Suchergebnisse

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

AbstractThis 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 São 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.

Software and data availability The source code for MAFIO as used in this work is available via the University of Aberdeen PURE repository: https://doi.org/10.20392/66f74663-ece3-4a52-8bed-f0cf52d0831a. The source code for EcH2O-iso is available at: https://bitbucket.org/sylka/ech2o_iso/src/master_2.0/. The Tulloch Burn datasets used in this study are available from the lead author on request. Acknowledgments Funding for this work from the Scottish Government's Hydro Nation Scholars Programme is gratefully acknowledged. Many thanks to Audrey Innes, Jonathan Dick, Claire Tunaley and Bernhard Scheliga for their assistance in analysing the isotope samples. In addition, thanks to Allan Sim, Duncan White and, in particular, Claire Abel and Adam Wyness for instruction and training on microbiological sampling and analysis techniques. Simulations with EcH2O-iso and MAFIO were undertaken on the Maxwell high performance computing cluster funded by the University of Aberdeen. Sylvain Kuppel and Aaron Smith are thanked for their assistance in troubleshooting occasional issues with EcH2O-iso. ; Peer reviewed ; Postprint

Software availability The source code for MAFIO as outlined in this work is available via the University of Aberdeen PURE repository: https://doi.org/10.20392/66f74663-ece3-4a52-8bed-f0cf52d0831a The source code for EcH2O-iso is available at: https://bitbucket.org/sylka/ech2o_iso/src/master_2.0/ Acknowledgements Funding for this work from the Scottish Government's Hydro Nation Scholars Programme is gratefully acknowledged. Many thanks go to Sim Reaney and John Wainwright for initial discussions regarding environmental agent-based modelling approaches and methods for their implementation. ; Peer reviewed ; Postprint

15 Pags.- 9 Figs.- 2 Tabls. ; We analyzed the relationships between meteorological drought and hydrological drought using very dense and diverse network of gauged natural drainage basins across the conterminous U.S. Specifically, this work utilized a dataset of 289 gauging stations, covering the period 1940–2013. Drainage basins were obtained for each gauging station using a digital terrain model. In addition to meteorological data (e.g., precipitation, air temperature and the atmospheric evaporative demand), we obtained a number of topographic, soil and remote sensing variables for each defined drainage basin. A hydrological drought index (the Standardized Streamflow Index; SSI) was computed for each basin and linked to the Standardized Precipitation Evapotranspiration Index (SPEI), which was used as a metric of climatic drought severity. The relationships between different SPEI time-scales and their corresponding SSI were assessed by means of a Pearson correlation coefficient. Also, the general patterns of response of hydrological droughts to climatic droughts were identified using a principal component analysis. Overall, results demonstrate a positive response of SSI to SPEI at shorter time-scales, with strong seasonality and clear spatial differences. We also assessed the role of some climatic and environmental factors in explaining these different responses using a predictive discriminant analysis. Results indicate that elevation and vegetation coverage are the main drivers of the diverse response of SSI to SPEI time-scales. Similar analyses were made for three sub-periods (1940–1964, 1965–1989 and 1989–2013), whose results confirm considerable differences in the response of SSI to SPEI over the past eighty years. ; This work was supported by the research project I-Link1001 (Validation of climate drought indices for multi-sectorial applications in North America and Europe under a global warming scenario) financed by CSIC, PCIN-2015-220 and CGL2014-52135-C03-01 financed by the Spanish Commission of Science and Technology and FEDER, IMDROFLOOD financed by the Water Works 2014 co-funded call of the European Commission and INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Marina Peña-Gallardo was granted by the Spanish Ministry of Economy and Competitiveness. Miquel Tomas-Burguera was supported by a doctoral grant by the Spanish Ministry of Education, Culture and Sport. Jamie Hannaford was supported by the Belmont Forum project 'DrIVER', NERC Grant Number (grant NE/L010038/1). Marco Maneta acknowledges support from the USDA NIFA grant 2016-67026-25067. ; Peer reviewed

23 Pags.- 12 Figs.- 4 Tabls. ; This article developed and implemented a new methodology for calculating the standardized evapotranspiration deficit index (SEDI) globally based on the log-logistic distribution to fit the evaporation deficit (ED), the difference between actual evapotranspiration (ETa) and atmospheric evaporative demand (AED). Our findings demonstrate that, regardless of the AED dataset used, a log-logistic distribution most optimally fitted the ED time series. As such, in many regions across the terrestrial globe, the SEDI is insensitive to the AED method used for calculation, with the exception of winter months and boreal regions. The SEDI showed significant correlations (p < 0.05) with the standardized precipitation evapotranspiration index (SPEI) across a wide range of regions, particularly for short (<3 month) SPEI time scales. This work provides a robust approach for calculating spatially and temporally comparable SEDI estimates, regardless of the climate region and land surface conditions, and it assesses the performance and the applicability of the SEDI to quantify drought severity across varying crop and natural vegetation areas. ; This work was supported by the re-search projects PCIN-2015-220 and CGL2014-52135-C03-01 financed by the Spanish Commission of Science and Technology and FEDER. IMDROFLOOD financed by the Water Works 2014 co-funded all of the European Commission and INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (Sweden), DLR (Germany), BMWFW (Austria), IFD (Denmark), MINECO (Spain), and ANR (France), with co-funding by the European Union (Grant 690462). Diego G. Miralles acknowledges support from the European Research Council (ERC) under Grant Agreement 715254 (DRY–2–DRY). Marina Peña-Gallardo was supported by the Spanish Ministry of Economy and Competitiveness and Miquel Tomás-Burguera was sup-ported by a doctoral grant by the Ministerio de Educación, Cultura y Deporte. ; Peer reviewed

We present an analysis of the sensitivity of three key crops (alfalfa, barley and winter wheat) produced in the northwestern United States to climatic and agricultural market anomalies using widely used standardized indices. Rather than investigating sensitivity of crop yields (production per unit area), we focus on agricultural production (yield * harvested area) anomalies, which captures both variations in yield and the effect of decision-making factors such as allocation of cropping area. We used two well-known standardized precipitation and reference evapotranspiration (ETo) indices (SPI and EDDI, respectively) and a standardized crop value index in a multivariate linear regression analysis to determine the characteristic timing and time-scales of precipitation and ETo anomalies that best explain annual crop production anomalies. Since climatic and market factors are standardized, regression coefficients are interpreted as a sensitivity measure that captures the relative effect of climatic and agricultural markets on agricultural production. Results show that alfalfa production was most sensitive climatic anomalies while barley and wheat production was more responsive to crop prices. Sensitivity to precipitation anomalies followed gradients in precipitation, temperature, and soil moisture regimes across the study area where drier and warmer climates were associated with increased sensitivity to climatic anomalies. We found that irrigation decoupled alfalfa production from climatic variability, but the effect of irrigation on decoupling barley production was less clear. Winter wheat production was most sensitive to price anomalies, and alfalfa was least sensitive. Omitting agricultural market conditions and other farmer incentives may introduce biases in our understanding of how drought and climate change impact agricultural production. ; United States Department of Agriculture, USDA-NIFA [2016-67026-25067]; NASA EPSCoR program [80NSSC18M0025]; NSF EPSCoRNational Science Foundation (NSF)NSF - Office of the Director (OD) [EPS-1101342] ; This work has been supported by the United States Department of Agriculture, USDA-NIFA research grant 2016-67026-25067, and by the NASA EPSCoR program research grant 80NSSC18M0025. P.W. also acknowledges partial support from the NSF EPSCoR cooperative agreement EPS-1101342. ; Public domain authored by a U.S. government employee