Detection of Changes in Glacier Mass Balance Using Satellite and Meteorological Data in Tirungkhad Basin Located in Western Himalaya

Himalayan glaciers and their mass balance are poorly sampled through direct mass balance measurements. Thus, based on Landsat datasets of ETM⁺ (2000), ETM⁺ (2006) and TM (2011), mass balance studies of 32 glaciers was carried out using accumulation area ratio (AAR) method in the Tirungkhad river basin, a tributary of Satluj River, located in western Himalayan region. The overall specific mass balance was negative varying from ?27 cm (2000) to ?41 cm (2011). Out of 32 glaciers, 27 glaciers (81.2 %) showed negative mean mass balance and 5 glaciers (18.7 %) showed positive mean mass balance. Mean of specific mass balance for the year 2000, 2006 and 2011 was found to be ?48 cm, ?55 cm and ?0.61 cm respectively, in case of glaciers with negative mass balance while in case of glaciers with positive mass balance, it was 0.67 cm (2000), 0.56 cm (2006) and 0.47 cm (2011). The investigations suggested a loss of ?0.034 km³ of glacial ice for 2000, 0.036 km³ for 2006 and 0.038 km³ for 2011 respectively. The negative mass balance of the glaciers since 2000 correlates well with the increasing trend of annual mean temperature and decreasing trend of precipitation (snow water equivalent (SWE) and rainfall). Based on Mann Kendall test the temperature and SWE trends were significant at 95 % confidence level, however, the rainfall trend was insignificant.     

A Case Study: Heavy Rainfall Event Comparison Between Daily Satellite Rainfall Estimation Products with IMD Gridded Rainfall Over Peninsular India During 2015 Winter Monsoon

India Meteorological department (IMD) used INSAT-3D Metrological Satellite Imager data to drive two type rainfall estimation products viz-Hydro Estimate (HE) and INSAT Multi-Spectral Rainfall Algorithm (IMSRA) on half hourly rainfall rate and daily accumulated rainfall in millimeter (mm). Integrated Multi-Satellite Retrieval for GPM (IMERG) product is being derived by NASA and JAXA by using Global Precipitation Mission (GPM) satellites data. IMSRA and GPM (IMERG) are gridded data at 10 km spatial resolution and HE is available at pixel level (4 km at Nadir). IMD provides gridded rainfall data at 0.25° × 0.25° resolution which is based on wide coverage of 6955 actual observation. In present study, validation of INSAT-3D based Hydro Estimator (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) and Integrated Multi-Satellite Retrieval for GPM (IMERG) of Global Precipitation Mission (GPM) satellites are carried out with IMD gridded data set for heavy rainfall event during winter monsoon, over peninsular India (November–December 2015). In validation, Nash–Sutcliffe efficiencies (NSE), RMSE, Correlation, Skilled scores are calculated at grid level for heavy and very heavy rain categories and the values of NSE of HE (? 32.36, ? 3.12), GPM (? 68.67, ? 2.39) and IMSRA (? 0.02, 0.28) on 16th November 2015 and HE (? 13.65, ? 1.69), GPM (? 43.79, ? 2.94) and IMSRA (? 1.08, ? 1.60) on 2nd Dec 2015, for heavy and very heavy rainfall. On both days, HE is showing better rainfall estimate compare to GPM for Heavy rainfall and GPM showing better estimation for very heavy rainfall events. In all the cases IMSRA is underestimating, if daily rain fall exceeded 75 mm.     

Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment,Central Java,Indonesia

Satellite rainfall products for landslide early warning prediction have been spotlighted by several researchers, in the last couple of decades. This study investigates the use of TRMM and ERA-Interim data, for the determination of rainfall thresholds and the prediction of precipitation, respectively, to be used for landslide early warning purposes at the Bogowonto catchment, Central Java, Indonesia. A landslide inventory of 218 landslides for the period of 2003–2016 was compiled, and rainfall data were retrieved for the landslide locations, as given by 6 ground stations, TRMM, and ERA-Interim data. First, rainfall data from the three different sources was compared in terms of correlation and extreme precipitation indices. Second, a procedure for the calculation of rainfall thresholds for landslide occurrence was followed consisting of four steps: i) the TRMM-based rainfall data was reconstructed for selected dates and locations characterized by landslide occurrence and non-occurrence; ii) the antecedent daily rainfall was calculated for 3, 5, 10, 15, 20 and 30 days for the selected dates and locations; iii) two-parameter daily rainfall-antecedent rainfall thresholds were calculated for the aforementioned dates; after analysis of the curves the optimum number of antecedent rainfall days was selected; and (iv) empirical rainfall thresholds for landslide occurrence were determined. The procedure was repeated for the entire landslide dataset, differentiating between forested and built-up areas, and between landslide occurrence in four temporal periods, in relation to the monsoon. The results indicated that TRMM performs well for the detection of very heavy precipitation and can be used to indicate the extreme rainfall events that trigger landslides. On the contrary, as ERA-Interim failed to detect those events, its applicability for LEWS remains limited. The 15-day antecedent rainfall was indicated to mostly affect the landslide occurrence in the area. The rainfall thresholds vary for forested and built-up areas, as well as for the beginning, middle and end of the rainy season.     

1960—2012年长江中下游极端降水与极端径流时空演变研究

利用1960—2012年间的长江中下游地区75个站点的逐日降水观测资料,基于11个极端降水指标,采用趋势分析、滑动平均、Mann-Kendall检验等方法对长江中下游地区各个子流域的极端降水事件的时空变化以及极端降水的集中度和集中期进行了研究.同时,分析了长江中下游干流区域4个水文站点的极端径流的变化情况,以期综合全面地分析极端事件的演变情况.结果表明:1)极端降水在各个子流域的大部分地区都表现为增加趋势,其中汉江流域西北部、中游干流区域东部、洞庭湖西部、鄱阳湖北部以及下游干流东北部显著增加;2)极端降水在总降水量中所占比例较大,这表明降水的形式更加趋于集聚化,并且比例自西北向东南逐渐减少,这表明越往西北部极端降水造成灾害的概率越大;3)长江中下游干流区域几个主要水文站点的极端径流各项指标均表现为较为显著地减小的趋势,极端降水和极端径流的变化趋势差异较大,这可能与人类活动有关(三峡工程).     

Vegetation response to intensive commercial horticulture and environmental changes within watersheds in central highlands,Kenya, using AVHRR NDVI data

Expansion and heterogeneous clustering of commercial horticulture within the central highlands of Kenya after the mid-1990s impact watersheds and the sustainable resource management. This is distressing since climate conditions for world horticultural regions are projected to change, making such farming extremely difficult and costly to the environment. To understand the scope of impact on vegetation, the study evaluated (1) interannual variability in averaged normalized difference vegetation index (NDVI); (2) trends in average annual NDVI before and after 1990 – the presumed onset of rapid horticulture; and (3) relationship between the average annual NDVI and large-scale commercial farms, population density, and mean annual rainfall in subwatersheds. Time-series analysis of long-term Global Inventory Modeling and Mapping Studies NDVI data were analyzed as indicator of vegetation condition. NDVI trends before 1990s (1982–1989) and after 1990s (1990–2006) were evaluated to determine the slope (sign), and the Spearman’s correlation coefficient (strength). Overall, results show considerable variations in vegetation condition due largely to mixed factors including intensive farming activities, drought, and rainfall variation. Statistical analysis shows significant differences in slopes before 1990 and after 1990 (p < 0.05 and p < 0.1 respectively). Negative (decline) trends were common after 1990, linked to increased commercial horticulture and related anthropogenic disturbances on land cover. There was decline in vegetation over densely populated subwatersheds, though low NDVI values in 1984 and 2000 were the effect of severe droughts. Understanding the linkage between vegetation responses to the effects of human-induced pressure at the subwatershed scale can help natural resource managers approach conservation measures more effectively.     

Variations in the Ice Phenology and Water Level of Ayakekumu Lake,Tibetan Plateau,Derived from MODIS and Satellite Altimetry Data

The alpine lakes on the Tibetan Plateau (TP) are highly sensitive to variations in climate changes, and the lake ice phenology and water level are considered to be direct indicators of regional climate variability. In this study, we first used 14 years of moderate resolution imaging spectroradiometer snow cover products to analyse the freeze dates, ablation dates, and ice coverage durations. The lake level changes during 2002–2015 were estimated, derived from satellite altimetry and Hydroweb data. Unexpectedly, the freeze dates of lake ice greatly advanced, and the ablation dates were markedly delayed. The complete freezing duration lengthened by approximately 77 days. As a result of the warm-wet climate in the northern TP, the lake area expanded from 770 to 995 km² during 2002–2015, and the water levels rose by 4.2 m in total, at a rate of 0.3 m/year. The progressive expansion of Ayakekumu Lake profoundly affected the ice phenology. Larger water volume with larger thermal capacity likely led to the delaying of ablation dates, with the freezing point depression caused by decreasing salinity. Some new narrow and shallow bays located in southern and eastern Ayakekumu Lake were conducive to early freezing of ice. Additionally, the changes in air temperature, precipitation, potential evaporation, and sunshine duration may be related to the prolonged ice cover duration since 2002. In sum, accurate measurements of lake ice and water levels are critical for understanding the water resource balance and hydrologic cycle in arid or semi-arid regions of China.     

Investigating the vegetation and agricultural responses to El Nino/Southern Oscillation using AVHRR data

This study explores the possible linkages of El Nino/Southern Oscillation (ENSO) with vegetation and rainfall patterns, vegetation activity and food grain yields, in arid and semi-arid regions of western India. A sequence of 20-year (1981–2000) monthly maximum Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) and monthly rainfall from 160 stations were examined to study the seasonal patterns and their relation to ENSO activity. In addition, a direct (ENSO-crop yield) linkage and an intermediate (ENSO-NDVI) linkage of agricultural responses to ENSO were also investigated. The results indicate below-normal seasonal NDVI and rainfall associated with El Nino (warm) events, except during 1997, while positive anomalies occur during La Nina (cold) events. Sea surface temperature (SST) anomalies from NINO 3 region (5°N–5°S; 150°W–90°W), as an indicator of ENSO were significantly correlated with NDVI anomalies, rainfall anomalies and yield anomalies but the Southern Oscillation Index (SOI) was significantly related to NDVI anomalies only. NDVI anomaly patterns correspond to rainfall variability including that associated with ENSO activity. The observed strong intermediate linkage between yield anomalies and NDVI anomaly signal (r = 0.609) indicates that NDVI is an ideal index for understanding and analysing agricultural response to ENSO climate teleconnections.     

Driving forces of grassland vegetation changes in Chen Barag Banner,Inner Mongolia

ABSTRACT

Inner Mongolia is an important ecological zone of northern China and 67% of its land area is grassland. This ecologically fragile region has experienced significant vegetation degradation during the last decades. Although the spatial extents and rates of vegetation change have previously been characterized through various remote sensing and GIS studies, the underlying driving factors of vegetation changes are still not well understood. In this study, we first used time-series MODIS NDVI data from 2000 to 2016 to characterize the temporal trend of vegetation changes. These vegetation change trends were compared with climate and socioeconomic variables to determine the potential drivers. We used a set of statistical methods, including multiple linear regression (MLR), spatial correlation analysis, and partial least squares (PLS) regression analyzes, to quantify the spatial distribution of the driving forces and their relative importance to vegetation changes. Results show that the main driving factors and their impact magnitude (weight) are in the order of human activities (r = -0.785, p < 0.01, VIP = 1.37), precipitation (r = 0.541, p < 0.05, VIP = 0.89), temperature (r = -0.319, p > 0.05 VIP = 0.59). The area affected by human activities was 10.57%. Specific human activities, such as coal mining and grazing were negatively associated with vegetation cover, while eco-engineering projects had positive impacts. This study provided thorough quantification of driving forces of vegetation change and enhanced our understanding of their interactions. Our integrated geospatial-statistical approach is particularly important for sustainable development of ecosystem balance in Chen Barag Banner and other areas facing similar challenges.     

LANDSAT Sub-pixel Analysis in Mapping Impact of Climatic Variability on Prairie Pothole Changes

The Prairie Pothole Region in the United States contains millions of seasonal, semi-permanent, or permanent lakes and wetlands that typically range in size from 0.1 to 10 ha. These lakes and wetlands are vulnerable to climate change, especially in our study area in South Dakota, in which a period of deluge following a sharp drought considerably expanded the areal extent of prairie pothole lakes during the last decade of the twentieth century. Preliminary estimates of lake areas, determined using LANDSAT 5 and 7 images, had appreciable errors especially for the smallest of these lakes. This article describes a new sub-pixel approach integrated with a CART (Classification and Regression Tree) model using a GIS (Geographical Information System) to quantify mixed water pixels along lake boundaries to improve area estimations for pothole lakes. Errors in estimated area were typically 10% or less for lakes greater than 1 ha in size. An analysis of lakes in our study area demonstrates how lake area changed with the transition from drought to deluge. Small lakes exhibited a distinct seasonal variation in contrast to large lakes that tended to follow precipitation trends more broadly. The total water area of lakes is consistent with broad variation in rainfall.     

顾及GRACE季节影响的华北平原水储量变化反演

基于移去-恢复方法,利用GRACE卫星重力数据研究了华北平原2003年1月—2014年6月的陆地水变化,提出了一种顾及季节影响尺度因子计算方法,用于减小GRACE后处理误差。将本文方法所得结果与水文模式和降水模型结合进行比较分析,结果表明：2008年之前华北平原发生比较明显的干旱现象,陆地水和地表水分别以（-7.9±2.4）mm/a和（-7.3±2.8）mm/a的速度下降,但地下水仅以（-0.6±1.4）mm/a的速度减少;2008年之后,陆地水和地表水分别以（4.3±1.3）mm/a和（10.9±2.1）mm/a的速度上升,期间地下水的超采严重,平均以（-6.5±1.2）mm/a的速度下降,但下降速度以0.9 mm/a²的趋势减缓。研究期间内,华北平原的陆地水、地表水和地下水整体分别呈现（-2.0±0.6）、（2.9±0.7）和（-4.8±0.7）mm/a的变化趋势。最后,利用交叉小波谱分析了GRACE滞后于降水的现象。研究结论表明,降水和地下水开采是影响华北平原水储量变化的两大决定性因素。     

Annual variations in water storage and precipitation in the Amazon Basin

We combine satellite gravity data from the gravity recovery and climate experiment (GRACE) and precipitation measurements from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) Merged Analysis of Precipitation (CMAP) and the Tropical Rainfall Measuring Mission (TRMM), over the period from mid-2002 to mid-2006, to investigate the relative importance of sink (runoff and evaporation) and source (precipitation) terms in the hydrological balance of the Amazon Basin. When linear and quadratic terms are removed, the time-series of land water storage variations estimated from GRACE exhibits a dominant annual signal of 250 mm peak-to-peak, which is equivalent to a water volume change of ~1,800 km³. A comparison of this trend with accumulated (i.e., integrated) precipitation shows excellent agreement and no evidence of basin saturation. The agreement indicates that the net runoff and evaporation contributes significantly less than precipitation to the annual hydrological mass balance. Indeed, raw residuals between the de-trended water storage and precipitation anomalies range from ±40 mm. This range is consistent with stream-flow measurements from the region, although the latter are characterized by a stronger annual signal than our residuals, suggesting that runoff and evaporation may act to partially cancel each other.     

Statistical study of GNSS L-band solar radio bursts

Intense L-band solar radio bursts (SRBs) can affect acquisition and tracking of signals from the Global Navigation Satellite Systems (GNSS) by receivers located in the sunlit hemisphere of the earth and have been regarded as a potential threat to GNSS stability and performance. To obtain the occurrence characteristics and evaluate the probability of GNSS L-band SRB events, we take a survey using the latest 20-year event set (1997–2016). The bursts data were archived and provided by the Space Weather Prediction Center (SWPC) of National Oceanic and Atmospheric Administration (NOAA). The results indicate that, during this period, a total of 2384 L-band radio bursts were observed, including 1384 minor events, 859 moderate events, 120 strong events, 17 severe events, and 4 extreme events. As expected, these bursts are generally closely related to solar activity and solar cycle. But, for the severe and extreme events, bursts occurrence is sporadic and random, which not only occurs at solar maximum but also at other intervals. Compared to X-ray solar flares, the L-band SRB events do not occur frequently, and the ratio of annual SRB events with peak flux exceeding 100 SFU to X-ray flares is approximately 0.12. Statistically, the occurrence frequency of strong, severe and extreme SRB events is conservatively about 6.00, 0.85, and 0.20 bursts per year, respectively. The data fitting shows that L-band SRB events yield a power law distribution with an exponent of about – 1.791.     

Seasonal trend analysis (STA) of MODIS vegetation index time series for the mangrove canopy of the Teacapan-Agua Brava lagoon system,Mexico

Monthly time series, from 2001 to 2016, of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from MOD13Q1 products were analyzed with Seasonal Trend Analysis (STA), assessing seasonal and long-term changes in the mangrove canopy of the Teacapan-Agua Brava lagoon system, the largest mangrove ecosystem in the Mexican Pacific coast. Profiles from both vegetation indices described similar phenological trends, but the EVI was more sensitive in detecting intra-annual changes. We identified a seasonal cycle dominated by Laguncularia racemosa and Rhizophora mangle mixed patches, with the more closed canopy occurring in the early autumn, and the maximum opening in the dry season. Mangrove patches dominated by Avicennia germinans displayed seasonal peaks in the winter. Curves fitted for the seasonal vegetation indices were better correlated with accumulated precipitation and solar radiation among the assessed climate variables (Pearson’s correlation coefficients, estimated for most of the variables, were r ≥ 0.58 p < 0.0001), driving seasonality for tidal basins with mangroves dominated by L. racemosa and R. mangle. For tidal basins dominated by A. germinans, the maximum and minimum temperatures and monthly precipitation fit better seasonally with the vegetation indices (r ≥ 0.58, p < 0.0001). Significant mangrove canopy reductions were identified in all the analyzed tidal basins (z values for the Mann-Kendall test ≤ ?1.96), but positive change trends were recorded in four of the basins, while most of the mangrove canopy (approximately 87%) displayed only seasonal canopy changes or canopy recovery (z > ?1.96). The most resilient mangrove forests were distributed in tidal basins dominated by L. racemosa and R. mangle (Mann-Kendal Tau t ≥ 0.4, p ≤ 0.03), while basins dominated by A. germinans showed the most evidence of disturbance.     

Use of high-resolution satellite data,GIS and NRCS-CN technique for the estimation of rainfall-induced run-off in small catchment of Jharkhand India

The present study demonstrates the use of NRCS-CN technique for rainfall-induced run-off estimation using high-resolution satellite data for small watershed of Palamu district, Jharkhand. The CN model was applied to the daily rainfall data of 15 years (1986–2000) along with use of large-scale thematic maps (1:10,000) pertaining to land use/land cover using IRS-P6 LISS-IV satellite data. The LU/LC map was spatially intersected with the hydrological soil group map to calculate the watershed area under different hydrological similar units for assigning CN values to compute discharge. The study showed that Daltonganj watershed exhibits an average run-off volume of 7,881,019 m³ from an average cumulative monsoon rainfall of 821 mm and the average actual direct run-off generated during the southwest monsoon season was 203 mm. The strong correlation between rainfall and run-off as well as between observed run-off and estimated run-off indicated high accuracy of run-off estimation by NRCS-CN technique.     

Malaria incidence in Nairobi,Kenya and dekadal trends in NDVI and climatic variables

The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R²  = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.     

Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation, climate factor, and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets. First, most vegetation canopies demonstrated obvious seasonality, increasing with latitudinal gradient. Second, obvious dynamic trends were observed in both vegetation and climate change, especially the positive trends. Over 70% areas were observed with obvious vegetation greening up, with vegetation degradation principally in the Pearl River Delta, Yangtze River Delta, and desert. Overall warming trend was observed across the whole country (>98% area), stronger in Northern China. Although over half of area (58.2%) obtained increasing rainfall trend, around a quarter of area (24.5%), especially the Central China and most northern portion of China, exhibited significantly negative rainfall trend. Third, significantly positive normalized difference vegetation index (NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions, corresponding to their synchronous stronger seasonal pattern. Finally, at inter-annual level, the NDVI–climate relationship differed with climatic regions and their long-term trends: in humid regions, positive coefficients were observed except in regions with vegetation degradation; in arid, semiarid, and semihumid regions, positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature. This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process.     

海河流域NDVI对气候变化的响应研究

以海河流域为研究区,利用8 km分辨率AVHRR/NDVI数据和气象资料,逐像元对1981-2000年时段的流域NDVI值、年降水量和年均气温的变化率进行分析,计算了NDVI和年降水量、年均气温的相关关系.结果表明,1981-2000年时段内,海河流域年降水量变化总体呈现北部和南部增加,中部减少的趋势,其变化范围在-8...     

Snow cover area change and its relations with climatic variability in Kashmir Himalayas,India

Snowmelt makes an essential component of the hydrological system of Kashmir Himalayas. The present study was carried out to examine the status of Snow Cover Area (SCA) using Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day Snow Cover Product between 2000 and 2016. The intra- and inter-annual variability in SCA and in meteorological parameters was observed and various statistical tests were used to study the interrelationship. Results of statistical analysis indicate decrease in maximum temperature (?0.05 °C/year) and minimum temperatures (?0.02 °C/year) while rise in precipitation (19.13 mm/year). It also showed an increase in annual mean SCA (43.5 sq km) during the study period. The analysis was also carried out on a seasonal basis. The results revealed that in Kashmir Himalayas, climate plays a dominating role in controlling the SCA. The results depict the short-term fluctuations in SCA and show the magnitude of change between two successive values being very large in SCA.     

Evaluation and Comparison of Multi Resolution DEM Derived Through Cartosat-1 Stereo Pair – A Case Study of Damanganga Basin

The study evaluates and compares Digital Elevation Model (DEM) data of various grid spacing derived using high resolution Cartosat 1 stereo data for hydrologic applications. DEM is essential in modeling different environmental processes which depend on surface elevation. The accuracy of derived DEM varies with grid spacing and source. The CartoDEM is the photogrammetric DEM derived from stereo pairs. Damanganga basin lying in the Western Ghats was analysed using 11 Carto stereo pairs. The process of triangulation resulted in RMSE of 0.42. DEM was extracted at 10 m, 20 m, 30 m, 40 m, 50 m and 90 m grid spacing and compared with ASTER GDEM (30 m) and SRTM DEM (90 m). DEM accuracy was checked with Root Mean Square Error (RMSE) statistic for random points generated in different elevation zones. Extracted stream networks were compared based on Correctness Index and Figure of Merit index, calculated for all the Digital Elevation Models at varying cell sizes. In order to further evaluate the DEM’s, a simple flood simulation with no water movement and no consideration of real time precipitation data was carried out and relationship between heights of flood stage and inundation area for each Digital Elevation Model was also established.     

Developing a satellite-based combined drought indicator to monitor agricultural drought: a case study for Ethiopia

Developing a robust drought monitoring tool is vital to mitigate the adverse impacts of drought. A drought monitoring system that integrates multiple agrometeorological variables into a single drought indicator is lacking in areas such as Ethiopia, which is extremely susceptible to this natural hazard. The overarching goal of this study is to develop a combined drought indicator (CDI-E) to monitor the spatial and temporal extents of historic agricultural drought events in Ethiopia. The CDI-E was developed by combining four satellite-based agrometeorological input parameters – the Standardized Precipitation Index (SPI), Land Surface Temperature (LST) anomaly, Standardized Normalized Difference Vegetation Index (stdNDVI) and Soil Moisture (SM) anomaly – for the period from 2001 to 2015. The method used to combine these indices is based on a quantitative approach that assigns a weight to each input parameter using Principal Component Analysis (PCA). The CDI-E results were evaluated using satellite-based gridded rainfall (3-month SPI) and crop yield data for 36 intra-country crop growing zones for a 15-year period (2001 to 2015). The evaluation was carried out for the main rainfall season, Kiremt (June-September), and the short rainfall season, Belg (February-May). The results showed that moderate to severe droughts were detected by the CDI-E across the food insecure regions reported by FEWS NET during Kiremt and Belg rainfall seasons. Relatively higher correlation coefficient values (r > 0.65) were obtained when CDI-E was compared with the 3-month SPI across the majority of Ethiopia. The spatial correlation analyses of CDI-E and cereal crop yields showed relatively good correlations (r > 0.5) in some of the crop growing zones in the northern, eastern and southwestern parts of the country. The CDI-E generally mapped the spatial and temporal patterns of historic drought and non-drought years and hence the CDI-E could potentially be used to develop an agricultural drought monitoring and early warning system in Ethiopia. Moreover, decision makers and donors may potentially use CDI-E to more accurately monitor crop yields across the food-insecure regions in Ethiopia.