Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Dynamics Of Flood Flow In Red River Basin

In recent decades, flooding has become a major issue in many areas of the Upper Midwest. Many rivers and streams in the region had considerable increases in mean annual peak flows during this period, which was driven by a combination of natural factors including discharge synchrony with the spring thaw, ice jams, glacial lake plain, and a decrease in gradient downstream. The Red River of the North is a prominent river in the United States and Canada\u27s Upper Midwest. It flows from its headwaters in Minnesota and North Dakota to Lake Winnipeg in Manitoba. The river is well-known for its spring floods, which can cause havoc on communities along its banks. There is an increasing need to improve the characterization and identification of precursors in the Red River basin that affect the hydrological conditions that cause spring snowmelt floods and improve predictions to reduce Red River flood damage. This dissertation has developed different research that concerns the dynamics of floods in the Red River basin by integrating hydrological, hydraulic, and machine-learning models. The primary objectives were to improve flood prediction accuracy by deriving the parameters of the Muskingum Routing method using discharge measurements obtained by an Autonomous Surface Vehicle, to predict scour potential of the river through HEC-RAS modeling, and to provide an estimate of the flood progression downstream based on the flow characteristics. The study also compared the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM) algorithms for flood prediction. Additionally, the research investigated the surface water area variation and response to wet and dry seasons across the entire Red River basin, which can inform the development of effective flood mitigation strategies. The results of this study contributed to a better understanding of flood control strategies in the Red River Basin and helped to inform policy decisions related to flood mitigation in the region. Ultimately, this research aimed to understand the complex dynamics of the RRB and derive hydrological and hydraulic models that could help to improve flood prediction. The first research developed a linear and nonlinear Muskingum model with lateral inflows for flood routing in the Red River Basin using Salp Swarm Algorithm (SSA). The distributed Muskingum model is introduced to improve the accuracy and efficiency of the calculations. The study focuses on developing a linear and nonlinear Muskingum model for the Grand Forks and Drayton USGS stations deriving the parameters of the Muskingum Routing method using discharge measurements based on spatial variable exponent parameters. The suggested approach minimizes the Sum of Square Errors (SSE) between observed and routed outflows. The results show for an icy river like Red River, the Muskingum method proposed is a convenient way to predict outflow hydrographs caused by snowmelt. The second study improved flood inundation mapping accuracy in flood-prone rivers, such as the Red River of the North, by using simulation tools in HEC-RAS for flood modeling and determining Manning\u27s n coefficient. An Autonomous Surface Vehicle (ASV) was used to collect bathymetry and discharge data, including a flood event with a 16.5-year return period in 2022. The results showed that Manning\u27s n-coefficient of 0.07 and 0.15 for the channel and overbanks, respectively, agreed well with the observed and simulated water level values under steady flow conditions. The study also demonstrated the efficiency of using ASVs for flood mapping and examined the scour potential and any local scour development in the streambed near the bridge piers. The third study of this dissertation used hourly level records from three USGS stations to evaluate water level predictions using three methods: SARIMA, RF, and LSTM. The LSTM method outperformed the other methods, demonstrating high precision for flood water level prediction. The results showed that the LSTM method was a reliable choice for predicting flood water levels up to one week in advance. This study contributes to the development of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. This last study focused on the spatiotemporal dynamics of surface water area in the Red River Basin (RRB) by using a high-resolution global surface water dataset to investigate the changes in surface water extent from 1990 to 2019. The results showed that there were four distinct phases of variation in surface water: wetting (1990-2001), dry (2002-2005), recent wetting (2006-2013), and recent drying (2014-2019). The transition from bare land to permanent and seasonal water area was observed during the wetting phase, while the other phases experienced relatively little fluctuation. Overall, this study contributes to a better understanding of the spatiotemporal variation of surface water area in the RRB and provides insights into the impact of recent wetting and drying periods on the lakes and wetlands of the RRB

A contemporary review on drought modeling using machine learning approaches

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include singular vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics

Forecasting seasonal hydrologic response in major river basins.

Seasonal precipitation variation due to natural climate variation influences stream flow and the apparent frequency and severity of extreme hydrological conditions such as flood and drought. To study hydrologic response and understand the occurrence of extreme hydrological events, the relevant forcing variables must be identified. This study attempts to assess and quantify the historical occurrence and context of extreme hydrologic flow events and quantify the relation between relevant climate variables. Once identified, the flow data and climate variables are evaluated to identify the primary relationship indicators of hydrologic extreme event occurrence. Existing studies focus on developing basin-scale forecasting techniques based on climate anomalies in El Nino/La Nina episodes linked to global climate. Building on earlier work, the goal of this research is to quantify variations in historical river flows at seasonal temporal-scale, and regional to continental spatial-scale. The work identifies and quantifies runoff variability of major river basins and correlates flow with environmental forcing variables such as El Nino, La Nina, sunspot cycle. These variables are expected to be the primary external natural indicators of inter-annual and inter-seasonal patterns of regional precipitation and river flow. Relations between continental-scale hydrologic flows and external climate variables are evaluated through direct correlations in a seasonal context with environmental phenomenon such as sun spot numbers (SSN), Southern Oscillation Index (SOI), and Pacific Decadal Oscillation (PDO). Methods including stochastic time series analysis and artificial neural networks are developed to represent the seasonal variability evident in the historical records of river flows. River flows are categorized into low, average and high flow levels to evaluate and simulate flow variations under associated climate variable variations. Results demonstrated not any particular method is suited to represent scenarios leading to extreme flow conditions. For selected flow scenarios, the persistence model performance may be comparable to more complex multivariate approaches, and complex methods did not always improve flow estimation. Overall model performance indicates inclusion of river flows and forcing variables on average improve model extreme event forecasting skills. As a means to further refine the flow estimation, an ensemble forecast method is implemented to provide a likelihood-based indication of expected river flow magnitude and variability. Results indicate seasonal flow variations are well-captured in the ensemble range, therefore the ensemble approach can often prove efficient in estimating extreme river flow conditions. The discriminant prediction approach, a probabilistic measure to forecast streamflow, is also adopted to derive model performance. Results show the efficiency of the method in terms of representing uncertainties in the forecasts

Indicator-to-impact links to help improve agricultural drought preparedness in Thailand

Droughts in Thailand are becoming more severe due to climate change. Developing a reliable Drought Monitoring and Early Warning System (DMEWS) is essential to strengthen a country&rsquo;s resilience to droughts. However, for a DMEWS to be valuable, the drought indicators it provides stakeholders must have relevance to tangible impacts on the ground. Here, we analyse drought indicator-to-impact relationships in Thailand, using a combination of correlation analysis and machine learning techniques (random forest). In the correlation analysis, we study the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop-yield and forest-growth impacts. Our analysis shows that this link varies depending on land use, season, and region. The random forest models built to estimate regional crop productivity allow a more in-depth analysis of the crop-/region-specific importance of different drought indicators. The results highlight seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effects are somewhat attenuated in irrigated regions. Integration of the approaches provides new detailed knowledge of crop-/region-specific indicator-to-impact links, which can form the basis of targeted mitigation actions in an improved DMEWS in Thailand, and could be applied in other parts of Southeast Asia and beyond.</p

Streamflow and soil moisture forecasting with hybrid data intelligent machine learning approaches: case studies in the Australian Murray-Darling basin

For a drought-prone agricultural nation such as Australia, hydro-meteorological imbalances and increasing demand for water resources are immensely constraining terrestrial water reservoirs and regional-scale agricultural productivity. Two important components of the terrestrial water reservoir i.e., streamflow water level (SWL) and soil moisture (SM), are imperative both for agricultural and hydrological applications. Forecasted SWL and SM can enable prudent and sustainable decisionmaking for agriculture and water resources management. To feasibly emulate SWL and SM, machine learning data-intelligent models are a promising tool in today’s rapidly advancing data science era. Yet, the naturally chaotic characteristics of hydro-meteorological variables that can exhibit non-linearity and non-stationarity behaviors within the model dataset, is a key challenge for non-tuned machine learning models. Another important issue that could confound model accuracy or applicability is the selection of relevant features to emulate SWL and SM since the use of too fewer inputs can lead to insufficient information to construct an accurate model while the use of an excessive number and redundant model inputs could obscure the performance of the simulation algorithm. This research thesis focusses on the development of hybridized dataintelligent models in forecasting SWL and SM in the upper layer (surface to 0.2 m) and the lower layer (0.2–1.5 m depth) within the agricultural region of the Murray-Darling Basin, Australia. The SWL quantifies the availability of surface water resources, while, the upper layer SM (or the surface SM) is important for surface runoff, evaporation, and energy exchange at the Earth-Atmospheric interface. The lower layer (or the root zone) SM is essential for groundwater recharge purposes, plant uptake and transpiration. This research study is constructed upon four primary objectives designed for the forecasting of SWL and SM with subsequent robust evaluations by means of statistical metrics, in tandem with the diagnostic plots of observed and modeled datasets. The first objective establishes the importance of feature selection (or optimization) in the forecasting of monthly SWL at three study sites within the Murray-Darling Basin. Artificial neural network (ANN) model optimized with iterative input selection (IIS) algorithm named IIS-ANN is developed whereby the IIS algorithm achieves feature optimization. The IIS-ANN model outperforms the standalone models and a further hybridization is performed by integrating a nondecimated and advanced maximum overlap discrete wavelet transformation (MODWT) technique. The IIS selected inputs are transformed into wavelet subseries via MODWT to unveil the embedded features leading to IIS-W-ANN model. The IIS-W-ANN outperforms the comparative IIS-W-M5 Model Tree, IIS-based and standalone models. In the second objective, improved self-adaptive multi-resolution analysis (MRA) techniques, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are utilized to address the non-stationarity issues in forecasting monthly upper and lower layer soil moisture at seven sites. The SM time-series are decomposed using EEMD/CEEMDAN into respective intrinsic mode functions (IMFs) and residual components. Then the partial-auto correlation function based significant lags are utilized as inputs to the extreme learning machine (ELM) and random forest (RF) models. The hybrid EEMD-ELM yielded better results in comparison to the CEEMDAN-ELM, EEMD-RF, CEEMDAN-RF and the classical ELM and RF models. Since SM is contingent upon many influential meteorological, hydrological and atmospheric parameters, for the third objective sixty predictor inputs are collated in forecasting upper and lower layer soil moisture at four sites. An ANN-based ensemble committee of models (ANN-CoM) is developed integrating a two-phase feature optimization via Neighborhood Component Analysis based feature selection algorithm for regression (fsrnca) and a basic ELM. The ANN-CoM shows better predictive performance in comparison to the standalone second order Volterra, M5 Model Tree, RF, and ELM models. In the fourth objective, a new multivariate sequential EEMD based modelling is developed. The establishment of multivariate sequential EEMD is an advancement of the classical single input EEMD approach, achieving a further methodological improvement. This multivariate approach is developed to allow for the utilization of multiple inputs in forecasting SM. The multivariate sequential EEMD optimized with cross-correlation function and Boruta feature selection algorithm is integrated with the ELM model in emulating weekly SM at four sites. The resulting hybrid multivariate sequential EEMD-Boruta-ELM attained a better performance in comparison with the multivariate adaptive regression splines (MARS) counterpart (EEMD-Boruta-MARS) and standalone ELM and MARS models. The research study ascertains the applicability of feature selection algorithms integrated with appropriate MRA for improved hydrological forecasting. Forecasting at shorter and near-real-time horizons (i.e., weekly) would help reinforce scientific tenets in designing knowledge-based systems for precision agriculture and climate change adaptation policy formulations

The predictability of UK drought using European weather patterns

PhD thesisThis thesis explores the use of a 167-year daily weather pattern (WP) classification (MO-30) in UK meteorological drought prediction. As MO-30 was recently introduced, necessary analyses as a precursor to building a forecast model are conducted. First, an exploratory analysis of MO30’s fundamental characteristics and its relation to UK precipitation and drought climatology is carried out. Second, two novel methods to find weekly to seasonal persistence in MO-30 are used in order to assess if there is any inherent predictability within MO-30. Third, a statistical model based on historical analogues for predicting 30-day periods of WPs is constructed, from which precipitation forecasts are derived. Finally, a dynamical ensemble prediction system is applied to forecast WPs, with resultant precipitation estimated in the same way as for the statistical method. MO-30 is shown to be suitable for precipitation-based analyses in the UK. Furthermore, intraWP precipitation variability, defined by the interquartile range, is lower in MO-30 compared to another commonly used WP classification. Six WPs are associated with nationwide drought, with several other WPs linked to regional drought. Results from the persistence analysis show that there are multi-month periods when small sets of four to six WPs dominate, and some of these periods coincide with notable meteorological events, including droughts and storms. Some WPs also behave as ‘attractors’, showing increased probability of reoccurrence despite other WPs occurring in-between. The statistical method for WP and precipitation forecasts is no more skilful than climatology, suggesting that the model did not adequately exploit the persistence identified previously. However, WPs are shown to be potentially useful for drought forecasting, as an idealised, perfect prognostic model (with WP observations as inputs rather than predictions) substantially improves skill, with a skill score of almost 0.5 (out of one) for north-eastern regions. Using a dynamical model to predict WPs, while keeping the precipitation estimation procedure the same as for the purely statistical method, yields overall higher skill compared to a benchmark statistical method for predicting droughts. The model also outperforms direct (modelled) dynamical precipitation forecasts for lead-times greater than 16 days during winter and autumn, with the greatest skill advantage for western regions. This is despite the relatively modest skill scores of all forecast models (rarely above 0.4). Again, high skill scores, of almost 0.8 on occasions, are achieved by the perfect prognostic model, demonstrating the potential for incorporating WPs into precipitation and drought forecast systems

ANALYZING THE RELATIONSHIP BETWEEN LARGE SCALE CLIMATE VARIABILITY AND STREAMFLOW OF THE CONTINENTAL UNITED STATES

Over the years there is an increasing evidence of climate change on the available water resources. The interaction of hydrological cycle with climate variability and change may provide information related with several water management issues. The current study analyzes streamflow variability of the United States due to large-scale ocean-atmospheric climate variability. In addition, forecast lead-time is also improved by coupling climate information in a data driven modeling framework. The spatial-temporal correlation between streamflow and oceanic-atmospheric variability represented by sea surface temperature (SST), 500-mbar geopotential height (Z500), 500-mbar specific humidity (SH500), and 500-mbar east-west wind (U500) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). For forecasting of streamflow, SVD significant regions are weighted using a non-parametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed forecasting model for the period of 1965-2014. The April-August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1-13 months. To understand the effect of predefined indices such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) on the regional streamflow, a wavelet analysis is also performed for regions developed by from 2014 National Climate Assessment (NCA). Moreover, different SVD approach is performed for streamflow of each of the six NCA regions named as Great Plains, Midwest, Northeast, Northwest, Southeast, and Southwest. In regional case, SVD is applied initially with streamflow and SST; and that spatial-temporal correlation is later correlated with Z500, SH500, and U500 separately to evaluate the interconnections between climate variables. SVD result showed that the streamflow variability of the URGRB was better explained by SST and U500 as compared to Z500 and SH500. The SVM model showed satisfactory forecasting ability as the observed and forecasted streamflow volume for different selected sites were well correlated. The best results were achieved using a 1-month lead to forecast the following 4-month period. Overall, the SVM results showed excellent predictive ability with average linear correlation coefficient of 0.89 and Nash-Sutcliffe efficiency of 0.79. Whereas regional SVD analysis showed that streamflow variability in the Great Plains, Midwest, and Southwest region is strongly associated with SST of ENSO-like region. However, for Northeast and Southeast region, U500 and SH500 were strongly correlated with streamflow as compared to the SST of the Pacific Ocean. The continuous wavelet analysis of ENSO/PDO/AMO and the regional streamflow patterns revealed different significant timescale bands that affected their variation over the study period. Identification of several teleconnected regions of the climate variables and the association with the streamflow can be helpful to improve long-term prediction of streamflow resulting in better management of water resources in the regional scale