We conducted experiments with a state-of-the-art, high-resolution version of SWAT - calibrated and validated for the MRB - to estimate impacts of the PDO, TAG, and WPWP variabilities on spring and winter wheat yields. Impacts of positive and negative phases of individual DCV phenomena as well as of combinations of the three DCV phenomena were simulated by forcing SWAT at approximately 13,500 locations (~ grid spacing of 12 km longitude - 12 km latitude) with idealized hydro-meteorological scenarios associated with the DCV phenomena. Experiments were conducted with average magnitudes and with extreme magnitudes of indices of the three DCV phenomena as observed from 1949 to 2010. Major results/products of this study are:
¤A methodology sensitive enough to detect effects of average magnitudes of major DCV phenomena on crop yields from the local (12-digit basin) to the regional (Major Water Resource Region; 2-digit basin) scale has been developed.
¤All six individual DCV scenarios at their average magnitudes affect hydro-meteorology and wheat yields in the MRB, with PDO making the largest impacts followed by TAG and then WPWP. Impacts of (PDO+, TAG-) and (PDO-, TAG+) combinations on spring and winter wheat yields are substantially stronger (up to 30-40% or more with respect to recent average yields) than those of either DCV phenomena alone. All eight multiple-DCV scenarios affect hydro-meteorology and wheat yields of the Basin, with (PDO+, TAG-, WPWP+/-) and (PDO-, TAG+, WPWP+/-) scenarios having the maximum overall impacts. Extreme magnitudes of the three DCV phenomena are associated with severe to extreme impacts on wheat yields in the MRB.
¤ Local conditions appear to influence wheat yield responses to DCV phenomena in South Dakota, northern Kansas, northern Missouri, and Montana under extreme scenarios.
A comparison of simulated impacts of idealized DCV scenarios on spring and winter wheat yields – described in this paper – with our exploratory study (Mehta et al., 2012) shows that while the overall spatial patterns of impacts of individual DCV phenomena on wheat yields in the MRB are generally similar, impacts are much larger in magnitude at some locations and in some 4-digit basins in the present study. This difference may be due largely to the much higher resolution employed in the present study, and to the meticulous calibration and validation of the SWAT model. There are 180 times more samples in the present study (approximately 13,500 locations), so analyses results are much more accurate and reliable compared to the exploratory study where there were only 75 locations in the entire MRB. Another major difference is that the exploratory study did not simulate impacts of multiple, simultaneous DCV phenomena on wheat yields. As shown by this study, impacts of (PDO+, TAG-) and (PDO-, TAG+) combinations can be much larger in magnitude than those of an individual DCV phenomenon at its average amplitude, the former combination potentially causing major periods of abundant wheat yields and the latter devastating decreases in wheat yields. Instances of associations between larger spring and winter wheat productions and relatively large-magnitude (PDO+, TAG-) combination can be seen in Figure 1 in the 1990 to 1998 and 2013 to 2016 periods; instances of associations between relatively smaller spring wheat production and relatively large-magnitude (PDO-, TAG+) combination can be seen in Figure 1 in the 1998 to 2012 period. Of course, the biggest difference between this study and our exploratory study is the usability of the present results by stakeholders and policymakers as explained in Section 1.2.
So, how can stakeholders and policymakers apply results of this study to adapt to DCV impacts? As mentioned briefly in section 1.2, Mehta et al. (2013a) reported the usefulness of DCV and impacts information as indicated by over 125 stakeholders and policymakers in the MRB. Since the various ways such information can be useful to agriculture and other sectors is described in detail by Mehta et al. (2013a), only the highlights of the usefulness for the agriculture sector are mentioned here. Decadal climate outlooks and their possible impacts on agriculture, as simulated in the present study, can be useful for (1) selecting crops and cultivars, (2) changes in crop rotation or tillage practices, (3) irrigation planning, (4) purchasing crop insurance, (5) decisions between dryland and irrigated crops, (6) decisions between food crops and biofuel crops which are more drought-resistant, (7) investments in irrigation systems, and (8) assessing viability of operations if a multiyear to decadal dry/wet epoch is predicted. As mentioned in the Introduction (section 1), Fernandez et al. (2016) and Rhodes and McCarl (2020) estimated that the monetary value of such adaptation options to MRB agriculture and U.S. agriculture, respectively, would be very substantial. Since the MRB produces a major portion of the wheat produced in the U.S., impacts of multiyear to decadal dry/wet epochs, as simulated in the present study, can make very substantial impacts on U.S. wheat prices and exports, thereby impacting national and global food security. Therefore, DCV and impacts predictions can also be very useful in adapting national and global food security to multiyear to decadal dry/wet epochs.
Because the three DCV phenomena whose impacts on non-irrigated wheat yields were modeled in this study are known to influence climate and hydro-meteorology in other parts of the world as well (as do other DCV phenomena not addressed in this study), our methodology should also be applicable to other non-irrigated agricultural regions. It is certainly possible that the evolution of major DCV phenomena will be forecast with some skill in the foreseeable future (Meehl et al. 2021). When this happens, it may become possible, with SWAT and other well-validated land use-hydrology-crop models, to forecast DCV phenomena’s multiyear to decadal impacts on water and crop yields in those regions known to be affected by DCV.
Our results show that the three DCV phenomena considered in this study, if they occur individually, can significantly impact wheat yields in the MRB. As these DCV phenomena can persist in either of their phases for a few years to a decade or longer, and as the simultaneous correlation among them is negligibly small, their combined and cumulative effects on the MRB hydro-meteorology and wheat yields should be sufficient to impact not only the agriculture sector but also transportation, irrigation, and other water-related sectors as shown by Mehta et al. (2016) by analyses of the same SWAT experiments described here; and local, state, and perhaps even national economies. These results also imply important consequences of these impacts on crop yields to feedbacks to the climate system via water vapor, heat, momentum, and carbon fluxes; and for nutrient content of run-off from wheat fields into streams/rivers and thereby on water quality for other sectors.
Results of the present study are also very relevant and applicable to climatic change impacts. It is well-known (see, for example, Henley and King (2017)) that natural DCV confounds the detection and attribution of anthropogenic climatic change. Similarly, DCV impacts on yields and productions of wheat and other crops would also confound the detection and attribution of future climatic change impacts on agriculture. For example, approximately one half-cycle of DCV impacts can exacerbate climatic change impacts and the other half-cycle can reduce climatic change impacts substantially. Therefore, it is very important for any plan to adapt to changing climate to include realistic simulations and predictions of DCV impacts to avoid misleading conclusions and actions. This study and Mehta et al. (2016) offer insights to past, current, and future weather-driven crop yield and water yield variability at multiyear to decadal timescales that may aid in projection of long-term climatic change effects on both. Most research on crop yields and water availability under climatic change assumes a uni-directional change (for example, increasingly hotter and drier) occurring at a reasonably accurate projectable/predictable rate driven by greenhouse gas emissions. But, future climate scenarios based on past data of natural DHCs, driven by DCV phenomena, provide the natural complexity and realism of seasonal and annual weather variability within an epoch. It is from documentation of such epochs’ history and assessment of their impacts that climatic change projections/predictions can be made more complex and realistic, and hence, more useful. In addition, impacts of DCV phenomena on crop yields are qualitatively and quantitatively different from impacts of seasonal to interannual climate variability. For example, multiyear to decadal droughts associated with DCV can even induce farmers to consider selling their lands and quit farming as a serious option which would be similar to farmers’ future decisions to quit farming if local climate is projected to become perpetually drought-like. Adaptation options based on simulations of DCV impacts on crop yields, as described in this paper and in Mehta et al. (2013a), can also be used to develop adaptation options for a future, climatic change-driven, perpetual drought-like situation. Finally, rigorous testing of SWAT and other such hydrology-crop models, with respect to past dry/wet epochs and crop yield data, as described here can inspire high confidence in such models’ use for simulating/projecting climatic change impacts on water and agriculture.