scholarly journals Characterizing and Monitoring Drought over Upper Blue Nile of Ethiopia with the Aid of Copula Analysis

2018 ◽  
pp. 283-294 ◽  
Author(s):  
Abebe Kebede ◽  
U. Jaya Prakash Raju ◽  
Diriba Koricha ◽  
Melessew Nigussie
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Joint Distribution ◽  
Goodness Of Fit ◽  
Severe Drought ◽  
Precipitation Data ◽  
Moisture Index ◽  
Drought Duration ◽  
Distribution Method ◽  
Blue Nile

The main aim of this study is to characterize and monitor drought distribution and expansion over Upper Blue Nile of Ethiopia by using univariate standard precipitation index (SPI) and standardized soil moisture index (SMI) whose joint distribution leads to multi standardized drought index (MSDI). The soil moisture and CHIRPS precipitation data from first January 1980 to 2016 are modeled. The indices of SPI, SMI and the joint MSDI value over the Upper Blue Nile are analyzed. The SPI for different time scales is implemented. The correlation between severity, duration and intensity including wetness and drought strengths is computed and analyzed. It is found that the correlation between duration and severity is 0.96 and normal conditions for SPI 3, 6, 12 month time scales are frequently observed rather than moderate, severe and extreme severe drought or wetness. Building on soil moisture and precipitation data of the summer season, the Clayton copula model is selected based on goodness of fit parameters. After setting the best copula family for the Upper Blue Nile then we applied the joint distribution method is applied for characterizing and monitoring drought. It is found that the MSDI more clearly showed that the severity of drought across the time series of each time scales, than SPI and SMI. As the time scale increases there is decline of fluctuation or frequency of drought and the rising of drought duration is shown by SPI, SMI and MSDI. By using SPI6, SMI6 and MSDI6 the spatial distribution of drought is determined from June to August in the years 1984 and 2015 indicate the drought expansions in the eastern and western parts of Upper Blue Nile during the respective years.

scholarly journals Multi-Dimensional Drought Assessment in Abbay/Upper Blue Nile Basin: The Importance of Shared Management and Regional Coordination Efforts for Mitigation

2021 ◽  
Vol 13 (9) ◽  
pp. 1835
Author(s):  
Yared Bayissa ◽  
Semu Moges ◽  
Assefa Melesse ◽  
Tsegaye Tadesse ◽  
Anteneh Z. Abiy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Earth Observation ◽  
Drought Indices ◽  
Severe Drought ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Small Streams ◽  
Upper Blue Nile Basin

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.

scholarly journals Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 705 ◽  
Author(s):  
Haekyung Park ◽  
Kyungmin Kim ◽  
Dong kun Lee
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Meteorological Data ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Absolute Error ◽  
Severe Drought ◽  
Drought Forecasting ◽  
Moisture Index ◽  
Training Area

The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage.

scholarly journals Development of the Soil Moisture Index to Quantify Agricultural Drought and Its “User Friendliness” in Severity-Area-Duration Assessment

2008 ◽  
Vol 9 (4) ◽  
pp. 660-676 ◽  
Author(s):  
Venkataramana Sridhar ◽  
Kenneth G. Hubbard ◽  
Jinsheng You ◽  
Eric D. Hunt
Keyword(s):  
Soil Moisture ◽  
Agricultural Drought ◽  
Extreme Drought ◽  
Weather Data ◽  
Moisture Index ◽  
Drought Duration ◽  
Available Water ◽  
Soil Moisture Index ◽  
The Difference ◽  
Wilting Point

Abstract This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additionally, with the aid of an in-house hydrology model, soil moisture was simulated in order to compute model-based SMI and to compare the drought duration and severity for various sites. The results suggest that the soil moisture influence, a positive feedback process reported in many earlier studies, is unquestionably a quantitative indicator of drought. Also, the severity and duration of drought across Nebraska has a clear gradient from west to east, with the Panhandle region experiencing severe to extreme drought in the deeper soil layers for longer periods (>200 days), than the central and southwestern regions (125–150 days) or the eastern regions about 100 days or less. The anomalous rainfall years can eliminate the distinction among these regions with regard to their drought extent, severity, and persistence, thus making drought a more ubiquitous phenomenon, but the recovery from drought can be subject to similar gradations. The spatial SMI maps presented in this paper can be used with the Drought Monitor maps to assess the local drought conditions more effectively.

scholarly journals Predicting U.S. Drought Monitor States Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part I: Development of a Nondiscrete USDM Index

2017 ◽  
Vol 18 (7) ◽  
pp. 1943-1962 ◽  
Author(s):  
David J. Lorenz ◽  
Jason A. Otkin ◽  
Mark Svoboda ◽  
Christopher R. Hain ◽  
Martha C. Anderson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Goodness Of Fit ◽  
Forecasting Methods ◽  
Different Seasons ◽  
Numerous Data ◽  
Empirical Methodology ◽  
Probability Density Function Pdf ◽  
Drought Classes ◽  
The U.S

Abstract The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a nondiscrete USDM index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th, and 2nd percentiles—corresponding with USDM definitions for the D4–D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the probability density function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness-of-fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method is well correlated with the current USDM analyses. In Part II, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next-higher drought category.

scholarly journals Computation of the Standardized Precipitation Index (SPI) and Its Use to Assess Drought Occurrences in Cameroon over Recent Decades

2014 ◽  
Vol 53 (10) ◽  
pp. 2310-2324 ◽  
Author(s):  
Guy Merlin Guenang ◽  
F. Mkankam Kamga
Keyword(s):  
Time Scales ◽  
Goodness Of Fit ◽  
Standardized Precipitation Index ◽  
Research Unit ◽  
Distribution Functions ◽  
Precipitation Index ◽  
Severe Drought ◽  
East Anglia ◽  
The Standardized Precipitation Index ◽  
Short Time

AbstractThe standardized precipitation index (SPI) is computed and analyzed using 55 years of precipitation data recorded in 24 observation stations in Cameroon along with University of East Anglia Climate Research Unit (CRU) spatialized data. Four statistical distribution functions (gamma, exponential, Weibull, and lognormal) are first fitted to data accumulated for various time scales, and the appropriate functions are selected on the basis of the Anderson–Darling goodness-of-fit statistic. For short time scales (up to 6 months) and for stations above 10°N, the gamma distribution is the most frequent choice; below this belt, the Weibull distribution predominates. For longer than 6-month time scales, there are no consistent patterns of fitted distributions. After calculating the SPI in the usual way, operational drought thresholds that are based on an objective method are determined at each station. These thresholds are useful in drought-response decision making. From SPI time series, episodes of severe and extreme droughts are identified at many stations during the study period. Moderate/severe drought occurrences are intra-annual in short time scales and interannual for long time scales (greater than 9 months), usually spanning many years. The SPI calculated from CRU gridded precipitation shows similar results, with some discrepancies at longer scales. Thus, the spatialized dataset can be used to extend such studies to a larger region—especially data-scarce areas.

scholarly journals Characteristics of droughts in Argentina's Core Crop Region

2020 ◽  
Author(s):  
Leandro Carlos Sgroi ◽  
Miguel Angel Lovino ◽  
Ernesto Hugo Berbery ◽  
Gabriela Viviana Müller
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Crop Yields ◽  
Growth Period ◽  
Drought Severity ◽  
Corn Yield ◽  
Drought Duration ◽  
Dry Spells ◽  
One Year ◽  
The Relationship

Abstract. The current study advances the documentation of dry episodes over Argentina’s Core Crop Region, where the production of major crops like wheat, corn, and soybean is most intense and represents the main contribution to the country’s Gross Domestic Product. Our analysis focuses on the properties of droughts that include their magnitude, frequency at different time scales, duration, and severity. It is of interest to assess the relationship between those properties and the crop yields. We analyzed 40 years of precipitation and soil moisture at resolutions suitable for regional studies. The analysis of precipitation and soil moisture anomalies is complemented with the corresponding standardized indices estimated at time scales of 3- and 6-months. Most droughts tend to occur for periods shorter than three months, but a few can extend up to one year and fewer even longer. However, if a multiyear drought experienced breaks, each period would be considered a separate case. Analysis of the frequency distribution indicates that cases of water deficit conditions are more common than instances of water excess. As relevant as the drought duration is its timing and severity. Even short dry spells may have large impacts if they occur at the time of the critical growth period of a given crop. In the core crop region, corn yield is the most sensitive to drought severity. For these reasons, the quantification of severity during the crop-sensitive months is an indicator of what crop yields could be on the next campaign.

scholarly journals Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite

2010 ◽  
Vol 14 (3) ◽  
pp. 459-478 ◽  
Author(s):  
J.-P. Vidal ◽  
E. Martin ◽  
L. Franchistéguy ◽  
F. Habets ◽  
J.-M. Soubeyroux ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Time Scales ◽  
Time Scale ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Multiple Time Scales ◽  
Flow Index ◽  
Severe Drought ◽  
Hydrological Droughts

Abstract. Physically-based droughts can be defined as a water deficit in at least one component of the land surface hydrological cycle. The reliance of different activity domains (water supply, irrigation, hydropower, etc.) on specific components of this cycle requires drought monitoring to be based on indices related to meteorological, agricultural, and hydrological droughts. This paper describes a high-resolution retrospective analysis of such droughts in France over the last fifty years, based on the Safran-Isba-Modcou (SIM) hydrometeorological suite. The high-resolution 1958–2008 Safran atmospheric reanalysis was used to force the Isba land surface scheme and the hydrogeological model Modcou. Meteorological droughts are characterized with the Standardized Precipitation Index (SPI) at time scales varying from 1 to 24 months. Similar standardizing methods were applied to soil moisture and streamflow for identifying multiscale agricultural droughts – through the Standardized Soil Wetness Index (SSWI) – and multiscale hydrological droughts, through the Standardized Flow Index (SFI). Based on a common threshold level for all indices, drought event statistics over the 50-yr period – number of events, duration, severity and magnitude – have been derived locally in order to highlight regional differences at multiple time scales and at multiple levels of the hydrological cycle (precipitation, soil moisture, streamflow). Results show a substantial variety of temporal drought patterns over the country that are highly dependent on both the variable and time scale considered. Independent spatio-temporal drought events have then been identified and described by combining local characteristics with the evolution of area under drought. Summary statistics have finally been used to compare past severe drought events, from multi-year precipitation deficits (1989–1990) to short hot and dry periods (2003). Results show that the ranking of drought events depends highly on both the time scale and the variable considered. This multilevel and multiscale drought climatology will serve as a basis for assessing the impacts of climate change on droughts in France.

scholarly journals Application of probability decision system and particle swarm optimization for improving soil moisture content

2021 ◽  
Author(s):  
Panpan Zhu ◽  
Hossein Saadati ◽  
Majid Khayatnezhad
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Natural Disasters ◽  
Goodness Of Fit ◽  
Soil Moisture Content ◽  
Root Zone ◽  
Distribution Functions ◽  
Drought Duration ◽  
Groundwater Exploitation ◽  
The Impact

Abstract Drought is one of the natural disasters having the highest degrees in comparison to the other natural disasters in terms of rate, intensity, incident duration, region expansion, life losses, economic damages, and long-term effects. Hence, the prediction of drought as a meteorological phenomenon should be evaluated to determine the groundwater exploitation strategies in agriculture. The present study aims at investigating the impact of the drought duration and severity on soil moisture supplement for agricultural activities in Baghmalek plain, Khuzestan province, Iran. For this objective, a non-dimensional index of precipitation depth was defined for quantifying the drought characteristics. Furthermore, marginal distribution functions, correlation coefficients and joint functions were incorporated to a probabilistic decision-making framework to predict the variables in different return periods from 2-year to 100-year. Results showed that t copula was the best function for constructing the multivariate distribution in the study area based on the goodness-of-fit tests. Moreover, soil moisture content in the root zone achieved by the predetermined amounts of precipitation could be increased in the seasonal average.

scholarly journals Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

2021 ◽  
Vol 13 (9) ◽  
pp. 4926
Author(s):  
Nguyen Duc Luong ◽  
Nguyen Hoang Hiep ◽  
Thi Hieu Bui
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Temporal Dynamics ◽  
Regional Scale ◽  
Dry Season ◽  
Red River ◽  
Agricultural Drought ◽  
Severe Drought ◽  
Red River Basin ◽  
Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

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