scholarly journals Short-to-medium range hydrologic forecast to manage water and agricultural resources in India

2016 ◽  
Author(s):  
Reepal Shah ◽  
Atul Kumar Sahai ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Root Zone ◽  
Tropical Meteorology ◽  
Forecast System ◽  
Temperature Forecast ◽  
Ensemble Mean ◽  
Medium Range ◽  
Prediction Systems ◽  
Hydrologic Forecast

Abstract. Water resources and agriculture are often affected by the weather anomalies in India resulting in a disproportionate damage. While short to medium range prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), Global Ensemble Forecast System (GEFSv2) and four products from Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from IITM) can be used operationally to provide hydrologic forecast in India at 7–45 days lead time. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Forecast based on the IITM-ensemble mean showed better skill in majority of India for all the lead times (7–45 days) in comparison to the other forecast products. Moreover, the VIC simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe droughts years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.

scholarly journals Short to sub-seasonal hydrologic forecast to manage water and agricultural resources in India

2017 ◽  
Vol 21 (2) ◽  
pp. 707-720 ◽  
Author(s):  
Reepal Shah ◽  
Atul Kumar Sahai ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Root Zone ◽  
Tropical Meteorology ◽  
Precipitation Forecast ◽  
Severe Drought ◽  
Forecast System ◽  
Infiltration Capacity ◽  
Ensemble Mean ◽  
Hydrologic Forecast

Abstract. Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7–45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all-India median mean absolute error – MAE), while all-India median bias corrected temperature forecast was improved by 2.1 °C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.

scholarly journals Utility of Global Ensemble Forecast System (GEFS) Reforecast for Medium-Range Drought Prediction in India

2016 ◽  
Vol 17 (6) ◽  
pp. 1781-1800 ◽  
Author(s):  
Reepal D. Shah ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Monsoon Season ◽  
Ensemble Forecast ◽  
Extreme Weather Events ◽  
Forecast System ◽  
Drought Prediction ◽  
Air Temperatures ◽  
Medium Range ◽  
Precipitation And Temperature

Abstract Medium-range (~7 days) forecasts of agricultural and hydrologic droughts can help in decision-making in agriculture and water resources management. India has witnessed severe losses due to extreme weather events during recent years and medium-range forecasts of precipitation, air temperatures (maximum and minimum), and hydrologic variables (root-zone soil moisture and runoff) can be valuable. Here, the skill of the Global Ensemble Forecast System (GEFS) reforecast of precipitation and air temperatures is evaluated using retrospective data for the period of 1985–2010. It is found that the GEFS forecast shows better skill in the nonmonsoon season than in the monsoon season in India. Moreover, skill in temperature forecast is higher than that of precipitation in both the monsoon and nonmonsoon seasons. The lower skill in forecasting precipitation during the monsoon season can be attributed to representation of intraseasonal variability in precipitation from the GEFS. Among the selected regions, the northern, northeastern, and core monsoon region showed relatively lower skill in the GEFS forecast. Temperature and precipitation forecasts were corrected from the GEFS using quantile–quantile (Q–Q) mapping and linear scaling, respectively. Bias-corrected forecasts for precipitation and air temperatures were improved over the raw forecasts. The influence of corrected and raw forcings on medium-range soil moisture, drought, and runoff forecasts was evaluated. The results showed that because of high persistence, medium-range soil moisture forecasts are largely determined by the initial hydrologic conditions. Bias correction of precipitation and temperature forecasts does not lead to significant improvement in the medium-range hydrologic forecasting of soil moisture and drought. However, bias correcting raw GEFS forecasts can provide better predictions of the forecasts of precipitation and temperature anomalies over India.

Exploring trends for the H SAF ASCAT root-zone soil moisture data records

2021 ◽  
Author(s):  
David Fairbairn ◽  
Patricia de Rosnay ◽  
Peter Weston
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Weather Prediction ◽  
Climate Change Impacts ◽  
Soil Moisture Data ◽  
Negative Root ◽  
In Situ Observations ◽  
Prediction Systems ◽  
Water Scarce

<p>Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM.<br>ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.</p>

scholarly journals Soil Moisture Droughts under the Retrospective and Projected Climate in India*

2014 ◽  
Vol 15 (6) ◽  
pp. 2267-2292 ◽  
Author(s):  
Vimal Mishra ◽  
Reepal Shah ◽  
Bridget Thrasher
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Monsoon Season ◽  
Areal Extent ◽  
Gangetic Plain ◽  
Growing Period ◽  
Growing Seasons ◽  
Ensemble Mean ◽  
Projected Changes ◽  
Sowing Period

Abstract Changes in precipitation, air temperature, and model-simulated soil moisture were examined for the observed (1950–2008) and projected (2010–99) climate for the sowing period of Kharif and Rabi [KHARIF_SOW (May–July) and RABI_SOW (October–December)] and the entire Kharif and Rabi [KHARIF (May–October) and RABI (October–April)] crop-growing periods in India. During the KHARIF_SOW and KHARIF periods, precipitation declined significantly in the Gangetic Plain, which in turn resulted in declines in soil moisture. Statistically significant warming trends were noticed as all-India-averaged air temperature increased by 0.40°, 0.90°, and 0.70°C in the KHARIF, RABI_SOW, and RABI periods, respectively, during 1950–2008. Frequency and areal extent of soil moisture–based droughts increased substantially during the latter half (1980–2008) of the observed period. Under the projected climate (2010–99), precipitation, air temperature, and soil moisture are projected to increase in all four crop-growing seasons. In the projected climate, all-India ensemble mean precipitation, air temperature, and soil moisture are projected to increase up to 39% (RABI_SOW period), 2.3°C, and 5.3%, respectively, in the crop-growing periods. While projected changes in air temperature are robust across India, robust increases in precipitation and soil moisture are projected to occur in the end-term (2070–99) climate. Frequency and areal extents of soil moisture–based severe, extreme, and exceptional droughts are projected to increase in the near- (2010–39) and midterm (2040–69) climate in the majority of crop-growing seasons in India. However, frequency and areal extent of droughts during the crop-growing period are projected to decline in the end-term climate in the entire crop-growing period because of projected increases in the monsoon season precipitation.

scholarly journals Drought Indices Based on the Climate Forecast System Reanalysis and Ensemble NLDAS

2011 ◽  
Vol 12 (2) ◽  
pp. 181-205 ◽  
Author(s):  
Kingtse C. Mo ◽  
Lindsey N. Long ◽  
Youlong Xia ◽  
S. K. Yang ◽  
Jae E. Schemm ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
North American ◽  
Root Zone ◽  
The United States ◽  
Drought Indices ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract Drought indices derived from the Climate Forecast System Reanalysis (CFSR) are compared with indices derived from the ensemble North American Land Data Assimilation System (NLDAS) and the North American Regional Reanalysis (NARR) over the United States. Uncertainties in soil moisture, runoff, and evapotranspiration (E) from three systems are assessed by comparing them with limited observations, including E from the AmeriFlux data, soil moisture from the Oklahoma Mesonet and the Illinois State Water Survey, and streamflow data from the U.S. Geological Survey (USGS). The CFSR has positive precipitation (P) biases over the western mountains, the Pacific Northwest, and the Ohio River valley in winter and spring. In summer, it has positive biases over the Southeast and large negative biases over the Great Plains. These errors limit the ability to use the standardized precipitation indices (SPIs) derived from the CFSR to measure the severity of meteorological droughts. To compare with the P analyses, the Heidke score for the 6-month SPI derived from the CFSR is on average about 0.5 for the three-category classification of drought, floods, and neutral months. The CFSR has positive E biases in spring because of positive biases in downward solar radiation and high potential evaporation. The negative E biases over the Great Plains in summer are due to less P and soil moisture in the root zone. The correlations of soil moisture percentile between the CFSR and the ensemble NLDAS are regionally dependent. The correlations are higher over the area east of 100°W and the West Coast. There is less agreement between them over the western interior region.

GLEAM evaporation and soil moisture: current state and ongoing efforts

2020 ◽  
Author(s):  
Reyes Martin-Gonzalez ◽  
Brecht Martens ◽  
Gabrielle De Lannoy ◽  
Hans Lievens ◽  
Brianna R. Pagán ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Root Zone ◽  
Current Model ◽  
Satellite Observations ◽  
Cloud Formation ◽  
Detailed Calculation ◽  
Global Land

<p>Approximately two-thirds of continental precipitation is evaporated back into the atmosphere. This highlights the influence of terrestrial evaporation for the distribution of hydrological resources, from catchment to planetary scales. The ability to monitor terrestrial evaporation dynamics is critical for climatological applications, since evaporation directly affects air temperature, influences air humidity and cloud formation, and is intrinsically connected to photosynthesis. To date, terrestrial evaporation cannot be observed directly from space, and in situ networks remain too sparse for both research and practical activities, making terrestrial evaporation one of the most uncertain components of Earth’s energy and water balance. However, a range of approaches have been proposed over the last decade to indirectly derive evaporation by applying models that combine the satellite-observed environmental and climatic drivers of the flux. One of these pioneering methods is the Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al. 2011). </p><p>GLEAM combines global satellite observations of meteorological variables – (e.g.) precipitation, surface net radiation and air temperature – and surface characteristics – (e.g.) soil and vegetation water content and snow depth. Since its publication almost 10 years ago, the model has been widely used to analyse trends in the water cycle, study land–atmospheric feedbacks or benchmark climate models. Advantages of GLEAM over analogous methods are the estimation of evaporation under cloud conditions due to the exploitation of microwave data, the explicit estimation of root-zone soil moisture data, and the detailed calculation of rainfall interception. Current model development efforts concentrate on (a) the increase in spatial resolution for its application to water management and agricultural applications and (b) the assimilation of novel satellite observations. This presentation provides a general overview of the framework and concentrates on ongoing efforts that strive in the direction of assimilating Gravity Recovery and Climate Experiment (GRACE) and Soil Moisture Active–Passive (SMAP) observations to improve the root-zone soil moisture estimates.</p><p> </p><p><strong>References </strong> Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A. and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15(2), 453–469, doi:10.5194/hess-15-453-2011, 2011.</p>

scholarly journals Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization

2021 ◽  
Author(s):  
Randal D. Koster ◽  
Anthony M. DeAngelis ◽  
Siegfried D. Schubert ◽  
Andrea M. Molod
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Forecast Error ◽  
Surface Air Temperature ◽  
Forecast Bias ◽  
Temperature Forecast ◽  
Independent Observations ◽  
Moisture State ◽  
The Relationship ◽  
Soil Moisture Initialization

AbstractSoil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

scholarly journals Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

2015 ◽  
Vol 16 (4) ◽  
pp. 1456-1465 ◽  
Author(s):  
R. D. Koster ◽  
G. K. Walker
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Warm Season ◽  
Small Extent ◽  
Vegetation Phenology ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Area Index ◽  
Atmospheric Processes ◽  
Phenology Model

Abstract The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations—as well as quantifying the inherent predictability of temperature within each ensemble—shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

ВОДНЫЙ РЕЖИМ ОРОШАЕМЫХ СОЛОНЦОВЫХ КОМПЛЕКСНЫХ ПОЧВ И ОСОБЕННОСТИ ЕГО РЕГУЛИРОВАНИЯ

2020 ◽  
Author(s):  
Valery Yashin
Keyword(s):  
Soil Moisture ◽  
Ground Water ◽  
Irrigation Water ◽  
Surface Runoff ◽  
Root Zone ◽  
Irrigation Methods ◽  
Salt Flats

Представлены материалы исследований формирования режима влажности и динамики грунтовых вод орошаемых солонцовых комплексных почв при различных способах полива, проведенные в Волгоградском Заволжье. Установлена значительная неравномерность распределения влажности почвы при поливах дождеванием. Отмечается поверхностный сток по микрорельефу до 30% от поливной нормы, что приводит к недостаточности увлажнения корневой зоны на солонцах и переувлажнению почв в понижениях микрорельефа и потере оросительной воды на инфильтрационное питание грунтовых вод.The article presents the materials of research on the formation of the humidity regime and dynamics of ground water of irrigated saline complex soils under various irrigation methods, conducted in the Volgograd Zavolzhye. A significant unevenness in the distribution of soil moisture during irrigation with sprinkling has been established. There is a surface runoff on the microrelief of up to 30% of the irrigation norm, which leads to insufficient moisture of the root zone on the salt flats and waterlogging of the soil in the microrelief depressions and loss of irrigation water for infiltration feed of ground water.

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