Precipitation Time Series Data Research Articles

Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria

Precipitation is highly unpredictable over space and time, and is an indicator for climate change. The present work classified average yearly precipitation over the 36 states and the Federal Capital Territory (FCT) in Nigeria into various groups. The hierarchical and non- hierarchical (k-means) cluster method was used in the classification of 37- years precipitation data (1984-2020). The results showed that six major clusters were formed. The Precipitation across the 36 states and the FCT in Nigeria can be classified as Light precipitation for Sokoto, Borno, Kastina, Yobe and Kano States; Moderate precipitation for Kebbi, Zamfara, Dutse, Yola, Gombe and Bauchi States; Heavy precipitation for Kaduna, Plateau, Kwara, Taraba, Nassarawa, Benue and Kogi States; Very Heavy precipitation for Osun, Ogun, FCT, Ekiti, Niger and Lagos States; Shower for Edo, Abia, Delta, Anambra, Ebonyi, Enugu, Ondo and Oyo States and lastly, Heavy Shower for Akwaibom, Imo, Rivers, Bayelsa and Cross River States.. The rangs of precipitation based on the six classification are 1.5-1.8mm (Light precipitation), 1.9-2.7mm (Moderate precipitation), 2.8-3.6mm (Heavy precipitation), 3.7-4.3mm (Very Heavy precipitation), 4.4-5.4mm (Shower) and greater than 5.5mm (Heavy Shower). The hierarchical cluster method performed better than the non- hierarchical (k-means) cluster method in structuring the precipitation data and shaping it into groups (clusters) such that observations within the same group are more similar than those in different groups. The results of this study can contribute to a better understanding of the climate condition of the36 States and the FCT in Nigeria.

Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.

Human or climate? Differentiating the anthropogenic and climatic drivers of lake storage changes on spatial perspective via remote sensing data

Lakes are an essential part of the terrestrial water system in which storage changes are controlled by water balance and human impact. Although there are some attempts to define storage changes on a global scale, examination of spatial relations is poorly quantified. In this study, therefore, lake storage changes have been investigated using remote-sensing-derived data around the globe. Hence, 372 artificial/natural lakes were obtained, covering between 1992 and 2019. Watersheds belong to river was extracted via HydroSHED data. Based on watershed, dominant climate types were determined via Köppen-Geiger classification. Similarly, the areal average CRU TS v.4.05 monthly gridded precipitation time series and human footprint data based on watersheds were obtained to understand the drivers of lake storage changes. The nonparametric Mann-Kendall and Sen's slope trend analyses were applied to the lake storage change and precipiation values in order to determine long-term increases and decreases. A bivariate map was constructed between storage changes trend vs precipitation trend and human footprint to reveal the drivers of lake storage changes in terms of spatial aspects. The trend analysis and bivariate map results show that North America, the East African Highlands, and the Tibet plateau are important increasing hotspots, where precipitation is a significant driver for storage oscillations, except for the Tibet plateau. Besides, the Brazilian Highlands, Pacific Mountain System, and Intermontane of conterminous USA are other decreasing hotspots in which human footprint and decreasing precipitation collectively affect these changes. Furthermore, results clearly show that anthropogenic influence is low in the northern and mountainous areas, and storage changes have a linear relationship with precipitation. In contrast, intense human climate interaction influences lake changes in plains areas and arid/temperate climates.

AutoML-GWL: Automated machine learning model for the prediction of groundwater level

Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management.

Impacts of hydrometeorological factors on discharge simulation in the North West Himalayas: a SUFI-2 algorithm-driven investigation using the SWAT model.

The Soil and Water Assessment Tool (SWAT) is a computational hydrological model extensively utilised for developing sustainable strategies and viable approaches for prudent management of water resources. The central emphasis of this study is on the utilisation of SWAT model along with SWAT-CUP (SWAT calibration toolbox) to simulate streamflow in the upper Jhelum basin, the North West Himalayas, for a period of 20years from 2000 to 2019. The global sensitivity analysis algorithm, Sequential Uncertainty Fitting 2 (SUFI-2) of SWAT-CUP, is used for sensitivity and uncertainty analysis. The optimised parameter set estimated by SUFI-2 constitutes 11 parameters that are found to be sensitive with soil conservation service (SCS) curve number (CN) being the most influential parameter followed by snowmelt base temperature. Autocorrelation analysis using the autocorrelation function was conducted on the temperature and precipitation time series data, followed by a pre-whitening procedure to remove any autocorrelation effects. Subsequently, the modified Mann-Kendall (MMK) test was applied to examine trends in the annual temperature and precipitation data. The results indicated statistically significant positive trends in both datasets on an annual scale. The results for the calibration period (2003-2014) for monthly simulation displayed good model performance at three gauging stations, Rambiara, Sangam and Ram Munshi Bagh with R2 values of 0.83, 0.847, 0.829, P factor values of 0.73, 0.76, 0.75 and R factor values of 0.61, 0.58, 0.63, respectively. The validation results for monthly simulation for the 2015-2019 period showed good model agreement with R2 values of 0.817, 0.853, and 0.836, P factor values of 0.76, 0.8, and 0.75 and R factor values of 0.62, 0.53, and 0.65, respectively. The study concludes that the SWAT hydrological model can perform satisfactorily in high mountainous catchments and can be employed to analyse the impact of land use-land cover changes and the effect of climate variation on streamflow dynamics.

Changes in the associations between malaria incidence and climatic factors across malaria endemic countries in Africa and Asia-Pacific region

Empirical evidence supporting the associations between malaria incidence and climatic factors has remained controversial, buffering the progress in the Global Malaria Program that aims to eliminate 90% of the world malaria burden by 2030. This study has aimed to evaluate the nature and extent at which these relations are maintained across all the malaria endemic countries of Africa and Asia-Pacific region. We have utilized the last two decades of malaria incidence, annual minimum temperature, and annual precipitation time series data (2000–2020) for the two most malaria-impacted regions. These data were fitted in the generalized linear model and the mixed effects model. The results showed that there exists a large heterogeneity in malaria incidence across the countries, and between the regions. Last two decadal tendencies showed significant reductions in the disease burden in almost all the countries in the Asia Pacific, with several exceptions or relatively slowed reductions in the Africa. We found significant changes in the positive linear associations between malaria incidence, annual minimum temperature, and annual precipitation across African countries. Many Asia-Pacific countries namely Bangladesh, Bhutan, Indonesia, South Korea, Nepal, Thailand, Vietnam showed negative effects in the associations with the annual precipitation. This study indicates that increasing temperature within the range of 12–30 °C can generate positive effects on malaria incidence, but the nature and extent of precipitation effects vary across countries and at a large regional scale.

Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data

• A data-driven ensemble time-series data estimation model is developed. • Statistical variability and the exogenous factor are considered in the estimation. • The range of normal data is calculated based on the ensemble estimation result. • Contextual and collective outliers in time-series data were effectively identified. In this study, a method to estimate the normal range of groundwater level time-series data was developed to identify outliers in terms of the global, contextual, and collective sense. To evaluate the normal range of groundwater level time-series data, the statistical characteristics of the data and the patterns of the precipitation time-series data were incorporated into the LSTM (Long Short-Term Memory)-based ensemble regressor (i.e., the LER model). Based on the LER model, multiple possible trends of the groundwater level were generated, and the general rules of outlier identification methods (i.e., σ and Tukey’s fences (TF) rules) were applied to the LER ensemble estimation result to finally define the range of the normal data. For outlier identification performance validation, the actual groundwater level acquired from three groundwater monitoring stations in South Korea (i.e., the Pohang–Gibuk (PG), Namwon–Dotong (ND), and Jeju–Sangyae (JS) monitoring wells) and the corresponding precipitation data acquired from the nearest weather stations were applied to the study. As the reference method for comparative performance validation, simple applications of the σ and TF rules were used. For the monitoring data, the developed LER-based outlier identification method evaluates the range of the data that might be explained by the modelled influences of the interest (i.e., normal data range). The developed method showed an outlier identification performance of >70% in general while the performance of the σ and TF rules was mostly <50%. In particular, as the method effectively estimated the seasonal trend and the variability of the groundwater level with consideration of the precipitation patterns and statistics on the groundwater level variation, it is superior for identifying the contextual or collective outliers compared to the simple σ and TF rules. Through in-depth analysis, it can be concluded that the developed LER-based outlier identification method is effective for discriminating the abnormal data by considering the intrinsic statistical characteristics of the original data trend and the exogenous factors. In the aspect of the practical applicability, as the result can be automatically acquired based on real-time monitoring data, the developed method is expected to apply for more efficient maintenance of the monitoring devices by embedding the model as the management software into the monitoring network system.

MEEMD Decomposition-Prediction-Reconstruction Model of Precipitation Time Series.

To address the problem of low prediction accuracy of precipitation time series data, an improved overall mean empirical modal decomposition–prediction–reconstruction model (MDPRM) is constructed in this paper. First, the non-stationary precipitation time series are decomposed into multiple decomposition terms by the improved overall mean empirical modal decomposition (MEEMD). Then, a particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN) and recurrent neural network (RNN) models are used to make predictions according to the characteristics of different decomposition terms. Finally, the prediction results of each decomposition term are superimposed and reconstructed to form the final prediction results. In addition, the application is carried out with the summer precipitation in the Wujiang River basin of Guizhou Province from 1961 to 2018, using the first 38 years of data to train MDPRM and the last 20 years of data to test MDPRM, and comparing with a feedback neural network (BP), a support vector machine (SVM), a particle swarm optimization support vector machine (PSO-SVM), a convolutional neural network (CNN), and a recurrent neural network (RNN), etc. The results show that the mean relative error (MAPE) of the proposed MDPRM is reduced from 0.31 to 0.09, the root mean square error (RMSE) is reduced from 0.56 to 0.30, and the consistency index (α) is significantly improved from 0.33 to 0.86, which has a higher prediction accuracy. Finally, the trained MDPRM predicts the average summer precipitation in the Wujiang River basin from 2019 to 2028 to be 466.42 mm, the minimum precipitation in 2020 to be 440.94 mm, and the maximum precipitation in 2024 to be 497.94 mm. Based on the prediction results, the agricultural drought level is evaluated using the Z index, which indicates that the summer is normal in the 10-year period. The study provides technical support for the effective guidance of regional water resources’ allocation and scheduling and drought mitigation.

The Influence of Teleconnections on the Precipitation in Baluchistan

Precipitation plays a vital role in the economies of agricultural countries, such as Pakistan. Baluchistan is the largest province in Pakistan (in terms of land) and it is facing reoccurring droughts due to changing precipitation patterns. The landscape of the province consists of rugged terrain, mountains, hills, and valleys. The torrential rains lead to devastating flash floods due to the topography of the province, which has proven to be more catastrophic in nature. It is quite intriguing to observe the changing precipitation patterns in Baluchistan. Precipitation has become less frequent but intense, resulting in flash floods and landslides, as well as damage to agriculture, infrastructure, trade, environment, and the ecosystem. Baluchistan is under a drought warning and is already facing a water crisis. This study was performed on monthly precipitation time series data obtained from the Pakistan Meteorological Department (PMD) for determining trends in precipitation from 41 years of data (1977 to 2017) over 13 selected stations in Baluchistan. Due to the non-linear nature of the precipitation data, a non-parametric Mann–Kendall (MK) test was used to determine the increasing or decreasing trends in precipitation on a monthly basis. Large-scale atmospheric circulation and climate indices that affected precipitation were considered to determine their influence on precipitation. Statistical techniques of the partial Mann–Kendall (PMK) and Pearson correlation were applied to each station to ascertain the influence on precipitation due to climatic indices.

A New Ensemble Index for Extracting Predictable Drought Features from Multiple Historical Simulations of Climate

Drought assessment and forecasting under an ensemble of multiple climate simulation models play important role in early warning drought mitigation policies. This research provides a new ensemble drought measure – the Multivariate Multi-Scaler Forecastable Standardized Drought Index (MMFSDI). At a particular georeferenced point, the MMFSDI uses time-series data of precipitation from multiple climate simulation models for the characterization of drought. The methodology of MMFSDI is mainly based on Forecastable Component Analysis (FCA) and K-Component Gaussian Mixture Distribution (K-CGMD). In application, historical simulated data of precipitation from 23 climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6) at fifty grid points scattered over the Tibet Plateau region are considered to evaluate the applicability of MMFSDI. For comparative analysis, the forecasting performance of MMFSDI is compared with Standardized Precipitation Index (SPI) using Residual Mean Square Error (RMSE) and Mean Average Error (MAE) under Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models. Outcomes associated with this research shows that 1) the first component of FCA as an ensemble of multiple climate models is more describable than simple model averaging, 2) the strong consistency between MMFSDI and SPI makes MMFSDI as an alternative multi-model drought measure, and 3) the implications of ARIMA and ANN revealed that MMFSDI has inherited feature for forecasting drought. In summary, the finding of the research argues to substitute SPI with MMFSDI as MMFSDI has inherited forecasting ability.

Using Chaos theory fundamentals for analysing temperature, precipitation variability and trends in Northern Patagonia, Argentina

The fundamentals of Chaos theory allow the study of climatic conditions and long-term modifications produced by changes in their spatial and temporal scales. The aim of this work is to analyse the variability and changes produced in the annual cycles of temperature and precipitation in Northern Patagonia, Argentina, applying multifractal analysis as a practical mathematical tool of Chaos theory. Data from the NASA POWER Project (2021) was implemented as an alternative dataset for carrying out climatological studies in the area. Annual mean temperature and precipitation time-series data (1981–2019) were analysed at 72 grid points with 1° of spatial resolution. The Mann–Kendall test was used to calculate the trends through the annual cycles of the meteorological variables. Fractal dimension values were calculated using Multifractal Detrended Fluctuation Analysis. The Hurst exponent, complexity and asymmetry were the multifractal dimensions describing the persistence of time-series trends and climatic variability. The results showed changes in the annual cycles of both variables during the study period. The most significant finding was a large area in the centre and north of the study area, where the decrease in the rainfall regime was persistent. The Hurst exponent detected a sector in the Patagonian Andes mountain range where the temperature increase was constant. This work demonstrates that fractal geometry is useful to describe meteorological variability and obtain better short-, medium- and long-term forecasts.

A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data

Detecting and predicting extreme events are of major importance for socioeconomic, healthcare and ecological purposes. This study proposes an alternative model to detect extreme events based on analyses of probability distribution functionffnss (f(X)), called Optimum Probability Distribution Function Searcher Model (Opt.PDF-model). The Opt.PDF-model involves the optimization of a fitness function between an histogram and a set of theoretical f(X), and the subsequent evaluation of the Probability Point Function (PPF) of the fittest theoretical (f(X)) to assess threshold values for the classification of extreme events. Any occurrence in the dataset with a PPF value equal to or greater than 90% was considered an extreme event candidate. A satellite-derived precipitation time-series (Climate Hazards Group InfraRed Precipitation with Station data) was used to calibrate and validate the proposed model, with data on accumulated precipitation from more than 30 years (Jan.1981 to Dec.2018) of the Brazilian Amazon region. The proposed method was pairwise cross-validated with two other extreme event models based on Gamma and Gaussian distributions, as applied by the European Drought Observatory of the European Environment Agency. Aditionally, all three extreme event classification models were cross-validated relative to the El Niño Southern Oscillation (ENSO). By means of the Opt.PDF-model, it was possible to evidence two positive temporal trends for the area of study: one for more intense precipitation events, and another for less intense events. The pairwise cross-validation analysis returned specific Kappa’s similarity indices, and the highest similarity was observed between the Gamma and the Opt.PDF models (48% for PPF(97.7%)). This analysis indicated that extreme event detection models are highly sensitive to distribution family priors and to threshold definitions. The proposed approach and the results obtained here are potentially useful for climate change warnings, and can be extended to other scientific areas that involve time-series analyses.

Projection of Future Drought Characteristics under Multiple Drought Indices

Drought is a natural phenomenon caused by the variability of climate. This study was conducted in the Songhua River Basin of China. The drought events were estimated by using the Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) which are based on precipitation (P) and potential evapotranspiration (PET) data. Furthermore, drought characteristics were identified for the assessment of drought trends in the study area. Short term (3 months) and long term (12 months) projected meteorological droughts were identified by using these drought indices. Future climate precipitation and temperature time series data (2021–2099) of various Representative Concentration Pathways (RCPs) were estimated by using outputs of the Global Circulation Model downscaled with a statistical methodology. The results showed that RCP 4.5 have a greater number of moderate drought events as compared to RCP 2.6 and RCP 8.5. Moreover, it was also noted that RCP 8.5 (40 events) and RCP 4.5 (38 events) showed a higher number of severe droughts on 12-month drought analysis in the study area. A severe drought conditions projected between 2073 and 2076 with drought severity (DS-1.66) and drought intensity (DI-0.42) while extreme drying trends were projected between 2097 and 2099 with drought severity (DS-1.85) and drought intensity (DI-0.62). It was also observed that Precipitation Decile predicted a greater number of years under deficit conditions under RCP 2.6. Overall results revealed that more severe droughts are expected to occur during the late phase (2050–2099) by using RDI and SPI. A comparative analysis of 3- and 12-month drying trends showed that RDI is prevailing during the 12-month drought analysis while almost both drought indices (RDI and SPI) indicated same behavior of drought identification at 3-month drought analysis between 2021 and 2099 in the research area. The results of study will help to evaluate the risk of future drought in the study area and be beneficial for the researcher to make an appropriate mitigation strategy.

An Analysis of Spatio-temporal Characterisrice of Drought Using Non-stationary Time Dependent-standardized Precipitation

The SPI index was developed based on the fact that when drought occurs when reduced precipitation causes water shortage relative to the required water demand. However, the SPI index works under the assumption of stationarity or normality in which the probabilistic properties of the time series data do not change over time. It has limitations in reflecting information changing over time sufficiently, such as climate change. In this study, we used SPIt, a new frequency analysis method considering non-stationarity, which is different from the existing SPI index calculation. We first used daily precipitation data to construct the time series data of 7-day precipitation. For the duration of drought, both SPI and SPIt indices for 3 months were calculated on a monthly and weekly basis, and the reproducibility of each index was assessed for the areas experiencing restricted water supply. In addition, both indices were calculated for all national meteorological stations in South Korea, which was used to create a spatial distribution map and confirm their spatial reproducibility.

Climatic Trends in Different Bioclimatic Zones in the Chitwan Annapurna Landscape, Nepal

The Chitwan Annapurna Landscape (CHAL) is the central part of the Himalayas and covers all bioclimatic zones with major endemism of flora, unique agro-biodiversity, environmental, cultural and socio-economic importance. Not much is known about temperature and precipitation trends along the different bioclimatic zones nor how changes in these parameters might impact the whole natural process, including biodiversity and ecosystems, in the CHAL. Analysis of daily temperature and precipitation time series data (1970–2019) was carried out in seven bioclimatic zones extending from lowland Terai to the higher Himalayas. The non-parametric Mann-Kendall test was applied to determine the trends, which were quantified by Sen’s slope. Annual and decade interval average temperature, precipitation trends, and lapse rate were analyzed in each bioclimatic zone. In the seven bioclimatic zones, precipitation showed a mixed pattern of decreasing and increasing trends (four bioclimatic zones showed a decreasing and three bioclimatic zones an increasing trend). Precipitation did not show any particular trend at decade intervals but the pattern of rainfall decreases after 2000AD. The average annual temperature at different bioclimatic zones clearly indicates that temperature at higher elevations is increasing significantly more than at lower elevations. In lower tropical bioclimatic zone (LTBZ), upper tropical bioclimatic zone (UTBZ), lower subtropical bioclimatic zone (LSBZ), upper subtropical bioclimatic zone (USBZ), and temperate bioclimatic zone (TBZ), the average temperature increased by 0.022, 0.030, 0.036, 0.042 and 0.051 °C/year, respectively. The decade level temperature scenario revealed that the hottest decade was from 1999–2009 and average decade level increases of temperature at different bioclimatic zones ranges from 0.2 to 0.27 °C /decade. The average temperature and precipitation was found clearly different from one bioclimatic zone to other. This is the first time that bioclimatic zone level precipitation and temperature trends have been analyzed for the CHAL. The rate of additional temperature rise at higher altitudes compared to lower elevations meets the requirements to mitigate climate change in different bioclimatic zones in a different ways. This information would be fundamental to safeguarding vulnerable communities, ecosystem and relevant climate-sensitive sectors from the impact of climate change through formulation of sector-wise climate change adaptation strategies and improving the livelihood of rural communities.

Variable-Sized Cluster Analysis for 3D Pattern Characterization of Trends in Precipitation and Change-Point Detection

AbstractA novel analysis procedure, referred to as variable-sized cluster analysis (VSCA), was developed to identify trends and change points in precipitation time series data set. The procedure in...

An approach for runoff and sediment nexus analysis under multi-flow conditions in a hyper-concentrated sediment river, Southwest China

Nexus of runoff and sediment in watercourses is important for sustainable watershed management, especially in rivers with hyper-concentrated sediments. However, complex relationships between runoff and sediment were not fully understood. In this research, Mann-Kendall (MK) and moving t-test methods were used to confirm the suitable temporal range based on time series data of precipitation in the Anning River Basin from 1971 to 2017. At the same time, the month of typical precipitation (i.e., the largest annual average precipitation) was identified. A model of runoff and sediment over two stations from 2010 to 2015 were constructed respectively based on copula analysis method. Synchronous and asynchronous probabilities of runoff and sediment were analysed from the temporal and spatial aspects separately. The encounter probabilities of runoff and sediment over multiple periods and at two stations were analysed. The sediment distribution was illustrated under multiple runoff distribution conditions. The encounter probability of runoff and sediment distribution changed greatly, which was principally due to employment of reservoir operation, soil and water conservation measures. The variability of the joint probability was much obvious in tributary regions than that of the mainstream areas of Anning River basin. The results could be adopted to improve the understanding of interactive relationships between runoff and sediment and provide a significant references for regional management of soil and water.

Soil system dynamics for learning about complex, feedback-driven agricultural resource problems: model development, evaluation, and sensitivity analysis of biophysical feedbacks

Soils form the foundation of terrestrial ecosystems that regulate the processes and functions driving ecosystem goods and services provisions that humans rely on, including agriculture. Pressing agricultural resource challenges persist, including those involving irrigation, fertilization, and salinization, due to the complex, coupled, and feedback-driven connectivity of various soil processes that are difficult to manage. Soil moisture dynamics cross-cut these processes and is a logical integration point for generating understanding and new insights for improved agroecosystem management. This paper presents an integrated soil-water-nutrient-plant interaction model (built within a system dynamics framework) with the purpose of replicating soil moisture evolution for a set of unique soils and climates, examining model performance given common irrigation (e.g., frequency and application rates) and crop management considerations (e.g., fertilization, tillage, cover cropping), and evaluating via sensitivity analysis model robustness and quantifying influential management parameters effect on core bio-physical feedbacks at the soil-level. The model has four main state variables (soil moisture, soil nitrogen, soil sodium, and plant canopy cover) that interact dynamically via feedback processes (formulated as coupled partial differential equations) between them. Exogenous variables included precipitation time-series data and required climatic parameters to determine reference (potential) evapotranspiration. The time-unit used from simulation was 1 day (time-step = 0.25) with a simulation horizon of 365 days. The model was calibrated using a variety of sources in the literature and with comparison to observed soil moisture data from four sites in Texas, USA, and evaluated statistically for accuracy (mean bias), precision, (coefficient of determination), and overall fit (Theil inequality statistics). Sensitivity analyses were conducted for a variety of hydroclimate forcing and irrigation, fertilization, and crop management decisions to examine the impacts to soil moisture evolution, soil salinity, and cropping profitability, among other variables. Calibration results showed high degrees of agreement between observed and predicted values (mean r2 = 0.67, mean bias = 0.008%). Sensitivity results demonstrated that precipitation frequency was more influential than precipitation depth in regulating soil moisture, that irrigation threshold (i.e., the soil moisture level inducing irrigation) was the variable most influential to crop profitability (which was maximized at the lowest irrigation threshold value), and that several conservation management strategies (i.e., no-till with residue management or cover cropping) improved soil moisture and crop profitability, contrary to common management perceptions. Several other tests for alternative fertilization, irrigation, and tile drain installation strategies produced results that corroborate common observations of agroecosystem management (i.e., despite environmental risks, crop profitability was enhanced). Future model extensions include expansion of the irrigation, fertilization, and crop management decision making factors to better capture how decisions that respond to economic and policy signals influence resource use and soil system dynamics. Modeling these complex, feedback driven agroecosystems processes remains an arena for future modeling innovations that will support important resource management improvements.

Selection of Suitable Precipitation CMIP-5 Sets of GCMs for Iraq Using a Symmetrical Uncertainty Filter

Prediction of future water resources in Iraq for the period 2020 to 2100 based on different scenarios of climatic change models by harnessing many calibrated Arc-SWAT models needs to select future precipitation data from suitable sources. Therefore, the selection of the appropriate source of the possible future precipitation time series data sets was studied by examining 20 models of the fifth phase of the Coupled Model Inter-comparison Project (CMIP-5.0) for General Circulation Models (GCMs). The Symmetrical Uncertainty (SU) approach was used to determine the performance of the 20 CMIP-5.0 sets of GCMs against a monthly scale of historical precipitation datasets at each one of 35 rain gauge stations spread throughout Iraq, and an appropriate ensemble of GCMs selected. The examined models were ranked as 1st, 2nd, 3rd, etc. based on the SU values at each station and the final ranking of the models was carried out using a multi-criteria decision-making (MCDM) method. The results showed that the HadGEM2-AO and HadGEM2-ES were the best (1st ranked) models at 31 stations, while the MIROC-5 and CSIRO-Mk-3.6 were the best models at Zakho and Duhok stations, respectively, and the BCC.CSM1.1.m and FIO.ESM were the best models at Samarra and Hilla stations, respectively. There was a variation in simulation preference regionally between the two models HadGEM2-AO and HadGEM2-ES in the second ranking except at Tel-Afer and Najaf stations. No predominant models were found in other ranks throughout Iraq. The results of the final ranking of these 20 CMIP-5 sets by the MCDM method thus showed that there are only four suitable GCMs, HadGEM2-AO, HadGEM2-ES, CSIRO.Mk3.6, and MIROC5 for data projections studying scenarios involving future water resources in Iraq.

Comparative analysis of probability distributions for the Standardized Precipitation Index and drought evolution in China during 1961–2015

As a representative index for monitoring and assessing drought, the Standardized Precipitation Index (SPI) relies on a suitable probability distribution function (PDF) to describe a precipitation series, which allows interregional comparisons following normalization. In this study, we considered nine PDFs (exponential (EXP), extreme value (EV), gamma (GAM), generalized Pareto (GP), logistic (LO), log-logistic (LOL), log-normal (LON), normal (NOR), and Weibull (WEI)) as candidates for use in SPI calculations. Based on monthly precipitation time series data (1961–2015) from 582 stations across China, together with the Kolmogorov–Smirnov (K–S) and Akaike Information Criterion (AIC) methods, differences in optimal PDFs for SPI calculations were compared comprehensively from the perspectives of timescale, record length, and index value. Based on the SPI calculated using the optimal PDF at the 6-month timescale (SPI6-opt), we analyzed the spatiotemporal characteristics of drought trends in China using the Mann–Kendall method. Results indicated both the timescale and the record length would affect the selection of the optimal PDF. The performance of the WEI and GAM distributions was superior to other distributions in describing monthly precipitation (especially for long precipitation records) at short and multiple timescales, respectively. During the entire study period, areas of China with high frequency of drought have transferred from the northwest (1960s), to the northeast (2000s), and to the southwest (most recent 5 years). Trend analysis revealed a noticeable wetting tendency confined mainly to Northwest, Northeast, and Southeast China and a significant trend toward drought in Southwest China and on the North China Plain.