scholarly journals A seasonal agricultural drought forecast system for food-insecure regions of East Africa

2014 ◽  
Vol 18 (10) ◽  
pp. 3907-3921 ◽  
Author(s):  
S. Shukla ◽  
A. McNally ◽  
G. Husak ◽  
C. Funk
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
East Africa ◽  
Early Warning Systems ◽  
Hydrologic Model ◽  
Forecast Skill ◽  
Agricultural Drought ◽  
A Posteriori ◽  
Forecast System ◽  
A Posteriori Estimates

Abstract. The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agropastoral management decisions, support optimal allocation of the region's water resources, and mitigate socioeconomic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's (FEWS NET) science team. We evaluate this forecast system for a region of equatorial EA (2° S–8° N, 36–46° E) for the March-April-May (MAM) growing season. This domain encompasses one of the most food-insecure, climatically variable, and socioeconomically vulnerable regions in EA, and potentially the world; this region has experienced famine as recently as 2011. To produce an "agricultural outlook", our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming season. First, we forced the VIC model with high-quality atmospheric observations to produce baseline soil moisture (SM) estimates (here after referred as SM a posteriori estimates). These compared favorably (correlation = 0.75) with the water requirement satisfaction index (WRSI), an index that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM forecasts generated by this system on 5 March and 5 April of each year between 1993 and 2012 by comparing them with the corresponding SM a posteriori estimates. We found that initializing SM forecasts with start-of-season (SOS) (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the forecast was initialized with midseason (i.e., 5 April) SM conditions, the skill of forecasting SM estimates until the end-of-season improved (correlation > 0.5 over several grid cells). We also found these SM forecasts to be more skillful than the ones generated using the Ensemble Streamflow Prediction (ESP) method, which derives its hydrologic forecast skill solely from the knowledge of the initial hydrologic conditions. Finally, we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years (when standardized anomaly of MAM precipitation is below 0). This indicates that this system might be particularity useful for identifying drought events in this region and can support decision-making for mitigation or humanitarian assistance.

scholarly journals A seasonal agricultural drought forecast system for food-insecure regions of East Africa

2014 ◽  
Vol 11 (3) ◽  
pp. 3049-3081 ◽  
Author(s):  
S. Shukla ◽  
A. McNally ◽  
G. Husak ◽  
C. Funk
Keyword(s):  
Water Resources ◽  
East Africa ◽  
Optimal Allocation ◽  
Crop Yields ◽  
Early Warning Systems ◽  
Hydrologic Model ◽  
Agricultural Drought ◽  
Economic Losses ◽  
Forecast System ◽  
Infiltration Capacity

Abstract. The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

How useful are CORDEX products for the assessment of future agricultural drought propensity across the Indian subcontinent?

2020 ◽  
Author(s):  
Prabal Das ◽  
Kironmala Chanda ◽  
Rajib Maity
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
Indian Subcontinent ◽  
Regional Climate ◽  
Agricultural Drought ◽  
Historical Period ◽  
Drought Management ◽  
Future Evolution ◽  
Failure State ◽  
The Future

<p><strong>Abstract</strong></p><p>This study aims to evaluate the future evolution of agricultural drought propensity across the Indian subcontinent through Drought Management Index (DMI), a probabilistic measure based on the concept of Reliability-Resilience-Vulnerability (RRV) of soil moisture series at a location/region (Chanda et al., 2014; Chanda and Maity, 2017). In this study, monthly gridded soil moisture products from the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework are used after suitable bias correction, if needed. In the realm of RRV analysis, the fall of soil moisture below a threshold (e.g., Permanent Wilting Point, PWP) is considered as the ‘failure state’. The joint distribution of resilience (the ability of the soil moisture system to recover from a failure state) and vulnerability (severity of the deficit in soil moisture during a failure state) of soil moisture series is modelled through copulas (Nelsen, 2006; Maity, 2018) to develop the DMI.  The results of this study help to assess the evolution of agricultural drought propensity, in terms of DMI, in the near (2011-2040), intermediate (2041-2070) and far future (2071-2099). The findings from multiple emission pathways, designated as Representative Concentration Pathways (RCPs), are compared against each other during the future period and also against the historical period. As an outcome of the study, specific regions across the Indian mainland are identified that need immediate attention for managing sustainable agricultural and allied activities in future.</p><p><strong>Keywords: </strong>Drought Management Index (DMI), soil moisture, future drought propensity, Reliability-Resilience-Vulnerability (RRV), CORDEX</p><p><strong> </strong></p><p><strong> </strong></p><p><strong> </strong></p><p><strong> </strong></p><p><strong>References </strong></p><p>Chanda, K., Maity, R., Sharma, A., and Mehrotra, R. (2014). Spatiotemporal variation of long-term drought propensity through reliability-resilience-vulnerability based Drought Management Index, Water Resources Research, 50(10), 7662-7676.</p><p>Chanda, K., and Maity, R. (2017). Assessment of Trend in Global Drought Propensity in the Twenty-First Century Using Drought Management Index, Water Resources Management, 31(4), 1209-1225.</p><p>Maity, R. (2018). Statistical Methods in Hydrology and Hydroclimatology. Springer.</p><p>Nelsen, R. B. (2007). An introduction to copulas. Springer Science & Business Media.</p>

scholarly journals An Assessment of the CFS Real-Time Seasonal Forecasts

2010 ◽  
Vol 25 (3) ◽  
pp. 950-969 ◽  
Author(s):  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar
Keyword(s):  
Soil Moisture ◽  
Indian Ocean ◽  
Real Time ◽  
Tropical Pacific ◽  
Forecast Skill ◽  
Sea Surface ◽  
Cold Bias ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
The Tropical Pacific

Abstract This study assesses the real-time seasonal forecasts for 2005–08 with the current National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The forecasts are compared with retrospective forecasts (or hindcasts) for 1981–2004 to examine the consistency of the forecast system, and with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with observed sea surface temperatures (SSTs) to contrast the realized skill against the potential predictability due to the specification of the observed sea surface temperatures. The analysis focuses on the forecasts of SSTs, 2-m surface air temperature (T2M), and precipitation. The CFS forecasts maintained a good level of prediction skill for SSTs in the tropical Pacific, the western Indian Ocean, and the northern Atlantic. The SST forecast skill is within the range of hindcast skill levels calculated with 4-yr windows, which can vary greatly associated with the interannual El Niño–Southern Oscillation (ENSO) variability. Overall, the SST forecast skill over the globe is comparable to the average of the hindcast skill. For the tropical eastern Pacific, however, the forecast skill at lead times longer than 2 months is less than the average hindcast skill due to the relatively weaker ENSO variability during the forecast period (2005–08). The forecasts and hindcasts show a similar level of precipitation skill over most of the globe. For T2M, the spatial distribution of skill differs substantially between the forecasts and hindcasts. In particular, the T2M skill of the forecasts for the Northern Hemisphere during its warm seasons is lower than that of the hindcasts. Comparison with the AMIP simulations shows similar levels of precipitation skill over the tropical Pacific. Over the tropical Indian Ocean, the CFS forecasts show a substantially higher level of skill than the AMIP simulations for a large part of the period. This conforms with the results from previous studies that while interannual variability in the tropical Pacific atmosphere is slaved to the underlying SST anomalies, specification of SSTs (as for the AMIP simulations) in the Indian Ocean may lead to incorrect simulation of the atmospheric variability. Over the tropical Atlantic, the precipitation skill of both the CFS forecasts and AMIP simulations is low, suggesting that SSTs have less control over the atmospheric anomalies and the predictability is low. The analysis reveals several deficiencies in the current CFS that need to be corrected for improved seasonal forecasting. For example, the CFS tends to consistently forecast larger ENSO amplitude and delayed transition between the ENSO phases. Forecasts of T2M also have a strong cold bias in Northern Hemisphere mid- to high latitudes during warm seasons. This error is due to initial soil moisture anomalies, which appear to be too wet compared with two other observational analyses. The strong impacts of soil moisture on the seasonal forecasts, and large discrepancies among the soil moisture analyses, call for more accurate specification of soil moisture. Furthermore, average forecast SST and T2M anomalies for 2005–08 show a cold bias over the entire globe, indicating that the model is unable to maintain the observed long-term warming trend.

scholarly journals Application of the Land–Atmosphere Coupling Paradigm to the Operational Coupled Forecast System, Version 2 (CFSv2)

2016 ◽  
Vol 18 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Subhadeep Halder
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Atmospheric Stability ◽  
Surface States ◽  
Heat Fluxes ◽  
Forecast Skill ◽  
Surface Model ◽  
Forecast System ◽  
Potential Predictability ◽  
Near Surface

Abstract Retrospective forecasts from CFSv2 are evaluated in terms of three elements of land–atmosphere coupling at subseasonal to seasonal time scales: sensitivity of the atmosphere to variations in land surface states, the magnitude of variability of land states and fluxes, and the memory or persistence of land surface anomalies. The Northern Hemisphere spring and summer seasons are considered for the period 1982–2009. Ensembles are constructed from all available pairings of initial land and atmosphere/ocean states taken from the Climate Forecast System Reanalysis at the start of April, May, and June among the 28 years, so that the effect of initial land states on the evolving forecasts can be assessed. Finally, improvement and continuance of forecast skill derived from accurate land surface initialization is related to the three coupling elements. It is found that soil moisture memory is the most broadly important element for significant improvement of realistic land initialization on forecast skill. However, coupling strength manifested through the elements of sensitivity and variability are necessary to realize the potential predictability provided by memory of initial land surface anomalies. Even though there is clear responsiveness of surface heat fluxes, near-surface temperature, humidity, and daytime boundary layer development to variations in soil moisture over much of the globe, precipitation in CFSv2 is unresponsive. Failure to realize potential predictability from land surface states could be due to unfavorable atmospheric stability or circulation states; poor quality of what is considered realistic soil moisture analyses; and errors in the land surface model, atmospheric model, or their coupled interaction.

scholarly journals Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2592 ◽  
Author(s):  
María del Pilar Jiménez-Donaire ◽  
Juan Vicente Giráldez ◽  
Tom Vanwalleghem
Keyword(s):  
Soil Moisture ◽  
Soil Depth ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Agricultural Drought ◽  
Annual Precipitation ◽  
Drought Indices ◽  
Climate Data ◽  
Management Tools ◽  
The Standardized Precipitation Index

The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools).

scholarly journals Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture

2015 ◽  
Vol 16 (1) ◽  
pp. 295-305 ◽  
Author(s):  
Amy McNally ◽  
Gregory J. Husak ◽  
Molly Brown ◽  
Mark Carroll ◽  
Chris Funk ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
National Level ◽  
European Space Agency ◽  
Early Warning Systems ◽  
Water Requirement ◽  
Agricultural Drought ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index

Abstract The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

Development of a drought monitor for South-Asia

2020 ◽  
Author(s):  
Toma Rani Saha ◽  
Luis Samaniego ◽  
Pallav K Shrestha ◽  
Stephan Thober ◽  
Oldrich Rakovec
Keyword(s):  
Soil Moisture ◽  
South Asia ◽  
Real Time ◽  
Monitoring System ◽  
Hydrologic Model ◽  
Agricultural Drought ◽  
Cumulative Distribution ◽  
Food Storage ◽  
Drought Monitoring ◽  
Wide Range

<p>South Asia (SA) is highly vulnerable to extreme climatic events and experiences a wide range of natural hazards such as floods, drought, storms, and sea-level rise.  Droughts are recurrent in SA and its impact on regional agriculture, food storage, and livelihood is enormous. Agricultural droughts have severe consequences on the economy, society, health and water resources sectors. In this work, a state-of-the-art monitoring system of soil moisture drought in SA is developed. This study aims at improving the agricultural drought monitoring system for SA and contributing towards better adaptation solutions in the region. The SA drought monitoring system is inspired by the German Drought Monitor (www.ufz.de/duerremonitor)[1]. First, we implement the mesoscale hydrologic model (mHM, https://git.ufz.de/mhm) to reconstruct daily soil moisture from 1981 to 2019 using a near-real-time precipitation product (CHIRPS version 2, 0.25-degree resolution). Second, the SMI is estimated with a non-parametric kernel-based cumulative distribution function [2] based on mHM’s historic soil moisture reconstruction. The generated SMI maps are classified into five classes based on severity: abnormally dry, moderate drought, severe drought, extreme drought and exceptional drought. Third, we develop the South Asia Drought Monitor (SADM) which is an interactive web-portal (http://southasiadroughtmonitor.pythonanywhere.com/) for the dissemination of the simulated near-real-time drought classes. To achieve maximum dissemination, the daily and monthly SMI fields will be uploaded and published on the SADM portal. The SADM will help to inform decision-makers, the general public, researchers, and stakeholders in the SA. The drought monitoring system will allow the scientific community to conduct micro-level in-depth research and to enable policymakers to formulate proper planning and to take mitigation measures in sectors encompassing energy, health, forestry, and agriculture at local to regional scales.</p><p> </p><p>[1] Zink, M., Samaniego, L., Kumar, R., Thober, S., Mai, J., Schäfer, D., Marx, A., 2016: The German drought monitor, Environ. Res. Lett. 11 (7), art. 074002, DOI:10.1088/1748-9326/11/7/074002.</p><p>[2] Samaniego, L., Kumar, R. and Zink, M.,2013: Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany, Journal of Hydrometeorology, DOI: 10.1175/JHM-D-12-075.1.</p><p> </p>

scholarly journals Drought Risk Assessment for Upland Crops using Satellite-derived Evapotranspiration and Soil Available Water Capacity

2021 ◽  
Vol 21 (1) ◽  
pp. 25-33
Author(s):  
Min-Gi Jeon ◽  
Won-Ho Nam ◽  
Hee-Jin Lee ◽  
Eun-Mi Hong ◽  
Seonah Hwang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Practical Method ◽  
Early Warning Systems ◽  
The United States ◽  
Agricultural Drought ◽  
United States Geological Survey ◽  
Drought Risk ◽  
Agricultural Ecosystem ◽  
Moisture Movement ◽  
Available Water

Agricultural drought can have long-lasting and harmful impacts on both the agricultural ecosystem and economy. Recently, as climate change has increased global warming, the frequency and intensity of droughts are increasing as weather and environmental factors that directly affect agriculture are rapidly changing. In South Korea, severe droughts have occurred every year for the past seven years. Compared to paddies supplied with water from agricultural reservoirs, upland crops are highly vulnerable to drought due to a lack of irrigation facilities. The consumption requirements for upland crops cannot be satisfied by rainfall alone and require supplementation through irrigation. The amount of upland crop consumption and irrigation water should be calculated not only by the amount of evaporation but also by taking into account the soil moisture movement. Soil moisture is a key variable for defining the agricultural drought index; however, in situ soil moisture observations are unavailable for many areas. Remote sensing techniques can allow surface soil moisture observations at different tempo-spatial resolutions. Soil available water content is an important factor used in evaluating upland drought impacts. It is recognized as a major factor in water resource circulation. This study proposes a practical method to perform drought risk assessments for upland crops based on evaporation and soil moisture by utilizing Famine Early Warning Systems Network evaporation acidity satellite images provided by the United States Geological Survey.

Space-time variability of soil moisture based drought in the transboundary Koshi river basin of the Himalayan region

2020 ◽  
Author(s):  
Santosh Nepal ◽  
Saurav Pradhananga ◽  
Narayan Shrestha ◽  
Jayandra Shrestha ◽  
Manfred Fink ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
River Basin ◽  
Agricultural Productivity ◽  
Agricultural Drought ◽  
Himalayan Region ◽  
Severe Drought ◽  
Post Monsoon ◽  
Koshi River Basin ◽  
J2000 Model

<p>Soil moisture is an important part of the vegetation cycle and a controlling factor for agriculture. Withstanding the role of agricultural productivity in economic development of a nation, it is imperative that water resources planners and managers are able to assess and forecast agricultural drought. As agricultural drought is related to declining soil moisture, this paper studies the dynamics of soil moisture based drought in the transboundary Koshi river basin in the Himalayan region. By applying the J2000 hydrological model, the daily soil moisture is derived for the whole basin for a 28-year time frame (1980-2007). The soil moisture deficit index (SMDI) is calculated based on a fully distributed spatial representation by considering the derivation from the long term soil moisture on a weekly time scale. In order to analyze the variation of soil moisture drought spatially, the river basin is subdivided into three distinct geographical areas, i.e. Northern Tibet, High and Middle Mountains, and Southern Plain. Further, temporally the SMDI is calculated for four distinct seasons based on wetness and dryness patterns observed in the study area, i.e. monsoon, post-monsoon, winter and pre-monsoon. A multi-site and multi-variable (streamflow at one station and evapotranspiration at three stations) approach was used for the calibration and validation of the J2000 model. Results show that the J2000 model is able to simulate the hydrological cycle of the basin with high accuracy. The model properly represents the winter drought of 2005 and 2006 was the most severe drought in the 28-year time period. Results also show considerable increases in the frequency of pre-monsoon and post-monsoon soil moisture drought in recent years. Severe droughts have had a high frequency in recent years, which is also reflected by an increase of areas that were impacted. In summary, our results show that severity and occurrence of agricultural drought has increased in the Koshi river basin in the last three decades, especially in the winter and pre-monsoon. This will have serious implications for agricultural productivity and for water resources management of the basin.</p>

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