Dr Mehdi Khaki

Malaria is a mosquito-borne infectious disease transmitted by the bite of Anopheles mosquitoes. Accurate and timely identification of Anopheles larval habitats and analysis of environmental factors affecting the formation and stability of these locations are very effective in the prevention and spread of malaria. In the absence of sufficient field observations and environmental parameters required in a suitable spatial coverage, remote sensing data can be effective in predicting the habitats of Anopheles mosquitoes. In this article, high-risk depressions that have the potential for Anopheles larval habitats had been identified by fusing a Digital Surface Model (DSM) extracted from very high spatial resolution aerial stereo images with land-use and soil type maps. land-useFinally, the high-risk map of malaria based on the prone larval habitats of Anopheles was created. To evaluate the results, 22 important depressions were identified using field observations in the 1.5 km buffer around Qaleh-Ganj city, Kerman Province, Iran. Of these 22 samples, 14 of them were stable for more than one month at temperatures above 30 °C and the rest were able to store water for three to four weeks. 12 out of 14 samples were consistent with the identified 130 high-risk depressions in the buffer range. Also, 19 of these 22 samples were compatible with the optimal depressions in the buffer range. By comparing the proposed method with previous methods based on data with medium and low spatial resolution or meteorological data, it was concluded that the correct and accurate seasonal and temporary position of Anopheles larval habitats depend on higher spatial resolution of remote sensing data. In addition, this study demonstrated that the use of vegetation and water indices cannot predict the exact location of all habitats of Anopheles. Because many of these habitats were temporary and these indices could not estimate and predict their exact location. The proposed method can be applied to search for suitable larval habitats of Anopheles around local residential areas, where neither medium and low spatial resolution data nor limited field observations can be used.

During the period 2019¿2020, Lake Victoria water levels rose at an alarming rate that has caused various problems in the region. The influence of this phenomena on surface and subsurface water resources has not yet been investigated, largely due to lack of enough in situ measurements compounded by the spatial coverage of the lake¿s basin, incomplete/inconsistent hydrometeorological data, and unavailable governmental data. Within the framework of joint data assimilation into a land surface model from multi-mission satellite remote sensing, this study employs the state-of-art Gravity Recovery and Climate Experiment follow-on (GRACE-FO) time-variable terrestrial water storage (TWS), newly released ERA-5 reanalysis, and satellite radar altimetry products to understand the cause of the rise of Lake Victoria on the one hand, and the associated impacts of the rise on the total water storage compartments (surface and groundwater) triggered by the extreme climatic event on the other hand. In addition, the study investigates the impacts of large-scale ocean¿atmosphere indices on the water storage changes. The results indicate a considerable increase in water storage over the past two years, with multiple subsequent positive trends mainly induced by the Indian Ocean Dipole (IOD). Significant storage increase is also quantified in various water components such as surface water and water discharge, where the results show the lake¿s water level rose by ~ 1.4 m, leading to approximately 1750 gigatonne volume increase. Multiple positive trends are observed in the past two years in the lake¿s water storage increase with two major events in April¿May 2019 and December 2019¿January 2020, with the rainfall occurring during the short rainy season of September to November (SON) having had a dominant effect on the lake¿s rise.

Cyclone global navigation satellite system (CYGNSS) has provided a valuable opportunity for high spatiotemporal monitoring of land surface reflectivity over the past few years. CYGNSS with a constellation of eight microsatellites is able to constantly observe the 'scattered' global positioning system signals from the land. In this study, we validate the CYGNSS land reflectivity data in Australia for mapping the spatial extent of the inundated area and for determining temporal changes in surface soil moisture. CYGNSS level 1 data acquired for the period of 2017-2020 is assessed against various measurements, including satellite and ground-based measurements. Empirical mode decomposition is used to better analyze the CYGNSS time series and their relationship with the independent measurements. Furthermore, the mission's ability to capture surface reflectivity changes in response to extreme climatic events is analyzed. The results show that high spatial and temporal resolution CYGNSS data can largely represent the top layer ($\sim$5 cm) soil moisture spatial and temporal variations close to soil moisture active passive. CYGNSS surface reflectivity results are also found to be sensitive to surface water changes and able to depict inundated land surface.

Following extreme drought during the 2019¿2020 bushfire summer, the eastern part of Australia suffered from a week-long intense rainfall and extensive flooding in March 2021. Understanding how much water storage changes in response to these climate extremes is critical for developing timely water management strategies. To quantify prompt water storage changes associated with the 2021 March flooding, we processed the low-latency (1¿3¿days), high-precision intersatellite laser ranging measurements from GRACE Follow-On spacecraft and determined instantaneous gravity changes along spacecraft orbital passes. Such new data processing detected an abrupt surge of water storage approaching 60¿70 trillion liters (km3 of water) over a week in the region, which concurrently caused land subsidence of ~5¿mm measured by a network of ground GPS stations. This was the highest speed of ground water recharge ever recorded in the region over the last two decades. Compared to the condition in February 2020, the amount of recharged water was similar but the recharge speed was much faster in March 2021. While these two events together replenished the region up to ~80% of the maximum storage over the last two decades, the wet antecedent condition of soils in 2021 was distinctly different from the dry conditions in 2020 and led to generating extensive runoff and flooding in 2021.

Water scarcity and environmental challenges are affecting many parts of the world, particularly the arid Middle East region. Limited water resources, urbanization, groundwater over-extraction, and water usage for irrigation and agriculture have exacerbated the situation over this region and is risking the future development of its growing population. This study investigates the changes in various water storage components including groundwater, surface water, and soil moisture in the Middle East. To this end, a long-term reanalysis of land-hydrologic water storage components was generated from 1980 to 2019 by combining multiple satellite remote sensing observations and a hydrological model via a state-of-art data assimilation approach. The results indicate that assimilating multivariate satellite remote sensing significantly improves the model performance. The reanalysis data also outperforms some of existing model outputs. Based on the results, a considerable water storage depletion is observed across the Middle East region, not only over the dryer parts but also in areas with above-average annual rainfall including countries located close to the Mediterranean Sea. The water depletion is most pronounced for groundwater and over arid and semiarid areas in the central to southern parts involving Iran, Saudi Arabia, Bahrain, and the United Arab Emirates. Water storage decline is further found in the region's eastern, north-western, and western parts. The results also reveal a close link between water storage declines and other environmental factors such as dust storms and loss of vegetation canopies.

Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN.

One of the most important issues in nuclear reactor operation and its designing is considering the interaction between thermal hydraulics and neutronics physics because there is an important relationship between the states of fluid and neutron spectrum and power distribution. In this research, the MCNP4C and COBRA-EN nuclear codes were coupled with each other to precisely analyze the fuel assembly of the light water reactor core. This coupling was carried out using iterative processes between the linked neutronic and thermal-hydraulic codes applying successive procedures while the desired convergence was made in both. The newly designed code was checked for three test problems, and the obtained results showed the improvement of the computations procedures by the developed code.

Monitoring of the water levels within the seas and oceans has been enhanced by application of satellite radar altimetry missions, compared to the traditional in-situ tide gauge measurements, due to their vast coverage and better spatial resolution. Satellite radar altimetry, which is originally designed to measure global ocean surface height, has been applied to inland surface water hydrological studies. Satellite radar altimetry, well known as TOPEX/POSEIDON, JASON, ENVISAT, which have been originally designed to measure global ocean surface height, nowadays, also demonstrated a great potential for the applications of inland water body studies. Altimetry was designed to determine the sea surface height based on spatial technology, electronic technology and microwave technology and basically work with sending and receiving electromagnetic pulse. Waveform is actually a curve, which shows the power of mentioned pulse reflected back to the altimeter. Altimeter on board of the satellite measures the range by sending and receiving a short pulse and calculating its travel time. The most important output of this procedure is the altimeter range. Due to the effect of topography and heterogeneity of reflecting surface and atmospheric effects, the expected waveform for altimeter returns over land differs from that over the ocean surfaces and subsequently the range is not as accurate. As a result, sea surface height values derived from altimetry over ice sheets and inland water bodies (particularly close to the coast lines) represent more errors in compared to the waveforms returned from other part of the ocean surface and may include missing data. We have developed a water-detection algorithm based on statistical analysis of decadal TOPEX/POSEIDON and JASON-1 height measurement time series and also their ground passes of the sea surface height in Persian Gulf. The Persian Gulf is certainly one of the most vital bodies of water on the planet; as gas and oil from Middle Eastern countries flow through it, supplying much of the world's energy needs.This algorithm contains a noise elimination process that includes Outlier Detection and Elimination of Unwanted Waveforms (ODEUW), an unsupervised classification of the satellite waveforms, and finally a retracking procedure. An unsupervised classification algorithm is implemented to classify the waveforms into consistent groups for which the appropriate retracking algorithms are performed. On the other hand the waveforms belong to the same group which refer to almost the land with common properties. The waveform retracking method is mainly used to calculate the offset between the practical middle point of waveform leading edge and the designed gate, based on which the retracked distance correction can be computed. Four different methods are implemented for retracking the waveforms. This includes the three previously introduced algorithms, including off center of gravity, threshold retracking and optimized iterative least squares fitting, after some improvements. We also introduce a new method based on edge detection and extracting extremum point, which is called 'ExtR retracking method'. At the end two different methods for validation of our results are examined, first consider the SSH time series before and after retracking then compare those with in situ data, the second, retrack the ground pass track lines data from two satellites and compare them with the geoid data.