Manual Aridity: Droughts and Human Development

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Demise and Mutation of Nomadism.

Aridity: Droughts and Human Development by Monique Mainguet, Berlin: Springer, 1999.

Definition of Degrees of Aridity. The Different Types of Drought.

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Postglacial Aridification and Advance of the Deserts in Africa? Study of the Drought of in Kenya and Its Consequences. The Soils of Turan. The Specific Characteristics of Dryland Soils. Vegetation Covers Economic Potentials and Droughts. Heterogeneity of the Vegetaion Cover in Drylands. Adaptability of Plants to Aridity and Drought. The Open Vegetation Cover of the Sahel.

The Vocation of Grazing the Sahel Steppe. Potential Foraging Value and Carrying Capacity. Behaviour of the Vegetal Cover During the Drought of Resources vs Hydrological and Aeolian Constraints. Hydrological Constraints in Drylands. The Fugacity of the Surface Waters. The Allochthonous WaterCourses.

Rill Wash and Runoff in Tropical Drylands. The Underground Waters. Degradation of Soils by Water Erosion.

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The Omnipresent Wind in Dry Ecosystems. Aeolian Processes in Sand and Loess Drylands. Aeolian Deposits. A Proposal for the Classification of Dunes. Aridity Drought and the Birth of Agriculture. Aridity Drought and the Birth of Hydraulic Societies.

More extreme and more frequent: Drought and aridity in the 21st century

The Great Hydraulic Systems of the Drylands. Traditional and Modern Hydraulic Techniques. Parsimonious Management of Surface Waters. Traditional and Modern Irrigation. Traditional Irrigation. Modern Irrigation. Control of Barchans and Linear or Parabolic Dunes. Ten Principles Explaining Decadence. Next, we examine and compare temporal trends in E 0 between the three formulations for a selected region, i.

Here again, large differences are evident between the three formulations in the magnitude of E 0 , in the amplitude of interannual variability, and in long-term trends. The warm and dry bias in CanESM2 is expected to cause this difference. Despite these differences between the two GCMs, we find that the Penman-Monteith-based E 0 estimation is both higher in magnitude of mean seasonal totals as well as the amplitude of interannual variability relative to the other two formulations.

Priestley-Taylor exhibits the lowest interannual variability, similar to results found for coefficient of variation in Fig 3. Because both Priestley-Taylor and Hargreaves-Samani estimations yield overall lower magnitudes for climatological mean and amplitude of interannual variability in E 0 , they have a large influence on the trends in percent of historical mean and standardized anomaly Fig 5 , bottom two rows , particularly the latter. For example, in the plots for GFDL-ESM2M left column , Priestley-Taylor exhibits a comparably low E 0 climatology similar to Hargreaves-Samani and the lowest interannual variability, yielding the largest 21 st century trends in E 0 in both percent of historical mean and standardized anomalies.

Our analysis of E 0 responses across the three formulations shows that Penman-Monteith produces a more spatially heterogeneous response in E 0 as well as a more enhanced realization of the amplitude of interannual variability. We believe that both these features are a result of a more adequate treatment of wind-driven turbulence and relative humidity in Penman-Monteith, and therefore this formulation is particularly critical for regions, such as the southwestern US and the Great Plains, that are sensitive to these drivers.

In the next section, we focus on just the Penman-Monteith formulation and examine how the E 0 responses are further sensitive to the choices of input variables representing both the aerodynamic and radiative terms. We first examine the sensitivity of projected E 0 trends to different choices of radiative inputs into the Penman-Monteith formulation.

The choice of fixed versus modeled variable albedo has very little effect on the trends. Overall, the radiative input choices have an extremely limited effect on differentiating the E 0 timeseries in percent of mean and standardized E 0 anomalies during the 21 st century. Next, we examined the sensitivity of projected E 0 responses to input of wind speed and crop height, which appear in the aerodynamic term. Fig 6 right column shows MJJAS E 0 trends for the Northern Great Plains for differences in the aerodynamic term due to i modeling tall versus short reference crops, and ii using daily wind speeds versus monthly wind speeds averaged from daily values.

Furthermore, interannual variability is higher for daily wind speed and tall crop type. These factors also affect 21 st century trends in percent of historical mean and standardized anomalies, however differences due to the choice of input parameters are much smaller relative to the differences found between the three different E 0 formulations in the previous section see Fig 5. One approach to explore changing drought risk under climate change is to evaluate future projections of currently used drought indices.

Priestley-Taylor shows the strongest tendency for dry conditions throughout the 21 st century.

Forest Management for Mitigating Drought Impacts

Hargreaves-Samani shows a similar overall pattern to Priestley-Taylor, although the magnitude of the drying response is comparatively lower. Penman-Monteith shows the smallest trend for increases in the 21 st century drought risk, but still shows a positive drying trend in the late summer and fall period. But for both indices, Hargreaves-Samani and Priestley-Taylor show a more pronounced drying tendency relative to Penman-Monteith.

Daily EDDI or SPEI values are binned into specific percentile categories spanning between driest and wettest categories relative to the historical — distribution. Both GCMs show an increased tendency for droughts as defined during the historical period during the 21 st century, although there are large differences based on the choice of E 0 formulation and GCM. Therefore, trends and model bias in precipitation will affect the SPEI responses.

An increasing trend in precipitation, which is found for both models, can lessen or counteract a trend toward increasing dryness in SPEI. A positive trend in precipitation can also dampen the increase in E 0 through the complementary relationship [ 89 , 90 ] by increasing available moisture, thereby impacting EDDI. In other words, P-E 0 has a much wider distribution in the historic period than E 0 itself.

Therefore, when standardized relative to the historical distribution, future E 0 trends will appear more augmented than future P-E 0 trends, especially if future P trends are relatively small. GCM precipitation bias during the historical period can impact these drought indices in two ways. An arid climate is more prone to conditions where the surface evapotranspiration cannot meet E 0 leading to more enhanced E 0 and therefore, EDDI because of soil moisture-temperature feedbacks that amplify surface temperature and water vapor deficit i.

A more detailed investigation of the relationship between GCM bias and drought projections is a topic of future research, as regional temperature and precipitation biases inherent in GCMs can strongly influence the trends in the 21 st century projections of these drought indices.

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Appropriate bias-corrected GCM datasets could help in reducing the uncertainty in the projection of regional drought risks. In this study we evaluated how different methodological and dataset choices in the estimation of evaporative demand yield different assessments of temporal and spatial variability in E 0 and trends in 21 st century drought risk. First, we evaluated these differential responses across three of the more widely used formulations for E 0 estimations: Penman-Monteith, Hargreaves-Samani, and Priestley-Taylor.

This formulation gives a more spatially heterogeneous realization of E 0 because it is more sensitive to the variations in turbulent fluxes driven by wind speed and surface roughness and relative humidity. Penman-Monteith also facilitates a more physically robust depiction of the amplitude of interannual variability relative to the other two methods, whose interannual variabilities are much lower, particularly Priestley-Taylor. We also find regional patterns to this enhanced interannual variability in Penman-Monteith, such as in the Great Plains and southwestern US, which is corroborated by pan evaporation observations.

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This feature is not reproduced by Hargreaves-Samani and Priestley-Taylor. Previous studies have also shown a greater importance of drivers like wind speed and relative humidity in these regions in driving interannual variability in E 0 [ 5 , 72 , 88 ]. Therefore, the application of Penman-Monteith for E 0 estimation would especially be critical for regions where the influence of these drivers is strong.

We also find that Penman-Monteith is more appropriate for 21 st century E 0 projections and for drought indices that use E 0 , e.

The 21 st century E 0 trends generated in our analysis, particularly trends in percent of historical mean and standardized anomalies, are found to be sensitive to the long-term mean value and the amplitude of interannual variability; i. We find this to be the case with the Hargreaves-Samani and Priestley-Taylor formulations, where the future standardized E 0 trends are much larger than those from Penman-Monteith.

In examining the Penman-Monteith-driven E 0 response under various input variable choices related to radiation, surface roughness, and wind speed, we found differences in E 0 trends. However, these differences are much smaller than the differences in E 0 trends from the three E 0 formulations. We find that the use of the wind speed term averaged across different temporal scales daily versus monthly has a similar effect on both the estimation of long-term mean E 0 value and interannual variability, i.

We also found that different choices in estimating the radiation term have a very limited influence on E 0 trends into the 21 st century. In the absence of an adequate number of GCM ensemble member runs available, we performed a Monte Carlo resampling with replacement of the data to provide uncertainty ranges for these estimates 5th to 95th percentiles. Overall, the use of Penman-Monteith formulation leads to more moderate increases in future drought risk while, for the most part, Priestley-Taylor causes the largest increases in drought risk, and Hargreaves-Samani falls between the two.

Filled circles show the mean change, box plots show 25th, 50th and 75th percentiles, and whiskers show 5th and 95th percentiles. Confidence intervals shown here about the mean projected change are estimated based on Monte Carlo resampling. We have demonstrated here that methodological and dataset choices are crucial to the assessment of 21 st century drought risk under climate change. Based on our findings, we would recommend a revaluation of results from previous studies that are based on projected trends in the standardized drought parameters.

Furthermore, biases in GCMs affect the quantification of projected changes in evaporative demand and the drought indices based on it, introducing additional uncertainty.