Verification tools for probabilistic forecasts of continuous hydrological variables

In the present paper we describe some methods for verifying and evaluating probabilistic forecasts of hydrological variables. We propose an extension to continuous-valued variables of a verification method originated in the meteorological literature for the analysis of binary variables, and based on the use of a suitable cost-loss function to evaluate the quality of the forecasts. We find that this procedure is useful and reliable when it is complemented with other verification tools, borrowed from the economic literature, which are addressed to verify the statistical correctness of the probabilistic forecast. We illustrate our findings with a detailed application to the evaluation of probabilistic and deterministic forecasts of hourly discharge value

Corrigendum: Global effects of local food-production crises: a virtual water perspective

Correction to: Scientific Reports https://doi.org/10.1038/srep18803; published online 25 January 2016; updated 22 May 2018 The Acknowledgements section in this Article is incomplete. “The authors acknowledge funding from the Italian Ministry of Education, University and Research (MIUR) through the project “The global virtual-water network: social, economic, and environmental implications” (FIRB - RBFR12BA3Y).” should read: “The authors acknowledge funding from the Italian Ministry of Education, University and Research (MIUR) through the project “The global virtual-water network: social, economic, and environmental implications” (FIRB - RBFR12BA3Y). Funding from the European Research Council (ERC) is also acknowledged (CWASI Project, grant #647473)

Probabilistic nonlinear prediction of river flows

In the recent past the Nonlinear Prediction (NLP) method, initially developed in the context of nonlinear system dynamics, has been successfully applied to river flow deterministic forecasting. In this work we propose a probabilistic approach to the NLP method, which allows to estimate the full probability distribution of the predicted discharge values, thus providing a useful information to quantify the uncertainty related to the forecast. The ineffective search of the best point prediction is therefore abandoned in favour of the quantification of the forecast process reliability. An ensemble technique is also applied to the choice of the parameter values in order to optimise the prediction and to avoid problems of model calibration. This probabilistic NLP method is applied to a river flow time series, and the obtained results underline the effectiveness and reliability of the proposed approac

Improved large-scale crop water requirement estimation through new high-resolution reanalysis dataset

Estimation of crop water needs is essential to understand the role of agriculture in the waterbalance modeling at various scales. In turn, this is relevant for water management purposes andfor the fulfilling of water-related environmental regulations. In this study, a comprehensiveassessment of crop water requirement at large scale is presented, both in terms of rainfall (greenwater) and irrigation (blue water).A water-balance model is built to provide estimates of actual evapotranspiration andaccompanying soil moisture by using high space-time resolution data. The new ERA5 reanalysisdataset, published by the ECMWF within the Copernicus monitoring system and obtained fromsatellite data and ground measurements, provides the precipitation and temperature inputvariables to the model. Data available at the hourly time scale are all aggregated on a daily scaleand used in the water balance model over a grid of cultivated areas from the MIRCA2000 dataset.Cultivated areas are available for 26 crops for year 2000 at a spatial resolution of 5 arcmin (about 9km at the Equator). Data from MIRCA2000 are separated between rainfed areas and areasequipped for irrigation and are characterized by specific monthly calendars of the crop growingseasons.The model performs the daily soil water balance throughout the whole year, considering all cropsat their growth stage and assuming as initial condition at each crop sowing date a monthlyaverage soil moisture. Results quantify the volumes of green and blue water necessary for cropgrowth and describe the spatial variability of the water requirements of each individual crop. Thehigh spatial and temporal resolution of Copernicus ERA5 data enables a great improvement in thecharacterization of hydro-climatic forcings with respect to previous assessments and a greateraccuracy in the crop water requirement estimates.Finally, the knowledge of water requirements is an important step to quantify the irrigationvolumes used in agriculture, on which there is a high uncertainty and little spatially distributedinformation. The model proposed enables the investigation of spatio-temporal variabilityassociated to varying meteorological forcings and of the effects of different irrigation techniques,enabling an improved management of water resources

Climate-driven trends in agricultural water requirement: an ERA5-based assessment at daily scale over 50 years

The impact of climate variability on the water requirements of crops is a key issue in a globalized world with unprecedented population and unevenly distributed water resources. Changes of hydro-climatic forcings may have significant impacts on water resources use, considering the possible effects on irrigation requirements and crop water stress. In this work, a comprehensive estimation of crop water requirements over the 1970–2019 period is presented, considering 26 main agricultural products over a 5 arcmin resolution global grid. The assessment is based on a daily-scale hydrological model considering rainfed and irrigated scenarios, driven by hydro-climatic forcings derived from ERA5, the most recent climate reanalysis product within the Climate Change Service of the Copernicus Programme. Results show the heterogeneous impact of climate variability on harvested areas of the world, quantified by water stressed days and irrigation requirement rates. Increases of irrigation requirement rates were found on more than 60% of irrigated lands, especially in regions like South Europe, North-East China, West US, Brazil and Australia, where the mean rate increased more than 100 mm yr−1 from 1970s to 2010s. The daily analysis of water requirements shows that crops require significantly more days of irrigation per season, especially in Europe, Africa and South-East Asia. Statistically significant trends of water stress duration were found over 38% of rainfed croplands, while only 6% of croplands has been affected by negative trends and shorter stress duration, mainly in India, Malaysia, North Europe and coastal regions of central western Africa

Spatio-temporal variability of global crop water requirement, during 1950-2020

Intensification of studies of the agricultural water requirement is a main challenge in a globalizedworld, where food production is pushed to meet the needs of a growing population and theinternational trade network requires large-scale planning policies. Agriculture is the human activitythat consumes most of the withdrawn freshwater and climate change can greatly influence theamount of irrigation required by crops. In recent years, the widespread availability of satelliteimages is providing an important contribution to water resources management, offering data athigh spatio-temporal resolution over an interestingly long period of time.This study deals with the temporal variability of global water requirement of the main crops, whichis assessed through a comprehensive model, driven by climate forcings, that estimates the dailycrop water requirement on a spatial resolution of 5 arc-min (or 0.0833°) from 1950 to 2020. Themodel computes a soil water balance using daily input data of precipitation andevapotranspiration, based on the high-resolution ERA5 reanalysis dataset from the ClimateChange Service of the Copernicus Program, which combines satellite information and groundmeasurements. The distribution of harvested areas and the length of crop development phasesare kept constant, to analyze the variability of crop water requirement strictly related to climateforcings, both in terms of precipitation (green water) and irrigation (blue water). The modelconsiders the separation between irrigated and rainfed areas, in order to provide a consistentspatial distribution of irrigation requirements. Examining the spatio-temporal variability of thecrop water requirement can support considerations on the effects of global warming in differentareas in the world

Significant drivers of the virtual water trade evaluated with a multivariate regression analysis

International trade of food is vital for the food security of many countries, which rely on trade to compensate for an agricultural production insufficient to feed the population. At the same time, food trade has implications on the distribution and use of water resources, because through the international trade of food commodities, countries virtually displace the water used for food production, known as “virtual water”. Trade thus implies a network of virtual water fluxes from exporting to importing countries, which has been estimated to displace more than 2 billions of m3 of water per year, or about the 2% of the annual global precipitation above land. It is thus important to adequately identify the dynamics and the controlling factors of the virtual water trade in that it supports and enables the world food security. Using the FAOSTAT database of international trade and the virtual water content available from the Water Footprint Network, we reconstructed 25 years (1986–2010) of virtual water fluxes. We then analyzed the dependence of exchanged fluxes on a set of major relevant factors, that includes: population, gross domestic product, arable land, virtual water embedded in agricultural production and dietary consumption, and geographical distance between countries. Significant drivers have been identified by means of a multivariate regression analysis, applied separately to the export and import fluxes of each country; temporal trends are outlined and the relative importance of drivers is assessed by a commonality analysis. Results indicate that population, gross domestic product and geographical distance are the major drivers of virtual water fluxes, with a minor (but non-negligible) contribution given by the agricultural production of exporting countries. Such drivers have become relevant for an increasing number of countries throughout the years, with an increasing variance explained by the distance between countries and a decreasing role of the gross domestic product. The worldwide adjusted coefficient of determination of fitted gravity-law model is 0.57 (in 2010), and it has increased in time, confirming the good descriptive capability of selected drivers for the virtual water trad

ERA5-based global assessment of irrigation requirement and validation

While only 20% of harvested lands are actually irrigated, 40% of global agricultural production originates from irrigated areas. Therefore, assessing irrigation requirements is essential for the development of effective water-related policies for an efficient management of water resources. Moreover, global-scale analyses are becoming increasingly relevant, motivated by globalized production and international trade of food as well as by the need of common strategies to address climate change. In this study, a comprehensive model to estimate crop growth and irrigation requirements of 26 main crops at global scale is presented. The model computes a soil water balance using daily precipitation and reference evapotranspiration based on a high-resolution ERA5 reanalysis dataset from the European Copernicus Program. The irrigation requirement, defined as the minimum water volume to avoid water stress, is computed for year 2000 at the resolution of 5 arc-min (or 0.0833°) and aggregated at different spatial and temporal scales for relevant analyses. The estimated global irrigation requirements for 962 km3 is described in detail, also in relation to the spatial variability and to the monthly variation of the requirements. A focus on different areas of the world (California, Northern Italy and India) highlights the wealth of information provided by the model in different climatic conditions. National data of irrigation withdrawals have been used for an extensive comparison with model results. A crop-specific validation has also been made for the State of California, comparing model results with local data of irrigation volume and independent estimates of crop water use. In both cases, we found a good agreement between model results and real data

Global effects of local food-production crises: a virtual water perspective

By importing food and agricultural goods, countries cope with the heterogeneous global water distribution and often rely on water resources available abroad. The virtual displacement of the water used to produce such goods (known as virtual water) connects together, in a global water system, all countries participating to the international trade network. Local food-production crises, having social, economic or environmental origin, propagate in this network, modifying the virtual water trade and perturbing local and global food availability, quantified in terms of virtual water. We analyze here the possible effects of local crises by developing a new propagation model, parsimonious but grounded on data-based and statistically-verified assumptions, whose effectiveness is proved on the Argentinean crisis in 2008-09. The model serves as the basis to propose indicators of crisis impact and country vulnerability to external food-production crises, which highlight that countries with largest water resources have the highest impact on the international trade, and that not only water-scarce but also wealthy and globalized countries are among the most vulnerable to external crises. The temporal analysis reveals that global average vulnerability has increased over time and that stronger effects of crises are now found in countries with low food (and water) availability

A Fast Track approach to deal with the temporal dimension of crop water footprint

Population growth, socio-economic development and climate changes are placing increasing pressure on water resources. Crop water footprint is a key indicator in the quantification of such pressure. It is determined by crop evapotranspiration and crop yield, which can be highly variable in space and time. While the spatial variability of crop water footprint has been the objective of several investigations, the temporal variability remains poorly studied. In particular, some studies approached this issue by associating the time variability of crop water footprint only to yield changes, while considering evapotranspiration patterns as marginal. Validation of this Fast Track approach has yet to be provided. In this Letter we demonstrate its feasibility through a comprehensive validation, an assessment of its uncertainty, and an example of application. Our results show that the water footprint changes are mainly driven by yield trends, while evapotranspiration plays a minor role. The error due to considering constant evapotranspiration is three times smaller than the uncertainty of the model used to compute the crop water footprint. These results confirm the suitability of the Fast Track approach and enable a simple, yet appropriate, evaluation of time-varying crop water footprint