Spatio-temporal Drought Analysis Research Articles

Spatiotemporal Analysis of Drought Characteristics and Their Impact on Vegetation and Crop Production in Rwanda

In recent years, Rwanda, especially its Eastern Province, has been contending with water shortages, primarily due to prolonged dry spells and restricted water sources. This situation poses a substantial threat to the country’s agriculture-based economy and food security. The impact may escalate with climate change, exacerbating the frequency and severity of droughts. However, there is a lack of comprehensive spatiotemporal analysis of meteorological and agricultural droughts, which is an urgent need for a nationwide assessment of the drought’s impact on vegetation and agriculture. Therefore, the study aimed to identify meteorological and agricultural droughts by employing the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI). VHI comprises the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), both derived from the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). This study analyzed data from 31 meteorological stations spanning from 1983 to 2020, as well as remote sensing indices from 2001 to 2020, to assess the spatiotemporal patterns, characteristics, and adverse impact of droughts on vegetation and agriculture. The results showed that the years 2003, 2004, 2005, 2006, 2013, 2014, 2015, 2016, and 2017 were the most prolonged and severe for both meteorological and agricultural droughts, especially in the Southern Province and Eastern Province. These extremely dry conditions led to a decline in both vegetation and crop production in the country. It is recommended that policymakers engage in proactive drought mitigation activities, address climate change, and enforce water resource management policies in Rwanda. These actions are crucial to decreasing the risk of drought and its negative impact on both vegetation and crop production in Rwanda.

Quantifying land degradation and desertification in semi-arid regions through environmental monitoring and assessment (A case study of the Imiter area, Toudgha River catchment, Morocco)

This study focuses on the spatiotemporal drought analysis in the driest region of southeastern Morocco, specifically the Imiter region within the eastern Anti Atlas region. The region has witnessed various climate change events in recent years, necessitating a comprehensive investigation of the relationship between climate and environmental conditions. To achieve this, Landsat 7, 8 and 9 satellite data were utilized for drought analysis. Additionally, satellite-based drought indices including the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), were employed to assess the extent of drought evolution. Over a period of two decades (from 1998 to 2022), 24 annual spatial maps were generated for each index to identify the most affected areas. The analysis of NDVI and VCI from 1998 to 2011 revealed significant year-to-year variability, attributed to various factors such as declining piezometric levels, deforestation and mining activities. These findings contribute to a better understanding of drought dynamics in the study area and highlight the importance of monitoring environmental conditions for effective drought management and mitigation strategies.

Correction to: Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms

Correction to: Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms

Spatiotemporal analysis of drought in Oromia regional state of Ethiopia over the period 1989 to 2019

Spatiotemporal analysis of drought in Oromia regional state of Ethiopia over the period 1989 to 2019

Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms

Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms

Spatio-Temporal Analysis of Drought in the North-Eastern Coastal Region of Vietnam Using the Standardized Precipitation Index (SPI)

Spatio-temporal analysis of drought provides valuable information for drought management and damage mitigation. In this study, the Standardized Precipitation Index at the time scale of 6 months (SPI-6) is selected to reflect drought conditions in the North-Eastern coastal region of Vietnam. The drought events and their characteristics from 1981 to 2019 are detected at 9 meteorological stations and 10 Chirps rainfall stations. The spatio-temporal variation of drought in the study region is analyzed on the basis of the number, duration, severity, intensity, and peak of the detected drought events at the 19 stations. The results show that from 1981 to 2019 the drought events mainly occurred with 1-season duration and moderate intensity and peak. The number, duration, severity, and peak of the drought events were the greatest in the period 2001-2010 and were the smallest in the period 2011-2019. Among the 19 stations, the drought duration tends to decrease at 11 stations, increase at 7 stations, and has a slight variant at 1 station; the drought severity tends to decrease at 14 stations, increase at 4 stations, and has not a significant trend at 1 station; the drought intensity tends to decrease at 17 stations, increase at 1 station, and has a slight variant at 1 station; and the drought peak tends to decrease at 18 stations and increase at 1 station.

Spatiotemporal analysis of drought and rainfall in Pakistan via Standardized Precipitation Index: homogeneous regions, trend, wavelet, and influence of El Niño-southern oscillation

The phenomenon of drought is common in the world, especially in Pakistan. El Niño-Southern Oscillation (ENSO) influences the spatial and temporal variability of drought and rainfall in Pakistan. Therefore, the objectives of this study are to identify homogeneous rainfall regions and their trend regions, as well as the impact of ENSO phases. In this study, monthly rainfall data from 44 weather stations are used during 1980–2019. Moreover, descriptive and exploratory statistics tests (e.g., Pettitt and Mann-Kendall—MK), Sen method, and cluster analysis (CA) are evaluated along with the annual Standardized Precipitation Index (SPI) on spatiotemporal scales. ENSO occurrences were classified based on the Oceanic Nino Index (ONI) for region 3.4. Using the cophenetic correlation coefficient (CCC) and a significance level of 5%, seven methods were applied to the rainfall series, with the complete method (CCC > 0.9082) being the best. According to the CA method, Pakistan has four groups of homogeneous rainfall (G1, G2, G3, and G4). Descriptive and exploratory statistics showed that G1 differs from the other groups in size and spatial distribution. Pettitt’s technique identified the most extreme El Niño years in terms of spatial and temporal drought variability, along with the wettest months (March, August, September, June, and December) in Pakistan. Non-significant increases in Pakistan’s annual precipitation were identified via the MK test, with exceptions in the southern and northern regions, respectively. No significant increase in rainfall in Pakistan was found using the Sen method, especially in regions G2, G3, and G4. The severity of the drought in Pakistan is intensified by El Niño events, which demand attention from public managers in the management of water resources, agriculture, and the country’s economy.

Drought Events in Western Part of Timor Island Indonesia

Drought is a below-averaged condition of water availability, which has detrimental impacts on many sectors. Many studies have been performed on drought analysis in Indonesia, yet knowledge about drought in western Timor is still limited. This research carried out a historical meteorological drought analysis based on a 3-month Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) using global climate data for 1989-2018. The index value was then categorized into three groups: moderate, severe, and extreme. We assessed: (i) the influence of El Niño phenomena to drought events, (ii) drought class frequency, and (iii) drought trend. Based on historical data, western Timor had a monsoonal pattern with dominant dry period, which occurred in April to November. The results showed that the drought events were mostly influenced by El Niño. Seasonally, El Niño not only increased the drought frequency in July-August (JJA) season, but also in other seasons. In El Niño year of 2015, drought covered most parts of study area during September-November (SON) season, especially in the western part. Dry conditions increased in June, reached maximum in September-November, and decreased in December. Other findings show that an extreme drought consistently had a downtrend, while the moderate drought had upward trends. Spatiotemporal drought analysis using SPI and SPEI showed similar patterns, SPEI detected a higher frequency of drought classes compared to SPI. This study suggests that knowledge on drought-related El Niño will benefit on drought mitigation action in the future.

Spatiotemporal Analysis of Drought and Agriculture Standardized Residual Yield Series Nexuses across Punjab, Pakistan

Food security for the growing global population is closely associated with the variations in agricultural yield at the regional scale. Based on this perspective, the current study was designed to determine the impacts of drought on wheat production in the Punjab province, which is the agricultural hub of Pakistan. Wheat is a staple food in Pakistan, and Punjab provides a major contribution to the total wheat production of the country. Therefore, Punjab is vital to scientific concerns regarding the evaluation of climatic impacts on the annual wheat yield. The current study offers a better understanding of the drought impacts on wheat in Punjab during 2001–2019. The Standardized Precipitation Index was used to assess the impact of drought stress on the wheat yield. Its temporal evolution indicates the recurrent appearance of drought episodes during the wheat cropping season. Furthermore, meteorological drought was noticed in all study years except for 2019. The results reveal that 2002 experienced severe drought conditions. The frequency of drought was calculated as 29% for SPI-12. The relationships between soil moisture, the Standardized Yield Residual Series (SYRS), and the detrended SPI at lags of 1–12 months indicate that zones 1 and 2 are more sensitive to dry conditions. The results presented in this study provide evidence to authorities responsible for developing policies in the context of natural hazards, particularly droughts, and for preparing drought mitigation plans and implementing the adaptation strategies to minimize the effects of drought on wheat yields.

Spatiotemporal analysis of drought by CHIRPS precipitation estimates

Drought is one of the most devastating natural hazards causing considerable losses in all climatic zones of the world. It is one of the most complex and the least understood hazards at the same time because of its spatially heterogeneous and temporally variable character. Spatially dense and uniformly distributed ground-based meteorological data are needed for proper spatial and temporal drought analysis. In practice, such data are lacking in general due either to the nonexistence of ground stations or their uneven and scarce distribution over a region. This creates a great potential in the use of satellite precipitation estimates (SPEs) such as the long-period high-resolution Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data in drought analysis. In this study, we aim to analyze drought over the Kucuk Menderes River Basin in the western part of Turkey by using the CHIRPS data, which were found highly correlated with precipitation in the local ground stations. The analysis was performed by considering the spatial variability and temporal change in the drought characterization based on the Standardized Precipitation Index (SPI) calculated at the 3-month (seasonal) timescale. Drought in the river basin was found to have a within-year variability from month to month, and a spatial variability over the basin in any given month. Also, an over-year variability with a decreasing trend exists, which could be considered a signal for more strengthened droughts in the future. The study eventually demonstrates how the CHIRPS SPEs could be useful in the spatial and temporal drought analysis for regions with limited ground-based meteorological data.

Spatiotemporal analysis of droughts in Hirfanli Dam basin, Turkey by the Standardised Precipitation Evapotranspiration Index (SPEI)

Spatiotemporal analysis of droughts in Hirfanli Dam basin, Turkey by the Standardised Precipitation Evapotranspiration Index (SPEI)

Integrated meteorological drought monitoring framework using multi-sensor and multi-temporal earth observation datasets and machine learning algorithms: A case study of central India

Devising strategies for drought planning and risk mitigation in predominantly small holder agriculture regions like South Asia requires improved understanding of spatio-temporal characteristics of droughts at finer spatial scales. In this study, an integrated framework is developed to generate high-resolution gridded precipitation products from publicly available coarse scale data, and use the downscaled products for analysis of meteorological droughts. Data from a region in Central India is used to develop and test the framework. The Standard Precipitation Index (SPI) is used to characterize meteorological drought. The proposed framework incorporates: (i) a Random Forest machine learning algorithm to downscale CHIRPS (5 km) gridded long term rainfall data to a higher resolution gridded product (1 km) using high resolution multi-sensor and multi-temporal earth observation data of land surface characteristics (vegetation, temperature, and topography) as cofactors; (ii) Computation of 3-month SPI (SPI-3) for the downscaled gridded rainfall; (iii) Run theory applied to downscaled SPI grid to determine drought characteristics - duration, severity, intensity, frequency, and onset and cessation of droughts, and (iv) Principal Components Analysis (PCA) to combine effects of multiple drought characteristics into a single composite Effective Meteorological Drought Index (EMDI), to identify sub-regional drought patterns. The downscaled gridded precipitation product (1 km) was validated using: (i) original CHIRPS (5 km) data, and (ii) measured rainfall data from 65 rain gauge stations in the study area, with respect to conservation of both statistical properties and spatial structure. Comparisons were also made with drought characteristics obtained from Standardized Evapotranspiration Index (SPEI) derived from MODIS evapotranspiration data. The downscaled product captures spatio-temporal variability of droughts at finer village and sub-village scales, compared to original CHIRPS (5 km) data for which each pixel encompassed multiple villages. Spatial distribution of composite index EMDI delineates the study area into two characteristic drought risk regions. In one region, droughts are of longer duration, but are less frequent, with lower severity and intensity. In the second region droughts are of shorter duration but of higher severity, intensity and frequency. The integrated framework developed in this study for high resolution spatio-temporal analysis of droughts, starting from coarse scale precipitation data, is practical and sufficiently general to adopt in other regions to support local drought risk planning and targeting mitigation decisions and actions.

Spatio-temporal analysis of droughts in the Lake Chilwa Basin, Malawi

Drought phenomena are attributed to water availability deficit that is caused by low precipitation. However, droughts are quite complex and cannot simply be defined on the basis of precipitation as other factors may have an influence. In this study, we investigated the spatio-temporal patterns of droughts in Lake Chilwa Basin, an endorheic lake basin that has recently experienced major recurrent lake recessions. The standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 6- and 12-month timescales were used to evaluate drought severity variations from 1970 to 2018, in relation to the recessions. The stationarity difference in rainfall between 1973 to 1995 and 1996 to 2018 and climatological trends were tested using Mann-Whitney and Mann-Kendall tests, respectively. The El Niño Southern Oscilation (ENSO) influence on rainfall was also investigated. In general, the results show a statistically insignificant decreasing rainfall trend, coupled with statistically significant temperature increase (α=0.05). In addition, both indices broadly detected droughts within similar category ranges and variation patterns, suggesting minimal influence of temperature on droughts compared to rainfall. The study also reveals that not every ENSO event leads to low rainfall in the basin. It is further shown that unlike past major recessions, e.g., 1994/95, recent lake dry-ups of 2012 and 2015 were as a result of milder droughts. Moreover, the trigger threshold of lake dry-ups is shown to have shifted; such that average annual rainfall below 1000mm is likely to yield a dry-up in recent times than before, which may be attributable to anthropogenic pressure.

Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China

Drought refers to a meteorological disaster that causes insufficient soil moisture and damage to crop water balance due to long-term lack of precipitation. With the increasing shortage of water resources, drought has become one of the hot issues of global concern. The standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) can effectively reflect the changes in drought characteristics of different geomorphologies in Sichuan on time and space scales, to explore the difference in drought characteristics between different physiognomy types in Sichuan Province, We calculated the SPI and SPEI values based on the data of 44 meteorological stations in Sichuan Province from 1961 to 2019 and used Mann–Kendall trend test and multivariable linear regression method (MLR) to quantify the significance of the drought characteristic trends at different time and space scales. The results as follow: (1) The SPEI drought trend in plain and hilly regions was greater than that in plateau and mountain regions on all time scales (− 0.039 year−1 for 1-month in hilly, − 0.035 year−1 for 1-month in plain, − 0.14 year−1 for 1-month in plateau, − 0.026 year−1 for 1-month in mountain) and the magnitude of trend of eastern (− 4.4 to 0.1 year−1) was lager than western (− 2.1 to 2.7 year−1), means that the drought trends transfer from northwest to east. (2) The drought intensity in the western region gradually increased (0.54–1.05) and drought events mainly occurred in the southwest plateau and central mountainous regions (24–47 times), means that drought meteorological hotspots were mainly concentrated in the Sichuan basin. (3) The MLR indicated altitude (H) is not the main influencing factor that causes the spatial unevenness of precipitation in Sichuan Province, but altitude (H), temperature (T), longitude (Lo) and latitude (La) can co-determined the precipitation. The results of this study are instructive and practical for drought assessment, risk management and application decision-making in Sichuan Province, and have guiding significance for agricultural disaster prevention, mitigation and agricultural irrigation in Sichuan Province.

Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016

East African region is susceptible to drought due to high variation in monthly precipitation. Studying drought at regional scale is vital since droughts are considered a ‘creeping’ disaster by nature with devasting and extended impact often requiring long periods to reverse the recorded damages. This study assessed drought exceedance and return years over East Africa from 1920 to 2016 using Climate Research Unit (CRU) precipitation data records. Meteorological drought, where precipitation is the central quantity of interest, was adopted in the work. Standardize Precipitation Index (SPI) was used to study long term meteorological droughts and also to assess drought magnitude, frequency, exceedance probability and return years using Joint Probability Density Function (JPDF). Also, Mann-Kendall trend analysis was applied to precipitation and SPI to investigate the trend changes. Results showed that years with high drought magnitude ranged from 1920−22, 1926−29, 1942−46 and 1947−51 with values corresponding to 2.2, 3.2, 3.4 and 2.6, respectively while years with low drought magnitude ranged from 1930−31, 1988−89 and 2001−02 with values as 0.2, 0.12 and 0.15, respectively. The longest droughts occurred from 1926−29, 1937−41, 1942−46, 1947−51, 1952−56, and 1958−61 with values in years as 3, 4, 4, 4, 4, and 3 years, respectively, while the shortest droughts occurred in time period of 1 year and ranged from 1930−31, 1964−65, 1979−80, 1981−82, 1983−84, 1988−89, 1991−92, 1993−94, 1996−97 and 2001−02. Also, it was demonstrated that probability of drought occurrence is high when severity is low and such droughts occur at short time intervals and not all severest drought took longer periods. The SPI trends indicate high positive (negative) pixels above (below) the zero-trend mark, indicating that drought prevails in both low and high elevation areas up to 2000 m. There was no direct link between ENSO and drought but arguably the association of drought in most El Niño and La Niña years suggests that the impact of ENSO cannot be ruled out since peak ENSO events occur during October to March periods which coincides with the short (SON) and long (MAM) rainy seasons of East Africa. The study is particularly relevant in being able to depict continuous and synoptic drought condition all over East Africa, providing vital information to farmers and policy makers, using very cost-effective method.

Spatiotemporal Drought Characterization Using Gravity Recovery and Climate Experiment (GRACE) in the Central Plateau Catchment of Iran

Droughts that occurred in the central plateau catchment of Iran have been monitored by Terrestrial Water Storage Deficit Index (TSDI) and three recently modified indices, namely aSPI, aSPEI and eRDI. Terrestrial Water Storage Changes (TWSC) for this area during April 2002–2016 was calculated using the GRACE spherical harmonics. The leakage error and noise of TWSC were evaluated by inter-comparison of this time series with regional mascon solutions; furthermore, to better comprehend the effect of climate on drought, the studied area was divided into semi-arid, arid, and extra-arid zones by using the modified De Martonne method. Using the meteorological data of 105 synoptic stations, the selected indices were calculated within a time scale of 3, 6, 9 and 12 months, and were compared to TSDI. Accordingly, the study area experienced multiple droughts during the studied period. The spatial distribution map for 2008–2009 drought showed that all indices indicated good conformity. The coefficient correlations between TSDI and other indices were different in each zone, but generally, the similarity of TSDI pattern increased with increasing time scales and was the most consistent with 9 and 12-month indices. Based on the spatiotemporal analysis of droughts, the severity, duration, and frequency of drought in the semi-arid zone were more than the other zones while no significant drought occurred in the extra-arid zone. Overall, the result indicated that GRACE-derived TSDI could be implemented successfully to predict spatiotemporal characteristics of droughts in extensive basins.

Monitoring droughts in Eswatini: A spatiotemporal variability analysis using the Standard Precipitation Index.

The spatiotemporal analysis of drought is of great importance to Eswatini as the country has been facing recurring droughts with negative impacts on agriculture, the environment and the economy. In 2016, the country experienced the most severe drought in over 35 years, resulting in food shortages, drying up of rivers as well as livestock deaths. The frequent occurrence of extreme drought events makes the use of drought indices essential for drought monitoring, early warning and planning. The aim of this study was to assess the applicability of the Standard Precipitation Index (SPI) for near real-time and retrospective drought monitoring in Eswatini. The 3-, 6- and 12-month SPI were computed to analyse the severity and onset of meteorological drought between 1986 and 2017. The results indicated that the climate of Eswatini exhibits geospatial and temporal variability. Droughts intensified in terms of frequency, severity and geospatial coverage, with the worst drought years being 1985–1986, 2005–2006 and 2015–2016 agricultural seasons. Moderate droughts were the most prevalent, while the frequency of severe and very severe droughts was low. Most parts of the country were vulnerable to mild and moderate agricultural droughts. Spatial analysis showed that the most severe and extreme droughts were mostly experienced in the Lowveld and Middleveld agro-ecological zones. The 3-, 6- and 12-month SPI computations conducted in January detected the onset of early season drought, thereby affirming the applicability of the index for monitoring near real-time and retrospective droughts in Eswatini. Drought monitoring using the SPI provides information for early warning, particularly in drought-prone areas, by depicting a drought before the effects are felt.

Monitoring droughts in Eswatini: A spatiotemporal variability analysis using the Standard Precipitation Index.

The spatiotemporal analysis of drought is of great importance to Eswatini as the country has been facing recurring droughts with negative impacts on agriculture, environment and economy. In 2016, the country experienced the most severe drought in over 35 years resulting in food shortages, drying up of rivers as well as livestock deaths. The frequent occurrence of extreme drought events makes the use of drought indices essential for drought monitoring, early warning and planning. The aim of this study was therefore to assess the applicability of the Standard Precipitation Index (SPI) for near real-time and retrospective drought monitoring in Eswatini. The 3-, 6- and 12-month SPI were computed to analyse severity and onset of meteorological drought between 1986 and 2017. The results indicated that the climate of Eswatini exhibits geospatial and temporal variability. Droughts intensified in terms of frequency, severity and geospatial coverage, with the worst drought years being 1985–1986, 2005–2006 and 2015–2016 agricultural seasons. Moderate droughts were the most prevalent, while the frequency of severe and very severe droughts was low. Most parts of the country were vulnerable to mild and moderate agricultural droughts. Spatial analysis showed that the most severe and extreme droughts were mostly experienced in the Lowveld and Middleveld agro-ecological zones. The 3-, 6- and 12-month SPI computations conducted in January detected the onset of early season drought, thereby affirming the applicability of the index for monitoring near real-time and retrospective droughts in Eswatini. Drought monitoring using SPI provides information for early warning, particularly in drought-prone areas, by depicting a drought before the effects have begun to be felt.

Climate change-induced drought evolution over the past 50 years in the southern Chinese Loess Plateau

Understanding the spatiotemporal evolution of drought is vital for effective water resources management especially in arid and semi-arid regions and under climate change. In this study, we developed the Soil and Water Assessment Tool (SWAT)-based drought evaluation tool and used it to investigate the spatiotemporal change of drought and its driving factors over the past 50 years (1965–2014) in a typical semi-arid area, the Wei River Basin, in the Loess Plateau. The temporal trend analysis of precipitation showed an intensified hydrological cycle with a longer dry interval, and the substantially decreased wind speed resulted in a significant decrease in the evapotranspiration and a slight increase in the soil water content. The spatiotemporal analysis of drought identified the vulnerable areas and indicated that spring drought was exacerbating. Overall, this study can be informative and valuable for the drought assessment and disaster alleviation in the Loess Plateau area.

Spatio-Temporal Analysis of Drought Indicated by SPEI over Northeastern China

Drought is a natural extreme climate event which occurs in most parts of the world. Northeastern China is one of the major agricultural production areas in China and also a typical vulnerable climate zone. To understand the spatio-temporal characteristics of drought over northeastern China, we first assessed the trends of precipitation and temperature. Drought events were then characterized by Standardized Precipitation Evapotranspiration Index over various temporal scales. The Trend Free Prewhitening Mann–Kendall test and distinct empirical orthogonal function, were used to investigate the trends and spatio-temporal patterns of droughts. The results indicate precipitation increasing trends are mostly detected in Heilongjiang and Jinling provinces, however, the majority of the trends are insignificant. Temperature increasing trends are detected over the entire northeastern China and most of them are significant. Decreasing drought trends are observed in Heilongjiang province and some bordering area in Jilin province, whereas increasing trends are noticed in Liaoning province and some bordering area in Jilin province. Two main sub-regions of drought variability—the Liaohe River Plain and the Second Songhua River basin (LS region), and the Songnen Plain and the Lesser Hinggan Mountains (SL region) are identified, and the detected droughts for the two sub-regions correspond well with recorded drought loss. The results will be beneficial for regional water resource management and planning, agriculture production, and ecosystem protection in northeastern China.