Marco P. Maneta

Economic impacts of regional water scarcity in the Sao Francisco River Basin, Brazil: an application of a linked hydro-economic model

This paper presents a linked hydro-economic model and uses it to examine the regional effects of water use regulations and product price changes on the agriculture of the Sao Francisco River Basin, Brazil. The effects of weather on surface water availability are explicitly addressed using the hydrological model MIKE-Basin. Farmers’ adjustments to changes in precipitation, surface water availability, and other factors are quantified using an economic model based on non-linear programming techniques. The models are externally linked. Results show that regional impacts, at the sub-basin level, vary depending on the location of each sub-basin relative to river flows. The effects of water use regulations and of exogenous price shocks on agriculture depend on weather, location, product mix and production technology. Implications of these results for policies designed to manage agriculture and water use are discussed.

Assessing agriculture–water links at the basin scale: hydrologic and economic models of the São Francisco River Basin, Brazil

This article uses a basin-wide hydrologic model to assess the hydrologic and economic effects of expanding agriculture in the São Francisco River Basin, Brazil. It then uses a basin-wide economic model of agriculture to examine the effects of implementing water use regulations. Preliminary results suggest that substantially expanding agriculture would put pressure on some of the rivers environmental flows. Agricultural output and rural employment would increase, though not in spatially uniform ways. The economic model demonstrates how cropping area, crop mix and production technology respond simultaneously to water shortages. While farmers can adjust, the costs of doing so may be beyond the reach of resource-poor farmers.

Coastal development and precipitation drive pathogen flow from land to sea: evidence from a Toxoplasma gondii and felid host system

Rapidly developing coastal regions face consequences of land use and climate change including flooding and increased sediment, nutrient, and chemical runoff, but these forces may also enhance pathogen runoff, which threatens human, animal, and ecosystem health. Using the zoonotic parasite Toxoplasma gondii in California, USA as a model for coastal pathogen pollution, we examine the spatial distribution of parasite runoff and the impacts of precipitation and development on projected pathogen delivery to the ocean. Oocysts, the extremely hardy free-living environmental stage of T. gondii shed in faeces of domestic and wild felids, are carried to the ocean by freshwater runoff. Linking spatial pathogen loading and transport models, we show that watersheds with the highest levels of oocyst runoff align closely with regions of increased sentinel marine mammal T. gondii infection. These watersheds are characterized by higher levels of coastal development and larger domestic cat populations. Increases in coastal development and precipitation independently raised oocyst delivery to the ocean (average increases of 44% and 79%, respectively), but dramatically increased parasite runoff when combined (175% average increase). Anthropogenic changes in landscapes and climate can accelerate runoff of diverse pathogens from terrestrial to aquatic environments, influencing transmission to people, domestic animals, and wildlife.

A Spatially Distributed Model to Simulate Water, Energy, and Vegetation Dynamics Using Information from Regional Climate Models

Studies seeking to understand the impacts of climatevariability and change on the hydrology of a region need to take into account the dynamics of vegetation and its interaction with the hydrologic and energy cycles. Yet, most of the hydrologic models used for these kinds of studies assume that vegetation is static. This paper presents a dynamic, spatially explicit model that couples a vertical energy balance scheme (surface and canopy layer) to a hydrologic model and a forest growth component to capture the dynamic interactions between energy, vegetation, and hydrology at hourly to daily time scales. The model is designed to be forced with outputs from regional climate models. Lateral water transfers are simulated using a 1D kinematic wave model. Infiltration is simulated using the Green and Ampt approximation to Richards equation. The dynamics of soil moisture and energy drives carbon assimilation and forest growth, which in turn affect the distribution of energy and water through leaf dynamics by altering light interception, shading, and enhanced transpiration. The model is demonstrated in two case studies

Stochastic calibration and learning in nonstationary hydroeconomic models

Concern about water scarcity and adverse climate events over agricultural regions has motivated a number of efforts to develop operational integrated hydroeconomic models to guide adaptation and optimal use of water. Once calibrated, these models are used for water management and analysis assuming they remain valid under future conditions. In this paper, we present and demonstrate a methodology that permits the recursive calibration of economic models of agricultural production from noisy but frequently available data. We use a standard economic calibration approach, namely positive mathematical programming, integrated in a data assimilation algorithm based on the ensemble Kalman filter equations to identify the economic model parameters. A moving average kernel ensures that new and past information on agricultural activity are blended during the calibration process, avoiding loss of information and overcalibration for the conditions of a single year. A regularization constraint akin to the standard Tikhonov regularization is included in the filter to ensure its stability even in the presence of parameters with low sensitivity to observations. The results show that the implementation of the PMP methodology within a data assimilation framework based on the enKF equations is an effective method to calibrate models of agricultural production even with noisy information. The recursive nature of the method incorporates new information as an added value to the known previous observations of agricultural activity without the need to store historical information. The robustness of the method opens the door to the use of new remote sensing algorithms for operational water management.

Sustainable Root Zone Salinity and Shallow Water Table in the Context of Land Retirement

This study uses five years of field data from the Land Retirement Demonstration Project located in western Fresno County of California to develop a comprehensive theoretical and numerical modeling framework to evaluate the specific site conditions required for a sustainable land retirement outcome based on natural drainage. Using field data, principles of mass balance in a control volume, the HYDRUS-1D software package for simulating one-dimensional movement of water, heat, and multiple solutes in variably-saturated media, and a model-independent parameter optimizer, the processes of soil water and solute movement in root zone and deep vadose zone were investigated. The optimization of unsaturated soil hydraulic parameters and downward flux (natural drainage) from the control volume against observed vadose zone salinity levels and shallow groundwater levels yield difficult to obtain natural drainage rate as a function of water table height within the control volume. The results show that the unsaturated s...

What can we learn from multi-data calibration of a process-based ecohydrological model?

This work was funded by the European Research Council (project GA 335910 VeWa). M. Maneta acknowledges support from the U.S National Science Foundation (project GSS 1461576) and U.S National Science Foundation EPSCoR Cooperative Agreement #EPS1101342. All model runs were performed using the High Performance Computing (HPC) cluster of the University of Aberdeen, and the IT Service is thanked for its help in installing PCRaster and other libraries necessary to run EcH2O and post-processing Python routines on the HPC cluster. Finally, the authors are grateful to the many people who have been involved in establishing and continuing data collection at the Bruntland Burn, particularly Christian Birkel, Maria Blumstock, Jon Dick, Josie Geris, Konrad Piegat, Claire Tunaley, and Hailong Wang.

Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data

Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008–2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.

Changes to Snowpack Energy State from Spring Storm Events, Columbia River Headwaters, Montana

AbstractThe generation and release of meltwater during the spring snowmelt season can be delayed because of spring storm episodes with snow accumulation and/or sustained subfreezing temperatures. The delayed release of snowmelt often extends beyond the particular storm event because of changes to the internal state of energy in the snowpack that prevents transmission of meltwater. Following a storm, two energy deficits internal to the snowpack must be overcome before surface melt can drain and exit the snowpack: 1) cold content created by heat lost during the episode must be removed and 2) dry pore space must be filled with liquid water to residual saturation. This study investigates the role of these two processes in spring snowmelt following past storm episodes in western Montana. The analysis addresses ~10 yr of historical snowpack and air temperature data from 33 stations in the Columbia River headwaters. Results indicate that the addition of pore space has a greater impact on delaying snowmelt than d...

Spatial patterns of rural poverty: an exploratory analysis in the São Francisco River Basin, Brazil

This paper uses recently released municipio-level data on rural poverty in Brazil to identify and analyze spatial patterns of rural poverty in the Sao Francisco River Basin (SFRB). Moran’s I statistics are generated and used to test for spatial autocorrelation, and to prepare cluster maps that locate rural poverty “hot spots” and “cold spots.” Our results indicate that poverty reduction policies in the SFRB should take into account the spatial distribution of poverty. Not only is poverty in the SFRB clustered spatially, but the bulk of the basin’s poor resides in municipios that comprise the poverty ‘hot spots’ we identified. These clusters did not correspond to state-level boundaries (the political delineations often used to measure poverty and to manage poverty reduction programs), so scope may exist for geographically refocusing poverty reduction efforts to make them more efficient. Maybe more importantly the results set the stage for the use of spatial econometrics for a future multivariate analysis of rural poverty in the basin.