Association of farmers' wellbeing in a drought-prone area, Thailand: applications of SPI and VCI indices.

Drought has major consequences for agricultural activity, economy, and the environment. For improved drought management, it is necessary to assess drought severity, frequency, and the potential of drought occurrence. The purpose of this study is to use drought indices, standardized precipitation index (SPI), and vegetation condition index (VCI) to characterize the severity of drought and investigate the association between drought severity and subjective well-being among local farmers. The SPI was used to quantify precipitation deficits at various time scales, while the VCI was utilized to monitor crop and vegetation drought conditions. Throughout the period 2000-2017, satellite data were incorporated, as well as a household survey of rice farmers in the dry zone research region in northeastern Thailand. The findings suggest that extreme droughts occur more frequently in the central part of the northeastern region of Thailand than in the rest of the region. The influence of drought on farmers' wellbeing was evaluated at various drought severity levels. Drought and overall wellbeing are strongly linked at the household level. Thai farmers in drought-prone areas are dissatisfied with their livelihoods more than farmers in less-affected area. It is intriguing that farmers who live in drought-prone areas are more content with their lives, their communities, and their occupations than farmers who live in less drought-prone areas. In this context, using proper drought indices could potentially improve the utility of governmental interventions and community-based programs targeted at assisting drought-affected people.

A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level.

Droughts and heat waves are currently recognized as two of the most serious threats associated with climate changes. Drought is characterized by prolonged dry periods, low precipitation, and high temperature, while heat wave refers to an extended period of exceptionally high temperature, surpassing the region's average for that time of year. There is a close relationship between droughts and heat waves, as both are often caused by similar weather patterns and can exacerbate each other's impacts. Therefore, it is crucial to monitor and quantify both droughts and heat waves jointly at a regional level in order to develop sustainable policies and effectively manage water resources. This article develops a new index, the standardized composite index for climate extremes (SCICE), for joint monitoring and probabilistic quantification of extreme climate events at regional level. The procedure of SCICE is mainly based on the joint standardization of standardized precipitation index (SPI) and standardized temperature index (STI). In the application of SCICE, results reveal that the long-term probabilities of the joint occurrence of dry and hot events are significantly greater than those of wet and cold events. Furthermore, the outcomes of the comparative assessment support the validity of using SCICE as a compact statistical approach in regional drought analysis. In summation, the study demonstrates the capability of SCICE to effectively characterize and assess the joint monitoring of drought and heat waves at a regional level, providing a comprehensive approach to understanding the joint impact of climate extremes.

Future climate-driven drought events across Lake Urmia, Iran.

Climate change has increased the vulnerability of arid and semi-arid regions to recurrent and prolonged meteorological droughts. In light of this, our study has sought to assess the nature of future meteorological drought in Lake Urmia basin, Iran, within the context of future climate projections. To achieve this, data from 54 general circulation models (GCMs) was calibrated against both in situ and Global Precipitation Climatology Centre datasets. These GCMs were then employed to project drought conditions expected over 2016-2046 under RCP2.6 and RCP8.5 as the most optimistic and pessimistic scenarios, respectively. To provide a comprehensive analysis, these RCPs were combined with two different time scale Standardized Precipitation Index (SPI), leading to eight different scenarios. The SPI was calculated over two temporal scales for the past (1985-2015) and future (2016-2046), including the medium-term (SPI-6) and long-term (SPI-18) index. Results showed that while precipitation is expected to increase by up to 34%, parts of the basin are projected to face severe and prolonged droughts under both RCPs. The most severe drought event is expected to occur around 2045-2046 under the most pessimistic RCP8.5 scenario. Severe droughts with low frequency are also anticipated to increase under other scenarios. By characterizing meteorological drought conditions for Lake Urmia basin under future climate conditions, our findings call for urgent action for adaptation strategies to mitigate the future adverse effects of drought in this region and other regions facing similar challenges. Overall, this study provides valuable insight into the impacts of climate change on future droughts that can adversely influence water resources in arid and semi-arid regions.

Spatio-temporal drought assessment of the Subarnarekha River basin, India, using CHIRPS-derived hydrometeorological indices.

Precipitation studies have a crucial role in deciphering climate change and monitoring natural disasters such as droughts. Such studies lead to better assessment of rainfall amounts and spatial variabilities; and have a vital role in impact assessment, mitigation, and prediction of occurrence. Thus, this study has been undertaken in the Subarnarekha River basin using Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset. Precipitation datasets helped in deriving hydrometeorological indices such as the Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI) for the identification of drought occurrences. The core objective was to infer spatio-temporal drought scenarios and their trend characterization covering four decades over the years 1981 to 2020. Quantitative drought assessment was done using run theory for identifying the Drought Duration (DD), Drought Severity (DS), Drought Intensity (DI), and Drought Frequency (DF). Mann-Kendall (MK) test was performed to understand the precipitation and drought trends at annual and seasonal scales. Eight severe drought events were identified in the Subarnarekha River basin for the past 40 years and the average DI value of 0.8 was recorded. MK test results for the precipitation showed a significant positive trend (95% confidence level) for pre-monsoon periods. However, for SPI, a significant positive trend was observed over the intervals of 3 (SPI3), 6 (SPI6), and 12 (SPI12) months respectively at an annual timescale, suggesting wetter conditions within the study area. Moreover, there had been insignificant negative trends for SPI1 and SPI3 during winter. It indicates that during the short-term SPI scale, i.e., 1 month (SPI1) and 3 months (SPI3), the instances of negative SPI values inferred were high, which point to the increasing incidences of meteorological drought possibly due to deficient soil moisture. Thus, the results indicated that the CHIRPS precipitation product-derived hydrometeorological indices could act as a valuable tool for assessing the past spatio-temporal drought conditions of the Subarnarekha River basin. This may further be helpful in planning for sustainable water resource management of such river basins.

Determining the most appropriate probability distribution function for meteorological drought indices in Urmia Lake Basin, Iran.

Normalization is believed to be one of the most important parts of numerical computation in discrete mathematics. This process aims to transform a wide numerical range into a narrower one. Hence, in a number of fields of study, numerous distribution functions (DF) have been extended based on their applications, one of which is drought calculation. In this research, annual drought was calculated via standard precipitation index (SPI) and China Z Index (CZI) through seven three-parametric DFs (Pearson 5, Weibull, Pearson 3 (gamma), log Pearson, Fréchet, log-logistic, and fatigue life) in order to determine the most appropriate one for each index in Urmia Lake Basin. To this end, the results of both SPI and CZI, with DFs and without them, were compared with statistical analyzers (RMSE, ME, R2, and pearson correlation). The results indicated that Weibull-CZI and Pearson 5-SPI had the highest correlation with the normal ones. Therefore, they could be used as the best DFs for these drought indices in this basin. Moreover, among the studied years, Gelazchay and Daryanchay stations experienced the most severe drought in 2008 and 1999 based on the CZI and SPI, respectively. It should be noted that in another section of the current study, the correlation between the two indices was analyzed and the results showed high correlations between them.

Drought dynamics of Northwestern Teesta Floodplain of Bangladesh: a remote sensing approach to ascertain the cause and effect.

Drought is an affliction for a region that primarily depends on agriculture as economic activity. Commonly monitoring and characterizing of drought is performed by only analyzing the meteorological aspect, assuming precipitation as the primary source of water. However, in riverine Bangladesh, this can lead to an erroneous conclusion, as there is a multitude of available water sources. Consequently, in this study, vegetation condition (Standard Vegetation Index), soil moisture (Soil Moisture Index), and precipitation (Standard Precipitation Index) are separately investigated from 2003 to 2019, in the Northwestern Teesta floodplain. Subsequently, statistical regression analysis is performed to determine the relationships between different aspects of drought. In addition, information obtained from field visits and expert opinions has also been assimilated. Analysis of vegetation and soil moisture condition presents a progressively improving scenario. However, SPI shows an incessant decline in meteorological drought conditions, especially after 2007. Evidently, regression analysis does not provide any indication of an interrelationship between the studied agricultural and meteorological parameters. Presumably, this absence is instigated because the study area is highly irrigated as the groundwater table is suitably near the surface and the existence of nearby Teesta river allows for the utilization of surface water. Moreover, the cropping pattern is shifting toward crops that require much less water and to places where soil moisture is scarce. Thus, this study addresses the gap in knowledge regarding the nature of agricultural drought and the dynamics of different aspects of drought which will be invaluable for the water management and agricultural policy in the study area as well as other regions with a similar backdrop.

Short-term effects of drought on daily mortality in Spain from 2000 to 2009.

Spain is a country of southern Europe that is prone to drought, and it is likely that this type of hydrological extreme will become substantially more frequent and intense in the 21st century, which could lead to greater health risks if adequate adaptive measures are not taken. For the first time, we calculated the relative risks (RRs) of daily natural (ICD10: A00-R99), circulatory (ICD10: I00-I99), and respiratory (ICD: J00-J99) mortality associated with drought events in each province of Spain from 2000 to 2009. For this purpose, we compared the performance of the Standardized Precipitation Index (SPI) and Standardized Precipitation- Evapotranspiration Index (SPEI) obtained at 1 month of accumulation (denoted as SPI-1/SPEI-1) to estimate the short-term risks of droughts on daily mortality using generalised linear models. Attributable risks were calculated from the RR data. The main findings of this study revealed statistically significant associations between the different causes of daily mortality and drought events for the different provinces of Spain, and clear spatial heterogeneity was observed across the country. Western Spain (northwest to southwest) was the region most affected, in contrast to northern and eastern Spain, and daily respiratory mortality was the group most strongly linked to the incidence of drought conditions. Moreover, for a considerable number of provinces, the effect of SPI-1 and SPEI-1 largely reflected the impact of atmospheric pollution and/or heatwaves; however, for other regions, the effect of drought conditions on daily mortality remained when these different climatic events were controlled in Poisson models. When the performances of the SPEI and SPI were compared to identify and estimate the risks of drought on daily mortality, the results were very similar, although there were slight differences in the specific causes of daily mortality.

Quantifying soil moisture impacts on light use efficiency across biomes.

Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub-humid, semi-arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly-based drought indices. Counter to common assumptions, fLUE reductions are largest in drought-deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought-related assessments.

Assessment of MODIS-derived indices (2001-2013) to drought across Taiwan's forests.

Tropical and subtropical ecosystems, the largest terrestrial carbon pools, are very susceptible to the variability of seasonal precipitation. However, the assessment of drought conditions in these regions is often overlooked due to the preconceived notion of the presence of high humidity. Drought indices derived from remotely sensed imagery have been commonly used for large-scale monitoring, but feasibility of drought assessment may vary across regions due to climate regimes and local biophysical conditions. Therefore, this study aims to evaluate the feasibility of 11 commonly used MODIS-derived vegetation/drought index in the forest regions of Taiwan through comparison with the station-based standardized precipitation index with a 3-month time scale (SPI3). The drought indices were further transformed (standardized anomaly, SA) to make them better delineate the spatiotemporal variations of drought conditions. The results showed that the Normalized Difference Infrared Index utilizing the near-infrared and shortwave infrared bands (NDII6) may be more superior to other indices in delineating drought patterns. Overall, the NDII6 SA-SPI3 pair yielded the highest correlation (mean r ± standard deviation = 0.31 ± 0.13) and was most significant in central and south Taiwan (r = 0.50-0.90) during the cold, dry season (January and April). This study illustrated that the NDII6 is suitable to delineate drought conditions in a relatively humid region. The results suggested the better performance of the NDII6 SA-SPI3 across the high climate gradient, especially in the regions with dramatic interannual amplifications of rainfall. This synthesis was conducted across a wide bioclimatic gradient, and the findings could be further generalized to a much broader geographical extent.

Water Level Reconstruction and Prediction Based on Space-Borne Sensors: A Case Study in the Mekong and Yangtze River Basins.

Water level (WL) measurements denote surface conditions that are useful for monitoring hydrological extremes, such as droughts and floods, which both affect agricultural productivity and regional development. Due to spatially sparse in situ hydrological stations, remote sensing measurements that capture localized instantaneous responses have recently been demonstrated to be a viable alternative to WL monitoring. Despite a relatively good correlation with WL, a traditional passive remote sensing derived WL is reconstructed from nearby remotely sensed surface conditions that do not consider the remotely sensed hydrological variables of a whole river basin. This method's accuracy is also limited. Therefore, a method based on basin-averaged, remotely sensed precipitation from the Tropical Rainfall Measuring Mission (TRMM) and gravimetrically derived terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) is proposed for WL reconstruction in the Yangtze and Mekong River basins in this study. This study examines the WL reconstruction performance from these two remotely sensed hydrological variables and their corresponding drought indices (i.e., TRMM Standardized Precipitation Index (TRMM-SPI) and GRACE Drought Severity Index (GRACE-DSI)) on a monthly temporal scale. A weighting procedure is also developed to explore a further potential improvement in the WL reconstruction. We found that the reconstructed WL derived from the hydrological variables compares well to the observed WL. The derived drought indices perform even better than those of their corresponding hydrological variables. The indices' performance rate is owed to their ability to bypass the influence of El Niño Southern Oscillation (ENSO) events in a standardized form and their basin-wide integrated information. In general, all performance indicators (i.e., the Pearson Correlation Coefficient (PCC), Root-mean-squares error (RMSE), and Nash⁻Sutcliffe model efficiency coefficient (NSE)) reveal that the remotely sensed hydrological variables (and their corresponding drought indices) are better alternatives compared with traditional remote sensing indices (e.g., Normalized Difference Vegetation Index (NDVI)), despite different geographical regions. In addition, almost all results are substantially improved by the weighted averaging procedure. The most accurate WL reconstruction is derived from a weighted TRMM-SPI for the Mekong (and Yangtze River basins) and displays a PCC of 0.98 (and 0.95), a RMSE of 0.19 m (and 0.85 m), and a NSE of 0.95 (and 0.89); by comparison, the remote sensing variables showed less accurate results (PCC of 0.88 (and 0.82), RMSE of 0.41 m (and 1.48 m), and NSE of 0.78 (and 0.67)) for its inferred WL. Additionally, regardless of weighting, GRACE-DSI displays a comparable performance. An external assessment also shows similar results. This finding indicates that the combined usage of remotely sensed hydrological variables in a standardized form and the weighted averaging procedure could lead to an improvement in WL reconstructions for river basins affected by ENSO events and hydrological extremes.

Monitoring of meteorological and hydrological droughts in the Vistula basin (Poland).

The article presents the course of meteorological droughts in Vistula subcatchments in years 1981-2010 and their influence on the occurrence of hydrological droughts. Using the Standardized Precipitation Index (SPI) as an indicator of meteorological drought on the one hand and the Standardized Water-level Index (SWI) and Standardized Runoff Index (SRI) as indicators of hydrological drought on the other, the mutual relationships between precipitation conditions and hydrological conditions were evaluated, as well as the relationships between the two drought types. Studies were conducted for three cumulative periods of these indices, of 12, 24, and 48 months. It was determined that meteorological droughts occurred earliest in the north-western and central part of the basin, and latest in areas lying above 300 m a.s.l. and in the south of Poland. Total duration, depending on the cumulative period, for SPI comprised from 38 to 41% of the analyzed period and for SWI (35-47%) and SRI (24-51%). The strongest relationships were identified in the central part of the Vistula (0.8 < r < 0.85), while the weakest relationships were recorded in the foothill region (r < 0.5). There were also indicated non-climate-related factors influencing those relationships (underground reservoirs, diverse Vistula water resource usage for municipal and industrial intake).

Spatiotemporal analysis of the effect of climate change on vegetation health in the Drakensberg Mountain Region of South Africa.

The impact of climate change on mountain ecosystems has been in the spotlight for the past three decades. Climate change is generally considered to be a threat to ecosystem health in mountain regions. Vegetation indices can be used to detect shifts in ecosystem phenology and climate change in mountain regions while satellite imagery can play an important role in this process. However, what has remained problematic is determining the extent to which ecosystem phenology is affected by climate change under increasingly warming conditions. In this paper, we use climate and vegetation indices that were derived from satellite data to investigate the link between ecosystem phenology and climate change in the Namahadi Catchment Area of the Drakensberg Mountain Region of South Africa. The time series for climate indices as well as those for gridded precipitation and temperature data were analyzed in order to determine climate shifts, and concomitant changes in vegetation health were assessed in the resultant epochs using vegetation indices. The results indicate that vegetation indices should only be used to assess trends in climate change under relatively pristine conditions, where human influence is limited. This knowledge is important for designing climate change monitoring strategies that are based on ecosystem phenology and vegetation health.

An improved approach for remotely sensing water stress impacts on forest C uptake.

Given that forests represent the primary terrestrial sink for atmospheric CO2 , projections of future carbon (C) storage hinge on forest responses to climate variation. Models of gross primary production (GPP) responses to water stress are commonly based on remotely sensed changes in canopy 'greenness' (e.g., normalized difference vegetation index; NDVI). However, many forests have low spectral sensitivity to water stress (SSWS) - defined here as drought-induced decline in GPP without a change in greenness. Current satellite-derived estimates of GPP use a vapor pressure deficit (VPD) scalar to account for the low SWSS of forests, but fail to capture their responses to water stress. Our objectives were to characterize differences in SSWS among forested and nonforested ecosystems, and to develop an improved framework for predicting the impacts of water stress on GPP in forests with low SSWS. First, we paired two independent drought indices with NDVI data for the conterminous US from 2000 to 2011, and examined the relationship between water stress and NDVI. We found that forests had lower SSWS than nonforests regardless of drought index or duration. We then compared satellite-derived estimates of GPP with eddy-covariance observations of GPP in two deciduous broadleaf forests with low SSWS: the Missouri Ozark (MO) and Morgan Monroe State Forest (MMSF) AmeriFlux sites. Model estimates of GPP that used VPD scalars were poorly correlated with observations of GPP at MO (r(2) = 0.09) and MMSF (r(2) = 0.38). When we included the NDVI responses to water stress of adjacent ecosystems with high SSWS into a model based solely on temperature and greenness, we substantially improved predictions of GPP at MO (r(2) = 0.83) and for a severe drought year at the MMSF (r(2) = 0.82). Collectively, our results suggest that large-scale estimates of GPP that capture variation in SSWS among ecosystems could improve predictions of C uptake by forests under drought.

Effects of interaction of multiple large-scale atmospheric circulations on precipitation dynamics in China.

Different scenarios of precipitation, that lead to such phenomena as droughts and floods are influenced by concurrent multiple teleconnection factors. However, the multivariate relationship between precipitation indices and teleconnection factors, including large-scale atmospheric circulations and sea surface temperature signals in China, is rarely explored. Understanding this relationship is crucial for drought early warning systems and effective response strategies. In this study, we comprehensively investigated the combined effects of multiple large-scale atmospheric circulation patterns on precipitation changes in China. Specifically, Pearson correlation analysis and Partial Wavelet Coherence (PWC) were used to identify the primary teleconnection factors influencing precipitation dynamics. Furthermore, we used the cross-wavelet method to elucidate the temporal lag and periodic relationships between multiple teleconnection factors and their interactions. Finally, the multiple wavelet coherence analysis method was used to identify the dominant two-factor and three-factor combinations shaping precipitation dynamics. This analysis facilitated the quantification and determination of interaction types and influencing pathways of teleconnection factors on precipitation dynamics, respectively. The results showed that: (1) the Atlantic Multidecadal Oscillation (AMO), EI Niño-Southern Oscillation (ENSO), East Asia Summer Monsoon (EASM), and Indian Ocean Dipole (IOD) were dominant teleconnection factors influencing Standardized Precipitation Index (SPI) dynamics; (2) significant correlation and leading or lagging relationships at different timescales generally existed for various teleconnection factors, where AMO was mainly leading the other factors with positive correlation, while ENSO and Southern Oscillation (SO) were mainly lagging behind other factors with prolonged correlations; and (3) the interactions between teleconnection factors were quantified into three types: enhancing, independent and offsetting effects. Specifically, the enhancing effect of two-factor combinations was stronger than the offsetting effect, where AMO + NAO (North Atlantic Oscillation) and AMO + AO (Atlantic Oscillation) had a larger distribution area in southern China. Conversely, the offsetting effect of three-factor combinations was more significant than that of the two-factor combinations, which was mainly distributed in northeast and northwest regions of China. This study sheds new light on the mechanisms of modulation and pathways of influencing various large-scale factors on seasonal precipitation dynamics.

Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto river basin, Cuba.

Drought is considered the most severe water-related disaster in the Cauto river basin, which is the longest river and the main agricultural producer in Cuba. Better understanding of drought characteristics is crucial to drought management. Given the sparsity of ground-based precipitation observations in the Cauto, this study aims at using gridded global precipitation to analyze the spatio-temporal variations of drought in this river basin. Firstly, the monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was calibrated with the gauged precipitation using the Thiessen polygon-based method and linear least squares regression equations. Then, the gridded standardized precipitation index (SPI) with time scales of 3, 6, 9 months and drought characteristics, namely, drought frequency, duration and intensity were calculated using the calibrated CHIRPS. Finally, the spatio-temporal analysis was performed to investigate the variations of drought in the Cauto river basin in time and space. The obtained results show that the calibrated CHIRPS is highly consistent with the gauged observations and is capable of determining the magnitude, time, and spatial extent of drought events in the Cauto river basin. The trend analysis by the Mann-Kendall test reveals that although the trend is not statistically significant, the SPI tends to decrease with time in the dry season, which indicates the more severe drought. The spatial analysis indicates that the lower altitude area of the Cauto river basin is suffered from longer drought duration and higher drought intensity than the upper one. This study expresses the importance of open global precipitation data sources in monitoring and quantifying drought characteristics in data-scarce regions.

The impacts of global atmospheric circulations on the water supply in select watersheds in the Indonesian Maritime Continent using SPI.

Despite the diverse atmospheric circulations affecting the Indonesian Maritime Continent (IMC), i.e., El Nino Southern Oscillation-ENSO, Indian Ocean Dipole-IOD, Madden Julian Oscillation-MJO, Monsoon, there is a lack of research on their interaction with hydrological events in watersheds. This study fills this gap by providing insights into the dominant atmospheric events and their correlation with the water supply in three characteristic watersheds, i.e., Tondano (north/Pacific Ocean), Jangka (south/Indian Ocean), and Kapuas (equatorial/interior) in IMC. The research used the standardized precipitation index for the 1-monthly (SPI1), 3-monthly (SPI3), and 6-monthly (SPI3) scale generated from 23 years (2000-2022) of monthly historical satellite rainfall data. The analysis compared each location's SPI indices with the monthly Nino 3.4, Dipole Mode Index (DMI), MJO (100E and 120E), Monsoon index, and streamflow data. The result shows that the dominant atmospheric events for the Tondano watershed were ENSO, IOD, and MJO, with correlation values of -0.62, -0.26, and -0.35, respectively. The MJO event was dominant for the Kapuas watershed, with a correlation value of -0.28. ENSO and IOD were dominant for the Jangka watershed, with correlation values of -0.27 and -0.28, respectively. The monsoon correlated less with the SPI3 in all locations, while it modulates the wet and dry period pattern annually. Most intense dry periods in Tondano occur with the activation of El Nino, while the intense wet period occurs even in normal atmospheric conditions. Most intense wet periods in Jangka occur with the activation of La Nina, while the intense dry period occurs even in normal atmospheric conditions. The occurrence of MJO compensates for the intense wet and dry periods in Kapuas. The correlation among SPI3, atmospheric circulation, and streamflow in the diverse watershed characteristics in the IMC watersheds could give strategic information for watershed management and applies to other watersheds with similar atmospheric circulation characteristics.

Trends in drought and effects on carbon sequestration over the Chinese mainland.

Recently, drought events have occurred frequently and have profoundly altered the carbon sequestration in terrestrial ecosystems. How drought affects carbon sequestration is an important issue which may assist in understanding and confronting the challenges of extreme climate change. Nevertheless, drought-induced carbon-cycle effects remain scarce from the perspective of drought indices. In this study, we quantified the impacts of potential evapotranspiration (PET), standardized precipitation evapotranspiration index (SPEI), downward short-wave radiation flux (SWDown), and soil water (Soil_w) on net ecosystem productivity (NEP). We showed that the spatiotemporal heterogeneity of drought was extremely significant, and the hot spots of aridification were mainly distributed in the southwestern Yungui Plateau (YG) and Northwest China (NW). Moreover, the "pan evaporation paradox" appeared across the Chinese mainland before the 1990s and subsequently disappeared. Similarly, in contrast to the moderate NEP fluctuation between 1981 and 1999, since the beginning of the 21st century, NEP has increased significantly across Chinese mainland, YG, the plains region of Changjiang (CJ), and Southeast China (SE). Meanwhile, there are obvious directional, temporal, and spatial differences in the effects of the drought indices on NEP. Specifically, a higher SPEI value results in a more obvious promoting effect on NEP in SE, North China (NN), and northeastern YG. An increase in SWDown can promote an increase in NEP, especially in the northeastern YG and central SE. The increase in Soil_w in parts of the Qinghai-Tibetan Plateau, Xinjiang Region (XJ), southeastern NW, NN, and Northeast China with poor water conditions can promote carbon sinks. The inhibition effect is particularly obvious in some areas of CJ, where water resources are abundant. The fluctuation in PET has a relatively low influence on NEP. This study provides a comprehensive assessment of drought change and its impact on carbon sequestration and may help in formulating appropriate policies for carbon management and ecological security.

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland.

The primary aim of this work is to assess the accuracy of the methods for spatial interpolation applied for the reconstruction of the spatial distribution of the Standardized Precipitation Index (SPI). The one-month version called SPI-1 is chosen for this purpose due to the known greatest variability of this index in comparison with its other versions. The analysis has been made for the territory of the entire country of Poland. At the same time the uncertainty related to the application of such computational procedures is determined based on qualitative and quantitative measures. The public data of two kinds are applied: (1) measurements of precipitation and (2) the locations of the meteorological stations in Poland. The analysis has been made for the period 1990-2020. However, all available observations since 1950 have been implemented. The number of available meteorological stations has decreased over the analyzed period. In January 1990 there were over one thousand stations making observations. In the end of the period of the study, the number of stations was below six hundred. Obviously, the temporal scarcity of data had an impact on the obtained results. The main tools applied were ArcGIS supported with Python scripting, including generally used modules and procedures dedicated to geoprocessing. Such an approach appeared crucial for the effective processing of the large number of data available. It also guaranteed the accuracy of the produced results and brought about drought maps based on SPI-1. The methods tested included: Inverse Distance Weighted, Natural Neighbor, Linear, Kriging, and Spline. The presented results prove that all the procedures are inaccurate and uncertain, but some of them provide satisfactory results. The worst method seems to be the interpolation based on Spline functions. The practical aspects related to the implementation of the methods led to removal of the Linear and Kriging interpolations from further use. Hence, Inverse Distance Weighted, as well as Natural Neighbor, seem to be well suited for this problem.

Wetland characteristics affect abundance and diversity of wintering birds: A case study in South-Western Iran.

Water availability is an important driver of bird population change, and its effects are likely to increase in coming decades under climate change. Here we assess effects of temperature, precipitation, and water area on wintering bird populations in Miyangaran Wetland in southwestern Iran. Modeling methods including, generalized linear model (GLM) and hierarchical partitioning were used to examine the relative importance of variables. The number of wintering species, inhabiting the wetland, varied among years, ranging from 10 to 48 species. The total number of wintering birds showed a significant decreasing trend. A significant increasing trend was obtained for shorebirds, while waterfowl species were significantly decreased. The GLM showed that species abundance, richness, and diversity were significantly correlated with the standardized precipitation index (SPI), annual precipitation, and normalized difference water index (NDWI). Hierarchical partitioning analysis also identified NDWI, SPI, and annual precipitation as the most important variables with average independent effects of 35, 36 (p < .01) and 17% (p < .05), respectively. Our results revealed that the water area plays a major role in determining the structure of bird diversity and abundance, affecting both waterfowl and shorebirds.