Drought dynamics of Northwestern Teesta Floodplain of Bangladesh: a remote sensing approach to ascertain the cause and effect

Abstract

Drought is an affliction for a region that primarily depends on agriculture as economic activity. Commonly monitoring and characterizing of drought is performed by only analyzing the meteorological aspect, assuming precipitation as the primary source of water. However, in riverine Bangladesh, this can lead to an erroneous conclusion, as there is a multitude of available water sources. Consequently, in this study, vegetation condition (Standard Vegetation Index), soil moisture (Soil Moisture Index), and precipitation (Standard Precipitation Index) are separately investigated from 2003 to 2019, in the Northwestern Teesta floodplain. Subsequently, statistical regression analysis is performed to determine the relationships between different aspects of drought. In addition, information obtained from field visits and expert opinions has also been assimilated. Analysis of vegetation and soil moisture condition presents a progressively improving scenario. However, SPI shows an incessant decline in meteorological drought conditions, especially after 2007. Evidently, regression analysis does not provide any indication of an interrelationship between the studied agricultural and meteorological parameters. Presumably, this absence is instigated because the study area is highly irrigated as the groundwater table is suitably near the surface and the existence of nearby Teesta river allows for the utilization of surface water. Moreover, the cropping pattern is shifting toward crops that require much less water and to places where soil moisture is scarce. Thus, this study addresses the gap in knowledge regarding the nature of agricultural drought and the dynamics of different aspects of drought which will be invaluable for the water management and agricultural policy in the study area as well as other regions with a similar backdrop.

Appendix

Year	6 months		3 months				12 months	3 months April	3 months November
	Oct–Mar	Apr–Sep	Oct–Dec	Jan–Mar	Apr–Jun	Jul–Sep
1998–1999	0.33	1.97	0.75	−1.76	1.22	2.04	1.84	1.22	−0.86
1999–2000	0.62	1.60	0.78	−0.35	1.79	0.93	1.59	1.79	−0.18
2000–2001	−0.87	0.32	−0.68	−0.06	0.29	0.28	0.03	0.29	1.20
2001–2002	1.83	2.06	1.61	0.62	1.89	1.58	2.42	1.89	2.17
2002–2003	0.70	0.12	−0.31	2.30	0.64	−0.37	0.29	0.64	0.65
2003–2004	1.01	1.07	1.08	−0.18	1.32	0.52	1.24	1.32	2.08
2004–2005	1.42	−0.01	1.23	0.71	−0.74	0.62	0.45	−0.74	0.33
2005–2006	1.39	−1.92	1.62	−2.46	−0.39	−2.74	−1.18	−0.39	−1.25
2006–2007	−0.62	−0.07	−0.42	−0.05	−0.20	0.09	−0.27	−0.20	0.03
2007–2008	−0.10	−1.34	−0.36	0.93	−1.51	−0.76	−1.28	−1.51	0.80
2008–2009	−1.07	−0.89	−0.68	−0.53	−1.43	−0.16	−1.13	−1.43	−1.57
2009–2010	0.26	0.66	0.51	−0.47	0.83	0.32	0.63	0.83	−0.95
2010–2011	−0.63	−0.50	−0.61	0.32	−1.46	0.43	−0.67	−1.46	0.31
2011–2012	−2.33	0.21	−1.71	−1.12	0.09	0.28	−0.30	0.09	0.01
2012–2013	0.33	−0.94	0.58	−0.55	−0.65	−0.86	−0.78	−0.65	−0.58
2013–2014	0.36	−0.59	0.56	−0.30	−0.59	−0.37	−0.45	−0.59	−1.06
2014–2015	−0.86	−0.28	−1.29	0.83	−0.10	−0.31	−0.52	−0.10	0.69
2015–2016	−1.06	−0.15	−1.47	0.69	0.28	−0.47	−0.44	0.28	−0.06
2016–2017	0.28	−0.29	0.05	0.96	−0.68	0.16	−0.22	−0.68	−0.73
2017–2018	0.17	−0.74	0.33	−0.08	0.46	−1.64	−0.66	0.46	−0.93
2018–2019	−1.15	−0.29	−1.58	0.64	−1.05	0.44	−0.59	−1.05	−0.33