Alireza Moghaddam Nia

Bio: Alireza Moghaddam Nia is an academic researcher from University of Tehran. The author has contributed to research in topics: Sampling (statistics) & Approximation error. The author has an hindex of 5, co-authored 12 publications receiving 77 citations.

Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting

Abstract: Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gained importance in hydrological studies in recent decades. In this study, the main objective was to compare the accuracy of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting. For this purpose, three scenarios were defined based on historical precipitation and streamflow series for 26 years of the Kentucky River basin located in eastern Kentucky, US. Statistical criteria including the coefficient of correlation ( $$R$$ ), Nash-Sutcliff coefficient of efficiency ( $$E$$ ), Nash-Sutcliff for High flow ( $${E}_{H}$$ ), Nash-Sutcliff for Low flow ( $${E}_{L}$$ ), normalized root mean square error ( $$NRMSE$$ ), relative error in estimating maximum flow ( $$REmax$$ ), threshold statistics ( $$TS$$ ), and average absolute relative error ( $$AARE$$ ) were employed to compare the performances of these methods. The results show that the LSTM network outperforms the other models in forecasting daily streamflow with the lowest values of $$NRMSE$$ and the highest values of $${E}_{H}$$ , $${E}_{L}$$ , and $$R$$ under all scenarios. These findings indicated that the LSTM is a robust data-driven technique to characterize the time series behaviors in hydrological modeling applications.

Spatiotemporal changes of 7-day low flow in Iran’s Namak Lake Basin: impacts of climatic and human factors

Abstract: Low flow is very sensitive to climate change and human intervention, especially in arid regions. In this study, changes of the 7-day low flow along the most important rivers of Iran’s Namak Lake Basin were investigated using nonparametric (Mann-Kendall and modified-Mann-Kendall) tests. A significant diminishing trend was observed in 72.2% of stations during the period of 1970–2012. The northern part of the basin lacked a significant trend, while in other parts of the basin, the descending trend was distributed uniformly. On the other hand, the changes of the annual rainfall during this period showed no clear trend (a significant trend in 36% and non-significant trend in 64% of stations), and the identified pattern of its changes was complicated on the basin scale and during the year. On a monthly scale, a significant decreasing trend was observed in March as one of the most productive months of the year in 49% of the stations. In addition, rainfall reduction was significant (over 35%) over the past 15 years in more than 71% of the stations. Also, changes in the proportion of seasonal rainfall and rainfall regime were considerable. The share of winter and spring rainfall showed a diminishing trend in 90% and 82% of stations, respectively. Also, rainfall regime based on precipitation concentration index (PCI) revealed a tendency to disorder (in 53% of stations). The annual temperature and temperature of October and February indicated a strong ascending trend in 92%, 71%, and 64% of stations, respectively, which can be effective during snow melting in basins with snow-rainy regimes and increasing evapotranspiration. Groundwater level changes also showed that, in the studied plains, the average water table drawdown was between 0.31 and 1.33 m/year. Therefore, the observed trend of low flow rates in this basin reflects the impact of climate change, where both direct and indirect human interference has led to the exacerbation of this situation.

Quantifying Positive and Negative Human-Modified Droughts in the Anthropocene: Illustration with Two Iranian Catchments

Abstract: In the Anthropocene, hydrological processes and the state of water in different parts of the terrestrial phase of the hydrological cycle can be altered both directly and indirectly due to human interventions and natural phenomena. Adaption and mitigation of future severe droughts need precise insights into the natural and anthropogenic drivers of droughts and understanding how variability in human drivers can alter anthropogenic droughts in positive or negative ways. The aim of the current study was expanding the “observation-modelling” approach to quantify different types of natural and human droughts. In addition, quantifying enhanced or alleviated modified droughts was the second parallel purpose of the research. The main principle of this approach is the simulation of the condition that would have happened in the absence of human interventions. The extended approach was tested in two Iranian catchments with notable human interventions and different climatic conditions. The drought events were identified through hydrological modelling by the Hydrologiska Byrans Vattenbalansavdelning (HBV) model, naturalizing the time series of hydrometeorological data for a period with no significant human interventions, and anomaly analysis. The obtained results have demonstrated that both catchments were almost the same in experiencing longer and more severe negative modified droughts than positive ones because of the negative pressure of human activities on the hydrological system. A large number of natural droughts have also been transformed into modified droughts because of the intensive exploitation of surface and sub-surface water resources and the lack of hydrological system recovery. The results show that the extended approach can detect and quantify different drought types in our human-influenced era.

Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009

Abstract: Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.

Drought under Global Warming: A Review

Abstract: This article reviews recent literature on drought of the last millennium, followed by an update on global aridity changes from 1950 to 2008. Projected future aridity is presented based on recent studies and our analysis of model simulations. Dry periods lasting for years to decades have occurred many times during the last millennium over, for example, North America, West Africa, and East Asia. These droughts were likely triggered by anomalous tropical sea surface temperatures (SSTs), with La Ni˜ na-like SST anomalies leading to drought in North America, and El-Ni˜ no-like SSTs causing drought in East China. Over Africa, the southward shift of the warmest SSTs in the Atlantic and warming in the Indian Ocean are responsible for the recent Sahel droughts. Local feedbacks may enhance and prolong drought. Global aridity has increased substantially since the 1970s due to recent drying over Africa, southern Europe, East and South Asia, and eastern Australia. Although El Ni˜ no-Southern Oscillation (ENSO), tropical Atlantic SSTs, and Asian monsoons have played a large role in the recent drying, recent warming has increased atmospheric moisture demand and likely altered atmospheric circulation patterns, both contributing to the drying. Climate models project increased aridity in the 21 st century over most of Africa, southern Europe and the Middle East, most of the Americas, Australia, and Southeast Asia. Regions like the United States have avoided prolonged droughts during the last 50 years due to natural climate variations, but might see persistent droughts in the next 20–50 years. Future efforts to predict drought will depend on models’ ability to predict tropical SSTs. 2010 JohnWiley &Sons,Ltd.WIREs Clim Change2010 DOI:10.1002/wcc.81

Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions

Abstract: The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN.