•Linear binary classification for flood-prone areas mapping in data-poor environments.•Definition of new morphological descriptors derived from DEMs.•A new Geomorphic Flood Index (GFI) is introduced ...for flood mapping.•The proposed methodology allows also the gap-filling of existing floodplain maps.
Knowing the location and the extent of the areas exposed to flood hazards is essential to any strategy for minimizing the risk. Unfortunately, in ungauged basins the use of traditional floodplain mapping techniques is prevented by the lack of the extensive data required. The present work aims to overcome this limitation by defining an alternative simplified procedure for a preliminary floodplain delineation based on the use of geomorphic classifiers. To validate the method in a data-rich environment, eleven flood-related morphological descriptors derived from remotely sensed elevation data have been used as linear binary classifiers over the Ohio River basin and its sub-catchments. Their performances have been measured at the change of the topography and the size of the calibration area, allowing to explore the transferability of the calibrated parameters, and to define the minimum extent of the calibration area. The best performing classifiers among those analysed have been applied and validated across the continental U.S. The results suggest that the classifier based on the Geomorphic Flood Index (GFI), is the most suitable to detect the flood-prone areas in data-scarce regions and for large-scale applications, providing good accuracies with low requirements in terms of data and computational costs. This index is defined as the logarithm of the ratio between the water depth in the element of the river network closest to the point under exam (estimated using a hydraulic scaling function based on contributing area) and the elevation difference between these two points.
The High Plains, Mississippi Embayment, and Central Valley aquifer systems within the United States are currently being overexploited for irrigation water supplies. The unsustainable use of ...groundwater resources in all three aquifer systems intensified from 2000 to 2008, making it imperative that we understand the consumptive processes and forces of demand that are driving their depletion. To this end, we quantify and track agricultural virtual groundwater transfers from these overexploited aquifer systems to their final destination. Specifically, we determine which US metropolitan areas, US states, and international export destinations are currently the largest consumers of these critical aquifers. We draw upon US government data on agricultural production, irrigation, and domestic food flows, as well as modeled estimates of agricultural virtual water contents to quantify domestic transfers. Additionally, we use US port-level trade data to trace international exports from these aquifers. In 2007, virtual groundwater transfers from the High Plains, Mississippi Embayment, and Central Valley aquifer systems totaled 17.93 km ³, 9.18 km ³, and 6.81 km ³, respectively, which is comparable to the capacity of Lake Mead (35.7 km ³), the largest surface reservoir in the United States. The vast majority (91%) of virtual groundwater transfers remains within the United States. Importantly, the cereals produced by these overexploited aquifers are critical to US food security (contributing 18.5% to domestic cereal supply). Notably, Japan relies upon cereals produced by these overexploited aquifers for 9.2% of its domestic cereal supply. These results highlight the need to understand the teleconnections between distant food demands and local agricultural water use.
Humans and ecosystems are deeply connected to, and through, the hydrological cycle. However, impacts of hydrological change on social and ecological systems are infrequently evaluated together at the ...global scale. Here, we focus on the potential for social and ecological impacts from freshwater stress and storage loss. We find basins with existing freshwater stress are drying (losing storage) disproportionately, exacerbating the challenges facing the water stressed versus non-stressed basins of the world. We map the global gradient in social-ecological vulnerability to freshwater stress and storage loss and identify hotspot basins for prioritization (n = 168). These most-vulnerable basins encompass over 1.5 billion people, 17% of global food crop production, 13% of global gross domestic product, and hundreds of significant wetlands. There are thus substantial social and ecological benefits to reducing vulnerability in hotspot basins, which can be achieved through hydro-diplomacy, social adaptive capacity building, and integrated water resources management practices.
Fresh water—the bloodstream of the biosphere—is at the center of the planetary drama of the Anthropocene. Water fluxes and stores regulate the Earth's climate and are essential for thriving aquatic ...and terrestrial ecosystems, as well as water, food, and energy security. But the water cycle is also being modified by humans at an unprecedented scale and rate. A holistic understanding of freshwater's role for Earth system resilience and the detection and monitoring of anthropogenic water cycle modifications across scales is urgent, yet existing methods and frameworks are not well suited for this. In this paper we highlight four core Earth system functions of water (hydroclimatic regulation, hydroecological regulation, storage, and transport) and key related processes. Building on systems and resilience theory, we review the evidence of regional‐scale regime shifts and disruptions of the Earth system functions of water. We then propose a framework for detecting, monitoring, and establishing safe limits to water cycle modifications and identify four possible spatially explicit methods for their quantification. In sum, this paper presents an ambitious scientific and policy grand challenge that could substantially improve our understanding of the role of water in the Earth system and cross‐scale management of water cycle modifications that would be a complementary approach to existing water management tools.
Plain language summary
Freshwater is crucially important for all life on Earth. There is abundant research and evidence on how different processes within the water cycle regulate climate and support ecosystems, and by extension, human societies. Humans are also a major force disturbing those processes and modifying the water cycle. These modifications include, for instance, surface water withdrawals, groundwater pumping, deforestation and other land cover change, and ice melt due to warming climate. As most previous research on human–water interactions focuses on understanding systems at smaller scales, such as a watershed or a nation, comprehensive understanding of what human modifications of the water cycle mean for the stability of the planet is still lacking. In this paper we propose a new framework for analysing and establishing limits to a variety of human modifications of the water cycle, to ensure that the stability of the Earth would not be compromised. We see this as an important and urgent scientific challenge that has the potential to substantially improve our understanding of the functioning of the Earth system and to inform local and global policy toward a more sustainable future.
Key Points
Earth system resilience depends on an improved understanding and management of water cycle modifications
We identify four key functions of freshwater in the Earth system and evidence of regional to global regime shifts and disruptions
The water planetary boundary is a compelling framework to improve our understanding and management of water cycle modifications in the Earth system
Food demands are rising due to an increasing population with changing food preferences, placing pressure on agricultural production. Additionally, climate extremes have recently highlighted the ...vulnerability of the agricultural system to climate variability. This study seeks to fill two important gaps in current knowledge: how irrigation impacts the large-scale response of crops to varying climate conditions and how we can explicitly account for uncertainty in yield response to climate. To address these, we developed a statistical model to quantitatively estimate historical and future impacts of climate change and irrigation on US county-level crop yields with uncertainty explicitly treated. Historical climate and crop yield data for 1970-2009 were used over different growing regions to fit the model, and five CMIP5 climate projections were applied to simulate future crop yield response to climate. Maize and spring wheat yields are projected to experience decreasing trends with all models in agreement. Winter wheat yields in the Northwest will see an increasing trend. Results for soybean and winter wheat in the South are more complicated, as irrigation can change the trend in projected yields. The comparison between projected crop yield time series for rainfed and irrigated cases indicates that irrigation can buffer against climate variability that could lead to negative yield anomalies. Through trend analysis of the predictors, the trend in crop yield is mainly driven by projected trends in temperature-related indices, and county-level trend analysis shows regional differences are negligible. This framework provides estimates of the impact of climate and irrigation on US crop yields for the 21st century that account for the full uncertainty of climate variables and the range of crop response. The results of this study can contribute to decision making about crop choice and water use in an uncertain future climate.
The increasing availability of remote sensing products for all components of the terrestrial water cycle makes it now possible to evaluate the potential of water balance closure purely from remote ...sensing sources. We take precipitation (P) from the TMPA and CMORPH products, a Penman‐Monteith based evapotranspiration (E) estimate derived from NASA Aqua satellite data and terrestrial water storage change (ΔS) from the GRACE satellite. Their combined ability to close the water budget is evaluated over the Mississippi River basin for 2003–5 by estimating streamflow (Q) as a residual of the water budget and comparing to streamflow measurements. We find that Q is greatly overestimated due mainly to the high bias in P, especially in the summer. Removal of systematic biases in P reduces the error significantly. However, uncertainties in the individual budget components due to simplifications in process algorithms and input data error are generally larger than the measured streamflow.
Socio‐hydrology focuses on studying the dynamics and co‐evolution of coupled human and water systems. Recently, several new socio‐hydrologic models have been published that explore these dynamics, ...and these models offer unique opportunities to better understand these coupled systems and to understand how water problems evolve similarly in different regions. These models also offer challenges, as decisions need to be made by the modeler on trade‐offs between generality, precision, and realism. In addition, traditional hydrologic model validation techniques, such as evaluating simulated streamflow, are insufficient, and new techniques must be developed. As socio‐hydrology progresses, these models offer a robust, invaluable tool to test hypotheses about the relationships between aspects of coupled human‐water systems. They will allow us to explore multiple working hypotheses to greatly expand insights and understanding of coupled socio‐hydrologic systems.
Key Points:
Socio‐hydrologic models can be seen as hypotheses of coupled system dynamics
Robust validation is required across multiple basins
Trade‐offs between generality, precision, and realism are required
Climate extremes can negatively impact crop production, and climate change is expected to affect the frequency and severity of extremes. Using a combination of in situ station measurements (Global ...Historical Climatology Network's Daily data set) and multiple other gridded data products, a derived 1° data set of growing season climate indices and extremes is compiled over the major growing regions for maize, wheat, soybean, and rice for 1951–2006. This data set contains growing season climate indices that are agriculturally relevant, such as the number of hot days, duration of dry spells, and rainfall intensity. Before 1980, temperature‐related indices had few trends; after 1980, statistically significant warming trends exist for each crop in the majority of growing regions. In particular, crops have increasingly been exposed to extreme hot temperatures, above which yields have been shown to decline. Rainfall trends are less consistent compared to temperature, with some regions receiving more rainfall and others less. Anomalous temperature and precipitation conditions are shown to often occur concurrently, with dry growing seasons more likely to be hotter, have larger drought indices, and have larger vapor pressure deficits. This leads to the confluence of a variety of climate conditions that negatively impact crop yields. These results show a consistent increase in global agricultural exposure to negative climate conditions since 1980.
Key Points
Agriculturally focused, historical climate data set is derived to assess agricultural climate risk
An acceleration in warming trends, including extreme heat exposure, has occurred since 1980 over the major growing regions for wheat, maize, soybean, and rice
Warming trends are accompanied by trends in drought indices and vapor pressure deficit, both of which may have deleterious impacts on crop yields
A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation ...techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984–2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, ...and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface‐subsurface interactions due to fine‐scale topography and vegetation; improved representation of land‐atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.
Key Points
Need for hyperresolution global models
Six challenges to hydrology that would benefit from hyper‐resolution models
The need for the community to come together in addressing the grand challenge
PDF
