at the county scale. This was attributed to the optimum field conditions considered in the model and the overall uncertainties. Comparison with NASS yield survey data indicates a 0.6 multiplicative factor provides good agreement with actual yields, and is recommended for county-scale simulations. Following the field/county scale model tests, a modeling framework was developed to simulate gridded crop yields. Results indicate that integrating spatial climatic information improved the regional performance of the Hybrid Maize model and this agro-climatic dataset shows good potential for developing agro-meteorological related applications. Finally, the impacts of the El Nino-Southern Oscillation (ENSO) on observed and simulated corn yields were examined. As a result, La Niña shows a significant negative impact on corn yield in the Corn Belt while the impact from El Niño is insignificant. It also has been found that La Niña correlates with relatively late planting dates in the Corn Belt. Based on a crop model study, the results indicate that for some counties, under optimal conditions, late planting dates can mitigate the negative impacts from the La Niña phase. Based on the studies above, reliable performance of the Hybrid Maize crop model and superior data ability of the new agro-climatic dataset have good potential to simulate regional corn yield with climate projections. The significant impacts of ENSO on corn yield indicate that advance ENSO warning may benefit field management in the Corn Belt

MSWEP : 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3hourly temporal and 0.25 degrees ffi spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0% of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km(2)) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byrans Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9% of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org

Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change

Modeling is a useful way to understand human and climate change impacts on the water resources of agricultural watersheds. Calibration and validation methodologies are crucial in forecasting assessments. This study explores the best calibration methodology depending on the level of hydrological alteration due to human-derived stressors. The Soil and Water Assessment Tool (SWAT) model is used to evaluate hydrology in South-West Europe in a context of intensive agriculture and water scarcity. The Index of Hydrological Alteration (IHA) is calculated using discharge observation data. A comparison of two SWAT calibration methodologies are done; a conventional calibration (CC) based on recorded in-stream water quality and quantity and an additional calibration (AC) adding crop managements practices. Even if the water quality and quantity trends are similar between CC and AC, water balance, irrigation and crop yields are different. In the context of rainfall decrease, water yield decreases in both CC and AC, while crop productions present opposite trends (+33% in CC and -31% in AC). Hydrological performance between CC and AC is correlated to IHA: When the level of IHA is under 80%, AC methodology is necessary. The combination of both calibrations appears essential to better constrain the model and to forecast the impact of climate change or anthropogenic influences on water resources

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s)