Crop switching reduces agricultural losses from climate change in the United States by half under RCP 8.5

A key strategy for agriculture to adapt to climate change is by switching crops and relocating crop production. We develop an approach to estimate the economic potential of crop reallocation using a Bayesian hierarchical model of yields. We apply the model to six crops in the United States, and show that it outperforms traditional empirical models under cross-validation. The fitted model parameters provide evidence of considerable existing climate adaptation across counties. If crop locations are held constant in the future, total agriculture profits for the six crops will drop by 31% for the temperature patterns of 2070 under RCP 8.5. When crop lands are reallocated to avoid yield decreases and take advantage of yield increases, half of these losses are avoided (16% loss), but 57% of counties are allocated crops different from those currently planted. Our results provide a framework for identifying crop adaptation opportunities, but suggest limits to their potential

Up-to-date probabilistic temperature climatologies

With ongoing global warming, climatologies based on average past temperatures are increasingly recognized as imperfect guides for current conditions, yet there is no consensus on alternatives. Here, we compare several approaches to deriving updated expected values of monthly mean temperatures, including moving average, exponentially weighted moving average, and piecewise linear regression. We go beyond most previous work by presenting updated climate normals as probability distributions rather than only point estimates, enabling estimation of the changing likelihood of hot and cold extremes. We show that there is a trade-off between bias and variance in these approaches, but that bias can be mitigated by an additive correction based on a global average temperature series, which has much less interannual variability than a single-station series. Using thousands of monthly temperature time series from the Global Historical Climatology Network (GHCN), we find that the exponentially weighted moving average with a timescale of 15 years and global bias correction has good overall performance in hindcasting temperatures over the last 30 years (1984–2013) compared with the other methods tested. Our results suggest that over the last 30 years, the likelihood of extremely hot months (above the 99th percentile of the temperature probability distribution as of the early 1980s) has increased more than fourfold across the GHCN stations, whereas the likelihood of very cold months (under the 1st percentile) has decreased by over two-thirds

Delaware Reservoirs’ Drought Risk Assessment: A Paleo View

The Delaware River provides half of New York City\u27s drinking water, is a habitat for wild trout, American shad and the federally endangered dwarf wedge mussel. It has suffered four 100‐year floods in the last seven years. A drought during the 1960s stands as a warning of the potential vulnerability of the New York City area to severe water shortages if a similar drought were to recur. The water releases from three New York City dams on the Delaware River\u27s headwaters impact not only the reliability of the city’s water supply, but also the potential impact of floods, and the quality of the aquatic habitat in the upper river. The goal of this work is to influence the Delaware River water release policies (FFMP/OST) to further benefit river habitat and fisheries without increasing New York City\u27s drought risk, or the flood risk to down basin residents. The Delaware water release policies are constrained by the dictates of two US Supreme Court Decrees (1931 and 1954) and the need for unanimity among four states: New York, New Jersey, Pennsylvania, and Delaware ‐‐ and New York City. Coordination of their activities and the operation under the existing decrees is provided by the Delaware River Basin Commission (DRBC). Questions such as the probability of the system approaching drought state based on the current FFMP plan and the severity of the 1960s drought are addressed using long record paleo‐reconstructions of flows. For this study, we developed reconstructed total annual flows (water year) for 3 reservoir inflows using regional tree rings going back upto 1754 (a total of 246 years). The reconstructed flows are used with a simple reservoir model to quantify droughts. We observe that the 1960s drought is by far the worst drought based on 246 years of simulations (since 1754)

Recent trends in the frequency and duration of global floods

Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985–2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1–7, moderate: 8–20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann–Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. Results show that flood frequency and the tails of the flood duration (long duration) have increased at both the global and the latitudinal scales. In the tropics, floods have increased 4-fold since the 2000s. This increase is 2.5-fold in the north midlatitudes. However, much of the trend in frequency and duration of the floods can be placed within the long-term climate variability context since the Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were the main atmospheric teleconnections explaining this trend. There is no monotonic trend in the frequency of short-duration floods across all the global and latitudinal scales. There is a significant increasing trend in the annual median of flood durations globally and each latitudinal belt, and this trend is not related to these teleconnections. While the DFO data come with a certain level of epistemic uncertainty due to imprecision in the estimation of floods, overall, the analysis provides insights for understanding the frequency and persistence in hydrologic extremes and how they relate to changes in the climate, organization of global and local dynamical systems, and country-scale socioeconomic factors

A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression

A hierarchical Bayesian regression model is presented for reconstructing the average summer streamflow at five gauges in the Delaware River basin using eight regional tree-ring chronologies. The model provides estimates of the posterior probability distribution of each reconstructed streamflow series considering parameter uncertainty. The vectors of regression coefficients are modeled as draws from a common multivariate normal distribution with unknown parameters estimated as part of the analysis. This leads to a multilevel structure. The covariance structure of the streamflow residuals across sites is explicitly modeled. The resulting partial pooling of information across multiple stations leads to a reduction in parameter uncertainty. The effect of no pooling and full pooling of station information, as end points of the method, is explored. The no-pooling model considers independent estimation of the regression coefficients for each streamflow gauge with respect to each tree-ring chronology. The full-pooling model considers that the same regression coefficients apply across all streamflow sites for a particular tree-ring chronology. The cross-site correlation of residuals is modeled in all cases. Performance on metrics typically used by tree-ring reconstruction experts, such as reduction of error, coefficient of efficiency, and coverage rates under credible intervals is comparable to, or better, for the partial-pooling model relative to the no-pooling model, and streamflow estimation uncertainty is reduced. Long record simulations from reconstructions are used to develop estimates of the probability of duration and severity of droughts in the region. Analysis of monotonic trends in the reconstructed drought events do not reject the null hypothesis of no trend at the 90% significance over 1754–2000

Coupled flow accumulation and atmospheric blocking govern flood duration

We present a physically based Bayesian network model for inference and prediction of flood duration that allows for a deeper understanding of the nexus of antecedent flow regime, atmospheric blocking, and moisture transport/release mechanisms. Distinct scaling factors at the land surface and regional atmospheric levels are unraveled using this Bayesian network model. Land surface scaling explains the variability in flood duration as a function of cumulative exceedance index, a new measure that represents the evolution of the flood in the basin. Dynamic atmospheric scaling explains the cumulative exceedance index using the interaction between atmospheric blocking system and the synergistic model of wind divergence and atmospheric water vapor. Our findings underline that the synergy between a large persistent low-pressure blocking system and a higher rate of divergent wind often triggers a long-duration flood, even in the presence of moderate moisture supply in the atmosphere. This condition in turn causes an extremely long-duration flood if the basin-wide cumulative flow prior to the flood event was already high. Thus, this new land-atmospheric interaction framework integrates regional flood duration scaling and dynamic atmospheric scaling to enable the coupling of ‘horizontal’ (for example, streamflow accumulation inside the basin) and ‘vertical’ flow of information (for example, interrelated land and ocean-atmosphere interactions), providing an improved understanding of the critical forcing of regional hydroclimatic systems. This Bayesian model approach is applied to the Missouri River Basin, which has the largest system of reservoirs in the United States. Our predictive model can aid in decision support systems for the protection of national infrastructure against long-duration flood events.info:eu-repo/semantics/publishedVersio

Can improved agricultural water use efficiency save India’s groundwater?

Irrigated agriculture is placing increasing pressure on finite freshwater resources, especially in developing countries, where water extraction is often unregulated, un-priced and even subsidized. To shift agriculture to a more sustainable use of water without harming the food security and livelihoods of hundreds of millions of smallholders, substantial improvements of water use efficiency will be required. Here, we use detailed hydroclimatic and agricultural data to estimate the potential for the widespread adoption of efficient irrigation technologies to halt the depletion of India\u27s groundwater resources. Even though we find substantial technical potential for reversing water table declines, we show that the impacts are highly sensitive to assumptions about farmers\u27 water use decisions. For example, we find that widespread adoption of proven technologies that include drip and sprinkler irrigation has the potential to reduce the amount of excessive extraction of groundwater by two thirds. However, under more realistic assumptions about farmers\u27 irrigation choices, half of these reductions are lost due to the expansion of irrigated area. Our results suggest that without the introduction of incentives for conservation, much of the potential impact of technology adoption on aquifers may be lost. The analysis provides quantitative input to the debate of incentive versus technology based water policies

Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India

Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions

How Does Flow Alteration Propagate Across a Large, Highly Regulated Basin? Dam Attributes, Network Context, and Implications for Biodiversity

Large dams are a leading cause of river ecosystem degradation. Although dams have cumulative effects as water flows downstream in a river network, most flow alteration research has focused on local impacts of single dams. Here we examined the highly regulated Colorado River Basin (CRB) to understand how flow alteration propagates in river networks, as influenced by the location and characteristics of dams as well as the structure of the river network—including the presence of tributaries. We used a spatial Markov network model informed by 117 upstream-downstream pairs of monthly flow series (2003–2017) to estimate flow alteration from 84 intermediate-to-large dams representing \u3e83% of the total storage in the CRB. Using Least Absolute Shrinkage and Selection Operator regression, we then investigated how flow alteration was influenced by local dam properties (e.g., purpose, storage capacity) and network-level attributes (e.g., position, upstream cumulative storage). Flow alteration was highly variable across the network, but tended to accumulate downstream and remained high in the main stem. Dam impacts were explained by network-level attributes (63%) more than by local dam properties (37%), underscoring the need to consider network context when assessing dam impacts. High-impact dams were often located in sub-watersheds with high levels of native fish biodiversity, fish imperilment, or species requiring seasonal flows that are no longer present. These three biodiversity dimensions, as well as the amount of dam-free downstream habitat, indicate potential to restore river ecosystems via controlled flow releases. Our methods are transferrable and could guide screening for dam reoperation in other highly regulated basins