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The Pampanga River basin: (a) river network and discharge gauges, (b) digital elevation model, (c) land use, and (d) soil map.
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Drought in the Philippines has been monitored for agricultural and economic losses but spatial and temporal characterization at the basin scale has not been quantified. The objectives of this study were: 1) to develop a standardized anomaly index (SA) for assessing impacts of the El Niño Southern-Oscillation (ENSO) on droughts at the basin scale; 2...
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Context 1
... The Pampanga River basin (10,061 km 2 ; see Figure 5a) is located in central Luzon, and lies in the provinces of Pampanga and Bulacan. It is bordered by the provinces of Bataan and Zambales to the west, Tarlac and Nueva Ecija to the north, the rest of Bulacan to the southeast, and drains to Manila Bay. ...
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... (Fig- ure 5a) were used in model calibration to achieve an opti- mal parameter set to obtain discharge at the basin outlet draining to Manila Bay. Meteorological forcing data used in the simulations were from the Japan Meteorological Agency (JMA) Japan Reanalysis (JRA) [Onogi et al., 2007] JRA25 fcst_phy2m data set (air temperature, specific hu- midity, air pressure, wind speed, downward solar, and long wave radiation). ...
Context 3
... topography used was the NASA SRTM (Shuttle Radar Topographic Mission) using three-arc sec- ond digital elevation data (approximately 90 m) resampled to a 100-m DEM. Figure 5b shows the northern and south- western upland areas (maximum elevation 1835 m) with the central plains at 5 m above sea level. ...
Context 4
... Land-use type consists mostly of deciduous, broad- leaf, and needleleaf evergreen trees (forest areas in the north- ern and central parts) with short vegetation and grassland areas scattered sparsely, and agricultural areas concentrated in the southwestern part of the watershed (Figure 5c). The land-use data are from the USGS global land cover data set. ...
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In 2011, massive floods occurred in the Chao Phraya River basin (CPRB) in Thailand. This affected both Thai socio-economics and the global industrial production supply-chain. There is also a risk of droughts in the CPRB during the dry season. The CPRB has two multi-purpose, large-scale reservoirs: the Bhumibol and Sirikit reservoirs. The aim of thi...
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... On the other hand, droughts and heat waves endanger crops and people's health. Droughts affect more people in the Philippines than other catastrophic events while not being as fatal as other calamities (Jaranilla-Sanchez et al., 2011). ...
Since ENSO influences the temperature and Precipitation that affects human
societies and Earth ecosystems, given that reason, this research discusses the
relationship of ENSO and the Sub Seasonal Variability of Southwest Monsoon in the
Philippines from 2004 to 2020. This research may serve as an extension for the
several researchers that pursue the same topic. This research used the historical data of Precipitation recorded by the Department of Science and Technology – Philippine Atmospheric Geophysical and Astronomical Services Administration (DOST PAGASA), and the data of ENSO was obtained from the National Oceanic and
Atmospheric Administration (NOAA). The data was obtained from different
meteorological stations (Science Garden, Subic Bay, Coron Palawan, Mactan Cebu,
Dipolog, and Zamboanga del Sur) located in the western part of the country.
Time Series Analysis-Additive Decomposition indicates a significant decreasing and increase from below -0.50 to 1.25 in the determinant of ENSO. It also shows the consistent Seasonal Pattern that changes over time of ENSO as time passes. Moreover, the researcher also used the same method to analyze the variability of Precipitation data in different meteorological stations. Upon interpretation by the researcher, the variability of southwest monsoon is high in all the Weather Stations in the country, with the highest peak of 1000 millimeters in 2013. The researcher also plotted the linear trendline of ENSO and Precipitation in different meteorological stations. Upon interpretation, it shows a low to no significant relationship between ENSO and the Sub Seasonal Variability of Southwest Monsoon in the Philippines. Based on the study, there is no significant relationship between ENSO and the Southwest Monsoon. The result between ENSO and Southwest Monsoon has been provided due to the result of Linear regression from which the Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and the Precipitation shows a low to negative correlation. Thus, the researcher concluded with an interpretation that there is no relationship between the variables.
... 역사적으로 보면 가뭄은 인류문명의 지속가능한 발전 에 큰 위협이 되고 있다. 특히, 가뭄은 1984년 나이지리아의 GDP를 4~6% 하락시켰고 짐바브웨 및 잠비아의 GDP를 8~9% 하락 사례에서도 알 수 있듯이 개발도상국의 경제발전 에 더 큰 위협이 되고 있다 (Lee, 2021 (Palmer, 1968;Hollinger et al., 1993;Heathcote, 1998;Kendy et al., 2003;Tadesse et al., 2005;Kim et al., 2006;Tsakiris et al., 2006;Ahn, 2008;Brown et al., 2008;Sheffield and Wood, 2008;Shukla and Wood, 2008;Jaranilla-Sanchez et al., 2011;Jung and Chang, 2012;Hogg et al., 2013;Nam et al., 2014;Nam et al., 2015;Tadesse et al., 2015) 그리고 하천유량, 저수지나 댐 저류량, 지하수 등을 이용하여 가뭄을 평가하는 수문학적 가뭄지수 (Dracup et al., 1980;Sen, 1980;Zelenhasic and Salvai, 1987;Chang and Stenson, 1990;Frick et al., 1990;Chang, 1991;Clausen and Pearson, 1995;Kwon et al., 2006;Kwon and Kim, 2007;Jo, 2008;Karamouz et al., 2009;Dogan et al., 2012;Assani, 2016;Jeong et al., 2017;Jo, 2017;Yoo et al., 2017) ...
Drought is caused by a long period of lack of rainfall and water resources, and has a great impact on the life and ecosystem of both humans and animals. It is particularly important for the agricultural sector, which is closely related to food security. Global warming, urbanization, and industrialization have led to a gradually increasing demand for agricultural water. In response, Korea has steadily developed its agricultural water sources to reduce rising damage to the agricultural sector due to climate change. The severity of drought is evaluated by using meteorological, agricultural, hydrological, and socioeconomic drought indexes. The agricultural drought index is evaluated using soil moisture and crop dryness, and the hydrological drought index is evaluated based on water shortage by comparing demands with water resources available for supply, such as rivers and groundwater, reservoirs and dams. However, these methods were found to over- or under-estimate the relatively low sensitivity of agricultural water as they assess the shortage of water for life, industry, and agriculture. Therefore, in this study, we developed the Agricultural Water Demand and Supply Drought Index (AWDSDI), which evaluates agricultural drought by analyzing water supply systems such as agricultural reservoirs, pumping stations and drainage, groundwater systems, and demands for agricultural water. In order to review the applicability of the developed drought index, AWDSDI was applied to 32 Eps and Myuns in three cities, including Jincheongun, Changnyeonggun, and Jangseonggun in the period June-August 2017, when drought damage was significant. It was found that AWDSDI reproduced the daily agricultural drought well in small administrative districts such as Eps and Myuns. In addition, in order to verify the developed drought index, the evaluation results of AWDSDI, the previously developed agricultural drought index and the hydrological drought index were compared together. The comparison found that the AWDSDI reproduced the drought period and drought depth in 32 Eps and Myuns in three cities better than previously developed drought indices.
... Also, Metropolitan Manila, the largest urban agglomeration in the country, is located in the Pasig-Laguna de Bay River Basin. By having specific analyses in a river basin context will provide valuable inputs in the Integrated River Basin Management approach being utilized by public agencies (DENR-RBCO, 2007;Jaranilla-Sanchez et al., 2011;Almaden, 2015;Salmivaara, 2015). ...
This study utilized the Analytic Hierarchical Process and spatial analysis using various datasets to produce sub-provincial vulnerability maps with 20 km resolution. Five (5) indicators for exposure, four (4) for sensitivity and seven (7) for adaptive capacity were selected and weighted using aggregated rankings from twenty-three (23) experts. Based on these indicators, gridded maps of exposure, sensitivity, adaptive capacity, and vulnerability were produced. Using river basins as the unit of analysis, the Pampanga River Basin was determined to be the most vulnerable, followed by the Agus River Basin, having the highest sensitivity, and Buayan River Basin, having the lowest adaptive capacity. These areas have large agricultural regions and river systems with high flooding risk. Coastal regions in southern Mindanao and eastern Visayas were also highly vulnerable to flooding. High poverty rates with high dependence on agricultural incomes and low adaptive capacities characterize these areas. Vulnerability hotspots can easily be identified through these maps, which have value in planning initiative to reduce potential damages of floods to agricultural areas.
... The data can be downloaded at http://data1.gfdl.noaa.gov. We resampled the monthly precipitation data from the native resolution to a resolution of 0.25 • using the bilinear interpolation method [48], [49]. To use the GFDL monthly precipitation as the input of the LSM which needs hourly meteorological forcings, we corrected the GLDAS 3-h precipitation to match the GFDL monthly precipitation every month, as done by [45] (see also Section II-D). ...
Agricultural drought monitoring and prediction technology are urgently needed. We applied an ecohydrological land data assimilation system (LDAS), which can simulate soil moisture and leaf area index (LAI) by data assimilation of microwave brightness temperature into a land surface model (LSM), to monitor and predict agricultural droughts in North Africa. We successfully monitor nationwide crop failures, which are characterized by the declines of the nationwide wheat production, in Morocco, Algeria, and Tunisia using LAI and soil moisture calculated by the LDAS. Our simulated LAI is well correlated with the nationwide wheat production (r= 0.70, 0.65, and 0.72 in Morocco, Algeria, and Tunisia, respectively). A general circulation model (GCM)-based seasonal meteorological prediction significantly contributes to accurately predicting LAI and agricultural droughts in 2-3-month lead time. In addition, it is found that initial conditions have an important role in predicting LAI. We demonstrate the capability of our framework to monitor and predict agricultural drought in North Africa. Our proposed framework can contribute to mitigating the negative impact of drought on agriculture in poorly gauged water-limited subcontinental regions.
... Thus, groundwater drought can be extrapolated from a time series of water level changes. In this study, groundwater drought events were identified from the standardized anomaly of groundwater levels, which was calculated by: (1) normalizing the hydrological variables, (2) standardizing the normalized distribution by removing the means of the transformed variable (i.e., long-term monthly means), and (3) dividing them by the transformed standard deviation (σ) [57]. When the standardized anomaly drops below -1, it indicates drought conditions. ...
... The periods of 2008-2009 and 2008-2015 experienced severely dry (SPI −1.99 ~ −1.50) to extremely dry (SPI < −2.0) conditions [54]. However, the standardized groundwater level anomaly [57,62,63], as indicated by [(observation-average)/standard deviation], varied among the monitoring wells, probably due to their location in terms of the groundwater flow system, the characteristics of aquifer materials, and the man-made impact of groundwater pumping. If the standardized anomaly dropped below -1, the index indicated groundwater drought conditions. ...
The sustainability of rural areas depends on the availability of water resources. The Mangyeong River Basin (MRB) in Korea faces a water supply shortage for agriculture and industry. Based on 11-year (2005–2015) precipitation and groundwater monitoring data, groundwater sustainability was evaluated in terms of natural and man-made factors and their spatio-temporal variations. A precipitation time-series revealed a declining trend, but there were different seasonal trends between wet and dry periods, with declining and rising trends, respectively. Groundwater hydrographs from five national groundwater monitoring wells showed temporal variations. Groundwater wells located in downstream areas showed both recharge from upgradient areas and local man-made impacts (e.g. from pumping), resulting in an ambiguous relationship between precipitation and water levels. However, other monitoring wells in the upstream areas displayed water level responses to precipitation events, with a declining trend. Using the standardized precipitation index at a time scale of 12 months (SPI-12) and the standardized groundwater level anomaly, meteorological and groundwater drought conditions were compared to infer the relationship between precipitation deficit and groundwater shortage in the aquifer. The SPI results indicated severely dry to extremely dry conditions during 2008–2009 and 2015. However, the standardized groundwater level anomaly showed various drought conditions for groundwater, which were dependent on the site-specific hydrogeological characteristics. Finally, groundwater sustainability was assessed using water budget modelling and water quality data. Presently, if groundwater is used above 39.2% of the recharge value in the MRB, groundwater drought conditions occur throughout the basin. Considering water quality issues, with nitrate being elevated above the natural background, this critical abstraction value becomes 28.4%. Consequently, in the MRB, sustainable groundwater management should embrace both natural and human-induced factors to regulate over-exploitation and prevent contamination.
... A comparison between the variability of the annual maximum discharges (AMAX) and their anomalies was performed in order to provide a clear visualization of the time series and its tendencies. The AMAX anomaly was calculated through the use of the Standardized Anomaly Index (SAI) introduced by E.B. Kraus [28,29], which is an index frequently used for climate change studies [30,31]. ...
The Sahelian regions are affected by an increasing number of catastrophic floods in recent years as a consequence of climate and land use/land cover changes. River flow data is key to understanding river behavior and develop flood mitigation and prevention strategies. The present study provides a revision and an update of the existing discharge dataset of the Sirba River with the aim of enhancing the reliability of these data. The revision also includes the recalibration of the Garbey Kourou rating curves. The analysis of the revised discharge time series strengthens the previous findings, evidencing a positive trend in flood frequency and intensity over the entire analyzed period of 1956–2018. This positive trend is more pronounced for the last 40 years due to a significant underestimation of the rating curves used. A relevant finding is a new changepoint in the time series, detected for 2008, which represents the beginning of the period in which the highest flood magnitudes were registered. The effect of land use/land cover changes and climate changes on the water resource is depicted using flow duration curves. This research produces a revised and more reliable discharge time series that will be a new starting point for future hydrological analyses.
... I identified and quantified the drought propagation using soil moisture and LAI of ECHLA. The drought index used in this study was the Standardized Anomaly (SA) [9,59]: ...
... The threshold of the LAI SA to identify drought events was set to −2.0. This threshold of SA is often used as "extremely dry" in the literature (e.g., [59]). If the LAI SA is less than −2.0 on the climatological day-of-year of the LAI's seasonal peak, I considered that the drought event occurred (Figure 3b . ...
... respectively. This threshold of SA (−1.0) is often used as "moderately dry" in the literature (e.g., [59]). If there are multiple drought events when the SA is less than −1.0 during the extracted 2-year period, I used the event which has the longest duration. ...
Despite the importance of the interaction between soil moisture and vegetation dynamics to understand the complex nature of drought, few land reanalyses explicitly simulate vegetation growth and senescence. In this study, I provide a new land reanalysis which explicitly simulates the interaction between sub-surface soil moisture and vegetation dynamics by the sequential assimilation of satellite microwave brightness temperature observations into a land surface model (LSM). Assimilating satellite microwave brightness temperature observations improves the skill of a LSM to simultaneously simulate soil moisture and the seasonal cycle of leaf area index (LAI). By analyzing soil moisture and LAI simulated by this new land reanalysis, I identify the drought events which significantly damage LAI on the climatological day-of-year of the LAI’s seasonal peak and quantify drought propagation from soil moisture to LAI in the global snow-free region. On average, soil moisture in the shallow soil layers (0–0.45 m) quickly recovers from the drought condition before the climatological day-of-year of the LAI’s seasonal peak while soil moisture in the deeper soil layer (1.05–2.05 m) and LAI recover from the drought condition approximately 100 days after the climatological day-of-year of the LAI’s seasonal peak.
... There are various indices that have been used to measure the drought characteristics, and their use depends on the type of results needed. Drought indices were also derived from other hydrological and ecological variables [5][6][7]. Drought indices based on different climatic and hydrological variables may depict different regional and temporal patterns [8]. These indices are mostly based on a deficit in precipitation or discharge [9] to identify the drought. ...
Since the climatic features of South Korea are highly complex and time variable,
spatio-temporal-based drought frequency analysis is a prerequisite for drought risk management.
The spatial extent of drought risk analysis in a bivariate framework has scarcely been applied in
South Korea before. In this study, the spatio-temporal changes in drought events are investigated
at 55 stations across South Korea during 1980–2015. A variety of probability distributions and
copulas are applied, and the best fitted is selected on the basis the goodness of fit. The spatial
distributions of primary and secondary return periods showed a high risk of drought due to
the unusual precipitation pattern in the western coast areas and at Uljin station and a relative low
risk of drought in the northwestern portion and surrounding areas of Yeongju, Uiseong, Boeun and
Daejeon stations. Overall, the spatial distribution patterns of primary and secondary (Kendall)
return periods are similar. However, their applicability changes according to the type of drought
risk to be considered. The spatio-temporal quantification of the return period can be used for
establishing the proper water demand and supply system and helps to overcome the challenges faced
in the hydrometeorological regulations of reservoirs in the southwest coast.
... Reliable forecasts of drought could lessen its impacts on the economy and human livelihood by, for instance, allowing for more informed reservoir operations or agricultural decisions (e.g., the number of animals to pasture or the variety of crops to plant), by enabling water management agencies and municipalities to preemptively issue restrictions or relief organizations to plan for allocation of resources, and by anticipating impacts to ecosystems and natural resource management. State-of-the-art drought forecasts are statistical, dynamical, or combinations of both, and produce predictions of drought indices such as the standardized precipitation index [Dutra et al., 2013; Belayneh et al., 2015; Mo and Lyon, 2015], soil moisture index [Thober et al., 2015], standardized streamflow index [Madadgar and Moradkhani, 2013] or standard anomaly index [Jaranilla-Sanchez et al., 2011]. Many of the statistical models use Bayesian [Madadgar and Moradkhani, 2013] or machine learning approaches such as artificial neural networks, trained on existing datasets [Jalalkamali et al., 2014; Belayneh et al., 2015], to project the value of drought indices into the future. ...
... These shortcomings may not apply to dynamical models. Those models often take the form of distributed hydrologic models that are forced by coupled atmosphere-ocean general circulation models [Thober et al., 2015] and produce estimates of river discharge, groundwater levels, and soil moisture [Jaranilla-Sanchez et al., 2011]. The most advanced of these mechanistic models also take into account feedback between vegetation communities, soil moisture, and regional exchange fluxes with the atmosphere [Sawada et al., 2014]. ...
A fundamental environmental challenge facing humanity in the 21st century and beyond is
predicting the impacts of global environmental change. This challenge is complicated by the fact
that we live on a non-stationary, unreplicated planet that is rapidly moving outside the envelope
of natural variability into an historical non-analog world. In other words, while the past helps
inform us about how the world has worked, it may no longer be the relevant frame of reference
for management, conservation, and sustainability. In this future world the two questions at the
foundation of sustainability are “How are ecosystems and the services they provide going to
change in the future?” and “How do human decisions affect this trajectory?” These are, at their
heart, questions about ecological forecasting.
The need for ecological forecasts traverses the entire discipline of ecology, with cutting-edge
research spanning disease dynamics, endangered and invasive species, wildlife and fisheries
management, disturbance, phenology, and biogeochemical cycles. However, at the moment,
the bulk of ecological forecasts are focused on mean climate change responses on centennial
time scales, and thus do not meet the needs of most environmental decision-making, which is
near-term and sensitive to variability and extremes. In addition to unmet demand, this focus on
long timescales means that we are not routinely comparing specific, quantitative predictions to
new data, which is one of the strongest tests of any scientific theory. Near-term forecasts
provide the opportunity to iteratively cycle between making forecasts, performing analyses, and
updating predictions in light of new evidence. Research across widely disparate disciplines has
shown that this iterative process of gaining feedback, building experience, and correcting
models and methods is critical for building accurate forecasts. Iterative forecasting is also a
crucial part of any decision making under high uncertainty and the cornerstone of adaptive
management.
Our aim is to articulate the needs and benefits of a concerted effort to develop iterative, nearterm
ecological forecasts. We specifically focus on the following questions:
1. What is the current state of iterative ecological forecasting?
2. What are the most immediate needs and opportunities, both scientifically and societally?
3. What are the hurdles to getting this done (theory, data, training, collaboration,
technology, funding, institutions, scientific culture)?
... A constant or a threshold can be used, and because a variable threshold level takes seasonal patterns into account, it has been widely used [3,4]. The threshold is usually assumed to be equal to a given percentile of the flow duration curve between the 30 percentile flow (Q 30 ) and the 5percentile flow (Q 5 ) [5], or the threshold can be obtained by fitting some kind of statistical function through the data (normal, gamma, beta, etc.) [6]. The threshold is 25percentile (Q 25 ), meaning that a drought occurs when the streamflow is below the 25th percentile flow from the duration curve. ...
This study investigates the sensitivity and uncertainty of hydrological droughts frequencies and severity in the Weihe Basin, China during 1960-2012, by using six commonly used univariate probability distributions and three Archimedean copulas to fit the marginal and joint distributions of drought characteristics. The Anderson-Darling method is used for testing the goodness-of-fit of the univariate model, and the Akaike information criterion (AIC) is applied to select the best distribution and copula functions. The results demonstrate that there is a very strong correlation between drought duration and drought severity in three stations. The drought return period varies depending on the selected marginal distributions and copula functions and, with an increase of the return period, the differences become larger. In addition, the estimated return periods (both co-occurrence and joint) from the best-fitted copulas are the closet to those from empirical distribution. Therefore, it is critical to select the appropriate marginal distribution and copula function to model the hydrological drought frequency and severity. The results of this study can not only help drought investigation to select a suitable probability distribution and copulas function, but are also useful for regional water resource management. However, a few limitations remain in this study, such as the assumption of stationary of runoff series.
