scholarly journals Malawi’s Shire River Fluctuations and Climate

2014 ◽  
Vol 15 (5) ◽  
pp. 2039-2049 ◽  
Author(s):  
Mark R. Jury
Keyword(s):  
Large Scale ◽  
River Flow ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Severity Index ◽  
Early Warning Systems ◽  
Climatic Research Unit ◽  
Climate Index ◽  
Drought Severity ◽  
High Pressure Cell

Abstract Hydrological fluctuations of Malawi’s Shire River and climatic drivers are studied for a range of time and space scales. The annual cycles of basin rainfall and river flow peak in summer and autumn, respectively. Satellite and model products at <50-km resolution resolve the water deficit in this narrow valley. The leading climate index fitting Shire River flow anomalies is the Climatic Research Unit (CRU) Palmer drought severity index, based on interpolated gauge rainfall minus Penman–Monteith potential evapotranspiration. Climate variables anticipate lake level changes by 2 months, while weather variables anticipate river flow surges by 2 days. Global climate patterns related to wet years include a Pacific La Niña cool phase and low pressure over northeastern Africa. Shire River floods coincide with a cyclonic looping wind pattern that amplifies the equatorial trough and draws monsoon flow from Tanzania. Hot spells are common in spring: daytime surface temperatures can reach 60°C causing rapid desiccation. An anticyclonic high pressure cell promotes evaporation losses of ~20 mm day−1 over brief periods. Flood and drought in Malawi are shown to be induced by the large-scale atmospheric circulation and rainfall in the surrounding highlands. Hence, early warning systems should consider satellite and radar coverage of the entire basin.

scholarly journals The Influence of Climate Model Biases on Projections of Aridity and Drought

2016 ◽  
Vol 29 (4) ◽  
pp. 1269-1285 ◽  
Author(s):  
Darren L. Ficklin ◽  
John T. Abatzoglou ◽  
Scott M. Robeson ◽  
Anna Dufficy
Keyword(s):  
United States ◽  
Climate Models ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Precipitation Amount ◽  
Severity Index ◽  
Global Climate Models ◽  
Drought Severity ◽  
Climate Conditions

Abstract Global climate models (GCMs) have biases when simulating historical climate conditions, which in turn have implications for estimating the hydrological impacts of climate change. This study examines the differences in projected changes of aridity [defined as the ratio of precipitation (P) over potential evapotranspiration (PET), or P/PET] and the Palmer drought severity index (PDSI) between raw and bias-corrected GCM output for the continental United States (CONUS). For historical simulations (1950–79) the raw GCM ensemble median has a positive precipitation bias (+24%) and negative PET bias (−7%) compared to the bias-corrected output when averaged over CONUS with the most acute biases over the interior western United States. While both raw and bias-corrected GCM ensembles project more aridity (lower P/PET) for CONUS in the late twenty-first century (2070–99), relative enhancements in aridity were found for bias-corrected data compared to the raw GCM ensemble owing to positive precipitation and negative PET biases in the raw GCM ensemble. However, the bias-corrected GCM ensemble projects less acute decreases in summer PDSI for the southwestern United States compared to the raw GCM ensemble (from 1 to 2 PDSI units higher), stemming from biases in precipitation amount and seasonality in the raw GCM ensemble. Compared to the raw GCM ensemble, bias-corrected GCM inputs not only correct for systematic errors but also can produce high-resolution projections that are useful for impact analyses. Therefore, changes in hydroclimate metrics often appear considerably different in bias-corrected output compared to raw GCM output.

scholarly journals Dynamical delimitation of the Central American Dry Corridor (CADC) using drought indices and aridity values

2019 ◽  
Vol 43 (5) ◽  
pp. 627-642 ◽  
Author(s):  
Luis Eduardo Quesada-Hernández ◽  
Oscar David Calvo-Solano ◽  
Hugo G Hidalgo ◽  
Paula M Pérez-Briceño ◽  
Eric J Alfaro
Keyword(s):  
Drought Index ◽  
Pacific Coast ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Rainfall Anomaly ◽  
Central American ◽  
Drought Indices ◽  
Drought Severity ◽  
Southern Mexico

The Central American Dry Corridor (CADC) is a sub-region in the isthmus that is relatively drier than the rest of the territory. Traditional delineations of the CADC’s boundaries start at the Pacific coast of southern Mexico, stretching south through Central America’s Pacific coast down to northwestern Costa Rica (Guanacaste province). Using drought indices (Standardized Precipitation Index, Modified Rainfall Anomaly Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Palmer Drought Z-Index and the Reconnaissance Drought Index) along with a definition of aridity as the ratio of potential evapotranspiration (representing demand of water from the atmosphere) over precipitation (representing the supply of water), we proposed a CADC delineation that changes for normal, dry and wet years. The identification of areas that change their classification during extremely dry conditions is important because these areas may indicate the location of future expansion of aridity associated with climate change. In the same way, the delineation of the CADC during wet extremes allows the identification of locations that remain part of the CADC even during the wettest years and that may require special attention from the authorities.

scholarly journals Juniper Tree-Ring Data from the Kuramin Range (Northern Tajikistan) Reveals Changing Summer Drought Signals in Western Central Asia

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 505 ◽  
Author(s):  
Feng Chen ◽  
Tongwen Zhang ◽  
Andrea Seim ◽  
Shulong Yu ◽  
Ruibo Zhang ◽  
...  
Keyword(s):  
Central Asia ◽  
Tree Ring ◽  
Large Scale ◽  
Southern Oscillation ◽  
Severity Index ◽  
Drought Severity ◽  
Summer Drought ◽  
Tree Ring Chronology ◽  
Tree Ring Data ◽  
June July

Coniferous forests cover the mountains in many parts of Central Asia and provide large potentials for dendroclimatic studies of past climate variability. However, to date, only a few tree-ring based climate reconstructions exist from this region. Here, we present a regional tree-ring chronology from the moisture-sensitive Zeravshan juniper (Juniperus seravschanica Kom.) from the Kuramin Range (Tajikistan) in western Central Asia, which is used to reveal past summer drought variability from 1650 to 2015 Common Era (CE). The chronology accounts for 40.5% of the variance of the June–July self-calibrating Palmer Drought Severity Index (scPDSI) during the instrumental period (1901 to 2012). Seven dry periods, including 1659–1696, 1705–1722, 1731–1741, 1758–1790, 1800–1842, 1860–1875, and 1931–1987, and five wet periods, including 1742–1752, 1843–1859, 1876–1913, 1921–1930, and 1988–2015, were identified. Good agreements between drought records from western and eastern Central Asia suggest that the PDSI records retain common drought signals and capture the regional dry/wet periods of Central Asia. Moreover, the spectral analysis indicates the existence of centennial (128 years), decadal (24.3 and 11.4 years), and interannual (8.0, 3.6, 2.9, and 2.0 years) cycles, which may be linked with climate forces, such as solar activity and El Niño-Southern Oscillation (ENSO). The analysis between the scPDSI reconstruction and large-scale atmospheric circulations during the reconstructed extreme dry and wet years can provide information about the linkages of extremes in our scPDSI record with the large-scale ocean–atmosphere–land circulation systems.

scholarly journals Terrestrial Aridity and Its Response to Greenhouse Warming across CMIP5 Climate Models

2015 ◽  
Vol 28 (14) ◽  
pp. 5583-5600 ◽  
Author(s):  
Jacob Scheff ◽  
Dargan M. W. Frierson
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Aridity Index ◽  
Global Climate Models ◽  
Greenhouse Warming ◽  
Dimensionless Ratio ◽  
The Tropics

Abstract The aridity of a terrestrial climate is often quantified using the dimensionless ratio of annual precipitation (P) to annual potential evapotranspiration (PET). In this study, the climatological patterns and greenhouse warming responses of terrestrial P, Penman–Monteith PET, and are compared among 16 modern global climate models. The large-scale climatological values and implied biome types often disagree widely among models, with large systematic differences from observational estimates. In addition, the PET climatologies often differ by several tens of percent when computed using monthly versus 3-hourly inputs. With greenhouse warming, land P does not systematically increase or decrease, except at high latitudes. Therefore, because of moderate, ubiquitous PET increases, decreases (drying) are much more widespread than increases (wetting) in the tropics, subtropics, and midlatitudes in most models, confirming and expanding on earlier findings. The PET increases are also somewhat sensitive to the time resolution of the inputs, although not as systematically as for the PET climatologies. The changes in the balance between P and PET are also quantified using an alternative aridity index, the ratio , which has a one-to-one but nonlinear correspondence with . It is argued that the magnitudes of changes are more uniformly relevant than the magnitudes of changes, which tend to be much higher in wetter regions. The ratio and its changes are also found to be excellent statistical predictors of the land surface evaporative fraction and its changes.

scholarly journals A New Global 0.5° Gridded Dataset (1901–2006) of a Multiscalar Drought Index: Comparison with Current Drought Index Datasets Based on the Palmer Drought Severity Index

2010 ◽  
Vol 11 (4) ◽  
pp. 1033-1043 ◽  
Author(s):  
S. M. Vicente-Serrano ◽  
S. Beguería ◽  
J. I. López-Moreno ◽  
M. Angulo ◽  
A. El Kenawy
Keyword(s):  
Spatial Resolution ◽  
Drought Index ◽  
Palmer Drought Severity Index ◽  
National Research Council ◽  
Severity Index ◽  
Research Unit ◽  
Climatic Research Unit ◽  
Drought Severity ◽  
Spatial And Temporal Variability ◽  
Drought Severity Index

Abstract A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI). The presented dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained using the Climatic Research Unit (CRU) TS3.0 dataset at a spatial resolution of 0.5°. The advantages of the new dataset are that (i) it improves the spatial resolution of the unique global drought dataset at a global scale; (ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI; and, in particular, (iii) it enables the identification of various drought types, given the multiscalar character of the SPEI. The dataset is freely available on the Web page of the Spanish National Research Council (CSIC) in three different formats [network Common Data Form (netCDF), binary raster, and plain text].

Long lead statistical forecasts of area burned in western U.S. wildfires by ecosystem province

2002 ◽  
Vol 11 (4) ◽  
pp. 257 ◽  
Author(s):  
Anthony L. Westerling ◽  
Alexander Gershunov ◽  
Daniel R. Cayan ◽  
Tim P. Barnett
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Forecast Model ◽  
Severity Index ◽  
Fire Season ◽  
Drought Severity ◽  
Canonical Correlations ◽  
Area Burned ◽  
Wildfire Season ◽  
Random Guess

A statistical forecast methodology exploits large-scale patterns in monthly U.S. Climatological Division Palmer Drought Severity Index (PDSI) values over a wide region and several seasons to predict area burned in western U.S. wildfires by ecosystem province a season in advance. The forecast model, which is based on canonical correlations, indicates that a few characteristic patterns determine predicted wildfire season area burned. Strong negative associations between anomalous soil moisture (inferred from PDSI) immediately prior to the fire season and area burned dominate in most higher elevation forested provinces, while strong positive associations between anomalous soil moisture a year prior to the fire season and area burned dominate in desert and shrub and grassland provinces. In much of the western U.S., above- and below-normal fire season forecasts were successful 57% of the time or better, as compared with a 33% skill for a random guess, and with a low probability of being surprised by a fire season at the opposite extreme of that forecast.

scholarly journals Evaluating Spatiotemporal Patterns and Trends of Drought in Japan Associated with Global Climatic Drivers

2021 ◽  
Author(s):  
Ke Shi ◽  
Yoshiya Touge ◽  
So Kazama
Keyword(s):  
Wavelet Transform ◽  
Potential Evapotranspiration ◽  
Kendall Test ◽  
Severity Index ◽  
Wavelet Coherence ◽  
Burned Area ◽  
Drought Severity ◽  
Arctic Oscillation Index ◽  
Climatic Drivers ◽  
Homogeneous Regions

Abstract. Drought disasters, such as water scarcity and wildfires, are serious natural disasters in Japan that are also affected by climate change. However, as drought generally has widespread impacts and the duration of drought can vary considerably, it is difficult to assess the spatiotemporal characteristics and the climatic causes of drought. Therefore, to identify the drought homogeneous regions and understand climatic causes of regional drought over Japan, this study provides a spatiotemporal analysis for historical droughts patterns and teleconnections associated with global climatic drivers. The trends of meteorological elements, which are the basis of drought index calculation, was first assessed. Then, drought characterized by the Self-calibrating Palmer Drought Severity Index (scPDSI) was investigated. Trends and patterns of drought were identified through the trend-free pre-whitening Mann-Kendall test and distinct empirical orthogonal function. The continuous wavelet transform and cross wavelet transform together with wavelet coherence were utilized to depict the links between drought and global climatic drivers. The results are described as follows: (1) the trends of precipitation were insignificant. However, temperature and potential evapotranspiration increasing trends were detected over Japan; (2) the drought trend over Japan varied seasonally, increasing in spring and summer and decreasing in autumn and winter; (3) two major subregions of drought variability—the western Japan (W region) and most of the northernmost Japan near the Pacific (N region) were identified; (4) wildfires with large burned area were more likely to occur when the scPDSI was less than −1; and (5) the North Atlantic Index (NAOI) showed the strongest coherence connections with Distinguished Principle Components-1 among four climatic drivers. Additionally, Distinguished Principle Components-2 showed stronger coherence connections with NAOI and Arctic Oscillation Index. This study is the first to identify homogeneous regions with distinct drought characteristics over Japan and connect the drought in Japan with the global climatic drivers.

scholarly journals Evaluation of Drought Characteristics in Iraq using SC-PDSI

2018 ◽  
Vol 6 (4) ◽  
pp. 177-182
Author(s):  
Thaer K. Jawad ◽  
Osama T. Al-Taai ◽  
Yaseen K. Al-Timimi
Keyword(s):  
Severity Index ◽  
Climatic Research Unit ◽  
Drought Severity ◽  
Arid Zones ◽  
Severe Drought ◽  
Drought Characteristics ◽  
Dry Spell ◽  
Desert Zone ◽  
Semi Arid ◽  
Run Theory

Evaluation of drought characteristics in Iraq by analysis annual growing season of Self-Calibrating Palmer Drought Severity Index (SC-PDSI) for three climatic zones using run theory method. The efficiency of SC-PDSI for drought monitoring was examined from compared with Rainfall Departure from the mean (RD) for three zones (Arid and Semi-Arid, Steppes and Desert) for the period 1981-2015, were derived from Climatic Research Unit (CRU). The spatial interpolation techniques in ArcGIS package has been used, to cover the whole extent of country and extracting the zones. Statistical methods were applied to compute the probability of drought events at every zone. The results showed the years 1999, 2000, 2008 and 2009 experienced droughts in all zones except the desert zone where was experienced severe drought in 2012, while the years 1982 and 1988 experienced received precipitation above-average in all zones. The values of standard deviation of precipitation were compared with precipitation anomalies for each zone, the drier seasons are (2007-2008) and (1998-1999) in all Zones. The wetter seasons are (1987-1988) in Arid and Semi-Arid and Steppes zone, (1994-1995) and (1997-1998) are wetter seasons in Desert zone. Using run theory, the steppes zone have experienced more severe droughts than other zones evaluated in this study and the most susceptible areas to dry spell are steppes and Arid and Semi-Arid Zones during study period. While the desert zone experienced less droughts.

scholarly journals How Wet and Dry Spells Evolve across the Conterminous United States Based on 555 Years of Paleoclimate Data

2018 ◽  
Vol 31 (16) ◽  
pp. 6633-6647 ◽  
Author(s):  
Michelle Ho ◽  
Upmanu Lall ◽  
Edward R. Cook
Keyword(s):  
United States ◽  
Large Scale ◽  
Viterbi Algorithm ◽  
Severity Index ◽  
Climate Forcing ◽  
Drought Severity ◽  
Spatial Features ◽  
Dry Spells ◽  
Temporal And Spatial ◽  
Wet Spells

Abstract Evolving patterns of droughts and wet spells in the conterminous United States (CONUS) are examined over 555 years using a tree-ring-based paleoclimate reconstruction of the modified Palmer drought severity index (PDSI). A hidden Markov model is used as an unsupervised method of classifying climate states and quantifying the temporal evolution from one state to another. Modeling temporal variability in spatial patterns of drought and wet spells provides the ability to objectively assess and simulate historical persistence and recurrence of similar patterns. The Viterbi algorithm reveals the probable sequence of states through time, enabling an examination of temporal and spatial features and associated large-scale climate forcing. Distinct patterns of sea surface temperature that are known to enhance or inhibit rainfall are associated with some states. Using the current CONUS PDSI field the model can be used to simulate the space–time PDSI pattern over the next few years, or unconditional simulations can be used to derive estimates of spatially concurrent PDSI patterns and their persistence and intensity across the CONUS.

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