scholarly journals Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India

2018 ◽  
Vol 22 (10) ◽  
pp. 5125-5141 ◽  
Author(s):  
Arun Ravindranath ◽  
Naresh Devineni ◽  
Upmanu Lall ◽  
Paulina Concha Larrauri

Abstract. Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.

2018 ◽  
Author(s):  
Arun Ravindranath ◽  
Naresh Devineni ◽  
Upmanu Lall ◽  
Paulina Concha Larrauri

Abstract. Water risk management is perhaps the most ubiquitous challenge a stakeholder in the water or agricultural sector faces. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest Rank Probability Skill Score and lowest Mean Squared Error in a leave-one-out cross validation mode. Adaptive forecasts were made over the years 2001 through 2013 using the identified predictors and a semi-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct nine out of thirteen times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress/risk indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.


MAUSAM ◽  
2021 ◽  
Vol 62 (1) ◽  
pp. 27-40
Author(s):  
MEHFOOZ ALI ◽  
U. P. SINGH ◽  
D. JOARDAR

The paper formulates a synoptic analogue model for issuing Quantitative Precipitation Forecast (QPF) for Lower Yamuna Catchment (LYC) based upon eleven years data (1998-2008) during southwest monsoon season. The results so derived were verified with realized Average Areal Precipitation (AAP) for the corresponding synoptic situation during 2009 southwest monsoon season. The performance of the model was observed Percentage Correct (PC) up to 86 % and for extreme events showed 100% correct with Heidke Skill Score (HSS) value 0.9. The experience during south west monsoon 2009 has shown that Synoptic analogue model can produce 24 hours advance QPF with accuracy and greater skill to facilitate the flood forecasters of Central Water Commission.


MAUSAM ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 459-474
Author(s):  
S. R. KALSI ◽  
RAJENDRA KUMAR JENAMANI ◽  
H. R. HATWAR

lkj & fiNys 14 o"kksZa ds nkSjku yxkrkj gqbZ vPNh ekulwu o"kkZ&_rq ds ckn Hkkjr esa o"kZ 2002 esa Hkh"k.k lw[kk iM+kA ;gk¡ rd fd ekfld le; eku ij Hkh 19 oha 'krkCnh ds e/; ls ysdj vc rd ds fjdkMZ ds bfrgkl esa tqykbZ dk eghuk o"kkZ dh n`f"V ls cgqr gh [kjkc eghuk jgk ftlesa vf[ky Hkkjrh; iSekus ij o"kkZ ds izfr’kr dk varj lkekU; ls 51-5 izfr’kr de jgkA ,d vU; egRoiw.kZ fo’ks"krk ;g jgh fd fiNys 133 o"kksaZ esa igyh ckj lEiw.kZ nf{k.kh if’peh ekulwu _rq ds nkSjku ,d Hkh vonkc vFkok pØokrh rwQku ugha cukA  Hkkjr esa ekulwu dbZ&dbZ fnuksa dh vo:)rk ds lkFk yxkrkj vkxs c<+kA 1960 ds ckn ls igyh ckj ,slk gqvk gS fd lqnwj mÙkjh&if’peh Hkkjr esa ekulwu] _rq ds iwok)Z esa ugha igq¡pkA bl 'kks/k&i= esa o"kZ 2002 ds nkSjku ekulwu ds fofHkUu y{k.kksa dh fo’ks"krkvksa tSls fd ekulwu dk vkjEHk] mldk vkxs c<+uk] :duk] fofHkUu flukWfIVd vkSj v)Z LFkk;h y{k.kksa rFkk nf{k.kh if’peh ekulwu o"kkZ _rq dh fo’ks"krkvksa dk foospu fd;k x;k gSA bu fo’ks"krkvksa dh rqyuk igys iMs+ lw[ks ds o"kksZa dh fo’ks"krkvksa  ds lkFk dh xbZ gSA tqykbZ 2002 ds nkSjku ekulwu o"kkZ dh Hkh"k.k deh ds laHkkfor dkj.kksa dk irk yxkus ds fy, fgan & iz’kkar ¼baMksislsfQd½ {ks= esa c`grLrjh; vkSlr ekfld vlkekU; egklkxjh; vkSj ok;qeaMyh; fLFkfr;ksa dh tk¡p dh xbZ gSA bl v/;;u ls izkIr gq, ifj.kkeksa ls ;g irk pyrk gS fd cgqr lh vlkekU; vkSj fof’k"V izdkj dh fo’ks"krkvksa ds dkj.k o"kZ 2002 ds nkSjku iwjs Hkkjr esa lw[kk iM+kA bl v/;;u ls ;g Hkh irk pyrk gS fd vuqdwy varjk&ekSleh {ks=h; fo’ks"krkvksa tSls fd ekulwu fo{kksHkksa vkSj v)Z LFkk;h ra=ksa] vR;ar ean yks ysoy tsV dh fo|ekurk] izcy e/; v{kka’kh; if’peh gokvksa ds izHkko] {ks= esa pØokr cuus dh vR;kf/kd vko`fr ds lkFk ekulwu _rq ds eghuksa ds nkSjku iz’kkar egklkxjh; fuuksa 4 {ks= esa vR;kf/kd m".k rhozrk ds lkFk ean ls lkekU; ,y fuuksa dk cuuk ,sls eq[; dkj.k gSa ftuds ifj.kkeLo:i  tqykbZ ds eghus esa o"kkZ dh vR;kf/kd deh gqbZ gSA India experienced severe drought in the year 2002 after 14 consecutive years of good monsoon. On the monthly time scale, July had the worst rainfall in the recorded history of monsoon dating back to middle of nineteenth century when the country as a whole registered rainfall deficiency of 51.5%. Another notable feature was that for the first time in the last 133 years, not a single depression or cyclonic storm formed during the whole southwest monsoon season. The advance of monsoon over India was accompanied with frequent as well as prolonged stagnations. The monsoon failed to arrive for the first time in extreme northwest India during the first half of the season since 1960. In the present study, various features of monsoon such as onset, progress, stagnation, different synoptic and semi-permanent features and characteristics of rainfall of southwest monsoon in 2002 over India have been discussed. A comparison of these features with those in the earlier drought years has been made. Large-scale mean monthly anomalous ocean and atmospheric conditions over Indo-Pacific region have also been investigated to find out the possible causes for drastic failure of the monsoon during July 2002. Results show that many abnormal and unique features during 2002 have resulted into all India drought. Study also shows that absence of favourable regional intra-seasonal features like monsoon disturbances and semi-permanent systems, presence of very weak low level jet, penetration of strong mid-latitude westerlies, weak to moderate El-Nino with most intense warming over Nino 4 region of Pacific Ocean during monsoon months together with higher frequency of typhoon formation over the region are the main causes that led to one of the highly pronounced rainfall deficiencies in the month of July.


MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 327-334
Author(s):  
G. C. DEBNATH ◽  
G. K. DAS

The Indian summer monsoon rainfall forecast and its verification has a direct impact on various sectors of public interest besides economy of the country. The present study highlights the verification of distribution forecast of synoptic method issued daily for six met subdivisions, comprising of five states of eastern India namely West Bengal, Sikkim, Bihar, Jharkhand and Odisha. Three years monsoon season rainfall data from 2011 through 2013 are used for the study area. The distribution-oriented verification is done for different rainfall classes like dry, isolated, scattered, fairly widespread and widespread to understand the usefulness of the synoptic method. Statistics are presented for both combined classes of Percentage Correct (PC) and Heidke Skill Score (HSS) of the met subdivision wise forecast and PC, POD and CSI for individual classes. It has been observed that among the met subdivision the efficiency of the method is highest in Sub Himalayan West Bengal (SHWB) & Sikkim followed by Gangetic West Bengal (GWB), Odisha, Jharkhand and Bihar.


MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 391-404
Author(s):  
ARTI BANDGAR ◽  
PRABHU PALLAVI ◽  
SREEJITH O P ◽  
PAI D S

This paper studies the summer monsoon 2017 and examines the number of parameters which we believe were important in understanding why monsoon failed in second half over India. The list of parameters includes monthly mean or anomalies of the following fields : sea surface temperature, outgoing longwave radiation, stream function of lower and upper atmosphere, velocity potential and monthly and seasonal precipitation. ENSO conditions were mainly neutral with warm ENSO neutral conditions observed in the first half and cool ENSO neutral conditions observed in the second half. As a result, influence on the monsoon from the large scale SST forcing from Pacific Ocean was nearly absent during the season. However, Positive IOD conditions over the Indian Ocean during the monsoon season, particularly during first half of the monsoon were prominent. The transition of warmer than normal SSTs (June and July) to normal SSTs (August) and then becoming cooler than normal SSTs (September) in the equatorial Indian Ocean had a significant influence which lead monsoon to fail in second half.   


Author(s):  
Andy H. Wong ◽  
Tae J. Kwon

Winter driving conditions pose a real hazard to road users with increased chance of collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built on one of the least explored yet most powerful geostatistical techniques, namely, regression kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-scale hot spot analysis is evaluated using the northeast quarter of the State of Iowa, spanning five winter seasons from 2013/14 to 2017/18. The findings of the case study assessed via three different statistical measures (mean squared error, root mean square error, and root mean squared standardized error) suggest that RK is very effective for modeling WC ratios, thereby further supporting its robustness and feasibility for a statewide implementation.


2021 ◽  
pp. 126419
Author(s):  
Lanlan Guo ◽  
TieWei Li ◽  
Deliang Chen ◽  
Junguo Liu ◽  
Bin He ◽  
...  

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.


Author(s):  
Elena Stepanovna Ustinovich ◽  
Tatyana Petrovna Boldyreva

It is clear to everyone that investment in the agricultural sector in developing countries is one of the most effective ways to reduce poverty and hunger in the world. Agricultural investment can generate a wide range of development opportunities. However, these benefi ts cannot be expected to arise automatically. Some forms of large-scale investment pose significant risks to investor states. It should be noted, however, that, despite discussions about the potential benefits and risks of international investment, there is still no evidence of negative actual consequences for the countries receiving investments. This article examines the issues of investment activity in relation to developing countries using the example of US agribusiness entities.


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